kittycad.models.ok_modeling_cmd_response
Classes
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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An empty response, used for any command that does not explicitly have a response defined here. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
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Create a new model by parsing and validating input data from keyword arguments. |
- class kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.camera_drag_end.CameraDragEnd'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['camera_drag_end']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd'>, 'config': {'title': 'OptionCameraDragEnd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd:94467871960016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_drag_end.CameraDragEnd'>, 'config': {'title': 'CameraDragEnd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_drag_end.CameraDragEnd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_drag_end.CameraDragEnd'>>]}, 'ref': 'kittycad.models.camera_drag_end.CameraDragEnd:94467863761360', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94467863769344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94467863725024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'CameraDragEnd', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'camera_drag_end', 'schema': {'expected': ['camera_drag_end'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCameraDragEnd', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f36bd0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037651d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007fa14e7f0370, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03767100, ), serializer: Fields( GeneralFieldsSerializer { fields: { "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007fa14b31b1f0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0375c3e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "w": SerField { key_py: Py( 0x00007fa14fa3e110, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007fa14d9ed0b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007fa14bbea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007fa14bbea7c0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007fa14b31b1b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "CameraDragEnd", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6e130, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "camera_drag_end", }, expected_py: None, name: "literal['camera_drag_end']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCameraDragEnd", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCameraDragEnd", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4f690, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4f6c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007fa14aa8f4f0, ), path: LookupPath( [ S( "settings", Py( 0x00007fa14aa8f530, ), ), ], ), }, name_py: Py( 0x00007fa14e7f0370, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14aa4e3a0, ), path: LookupPath( [ S( "center", Py( 0x00007fa14aa4e580, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007fa14aa4d710, ), path: LookupPath( [ S( "fov_y", Py( 0x00007fa14aa4d740, ), ), ], ), }, name_py: Py( 0x00007fa14bbea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007fa14aa8f430, ), path: LookupPath( [ S( "orientation", Py( 0x00007fa14aa8f3f0, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1f0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007fa14fa3e110, ), path: LookupPath( [ S( "w", Py( 0x00007fa14fa3e110, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e110, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0375c3e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007fa14aa4f4e0, ), path: LookupPath( [ S( "ortho", Py( 0x00007fa14aa4f5a0, ), ), ], ), }, name_py: Py( 0x00007fa14bbea7c0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007fa14aa8f4b0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007fa14aa8f470, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14aa4f5d0, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14aa4f600, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007fa14aa4f630, ), path: LookupPath( [ S( "up", Py( 0x00007fa14aa4f660, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed0b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03767100, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "CameraDragEnd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037651d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraDragEnd", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4f6f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4f720, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6e130, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "camera_drag_end": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6e130, ), ], }, expected_repr: "'camera_drag_end'", name: "literal['camera_drag_end']", }, ), validate_default: false, copy_default: false, name: "default[literal['camera_drag_end']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCameraDragEnd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f36bd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCameraDragEnd", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.camera_drag_end.CameraDragEnd, type: Literal['camera_drag_end'] = 'camera_drag_end') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
CameraDragEnd[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CameraDragEnd, required=True), 'type': FieldInfo(annotation=Literal['camera_drag_end'], required=False, default='camera_drag_end')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.camera_drag_move.CameraDragMove'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['camera_drag_move']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove'>, 'config': {'title': 'OptionCameraDragMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove:94467871948768', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_drag_move.CameraDragMove'>, 'config': {'title': 'CameraDragMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_drag_move.CameraDragMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_drag_move.CameraDragMove'>>]}, 'ref': 'kittycad.models.camera_drag_move.CameraDragMove:94467849402000', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94467863769344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94467863725024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'CameraDragMove', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'camera_drag_move', 'schema': {'expected': ['camera_drag_move'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCameraDragMove', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f33fe0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb029b3690, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007fa14e7f0370, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03767100, ), serializer: Fields( GeneralFieldsSerializer { fields: { "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007fa14b31b1b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007fa14bbea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007fa14bbea7c0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007fa14d9ed0b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007fa14b31b1f0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0375c3e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "w": SerField { key_py: Py( 0x00007fa14fa3e110, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "CameraDragMove", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6deb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "camera_drag_move", }, expected_py: None, name: "literal['camera_drag_move']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCameraDragMove", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCameraDragMove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4d530, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4d350, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007fa14aa8c7f0, ), path: LookupPath( [ S( "settings", Py( 0x00007fa14aa8c830, ), ), ], ), }, name_py: Py( 0x00007fa14e7f0370, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14aa4d590, ), path: LookupPath( [ S( "center", Py( 0x00007fa14aa4e430, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007fa14aa4d6b0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007fa14aa4e640, ), ), ], ), }, name_py: Py( 0x00007fa14bbea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007fa14af3a170, ), path: LookupPath( [ S( "orientation", Py( 0x00007fa14aa8c730, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1f0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007fa14fa3e110, ), path: LookupPath( [ S( "w", Py( 0x00007fa14fa3e110, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e110, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0375c3e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007fa14aa4ce70, ), path: LookupPath( [ S( "ortho", Py( 0x00007fa14aa4d2c0, ), ), ], ), }, name_py: Py( 0x00007fa14bbea7c0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007fa14aa8c7b0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007fa14aa8c770, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14aa4d5f0, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14aa4d830, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007fa14aa4d7d0, ), path: LookupPath( [ S( "up", Py( 0x00007fa14aa4d890, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed0b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03767100, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "CameraDragMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb029b3690, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraDragMove", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4c780, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4cb10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6deb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "camera_drag_move": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6deb0, ), ], }, expected_repr: "'camera_drag_move'", name: "literal['camera_drag_move']", }, ), validate_default: false, copy_default: false, name: "default[literal['camera_drag_move']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCameraDragMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f33fe0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCameraDragMove", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.camera_drag_move.CameraDragMove, type: Literal['camera_drag_move'] = 'camera_drag_move') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
CameraDragMove[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CameraDragMove, required=True), 'type': FieldInfo(annotation=Literal['camera_drag_move'], required=False, default='camera_drag_move')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionCameraDragStart(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.camera_drag_start.CameraDragStart'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['camera_drag_start']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragStart'>, 'config': {'title': 'OptionCameraDragStart'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragStart'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragStart'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragStart:94467871533712', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_drag_start.CameraDragStart'>, 'config': {'title': 'CameraDragStart'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_drag_start.CameraDragStart'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_drag_start.CameraDragStart'>>]}, 'ref': 'kittycad.models.camera_drag_start.CameraDragStart:94467849435184', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'CameraDragStart', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'camera_drag_start', 'schema': {'expected': ['camera_drag_start'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCameraDragStart', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ecea90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6ff70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "camera_drag_start", }, expected_py: None, name: "literal['camera_drag_start']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb029bb830, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "CameraDragStart", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCameraDragStart", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCameraDragStart", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa36310, ), path: LookupPath( [ S( "data", Py( 0x00007fa14abe2eb0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "CameraDragStart", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb029bb830, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraDragStart", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14abe3f30, ), path: LookupPath( [ S( "type", Py( 0x00007fa14abe2790, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6ff70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "camera_drag_start": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6ff70, ), ], }, expected_repr: "'camera_drag_start'", name: "literal['camera_drag_start']", }, ), validate_default: false, copy_default: false, name: "default[literal['camera_drag_start']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCameraDragStart", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ecea90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCameraDragStart", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.camera_drag_start.CameraDragStart, type: Literal['camera_drag_start'] = 'camera_drag_start') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
CameraDragStart[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CameraDragStart, required=True), 'type': FieldInfo(annotation=Literal['camera_drag_start'], required=False, default='camera_drag_start')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.center_of_mass.CenterOfMass'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['center_of_mass']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass'>, 'config': {'title': 'OptionCenterOfMass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass:94467872641536', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.center_of_mass.CenterOfMass'>, 'config': {'title': 'CenterOfMass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.center_of_mass.CenterOfMass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.center_of_mass.CenterOfMass'>>]}, 'ref': 'kittycad.models.center_of_mass.CenterOfMass:94467849492208', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center_of_mass': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitLength'>, 'members': [UnitLength.CM, UnitLength.FT, UnitLength.IN, UnitLength.M, UnitLength.MM, UnitLength.YD], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_length.UnitLength:94467861018320', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'CenterOfMass', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'center_of_mass', 'schema': {'expected': ['center_of_mass'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCenterOfMass', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fdd200, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6fb30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "center_of_mass", }, expected_py: None, name: "literal['center_of_mass']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb029c96f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "center_of_mass": SerField { key_py: Py( 0x00007fa14bc6fb30, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb034c76d0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "CenterOfMass", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCenterOfMass", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCenterOfMass", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaaa820, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaaaa30, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center_of_mass", lookup_key: Simple { key: "center_of_mass", py_key: Py( 0x00007fa14aafbd70, ), path: LookupPath( [ S( "center_of_mass", Py( 0x00007fa14aae1630, ), ), ], ), }, name_py: Py( 0x00007fa14bc6fb30, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14aaec8f0, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14aaec270, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb034c76d0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "yd": 5, "ft": 1, "m": 3, "in": 2, "cm": 0, "mm": 4, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c4c50, ), Py( 0x00007fa14b3c51f0, ), Py( 0x00007fa14b3c52b0, ), Py( 0x00007fa14b3c5310, ), Py( 0x00007fa14b3c5370, ), Py( 0x00007fa14b3c53d0, ), ], }, missing: None, expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'", strict: false, class_repr: "UnitLength", name: "str-enum[UnitLength]", }, ), frozen: false, }, ], model_name: "CenterOfMass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb029c96f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CenterOfMass", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae6dc0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae69d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6fb30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "center_of_mass": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6fb30, ), ], }, expected_repr: "'center_of_mass'", name: "literal['center_of_mass']", }, ), validate_default: false, copy_default: false, name: "default[literal['center_of_mass']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCenterOfMass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fdd200, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCenterOfMass", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.center_of_mass.CenterOfMass, type: Literal['center_of_mass'] = 'center_of_mass') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
CenterOfMass[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CenterOfMass, required=True), 'type': FieldInfo(annotation=Literal['center_of_mass'], required=False, default='center_of_mass')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionClosePath(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.close_path.ClosePath'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['close_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionClosePath'>, 'config': {'title': 'OptionClosePath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionClosePath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionClosePath'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionClosePath:94467871921632', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.close_path.ClosePath'>, 'config': {'title': 'ClosePath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.close_path.ClosePath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.close_path.ClosePath'>>]}, 'ref': 'kittycad.models.close_path.ClosePath:94467849519072', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ClosePath', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'close_path', 'schema': {'expected': ['close_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionClosePath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f2d5e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6f5b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "close_path", }, expected_py: None, name: "literal['close_path']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb029cffe0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "face_id": SerField { key_py: Py( 0x00007fa14b32c8a0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ClosePath", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionClosePath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionClosePath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4eee0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4ee80, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007fa14aa4f000, ), path: LookupPath( [ S( "face_id", Py( 0x00007fa14aa4efa0, ), ), ], ), }, name_py: Py( 0x00007fa14b32c8a0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "ClosePath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb029cffe0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ClosePath", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4f030, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4ecd0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6f5b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "close_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6f5b0, ), ], }, expected_repr: "'close_path'", name: "literal['close_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['close_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionClosePath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f2d5e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionClosePath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.close_path.ClosePath, type: Literal['close_path'] = 'close_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ClosePath, required=True), 'type': FieldInfo(annotation=Literal['close_path'], required=False, default='close_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.curve_get_control_points.CurveGetControlPoints'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_control_points']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints'>, 'config': {'title': 'OptionCurveGetControlPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints:94467872305952', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.curve_get_control_points.CurveGetControlPoints'>, 'config': {'title': 'CurveGetControlPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.curve_get_control_points.CurveGetControlPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.curve_get_control_points.CurveGetControlPoints'>>]}, 'ref': 'kittycad.models.curve_get_control_points.CurveGetControlPoints:94467849944512', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'control_points': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'CurveGetControlPoints', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_control_points', 'schema': {'expected': ['curve_get_control_points'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetControlPoints', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f8b320, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb02a37dc0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "control_points": SerField { key_py: Py( 0x00007fa14b1beb70, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[Point3d]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "CurveGetControlPoints", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd06c90, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_control_points", }, expected_py: None, name: "literal['curve_get_control_points']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetControlPoints", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetControlPoints", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaaa910, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaaa880, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "control_points", lookup_key: Simple { key: "control_points", py_key: Py( 0x00007fa14aa8e070, ), path: LookupPath( [ S( "control_points", Py( 0x00007fa14aabf070, ), ), ], ), }, name_py: Py( 0x00007fa14b1beb70, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "CurveGetControlPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02a37dc0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CurveGetControlPoints", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaaaa60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaaa6d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd06c90, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_control_points": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd06c90, ), ], }, expected_repr: "'curve_get_control_points'", name: "literal['curve_get_control_points']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_control_points']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetControlPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f8b320, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCurveGetControlPoints", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.curve_get_control_points.CurveGetControlPoints, type: Literal['curve_get_control_points'] = 'curve_get_control_points') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CurveGetControlPoints, required=True), 'type': FieldInfo(annotation=Literal['curve_get_control_points'], required=False, default='curve_get_control_points')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.curve_get_end_points.CurveGetEndPoints'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_end_points']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints'>, 'config': {'title': 'OptionCurveGetEndPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints:94467872449360', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.curve_get_end_points.CurveGetEndPoints'>, 'config': {'title': 'CurveGetEndPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.curve_get_end_points.CurveGetEndPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.curve_get_end_points.CurveGetEndPoints'>>]}, 'ref': 'kittycad.models.curve_get_end_points.CurveGetEndPoints:94467849956336', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'end': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'start': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CurveGetEndPoints', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_end_points', 'schema': {'expected': ['curve_get_end_points'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetEndPoints', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fae350, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb02a3abf0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "start": SerField { key_py: Py( 0x00007fa14fa3bce8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "end": SerField { key_py: Py( 0x00007fa14fa38368, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "CurveGetEndPoints", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb7ef0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_end_points", }, expected_py: None, name: "literal['curve_get_end_points']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetEndPoints", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetEndPoints", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae4960, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae4990, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "end", lookup_key: Simple { key: "end", py_key: Py( 0x00007fa14aae48a0, ), path: LookupPath( [ S( "end", Py( 0x00007fa14aae48d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa38368, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "start", lookup_key: Simple { key: "start", py_key: Py( 0x00007fa14aae4900, ), path: LookupPath( [ S( "start", Py( 0x00007fa14aae4930, ), ), ], ), }, name_py: Py( 0x00007fa14fa3bce8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CurveGetEndPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02a3abf0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CurveGetEndPoints", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae49c0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae49f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb7ef0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_end_points": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb7ef0, ), ], }, expected_repr: "'curve_get_end_points'", name: "literal['curve_get_end_points']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_end_points']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetEndPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fae350, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCurveGetEndPoints", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.curve_get_end_points.CurveGetEndPoints, type: Literal['curve_get_end_points'] = 'curve_get_end_points') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CurveGetEndPoints, required=True), 'type': FieldInfo(annotation=Literal['curve_get_end_points'], required=False, default='curve_get_end_points')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionCurveGetType(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.curve_get_type.CurveGetType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_type']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetType'>, 'config': {'title': 'OptionCurveGetType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetType'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetType:94467872318608', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.curve_get_type.CurveGetType'>, 'config': {'title': 'CurveGetType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.curve_get_type.CurveGetType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.curve_get_type.CurveGetType'>>]}, 'ref': 'kittycad.models.curve_get_type.CurveGetType:94467849975360', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'CurveType'>, 'members': [CurveType.LINE, CurveType.ARC, CurveType.NURBS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.curve_type.CurveType:94467849973584', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'CurveGetType', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_type', 'schema': {'expected': ['curve_get_type'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetType', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f8e490, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb02a3f640, ), serializer: Fields( GeneralFieldsSerializer { fields: { "curve_type": SerField { key_py: Py( 0x00007fa14beb7d30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb02a3ef50, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "CurveGetType", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb7f70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_type", }, expected_py: None, name: "literal['curve_get_type']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetType", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetType", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa8420, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa8690, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_type", lookup_key: Simple { key: "curve_type", py_key: Py( 0x00007fa14aacd1b0, ), path: LookupPath( [ S( "curve_type", Py( 0x00007fa14aaccf30, ), ), ], ), }, name_py: Py( 0x00007fa14beb7d30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb02a3ef50, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "line": 0, "nurbs": 2, "arc": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b38f8f0, ), Py( 0x00007fa14b38f950, ), Py( 0x00007fa14b38f9b0, ), ], }, missing: None, expected_repr: "'line', 'arc' or 'nurbs'", strict: false, class_repr: "CurveType", name: "str-enum[CurveType]", }, ), frozen: false, }, ], model_name: "CurveGetType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02a3f640, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CurveGetType", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa8870, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa83f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb7f70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_type": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb7f70, ), ], }, expected_repr: "'curve_get_type'", name: "literal['curve_get_type']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_type']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f8e490, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCurveGetType", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.curve_get_type.CurveGetType, type: Literal['curve_get_type'] = 'curve_get_type') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
CurveGetType[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CurveGetType, required=True), 'type': FieldInfo(annotation=Literal['curve_get_type'], required=False, default='curve_get_type')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionCurveSetConstraint(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.curve_set_constraint.CurveSetConstraint'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_set_constraint']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveSetConstraint'>, 'config': {'title': 'OptionCurveSetConstraint'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCurveSetConstraint'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveSetConstraint'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCurveSetConstraint:94467871275920', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.curve_set_constraint.CurveSetConstraint'>, 'config': {'title': 'CurveSetConstraint'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.curve_set_constraint.CurveSetConstraint'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.curve_set_constraint.CurveSetConstraint'>>]}, 'ref': 'kittycad.models.curve_set_constraint.CurveSetConstraint:94467849978592', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'CurveSetConstraint', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_set_constraint', 'schema': {'expected': ['curve_set_constraint'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveSetConstraint', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e8fb90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb7e30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_set_constraint", }, expected_py: None, name: "literal['curve_set_constraint']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb02a402e0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "CurveSetConstraint", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveSetConstraint", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveSetConstraint", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac96fd0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac97000, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "CurveSetConstraint", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb02a402e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CurveSetConstraint", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac97030, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac97060, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb7e30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_set_constraint": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb7e30, ), ], }, expected_repr: "'curve_set_constraint'", name: "literal['curve_set_constraint']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_set_constraint']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionCurveSetConstraint", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e8fb90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionCurveSetConstraint", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.curve_set_constraint.CurveSetConstraint, type: Literal['curve_set_constraint'] = 'curve_set_constraint') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CurveSetConstraint, required=True), 'type': FieldInfo(annotation=Literal['curve_set_constraint'], required=False, default='curve_set_constraint')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToScene(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_center_to_scene.DefaultCameraCenterToScene'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_center_to_scene']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToScene'>, 'config': {'title': 'OptionDefaultCameraCenterToScene'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToScene'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToScene'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToScene:94467871796464', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_center_to_scene.DefaultCameraCenterToScene'>, 'config': {'title': 'DefaultCameraCenterToScene'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_center_to_scene.DefaultCameraCenterToScene'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_center_to_scene.DefaultCameraCenterToScene'>>]}, 'ref': 'kittycad.models.default_camera_center_to_scene.DefaultCameraCenterToScene:94467864268080', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DefaultCameraCenterToScene', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_center_to_scene', 'schema': {'expected': ['default_camera_center_to_scene'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraCenterToScene', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f0ecf0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037e0d30, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DefaultCameraCenterToScene", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd07000, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_center_to_scene", }, expected_py: None, name: "literal['default_camera_center_to_scene']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraCenterToScene", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraCenterToScene", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4d230, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4d1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DefaultCameraCenterToScene", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037e0d30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraCenterToScene", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4d380, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4cba0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd07000, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_center_to_scene": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd07000, ), ], }, expected_repr: "'default_camera_center_to_scene'", name: "literal['default_camera_center_to_scene']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_center_to_scene']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraCenterToScene", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f0ecf0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraCenterToScene", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_center_to_scene.DefaultCameraCenterToScene, type: Literal['default_camera_center_to_scene'] = 'default_camera_center_to_scene') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraCenterToScene, required=True), 'type': FieldInfo(annotation=Literal['default_camera_center_to_scene'], required=False, default='default_camera_center_to_scene')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToSelection(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_center_to_selection.DefaultCameraCenterToSelection'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_center_to_selection']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToSelection'>, 'config': {'title': 'OptionDefaultCameraCenterToSelection'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToSelection'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToSelection'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraCenterToSelection:94467871791120', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_center_to_selection.DefaultCameraCenterToSelection'>, 'config': {'title': 'DefaultCameraCenterToSelection'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_center_to_selection.DefaultCameraCenterToSelection'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_center_to_selection.DefaultCameraCenterToSelection'>>]}, 'ref': 'kittycad.models.default_camera_center_to_selection.DefaultCameraCenterToSelection:94467864250752', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DefaultCameraCenterToSelection', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_center_to_selection', 'schema': {'expected': ['default_camera_center_to_selection'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraCenterToSelection', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f0d810, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037dc980, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DefaultCameraCenterToSelection", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2ad30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_center_to_selection", }, expected_py: None, name: "literal['default_camera_center_to_selection']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraCenterToSelection", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraCenterToSelection", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac97d80, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac97de0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DefaultCameraCenterToSelection", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037dc980, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraCenterToSelection", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac97cf0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac97ed0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2ad30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_center_to_selection": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2ad30, ), ], }, expected_repr: "'default_camera_center_to_selection'", name: "literal['default_camera_center_to_selection']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_center_to_selection']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraCenterToSelection", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f0d810, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraCenterToSelection", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_center_to_selection.DefaultCameraCenterToSelection, type: Literal['default_camera_center_to_selection'] = 'default_camera_center_to_selection') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraCenterToSelection, required=True), 'type': FieldInfo(annotation=Literal['default_camera_center_to_selection'], required=False, default='default_camera_center_to_selection')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_focus_on']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn'>, 'config': {'title': 'OptionDefaultCameraFocusOn'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn:94467872194688', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn'>, 'config': {'title': 'DefaultCameraFocusOn'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn'>>]}, 'ref': 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn:94467864254560', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DefaultCameraFocusOn', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_focus_on', 'schema': {'expected': ['default_camera_focus_on'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraFocusOn', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f70080, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037dd860, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DefaultCameraFocusOn", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb7ab0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_focus_on", }, expected_py: None, name: "literal['default_camera_focus_on']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraFocusOn", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraFocusOn", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa8fc0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa8ff0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DefaultCameraFocusOn", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037dd860, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraFocusOn", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa9020, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa9050, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb7ab0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_focus_on": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb7ab0, ), ], }, expected_repr: "'default_camera_focus_on'", name: "literal['default_camera_focus_on']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_focus_on']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraFocusOn", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f70080, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraFocusOn", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_focus_on.DefaultCameraFocusOn, type: Literal['default_camera_focus_on'] = 'default_camera_focus_on') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraFocusOn, required=True), 'type': FieldInfo(annotation=Literal['default_camera_focus_on'], required=False, default='default_camera_focus_on')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_get_settings']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings'>, 'config': {'title': 'OptionDefaultCameraGetSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings:94467871997488', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings'>, 'config': {'title': 'DefaultCameraGetSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings'>>]}, 'ref': 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings:94467864258544', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94467863769344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94467863725024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'DefaultCameraGetSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_get_settings', 'schema': {'expected': ['default_camera_get_settings'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraGetSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f3fe30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2ac90, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_get_settings", }, expected_py: None, name: "literal['default_camera_get_settings']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037de7f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007fa14e7f0370, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03767100, ), serializer: Fields( GeneralFieldsSerializer { fields: { "ortho_scale": SerField { key_py: Py( 0x00007fa14b31b1b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007fa14bbea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007fa14bbea7c0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007fa14d9ed0b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007fa14b31b1f0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0375c3e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "w": SerField { key_py: Py( 0x00007fa14fa3e110, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "DefaultCameraGetSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraGetSettings", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraGetSettings", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4e040, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4de90, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007fa14aa872f0, ), path: LookupPath( [ S( "settings", Py( 0x00007fa14aa87370, ), ), ], ), }, name_py: Py( 0x00007fa14e7f0370, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14aa4f7b0, ), path: LookupPath( [ S( "center", Py( 0x00007fa14aa4f810, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007fa14aa4f7e0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007fa14aa4f930, ), ), ], ), }, name_py: Py( 0x00007fa14bbea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007fa14aa384f0, ), path: LookupPath( [ S( "orientation", Py( 0x00007fa14aa39cf0, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1f0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007fa14fa3e110, ), path: LookupPath( [ S( "w", Py( 0x00007fa14fa3e110, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e110, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0375c3e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007fa14aa4f900, ), path: LookupPath( [ S( "ortho", Py( 0x00007fa14aa4f8a0, ), ), ], ), }, name_py: Py( 0x00007fa14bbea7c0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007fa14ad6ecb0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007fa14aa869b0, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14aa4f870, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14aa4f8d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007fa14aa4e370, ), path: LookupPath( [ S( "up", Py( 0x00007fa14aa4e280, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed0b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03767100, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "DefaultCameraGetSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037de7f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraGetSettings", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4de00, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4e220, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2ac90, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_get_settings": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2ac90, ), ], }, expected_repr: "'default_camera_get_settings'", name: "literal['default_camera_get_settings']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_get_settings']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraGetSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f3fe30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraGetSettings", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_get_settings.DefaultCameraGetSettings, type: Literal['default_camera_get_settings'] = 'default_camera_get_settings') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraGetSettings, required=True), 'type': FieldInfo(annotation=Literal['default_camera_get_settings'], required=False, default='default_camera_get_settings')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraLookAt(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_look_at.DefaultCameraLookAt'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_look_at']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraLookAt'>, 'config': {'title': 'OptionDefaultCameraLookAt'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraLookAt'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraLookAt'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraLookAt:94467871542784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_look_at.DefaultCameraLookAt'>, 'config': {'title': 'DefaultCameraLookAt'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_look_at.DefaultCameraLookAt'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_look_at.DefaultCameraLookAt'>>]}, 'ref': 'kittycad.models.default_camera_look_at.DefaultCameraLookAt:94467864299280', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DefaultCameraLookAt', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_look_at', 'schema': {'expected': ['default_camera_look_at'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraLookAt', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ed0e00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037e8710, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DefaultCameraLookAt", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb6cf0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_look_at", }, expected_py: None, name: "literal['default_camera_look_at']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraLookAt", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraLookAt", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac165e0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac165b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DefaultCameraLookAt", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037e8710, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraLookAt", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac16550, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac16490, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb6cf0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_look_at": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb6cf0, ), ], }, expected_repr: "'default_camera_look_at'", name: "literal['default_camera_look_at']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_look_at']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraLookAt", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ed0e00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraLookAt", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_look_at.DefaultCameraLookAt, type: Literal['default_camera_look_at'] = 'default_camera_look_at') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraLookAt, required=True), 'type': FieldInfo(annotation=Literal['default_camera_look_at'], required=False, default='default_camera_look_at')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraPerspectiveSettings(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_perspective_settings.DefaultCameraPerspectiveSettings'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_perspective_settings']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraPerspectiveSettings'>, 'config': {'title': 'OptionDefaultCameraPerspectiveSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraPerspectiveSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraPerspectiveSettings'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraPerspectiveSettings:94467871552304', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_perspective_settings.DefaultCameraPerspectiveSettings'>, 'config': {'title': 'DefaultCameraPerspectiveSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_perspective_settings.DefaultCameraPerspectiveSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_perspective_settings.DefaultCameraPerspectiveSettings'>>]}, 'ref': 'kittycad.models.default_camera_perspective_settings.DefaultCameraPerspectiveSettings:94467864304864', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DefaultCameraPerspectiveSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_perspective_settings', 'schema': {'expected': ['default_camera_perspective_settings'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraPerspectiveSettings', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ed3330, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a7e0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_perspective_settings", }, expected_py: None, name: "literal['default_camera_perspective_settings']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037e9ce0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DefaultCameraPerspectiveSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraPerspectiveSettings", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraPerspectiveSettings", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac16ca0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac16cd0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DefaultCameraPerspectiveSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037e9ce0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraPerspectiveSettings", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac16d00, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac16d30, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a7e0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_perspective_settings": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a7e0, ), ], }, expected_repr: "'default_camera_perspective_settings'", name: "literal['default_camera_perspective_settings']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_perspective_settings']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraPerspectiveSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ed3330, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraPerspectiveSettings", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_perspective_settings.DefaultCameraPerspectiveSettings, type: Literal['default_camera_perspective_settings'] = 'default_camera_perspective_settings') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraPerspectiveSettings, required=True), 'type': FieldInfo(annotation=Literal['default_camera_perspective_settings'], required=False, default='default_camera_perspective_settings')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetOrthographic(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_set_orthographic.DefaultCameraSetOrthographic'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_set_orthographic']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetOrthographic'>, 'config': {'title': 'OptionDefaultCameraSetOrthographic'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetOrthographic'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetOrthographic'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetOrthographic:94467871772320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_set_orthographic.DefaultCameraSetOrthographic'>, 'config': {'title': 'DefaultCameraSetOrthographic'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_set_orthographic.DefaultCameraSetOrthographic'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_set_orthographic.DefaultCameraSetOrthographic'>>]}, 'ref': 'kittycad.models.default_camera_set_orthographic.DefaultCameraSetOrthographic:94467864307808', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DefaultCameraSetOrthographic', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_set_orthographic', 'schema': {'expected': ['default_camera_set_orthographic'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraSetOrthographic', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f08ea0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037ea860, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DefaultCameraSetOrthographic", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a6f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_set_orthographic", }, expected_py: None, name: "literal['default_camera_set_orthographic']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraSetOrthographic", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraSetOrthographic", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4d3e0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4d410, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DefaultCameraSetOrthographic", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037ea860, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraSetOrthographic", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4d440, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4d470, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a6f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_set_orthographic": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a6f0, ), ], }, expected_repr: "'default_camera_set_orthographic'", name: "literal['default_camera_set_orthographic']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_set_orthographic']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraSetOrthographic", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f08ea0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraSetOrthographic", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_set_orthographic.DefaultCameraSetOrthographic, type: Literal['default_camera_set_orthographic'] = 'default_camera_set_orthographic') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraSetOrthographic, required=True), 'type': FieldInfo(annotation=Literal['default_camera_set_orthographic'], required=False, default='default_camera_set_orthographic')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetPerspective(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_set_perspective.DefaultCameraSetPerspective'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_set_perspective']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetPerspective'>, 'config': {'title': 'OptionDefaultCameraSetPerspective'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetPerspective'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetPerspective'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraSetPerspective:94467871781584', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_set_perspective.DefaultCameraSetPerspective'>, 'config': {'title': 'DefaultCameraSetPerspective'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_set_perspective.DefaultCameraSetPerspective'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_set_perspective.DefaultCameraSetPerspective'>>]}, 'ref': 'kittycad.models.default_camera_set_perspective.DefaultCameraSetPerspective:94467864311264', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DefaultCameraSetPerspective', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_set_perspective', 'schema': {'expected': ['default_camera_set_perspective'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraSetPerspective', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f0b2d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a650, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_set_perspective", }, expected_py: None, name: "literal['default_camera_set_perspective']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037eb5e0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DefaultCameraSetPerspective", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraSetPerspective", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraSetPerspective", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aca4b10, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac97810, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DefaultCameraSetPerspective", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037eb5e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraSetPerspective", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac97750, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac976f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a650, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_set_perspective": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a650, ), ], }, expected_repr: "'default_camera_set_perspective'", name: "literal['default_camera_set_perspective']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_set_perspective']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraSetPerspective", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f0b2d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraSetPerspective", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_set_perspective.DefaultCameraSetPerspective, type: Literal['default_camera_set_perspective'] = 'default_camera_set_perspective') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraSetPerspective, required=True), 'type': FieldInfo(annotation=Literal['default_camera_set_perspective'], required=False, default='default_camera_set_perspective')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_zoom.DefaultCameraZoom'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_zoom']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom'>, 'config': {'title': 'OptionDefaultCameraZoom'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom:94467872034464', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_zoom.DefaultCameraZoom'>, 'config': {'title': 'DefaultCameraZoom'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_zoom.DefaultCameraZoom'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_zoom.DefaultCameraZoom'>>]}, 'ref': 'kittycad.models.default_camera_zoom.DefaultCameraZoom:94467864313808', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94467863769344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94467863725024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'DefaultCameraZoom', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_zoom', 'schema': {'expected': ['default_camera_zoom'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraZoom', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f48ea0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037ebfd0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007fa14e7f0370, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03767100, ), serializer: Fields( GeneralFieldsSerializer { fields: { "orientation": SerField { key_py: Py( 0x00007fa14b31b1f0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0375c3e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "w": SerField { key_py: Py( 0x00007fa14fa3e110, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007fa14bbea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007fa14b31b1b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007fa14d9ed0b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007fa14bbea7c0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "DefaultCameraZoom", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb68b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_zoom", }, expected_py: None, name: "literal['default_camera_zoom']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraZoom", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraZoom", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4fa80, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4fb10, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007fa14aa9adf0, ), path: LookupPath( [ S( "settings", Py( 0x00007fa14aa9ae30, ), ), ], ), }, name_py: Py( 0x00007fa14e7f0370, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14aa4f540, ), path: LookupPath( [ S( "center", Py( 0x00007fa14aa4f1b0, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007fa14aa4f3f0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007fa14aa4f2d0, ), ), ], ), }, name_py: Py( 0x00007fa14bbea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007fa14aa9ad30, ), path: LookupPath( [ S( "orientation", Py( 0x00007fa14aa9acf0, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1f0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007fa14fa3e110, ), path: LookupPath( [ S( "w", Py( 0x00007fa14fa3e110, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e110, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0375c3e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007fa14aa4f510, ), path: LookupPath( [ S( "ortho", Py( 0x00007fa14aa4f240, ), ), ], ), }, name_py: Py( 0x00007fa14bbea7c0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007fa14aa9adb0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007fa14aa9ad70, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14aa4f3c0, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14aa4f480, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007fa14aa4f9f0, ), path: LookupPath( [ S( "up", Py( 0x00007fa14aa4f9c0, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed0b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03767100, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "DefaultCameraZoom", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037ebfd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DefaultCameraZoom", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4fae0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4fba0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb68b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_zoom": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb68b0, ), ], }, expected_repr: "'default_camera_zoom'", name: "literal['default_camera_zoom']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_zoom']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraZoom", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f48ea0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDefaultCameraZoom", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_zoom.DefaultCameraZoom, type: Literal['default_camera_zoom'] = 'default_camera_zoom') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraZoom, required=True), 'type': FieldInfo(annotation=Literal['default_camera_zoom'], required=False, default='default_camera_zoom')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDensity(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.density.Density'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['density']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDensity'>, 'config': {'title': 'OptionDensity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDensity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDensity'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDensity:94467872615520', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.density.Density'>, 'config': {'title': 'Density'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.density.Density'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.density.Density'>>]}, 'ref': 'kittycad.models.density.Density:94467864343744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'density': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitDensity'>, 'members': [UnitDensity.LB_FT3, UnitDensity.KG_M3], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_density.UnitDensity:94467863217504', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'Density', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'density', 'schema': {'expected': ['density'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDensity', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fd6c60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14cbce4c0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "density", }, expected_py: None, name: "literal['density']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037f34c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036e0560, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "density": SerField { key_py: Py( 0x00007fa14cbce4c0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Density", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDensity", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDensity", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae65e0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae6610, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "density", lookup_key: Simple { key: "density", py_key: Py( 0x00007fa14aae64f0, ), path: LookupPath( [ S( "density", Py( 0x00007fa14aae65b0, ), ), ], ), }, name_py: Py( 0x00007fa14cbce4c0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14ab01f70, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14ab01f30, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036e0560, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "kg:m3": 1, "lb:ft3": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7cb0, ), Py( 0x00007fa14b3c7d10, ), ], }, missing: None, expected_repr: "'lb:ft3' or 'kg:m3'", strict: false, class_repr: "UnitDensity", name: "str-enum[UnitDensity]", }, ), frozen: false, }, ], model_name: "Density", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037f34c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Density", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae6640, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae6670, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14cbce4c0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "density": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14cbce4c0, ), ], }, expected_repr: "'density'", name: "literal['density']", }, ), validate_default: false, copy_default: false, name: "default[literal['density']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDensity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fd6c60, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDensity", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.density.Density, type: Literal['density'] = 'density') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Density, required=True), 'type': FieldInfo(annotation=Literal['density'], required=False, default='density')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionDisableDryRun(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.disable_dry_run.DisableDryRun'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['disable_dry_run']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDisableDryRun'>, 'config': {'title': 'OptionDisableDryRun'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDisableDryRun'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDisableDryRun'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDisableDryRun:94467871266864', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.disable_dry_run.DisableDryRun'>, 'config': {'title': 'DisableDryRun'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.disable_dry_run.DisableDryRun'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.disable_dry_run.DisableDryRun'>>]}, 'ref': 'kittycad.models.disable_dry_run.DisableDryRun:94467864387072', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DisableDryRun', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'disable_dry_run', 'schema': {'expected': ['disable_dry_run'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDisableDryRun', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e8d830, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb6c30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "disable_dry_run", }, expected_py: None, name: "literal['disable_dry_run']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb037fde00, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DisableDryRun", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDisableDryRun", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDisableDryRun", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac96ac0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac96af0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DisableDryRun", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb037fde00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "DisableDryRun", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac96b20, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac96b50, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb6c30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "disable_dry_run": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb6c30, ), ], }, expected_repr: "'disable_dry_run'", name: "literal['disable_dry_run']", }, ), validate_default: false, copy_default: false, name: "default[literal['disable_dry_run']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionDisableDryRun", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e8d830, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionDisableDryRun", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.disable_dry_run.DisableDryRun, type: Literal['disable_dry_run'] = 'disable_dry_run') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
DisableDryRun[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DisableDryRun, required=True), 'type': FieldInfo(annotation=Literal['disable_dry_run'], required=False, default='disable_dry_run')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEdgeLinesVisible(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.edge_lines_visible.EdgeLinesVisible'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['edge_lines_visible']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEdgeLinesVisible'>, 'config': {'title': 'OptionEdgeLinesVisible'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEdgeLinesVisible'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEdgeLinesVisible'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEdgeLinesVisible:94467871126992', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.edge_lines_visible.EdgeLinesVisible'>, 'config': {'title': 'EdgeLinesVisible'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.edge_lines_visible.EdgeLinesVisible'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.edge_lines_visible.EdgeLinesVisible'>>]}, 'ref': 'kittycad.models.edge_lines_visible.EdgeLinesVisible:94467864467200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'EdgeLinesVisible', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'edge_lines_visible', 'schema': {'expected': ['edge_lines_visible'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEdgeLinesVisible', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e6b5d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14beb6f70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "edge_lines_visible", }, expected_py: None, name: "literal['edge_lines_visible']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03811700, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "EdgeLinesVisible", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEdgeLinesVisible", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEdgeLinesVisible", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa35440, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa36df0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "EdgeLinesVisible", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03811700, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EdgeLinesVisible", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa36820, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa36b50, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14beb6f70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "edge_lines_visible": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14beb6f70, ), ], }, expected_repr: "'edge_lines_visible'", name: "literal['edge_lines_visible']", }, ), validate_default: false, copy_default: false, name: "default[literal['edge_lines_visible']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEdgeLinesVisible", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e6b5d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEdgeLinesVisible", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.edge_lines_visible.EdgeLinesVisible, type: Literal['edge_lines_visible'] = 'edge_lines_visible') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EdgeLinesVisible, required=True), 'type': FieldInfo(annotation=Literal['edge_lines_visible'], required=False, default='edge_lines_visible')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEmpty(**data)[source][source]
An empty response, used for any command that does not explicitly have a response defined here.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['empty']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEmpty'>, 'config': {'title': 'OptionEmpty'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEmpty'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEmpty'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEmpty:94467871453520', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'empty', 'schema': {'expected': ['empty'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEmpty', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ebb150, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f9a8f60, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "empty", }, expected_py: None, name: "literal['empty']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionEmpty", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEmpty", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa36130, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa361f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f9a8f60, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "empty": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14f9a8f60, ), ], }, expected_repr: "'empty'", name: "literal['empty']", }, ), validate_default: false, copy_default: false, name: "default[literal['empty']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEmpty", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ebb150, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEmpty", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, type: Literal['empty'] = 'empty') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['empty'], required=False, default='empty')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEnableDryRun(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.enable_dry_run.EnableDryRun'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['enable_dry_run']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEnableDryRun'>, 'config': {'title': 'OptionEnableDryRun'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEnableDryRun'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEnableDryRun'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEnableDryRun:94467871257296', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.enable_dry_run.EnableDryRun'>, 'config': {'title': 'EnableDryRun'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.enable_dry_run.EnableDryRun'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.enable_dry_run.EnableDryRun'>>]}, 'ref': 'kittycad.models.enable_dry_run.EnableDryRun:94467864479744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'EnableDryRun', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'enable_dry_run', 'schema': {'expected': ['enable_dry_run'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEnableDryRun', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e8b2d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be49730, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "enable_dry_run", }, expected_py: None, name: "literal['enable_dry_run']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03814800, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "EnableDryRun", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEnableDryRun", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEnableDryRun", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac95380, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac94f30, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "EnableDryRun", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03814800, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EnableDryRun", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac95080, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac95350, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be49730, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "enable_dry_run": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be49730, ), ], }, expected_repr: "'enable_dry_run'", name: "literal['enable_dry_run']", }, ), validate_default: false, copy_default: false, name: "default[literal['enable_dry_run']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEnableDryRun", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e8b2d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEnableDryRun", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.enable_dry_run.EnableDryRun, type: Literal['enable_dry_run'] = 'enable_dry_run') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
EnableDryRun[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EnableDryRun, required=True), 'type': FieldInfo(annotation=Literal['enable_dry_run'], required=False, default='enable_dry_run')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEnableSketchMode(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.enable_sketch_mode.EnableSketchMode'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['enable_sketch_mode']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEnableSketchMode'>, 'config': {'title': 'OptionEnableSketchMode'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEnableSketchMode'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEnableSketchMode'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEnableSketchMode:94467871285440', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.enable_sketch_mode.EnableSketchMode'>, 'config': {'title': 'EnableSketchMode'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.enable_sketch_mode.EnableSketchMode'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.enable_sketch_mode.EnableSketchMode'>>]}, 'ref': 'kittycad.models.enable_sketch_mode.EnableSketchMode:94467864486928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'EnableSketchMode', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'enable_sketch_mode', 'schema': {'expected': ['enable_sketch_mode'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEnableSketchMode', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e920c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4c070, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "enable_sketch_mode", }, expected_py: None, name: "literal['enable_sketch_mode']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03816410, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "EnableSketchMode", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEnableSketchMode", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEnableSketchMode", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac974e0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac97510, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "EnableSketchMode", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03816410, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EnableSketchMode", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac97540, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac97570, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4c070, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "enable_sketch_mode": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4c070, ), ], }, expected_repr: "'enable_sketch_mode'", name: "literal['enable_sketch_mode']", }, ), validate_default: false, copy_default: false, name: "default[literal['enable_sketch_mode']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEnableSketchMode", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e920c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEnableSketchMode", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.enable_sketch_mode.EnableSketchMode, type: Literal['enable_sketch_mode'] = 'enable_sketch_mode') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EnableSketchMode, required=True), 'type': FieldInfo(annotation=Literal['enable_sketch_mode'], required=False, default='enable_sketch_mode')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEngineUtilEvaluatePath(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.engine_util_evaluate_path.EngineUtilEvaluatePath'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['engine_util_evaluate_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEngineUtilEvaluatePath'>, 'config': {'title': 'OptionEngineUtilEvaluatePath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEngineUtilEvaluatePath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEngineUtilEvaluatePath'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEngineUtilEvaluatePath:94467871454512', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.engine_util_evaluate_path.EngineUtilEvaluatePath'>, 'config': {'title': 'EngineUtilEvaluatePath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.engine_util_evaluate_path.EngineUtilEvaluatePath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.engine_util_evaluate_path.EngineUtilEvaluatePath'>>]}, 'ref': 'kittycad.models.engine_util_evaluate_path.EngineUtilEvaluatePath:94467864535456', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'EngineUtilEvaluatePath', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'engine_util_evaluate_path', 'schema': {'expected': ['engine_util_evaluate_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEngineUtilEvaluatePath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ebb530, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038221a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EngineUtilEvaluatePath", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a380, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "engine_util_evaluate_path", }, expected_py: None, name: "literal['engine_util_evaluate_path']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEngineUtilEvaluatePath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEngineUtilEvaluatePath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14abe10e0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14abe3cf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14abe2df0, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14abe3150, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), frozen: false, }, ], model_name: "EngineUtilEvaluatePath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038221a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EngineUtilEvaluatePath", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14abe3d80, ), path: LookupPath( [ S( "type", Py( 0x00007fa14abe3c90, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a380, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "engine_util_evaluate_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a380, ), ], }, expected_repr: "'engine_util_evaluate_path'", name: "literal['engine_util_evaluate_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['engine_util_evaluate_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEngineUtilEvaluatePath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ebb530, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEngineUtilEvaluatePath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.engine_util_evaluate_path.EngineUtilEvaluatePath, type: Literal['engine_util_evaluate_path'] = 'engine_util_evaluate_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EngineUtilEvaluatePath, required=True), 'type': FieldInfo(annotation=Literal['engine_util_evaluate_path'], required=False, default='engine_util_evaluate_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_circular_pattern.EntityCircularPattern'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_circular_pattern']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern'>, 'config': {'title': 'OptionEntityCircularPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern:94467872721424', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_circular_pattern.EntityCircularPattern'>, 'config': {'title': 'EntityCircularPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_circular_pattern.EntityCircularPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_circular_pattern.EntityCircularPattern'>>]}, 'ref': 'kittycad.models.entity_circular_pattern.EntityCircularPattern:94467864532928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityCircularPattern', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_circular_pattern', 'schema': {'expected': ['entity_circular_pattern'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityCircularPattern', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ff0a10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038217c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityCircularPattern", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4d670, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_circular_pattern", }, expected_py: None, name: "literal['entity_circular_pattern']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityCircularPattern", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityCircularPattern", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae7db0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae7de0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14ab126b0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14ab12670, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityCircularPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038217c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityCircularPattern", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae7e10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae7e40, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4d670, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_circular_pattern": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4d670, ), ], }, expected_repr: "'entity_circular_pattern'", name: "literal['entity_circular_pattern']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_circular_pattern']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityCircularPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ff0a10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityCircularPattern", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_circular_pattern.EntityCircularPattern, type: Literal['entity_circular_pattern'] = 'entity_circular_pattern') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityCircularPattern, required=True), 'type': FieldInfo(annotation=Literal['entity_circular_pattern'], required=False, default='entity_circular_pattern')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityFade(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_fade.EntityFade'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_fade']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityFade'>, 'config': {'title': 'OptionEntityFade'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityFade'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityFade'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityFade:94467871201664', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_fade.EntityFade'>, 'config': {'title': 'EntityFade'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_fade.EntityFade'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_fade.EntityFade'>>]}, 'ref': 'kittycad.models.entity_fade.EntityFade:94467864560464', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'EntityFade', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_fade', 'schema': {'expected': ['entity_fade'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityFade', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e7d980, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03828350, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "EntityFade", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4d730, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_fade", }, expected_py: None, name: "literal['entity_fade']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityFade", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityFade", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac94b70, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac94240, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "EntityFade", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03828350, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityFade", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac94030, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac940f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4d730, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_fade": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4d730, ), ], }, expected_repr: "'entity_fade'", name: "literal['entity_fade']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_fade']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityFade", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e7d980, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityFade", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_fade.EntityFade, type: Literal['entity_fade'] = 'entity_fade') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
EntityFade[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityFade, required=True), 'type': FieldInfo(annotation=Literal['entity_fade'], required=False, default='entity_fade')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_all_child_uuids']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids'>, 'config': {'title': 'OptionEntityGetAllChildUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids:94467871897376', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids'>, 'config': {'title': 'EntityGetAllChildUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids'>>]}, 'ref': 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids:94467864545824', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityGetAllChildUuids', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_all_child_uuids', 'schema': {'expected': ['entity_get_all_child_uuids'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetAllChildUuids', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f27720, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a240, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_all_child_uuids", }, expected_py: None, name: "literal['entity_get_all_child_uuids']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03824a20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetAllChildUuids", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetAllChildUuids", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetAllChildUuids", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4ebb0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4ebe0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14aa84670, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14aa84630, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityGetAllChildUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03824a20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityGetAllChildUuids", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4ec10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4ec40, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a240, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_all_child_uuids": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a240, ), ], }, expected_repr: "'entity_get_all_child_uuids'", name: "literal['entity_get_all_child_uuids']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_all_child_uuids']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetAllChildUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f27720, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetAllChildUuids", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids, type: Literal['entity_get_all_child_uuids'] = 'entity_get_all_child_uuids') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetAllChildUuids, required=True), 'type': FieldInfo(annotation=Literal['entity_get_all_child_uuids'], required=False, default='entity_get_all_child_uuids')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_child_uuid']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid'>, 'config': {'title': 'OptionEntityGetChildUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid:94467871860320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid'>, 'config': {'title': 'EntityGetChildUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid'>>]}, 'ref': 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid:94467864552768', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'EntityGetChildUuid', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_child_uuid', 'schema': {'expected': ['entity_get_child_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetChildUuid', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f1e660, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4d8b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_child_uuid", }, expected_py: None, name: "literal['entity_get_child_uuid']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03826540, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetChildUuid", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetChildUuid", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetChildUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4dd40, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4dd10, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14aa3b3f0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14aa3b470, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "EntityGetChildUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03826540, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityGetChildUuid", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4de60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4de30, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4d8b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_child_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4d8b0, ), ], }, expected_repr: "'entity_get_child_uuid'", name: "literal['entity_get_child_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_child_uuid']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetChildUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f1e660, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetChildUuid", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_child_uuid.EntityGetChildUuid, type: Literal['entity_get_child_uuid'] = 'entity_get_child_uuid') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetChildUuid, required=True), 'type': FieldInfo(annotation=Literal['entity_get_child_uuid'], required=False, default='entity_get_child_uuid')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_distance.EntityGetDistance'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_distance']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance'>, 'config': {'title': 'OptionEntityGetDistance'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance:94467872672832', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_distance.EntityGetDistance'>, 'config': {'title': 'EntityGetDistance'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_distance.EntityGetDistance'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_distance.EntityGetDistance'>>]}, 'ref': 'kittycad.models.entity_get_distance.EntityGetDistance:94467864555952', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'max_distance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'min_distance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'EntityGetDistance', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_distance', 'schema': {'expected': ['entity_get_distance'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetDistance', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fe4c40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038271b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "max_distance": SerField { key_py: Py( 0x00007fa14e8a9cb0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "min_distance": SerField { key_py: Py( 0x00007fa14b046770, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "EntityGetDistance", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4d9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_distance", }, expected_py: None, name: "literal['entity_get_distance']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetDistance", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetDistance", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae5a10, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae6fa0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "max_distance", lookup_key: Simple { key: "max_distance", py_key: Py( 0x00007fa14ab0a970, ), path: LookupPath( [ S( "max_distance", Py( 0x00007fa14ab0a930, ), ), ], ), }, name_py: Py( 0x00007fa14e8a9cb0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ab0a700, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "min_distance", lookup_key: Simple { key: "min_distance", py_key: Py( 0x00007fa14ab0a9f0, ), path: LookupPath( [ S( "min_distance", Py( 0x00007fa14ab0a9b0, ), ), ], ), }, name_py: Py( 0x00007fa14b046770, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14ab0a700, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "EntityGetDistance", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038271b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityGetDistance", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae6e50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae6e20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4d9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_distance": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4d9b0, ), ], }, expected_repr: "'entity_get_distance'", name: "literal['entity_get_distance']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_distance']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetDistance", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fe4c40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetDistance", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_distance.EntityGetDistance, type: Literal['entity_get_distance'] = 'entity_get_distance') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetDistance, required=True), 'type': FieldInfo(annotation=Literal['entity_get_distance'], required=False, default='entity_get_distance')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_num_children.EntityGetNumChildren'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_num_children']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren'>, 'config': {'title': 'OptionEntityGetNumChildren'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren:94467871865856', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_num_children.EntityGetNumChildren'>, 'config': {'title': 'EntityGetNumChildren'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_num_children.EntityGetNumChildren'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_num_children.EntityGetNumChildren'>>]}, 'ref': 'kittycad.models.entity_get_num_children.EntityGetNumChildren:94467864570912', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'num': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}}, 'model_name': 'EntityGetNumChildren', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_num_children', 'schema': {'expected': ['entity_get_num_children'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetNumChildren', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f1fc00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0382ac20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "num": SerField { key_py: Py( 0x00007fa14eb33720, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetNumChildren", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4dab0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_num_children", }, expected_py: None, name: "literal['entity_get_num_children']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetNumChildren", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetNumChildren", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4d0e0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4d140, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "num", lookup_key: Simple { key: "num", py_key: Py( 0x00007fa14aa4c8d0, ), path: LookupPath( [ S( "num", Py( 0x00007fa14aa4d170, ), ), ], ), }, name_py: Py( 0x00007fa14eb33720, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, ], model_name: "EntityGetNumChildren", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0382ac20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityGetNumChildren", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4cd50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4cd20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4dab0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_num_children": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4dab0, ), ], }, expected_repr: "'entity_get_num_children'", name: "literal['entity_get_num_children']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_num_children']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetNumChildren", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f1fc00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetNumChildren", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_num_children.EntityGetNumChildren, type: Literal['entity_get_num_children'] = 'entity_get_num_children') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetNumChildren, required=True), 'type': FieldInfo(annotation=Literal['entity_get_num_children'], required=False, default='entity_get_num_children')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_parent_id.EntityGetParentId'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_parent_id']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId'>, 'config': {'title': 'OptionEntityGetParentId'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId:94467871875888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_parent_id.EntityGetParentId'>, 'config': {'title': 'EntityGetParentId'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_parent_id.EntityGetParentId'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_parent_id.EntityGetParentId'>>]}, 'ref': 'kittycad.models.entity_get_parent_id.EntityGetParentId:94467864578800', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'EntityGetParentId', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_parent_id', 'schema': {'expected': ['entity_get_parent_id'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetParentId', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f22330, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0382caf0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetParentId", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4dbb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_parent_id", }, expected_py: None, name: "literal['entity_get_parent_id']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetParentId", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetParentId", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4e6a0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4e6d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14aa38e30, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14aa38e70, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "EntityGetParentId", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0382caf0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityGetParentId", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4e700, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4e730, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4dbb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_parent_id": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4dbb0, ), ], }, expected_repr: "'entity_get_parent_id'", name: "literal['entity_get_parent_id']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_parent_id']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetParentId", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f22330, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetParentId", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_parent_id.EntityGetParentId, type: Literal['entity_get_parent_id'] = 'entity_get_parent_id') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetParentId, required=True), 'type': FieldInfo(annotation=Literal['entity_get_parent_id'], required=False, default='entity_get_parent_id')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_sketch_paths']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths'>, 'config': {'title': 'OptionEntityGetSketchPaths'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths:94467871908992', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths'>, 'config': {'title': 'EntityGetSketchPaths'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths'>>]}, 'ref': 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths:94467864581856', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityGetSketchPaths', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_sketch_paths', 'schema': {'expected': ['entity_get_sketch_paths'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetSketchPaths', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f2a480, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0382d6e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetSketchPaths", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4dcb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_sketch_paths", }, expected_py: None, name: "literal['entity_get_sketch_paths']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetSketchPaths", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetSketchPaths", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4f090, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4f0c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14aa85a70, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14aa85a30, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityGetSketchPaths", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0382d6e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityGetSketchPaths", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4f0f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4f120, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4dcb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_sketch_paths": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4dcb0, ), ], }, expected_repr: "'entity_get_sketch_paths'", name: "literal['entity_get_sketch_paths']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_sketch_paths']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetSketchPaths", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f2a480, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityGetSketchPaths", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths, type: Literal['entity_get_sketch_paths'] = 'entity_get_sketch_paths') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetSketchPaths, required=True), 'type': FieldInfo(annotation=Literal['entity_get_sketch_paths'], required=False, default='entity_get_sketch_paths')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_linear_pattern.EntityLinearPattern'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_linear_pattern']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern'>, 'config': {'title': 'OptionEntityLinearPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern:94467872708800', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_linear_pattern.EntityLinearPattern'>, 'config': {'title': 'EntityLinearPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_linear_pattern.EntityLinearPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_linear_pattern.EntityLinearPattern'>>]}, 'ref': 'kittycad.models.entity_linear_pattern.EntityLinearPattern:94467864601328', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityLinearPattern', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_linear_pattern', 'schema': {'expected': ['entity_linear_pattern'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityLinearPattern', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fed8c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038322f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityLinearPattern", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4ddb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_linear_pattern", }, expected_py: None, name: "literal['entity_linear_pattern']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityLinearPattern", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityLinearPattern", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae78a0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae78d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14ab112b0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14ab11270, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityLinearPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038322f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityLinearPattern", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae7900, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae7930, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4ddb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_linear_pattern": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4ddb0, ), ], }, expected_repr: "'entity_linear_pattern'", name: "literal['entity_linear_pattern']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_linear_pattern']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityLinearPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fed8c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityLinearPattern", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_linear_pattern.EntityLinearPattern, type: Literal['entity_linear_pattern'] = 'entity_linear_pattern') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityLinearPattern, required=True), 'type': FieldInfo(annotation=Literal['entity_linear_pattern'], required=False, default='entity_linear_pattern')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_linear_pattern_transform']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform'>, 'config': {'title': 'OptionEntityLinearPatternTransform'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform:94467872695408', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform'>, 'config': {'title': 'EntityLinearPatternTransform'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform'>>]}, 'ref': 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform:94467864607936', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityLinearPatternTransform', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_linear_pattern_transform', 'schema': {'expected': ['entity_linear_pattern_transform'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityLinearPatternTransform', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fea470, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03833cc0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityLinearPatternTransform", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a1f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_linear_pattern_transform", }, expected_py: None, name: "literal['entity_linear_pattern_transform']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityLinearPatternTransform", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityLinearPatternTransform", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae7390, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae73c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14ab0bdb0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14ab0bd70, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityLinearPatternTransform", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03833cc0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityLinearPatternTransform", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae73f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae7420, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a1f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_linear_pattern_transform": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a1f0, ), ], }, expected_repr: "'entity_linear_pattern_transform'", name: "literal['entity_linear_pattern_transform']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_linear_pattern_transform']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityLinearPatternTransform", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fea470, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityLinearPatternTransform", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform, type: Literal['entity_linear_pattern_transform'] = 'entity_linear_pattern_transform') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityLinearPatternTransform, required=True), 'type': FieldInfo(annotation=Literal['entity_linear_pattern_transform'], required=False, default='entity_linear_pattern_transform')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityMakeHelix(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_make_helix.EntityMakeHelix'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_make_helix']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMakeHelix'>, 'config': {'title': 'OptionEntityMakeHelix'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMakeHelix'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMakeHelix'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityMakeHelix:94467871561472', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_make_helix.EntityMakeHelix'>, 'config': {'title': 'EntityMakeHelix'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_make_helix.EntityMakeHelix'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_make_helix.EntityMakeHelix'>>]}, 'ref': 'kittycad.models.entity_make_helix.EntityMakeHelix:94467864614544', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'EntityMakeHelix', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_make_helix', 'schema': {'expected': ['entity_make_helix'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityMakeHelix', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ed5700, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4df30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_make_helix", }, expected_py: None, name: "literal['entity_make_helix']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03835690, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "EntityMakeHelix", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityMakeHelix", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityMakeHelix", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac170f0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac171b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "EntityMakeHelix", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03835690, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityMakeHelix", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac171e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac17210, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4df30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_make_helix": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4df30, ), ], }, expected_repr: "'entity_make_helix'", name: "literal['entity_make_helix']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_make_helix']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityMakeHelix", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ed5700, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityMakeHelix", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_make_helix.EntityMakeHelix, type: Literal['entity_make_helix'] = 'entity_make_helix') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
EntityMakeHelix[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityMakeHelix, required=True), 'type': FieldInfo(annotation=Literal['entity_make_helix'], required=False, default='entity_make_helix')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityMirror(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_mirror.EntityMirror'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_mirror']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMirror'>, 'config': {'title': 'OptionEntityMirror'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMirror'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMirror'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityMirror:94467871571168', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_mirror.EntityMirror'>, 'config': {'title': 'EntityMirror'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_mirror.EntityMirror'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_mirror.EntityMirror'>>]}, 'ref': 'kittycad.models.entity_mirror.EntityMirror:94467864617792', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'EntityMirror', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_mirror', 'schema': {'expected': ['entity_mirror'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityMirror', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ed7ce0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4dfb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_mirror", }, expected_py: None, name: "literal['entity_mirror']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03836340, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "EntityMirror", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityMirror", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityMirror", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac17690, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac176c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "EntityMirror", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03836340, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityMirror", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac176f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac17720, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4dfb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_mirror": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4dfb0, ), ], }, expected_repr: "'entity_mirror'", name: "literal['entity_mirror']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_mirror']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityMirror", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ed7ce0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityMirror", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_mirror.EntityMirror, type: Literal['entity_mirror'] = 'entity_mirror') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
EntityMirror[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityMirror, required=True), 'type': FieldInfo(annotation=Literal['entity_mirror'], required=False, default='entity_mirror')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntityMirrorAcrossEdge(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_mirror_across_edge.EntityMirrorAcrossEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_mirror_across_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMirrorAcrossEdge'>, 'config': {'title': 'OptionEntityMirrorAcrossEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMirrorAcrossEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityMirrorAcrossEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityMirrorAcrossEdge:94467871580320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_mirror_across_edge.EntityMirrorAcrossEdge'>, 'config': {'title': 'EntityMirrorAcrossEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_mirror_across_edge.EntityMirrorAcrossEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_mirror_across_edge.EntityMirrorAcrossEdge'>>]}, 'ref': 'kittycad.models.entity_mirror_across_edge.EntityMirrorAcrossEdge:94467864589504', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'EntityMirrorAcrossEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_mirror_across_edge', 'schema': {'expected': ['entity_mirror_across_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityMirrorAcrossEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03eda0a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0382f4c0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "EntityMirrorAcrossEdge", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2a150, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_mirror_across_edge", }, expected_py: None, name: "literal['entity_mirror_across_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityMirrorAcrossEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityMirrorAcrossEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14abe0780, ), path: LookupPath( [ S( "data", Py( 0x00007fa14abe3d50, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "EntityMirrorAcrossEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0382f4c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntityMirrorAcrossEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14abe07b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14abe29a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2a150, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_mirror_across_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2a150, ), ], }, expected_repr: "'entity_mirror_across_edge'", name: "literal['entity_mirror_across_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_mirror_across_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntityMirrorAcrossEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eda0a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntityMirrorAcrossEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_mirror_across_edge.EntityMirrorAcrossEdge, type: Literal['entity_mirror_across_edge'] = 'entity_mirror_across_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityMirrorAcrossEdge, required=True), 'type': FieldInfo(annotation=Literal['entity_mirror_across_edge'], required=False, default='entity_mirror_across_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionEntitySetOpacity(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_set_opacity.EntitySetOpacity'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_set_opacity']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntitySetOpacity'>, 'config': {'title': 'OptionEntitySetOpacity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntitySetOpacity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntitySetOpacity'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntitySetOpacity:94467871192608', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_set_opacity.EntitySetOpacity'>, 'config': {'title': 'EntitySetOpacity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_set_opacity.EntitySetOpacity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_set_opacity.EntitySetOpacity'>>]}, 'ref': 'kittycad.models.entity_set_opacity.EntitySetOpacity:94467864599072', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'EntitySetOpacity', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_set_opacity', 'schema': {'expected': ['entity_set_opacity'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntitySetOpacity', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e7b620, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03831a20, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "EntitySetOpacity", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e130, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_set_opacity", }, expected_py: None, name: "literal['entity_set_opacity']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntitySetOpacity", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntitySetOpacity", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac95470, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac953e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "EntitySetOpacity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03831a20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "EntitySetOpacity", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac955c0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac95230, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e130, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_set_opacity": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e130, ), ], }, expected_repr: "'entity_set_opacity'", name: "literal['entity_set_opacity']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_set_opacity']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionEntitySetOpacity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e7b620, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionEntitySetOpacity", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_set_opacity.EntitySetOpacity, type: Literal['entity_set_opacity'] = 'entity_set_opacity') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntitySetOpacity, required=True), 'type': FieldInfo(annotation=Literal['entity_set_opacity'], required=False, default='entity_set_opacity')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionExport(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.export.Export'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['export']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionExport'>, 'config': {'title': 'OptionExport'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionExport'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionExport'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionExport:94467871820096', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.export.Export'>, 'config': {'title': 'Export'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.export.Export'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.export.Export'>>]}, 'ref': 'kittycad.models.export.Export:94467864697440', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'files': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.export_file.ExportFile'>, 'config': {'title': 'ExportFile'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.export_file.ExportFile'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.export_file.ExportFile'>>]}, 'ref': 'kittycad.models.export_file.ExportFile:94467864688288', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'contents': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <bound method Base64Data.validate of <class 'kittycad.models.base64data.Base64Data'>>, 'type': 'no-info'}, 'schema': {'choices': [{'type': 'str'}, {'type': 'bytes'}], 'type': 'union'}, 'serialization': {'function': <bound method Base64Data.serialize of <class 'kittycad.models.base64data.Base64Data'>>, 'type': 'function-plain'}, 'type': 'function-after'}, 'type': 'model-field'}, 'name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ExportFile', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Export', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'export', 'schema': {'expected': ['export'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExport', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f14940, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03849a60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "files": SerField { key_py: Py( 0x00007fa14f9f0980, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055eb038476a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "name": SerField { key_py: Py( 0x00007fa14fa3a388, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "contents": SerField { key_py: Py( 0x00007fa14f9f4760, ), alias: None, alias_py: None, serializer: Some( Function( FunctionPlainSerializer { func: Py( 0x00007fa14b077a00, ), name: "plain_function[serialize]", function_name: "serialize", return_serializer: Any( AnySerializer, ), fallback_serializer: None, when_used: Always, is_field_serializer: false, info_arg: false, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "ExportFile", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[ExportFile]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Export", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14e164660, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "export", }, expected_py: None, name: "literal['export']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionExport", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExport", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4dbc0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4dbf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "files", lookup_key: Simple { key: "files", py_key: Py( 0x00007fa14aa4db60, ), path: LookupPath( [ S( "files", Py( 0x00007fa14aa4db90, ), ), ], ), }, name_py: Py( 0x00007fa14f9f0980, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "contents", lookup_key: Simple { key: "contents", py_key: Py( 0x00007fa14aa45330, ), path: LookupPath( [ S( "contents", Py( 0x00007fa14aa45370, ), ), ], ), }, name_py: Py( 0x00007fa14f9f4760, ), validator: FunctionAfter( FunctionAfterValidator { validator: Union( UnionValidator { mode: Smart, choices: [ ( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), None, ), ( Bytes( BytesValidator { strict: false, bytes_mode: ValBytesMode { ser: Utf8, }, }, ), None, ), ], custom_error: None, strict: false, name: "union[str,bytes]", }, ), func: Py( 0x00007fa14b077980, ), config: Py( 0x00007fa14aa456c0, ), name: "function-after[validate(), union[str,bytes]]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "name", lookup_key: Simple { key: "name", py_key: Py( 0x00007fa14aa4db00, ), path: LookupPath( [ S( "name", Py( 0x00007fa14aa4db30, ), ), ], ), }, name_py: Py( 0x00007fa14fa3a388, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "ExportFile", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038476a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ExportFile", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "Export", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03849a60, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Export", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4dc20, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4dc50, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14e164660, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "export": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14e164660, ), ], }, expected_repr: "'export'", name: "literal['export']", }, ), validate_default: false, copy_default: false, name: "default[literal['export']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionExport", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f14940, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionExport", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.export.Export, type: Literal['export'] = 'export') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Export, required=True), 'type': FieldInfo(annotation=Literal['export'], required=False, default='export')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionExtendPath(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.extend_path.ExtendPath'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['extend_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionExtendPath'>, 'config': {'title': 'OptionExtendPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionExtendPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionExtendPath'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionExtendPath:94467871487120', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.extend_path.ExtendPath'>, 'config': {'title': 'ExtendPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.extend_path.ExtendPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.extend_path.ExtendPath'>>]}, 'ref': 'kittycad.models.extend_path.ExtendPath:94467864710032', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'ExtendPath', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'extend_path', 'schema': {'expected': ['extend_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExtendPath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ec3490, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bf7e630, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "extend_path", }, expected_py: None, name: "literal['extend_path']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0384cb90, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "ExtendPath", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionExtendPath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExtendPath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa366d0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa36700, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "ExtendPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0384cb90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ExtendPath", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa36730, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa36760, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bf7e630, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "extend_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bf7e630, ), ], }, expected_repr: "'extend_path'", name: "literal['extend_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['extend_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionExtendPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ec3490, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionExtendPath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.extend_path.ExtendPath, type: Literal['extend_path'] = 'extend_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
ExtendPath[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ExtendPath, required=True), 'type': FieldInfo(annotation=Literal['extend_path'], required=False, default='extend_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionExtrude(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.extrude.Extrude'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['extrude']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionExtrude'>, 'config': {'title': 'OptionExtrude'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionExtrude'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionExtrude'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionExtrude:94467871496640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.extrude.Extrude'>, 'config': {'title': 'Extrude'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.extrude.Extrude'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.extrude.Extrude'>>]}, 'ref': 'kittycad.models.extrude.Extrude:94467864830272', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'Extrude', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'extrude', 'schema': {'expected': ['extrude'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExtrude', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ec59c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14c15c990, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "extrude", }, expected_py: None, name: "literal['extrude']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0386a140, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "Extrude", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionExtrude", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExtrude", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa36bb0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa36be0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "Extrude", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0386a140, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Extrude", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa36c10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa36c40, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14c15c990, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "extrude": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14c15c990, ), ], }, expected_repr: "'extrude'", name: "literal['extrude']", }, ), validate_default: false, copy_default: false, name: "default[literal['extrude']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionExtrude", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ec59c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionExtrude", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.extrude.Extrude, type: Literal['extrude'] = 'extrude') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Extrude, required=True), 'type': FieldInfo(annotation=Literal['extrude'], required=False, default='extrude')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['extrusion_face_info']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo'>, 'config': {'title': 'OptionExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo:94467867060480', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, 'config': {'title': 'ExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo:94467864852048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'cap': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'ExtrusionFaceCapType'>, 'members': [ExtrusionFaceCapType.NONE, ExtrusionFaceCapType.TOP, ExtrusionFaceCapType.BOTTOM], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.extrusion_face_cap_type.ExtrusionFaceCapType:94467864849104', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'extrusion_face_info', 'schema': {'expected': ['extrusion_face_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03a8a900, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0386f650, ), serializer: Fields( GeneralFieldsSerializer { fields: { "face_id": SerField { key_py: Py( 0x00007fa14b32c8a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "cap": SerField { key_py: Py( 0x00007fa14d7b6b50, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb0386ead0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "curve_id": SerField { key_py: Py( 0x00007fa14b09ad70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "ExtrusionFaceInfo", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e6f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "extrusion_face_info", }, expected_py: None, name: "literal['extrusion_face_info']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionExtrusionFaceInfo", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExtrusionFaceInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae5260, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae5020, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "cap", lookup_key: Simple { key: "cap", py_key: Py( 0x00007fa14aae5d10, ), path: LookupPath( [ S( "cap", Py( 0x00007fa14aae4600, ), ), ], ), }, name_py: Py( 0x00007fa14d7b6b50, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb0386ead0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "bottom": 2, "none": 0, "top": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b0423f0, ), Py( 0x00007fa14b042450, ), Py( 0x00007fa14b0424b0, ), ], }, missing: None, expected_repr: "'none', 'top' or 'bottom'", strict: false, class_repr: "ExtrusionFaceCapType", name: "str-enum[ExtrusionFaceCapType]", }, ), frozen: false, }, Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007fa14ab13730, ), path: LookupPath( [ S( "curve_id", Py( 0x00007fa14ab136f0, ), ), ], ), }, name_py: Py( 0x00007fa14b09ad70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007fa14aae4ed0, ), path: LookupPath( [ S( "face_id", Py( 0x00007fa14aae51a0, ), ), ], ), }, name_py: Py( 0x00007fa14b32c8a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "ExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0386f650, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ExtrusionFaceInfo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae5050, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae6340, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e6f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "extrusion_face_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e6f0, ), ], }, expected_repr: "'extrusion_face_info'", name: "literal['extrusion_face_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['extrusion_face_info']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03a8a900, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionExtrusionFaceInfo", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.extrusion_face_info.ExtrusionFaceInfo, type: Literal['extrusion_face_info'] = 'extrusion_face_info') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ExtrusionFaceInfo, required=True), 'type': FieldInfo(annotation=Literal['extrusion_face_info'], required=False, default='extrusion_face_info')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.face_get_center.FaceGetCenter'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['face_get_center']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter'>, 'config': {'title': 'OptionFaceGetCenter'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter:94467872509008', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.face_get_center.FaceGetCenter'>, 'config': {'title': 'FaceGetCenter'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.face_get_center.FaceGetCenter'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.face_get_center.FaceGetCenter'>>]}, 'ref': 'kittycad.models.face_get_center.FaceGetCenter:94467864839248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'FaceGetCenter', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_center', 'schema': {'expected': ['face_get_center'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetCenter', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fbcc50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e7b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_center", }, expected_py: None, name: "literal['face_get_center']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0386c450, ), serializer: Fields( GeneralFieldsSerializer { fields: { "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "FaceGetCenter", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceGetCenter", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetCenter", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae4c90, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae4d50, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14aae4e10, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14aae4cc0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), frozen: false, }, ], model_name: "FaceGetCenter", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0386c450, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "FaceGetCenter", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae4de0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae4db0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e7b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_center": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e7b0, ), ], }, expected_repr: "'face_get_center'", name: "literal['face_get_center']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_center']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionFaceGetCenter", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fbcc50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFaceGetCenter", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.face_get_center.FaceGetCenter, type: Literal['face_get_center'] = 'face_get_center') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
FaceGetCenter[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=FaceGetCenter, required=True), 'type': FieldInfo(annotation=Literal['face_get_center'], required=False, default='face_get_center')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.face_get_gradient.FaceGetGradient'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['face_get_gradient']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient'>, 'config': {'title': 'OptionFaceGetGradient'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient:94467872524864', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.face_get_gradient.FaceGetGradient'>, 'config': {'title': 'FaceGetGradient'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.face_get_gradient.FaceGetGradient'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.face_get_gradient.FaceGetGradient'>>]}, 'ref': 'kittycad.models.face_get_gradient.FaceGetGradient:94467864859344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'df_du': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'df_dv': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'normal': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'FaceGetGradient', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_gradient', 'schema': {'expected': ['face_get_gradient'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetGradient', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fc0a40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038712d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "df_dv": SerField { key_py: Py( 0x00007fa14b062370, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "df_du": SerField { key_py: Py( 0x00007fa14b0622e0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "normal": SerField { key_py: Py( 0x00007fa14e172f10, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "FaceGetGradient", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e870, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_gradient", }, expected_py: None, name: "literal['face_get_gradient']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceGetGradient", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetGradient", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae53b0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae53e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "df_du", lookup_key: Simple { key: "df_du", py_key: Py( 0x00007fa14aae5200, ), path: LookupPath( [ S( "df_du", Py( 0x00007fa14aae52c0, ), ), ], ), }, name_py: Py( 0x00007fa14b0622e0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "df_dv", lookup_key: Simple { key: "df_dv", py_key: Py( 0x00007fa14aae52f0, ), path: LookupPath( [ S( "df_dv", Py( 0x00007fa14aae5320, ), ), ], ), }, name_py: Py( 0x00007fa14b062370, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "normal", lookup_key: Simple { key: "normal", py_key: Py( 0x00007fa14aae5350, ), path: LookupPath( [ S( "normal", Py( 0x00007fa14aae5380, ), ), ], ), }, name_py: Py( 0x00007fa14e172f10, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "FaceGetGradient", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038712d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "FaceGetGradient", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae5410, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae5440, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e870, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_gradient": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e870, ), ], }, expected_repr: "'face_get_gradient'", name: "literal['face_get_gradient']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_gradient']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionFaceGetGradient", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fc0a40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFaceGetGradient", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.face_get_gradient.FaceGetGradient, type: Literal['face_get_gradient'] = 'face_get_gradient') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
FaceGetGradient[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=FaceGetGradient, required=True), 'type': FieldInfo(annotation=Literal['face_get_gradient'], required=False, default='face_get_gradient')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.face_get_position.FaceGetPosition'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['face_get_position']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition'>, 'config': {'title': 'OptionFaceGetPosition'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition:94467872480432', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.face_get_position.FaceGetPosition'>, 'config': {'title': 'FaceGetPosition'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.face_get_position.FaceGetPosition'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.face_get_position.FaceGetPosition'>>]}, 'ref': 'kittycad.models.face_get_position.FaceGetPosition:94467864876576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'FaceGetPosition', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_position', 'schema': {'expected': ['face_get_position'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetPosition', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fb5cb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03875620, ), serializer: Fields( GeneralFieldsSerializer { fields: { "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0330e750, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "FaceGetPosition", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e930, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_position", }, expected_py: None, name: "literal['face_get_position']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceGetPosition", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetPosition", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae4780, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae4810, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14aaabc90, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14aae4b10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0330e750, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point3d", }, ), frozen: false, }, ], model_name: "FaceGetPosition", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03875620, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "FaceGetPosition", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae46c0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae46f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e930, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_position": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e930, ), ], }, expected_repr: "'face_get_position'", name: "literal['face_get_position']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_position']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionFaceGetPosition", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fb5cb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFaceGetPosition", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.face_get_position.FaceGetPosition, type: Literal['face_get_position'] = 'face_get_position') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
FaceGetPosition[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=FaceGetPosition, required=True), 'type': FieldInfo(annotation=Literal['face_get_position'], required=False, default='face_get_position')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.face_is_planar.FaceIsPlanar'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['face_is_planar']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar'>, 'config': {'title': 'OptionFaceIsPlanar'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar:94467872462752', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.face_is_planar.FaceIsPlanar'>, 'config': {'title': 'FaceIsPlanar'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.face_is_planar.FaceIsPlanar'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.face_is_planar.FaceIsPlanar'>>]}, 'ref': 'kittycad.models.face_is_planar.FaceIsPlanar:94467864894832', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'origin': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'x_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'y_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'z_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'FaceIsPlanar', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_is_planar', 'schema': {'expected': ['face_is_planar'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceIsPlanar', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fb17a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03879d70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "origin": SerField { key_py: Py( 0x00007fa14fa3aa98, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, "y_axis": SerField { key_py: Py( 0x00007fa14b063030, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, "z_axis": SerField { key_py: Py( 0x00007fa14b063120, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, "x_axis": SerField { key_py: Py( 0x00007fa14b062f40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "FaceIsPlanar", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4e9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_is_planar", }, expected_py: None, name: "literal['face_is_planar']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceIsPlanar", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceIsPlanar", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaaa1c0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaaadc0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "origin", lookup_key: Simple { key: "origin", py_key: Py( 0x00007fa14aaab8a0, ), path: LookupPath( [ S( "origin", Py( 0x00007fa14aaa8a20, ), ), ], ), }, name_py: Py( 0x00007fa14fa3aa98, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "x_axis", lookup_key: Simple { key: "x_axis", py_key: Py( 0x00007fa14aaa9260, ), path: LookupPath( [ S( "x_axis", Py( 0x00007fa14aaa8cf0, ), ), ], ), }, name_py: Py( 0x00007fa14b062f40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "y_axis", lookup_key: Simple { key: "y_axis", py_key: Py( 0x00007fa14aaa8810, ), path: LookupPath( [ S( "y_axis", Py( 0x00007fa14aaaa310, ), ), ], ), }, name_py: Py( 0x00007fa14b063030, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "z_axis", lookup_key: Simple { key: "z_axis", py_key: Py( 0x00007fa14aaaa160, ), path: LookupPath( [ S( "z_axis", Py( 0x00007fa14aaaa280, ), ), ], ), }, name_py: Py( 0x00007fa14b063120, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "FaceIsPlanar", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03879d70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "FaceIsPlanar", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa9200, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaaaf10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4e9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_is_planar": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4e9b0, ), ], }, expected_repr: "'face_is_planar'", name: "literal['face_is_planar']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_is_planar']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionFaceIsPlanar", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fb17a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionFaceIsPlanar", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.face_is_planar.FaceIsPlanar, type: Literal['face_is_planar'] = 'face_is_planar') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
FaceIsPlanar[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=FaceIsPlanar, required=True), 'type': FieldInfo(annotation=Literal['face_is_planar'], required=False, default='face_is_planar')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionGetEntityType(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.get_entity_type.GetEntityType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_entity_type']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionGetEntityType'>, 'config': {'title': 'OptionGetEntityType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionGetEntityType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionGetEntityType'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionGetEntityType:94467872292816', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.get_entity_type.GetEntityType'>, 'config': {'title': 'GetEntityType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.get_entity_type.GetEntityType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.get_entity_type.GetEntityType'>>]}, 'ref': 'kittycad.models.get_entity_type.GetEntityType:94467865284128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'EntityType'>, 'members': [EntityType.ENTITY, EntityType.OBJECT, EntityType.PATH, EntityType.CURVE, EntityType.SOLID2D, EntityType.SOLID3D, EntityType.EDGE, EntityType.FACE, EntityType.PLANE, EntityType.VERTEX], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.entity_type.EntityType:94467864596176', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'GetEntityType', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_entity_type', 'schema': {'expected': ['get_entity_type'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetEntityType', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f87fd0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038d8e20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_type": SerField { key_py: Py( 0x00007fa14be4e1f0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03830ed0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "GetEntityType", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4f870, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_entity_type", }, expected_py: None, name: "literal['get_entity_type']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionGetEntityType", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetEntityType", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaaaac0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaaaaf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_type", lookup_key: Simple { key: "entity_type", py_key: Py( 0x00007fa14aabfd30, ), path: LookupPath( [ S( "entity_type", Py( 0x00007fa14aabfcf0, ), ), ], ), }, name_py: Py( 0x00007fa14be4e1f0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03830ed0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "object": 1, "solid3d": 5, "edge": 6, "face": 7, "vertex": 9, "entity": 0, "path": 2, "plane": 8, "curve": 3, "solid2d": 4, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b0411f0, ), Py( 0x00007fa14b041250, ), Py( 0x00007fa14b0412b0, ), Py( 0x00007fa14b041310, ), Py( 0x00007fa14b041370, ), Py( 0x00007fa14b0413d0, ), Py( 0x00007fa14b041430, ), Py( 0x00007fa14b041490, ), Py( 0x00007fa14b0414f0, ), Py( 0x00007fa14b041550, ), ], }, missing: None, expected_repr: "'entity', 'object', 'path', 'curve', 'solid2d', 'solid3d', 'edge', 'face', 'plane' or 'vertex'", strict: false, class_repr: "EntityType", name: "str-enum[EntityType]", }, ), frozen: false, }, ], model_name: "GetEntityType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038d8e20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "GetEntityType", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaaab20, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaaab50, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4f870, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_entity_type": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4f870, ), ], }, expected_repr: "'get_entity_type'", name: "literal['get_entity_type']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_entity_type']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGetEntityType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f87fd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGetEntityType", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.get_entity_type.GetEntityType, type: Literal['get_entity_type'] = 'get_entity_type') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
GetEntityType[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=GetEntityType, required=True), 'type': FieldInfo(annotation=Literal['get_entity_type'], required=False, default='get_entity_type')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.get_num_objects.GetNumObjects'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_num_objects']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects'>, 'config': {'title': 'OptionGetNumObjects'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects:94467872146208', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.get_num_objects.GetNumObjects'>, 'config': {'title': 'GetNumObjects'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.get_num_objects.GetNumObjects'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.get_num_objects.GetNumObjects'>>]}, 'ref': 'kittycad.models.get_num_objects.GetNumObjects:94467865298400', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'num_objects': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}}, 'model_name': 'GetNumObjects', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_num_objects', 'schema': {'expected': ['get_num_objects'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetNumObjects', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f64320, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038dc5e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "num_objects": SerField { key_py: Py( 0x00007fa14c51ca70, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "GetNumObjects", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4f8f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_num_objects", }, expected_py: None, name: "literal['get_num_objects']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionGetNumObjects", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetNumObjects", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa8360, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa83c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "num_objects", lookup_key: Simple { key: "num_objects", py_key: Py( 0x00007fa14abfbaf0, ), path: LookupPath( [ S( "num_objects", Py( 0x00007fa14aaa3370, ), ), ], ), }, name_py: Py( 0x00007fa14c51ca70, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, ], model_name: "GetNumObjects", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038dc5e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "GetNumObjects", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa8390, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa84e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4f8f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_num_objects": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4f8f0, ), ], }, expected_repr: "'get_num_objects'", name: "literal['get_num_objects']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_num_objects']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGetNumObjects", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f64320, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGetNumObjects", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.get_num_objects.GetNumObjects, type: Literal['get_num_objects'] = 'get_num_objects') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
GetNumObjects[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=GetNumObjects, required=True), 'type': FieldInfo(annotation=Literal['get_num_objects'], required=False, default='get_num_objects')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_sketch_mode_plane']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane'>, 'config': {'title': 'OptionGetSketchModePlane'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane:94467872655072', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane'>, 'config': {'title': 'GetSketchModePlane'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane'>>]}, 'ref': 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane:94467865314176', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'origin': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'x_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'y_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'z_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'GetSketchModePlane', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_sketch_mode_plane', 'schema': {'expected': ['get_sketch_mode_plane'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetSketchModePlane', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fe06e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038e0380, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x_axis": SerField { key_py: Py( 0x00007fa14b062f40, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "z_axis": SerField { key_py: Py( 0x00007fa14b063120, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "origin": SerField { key_py: Py( 0x00007fa14fa3aa98, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "y_axis": SerField { key_py: Py( 0x00007fa14b063030, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "GetSketchModePlane", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4f9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_sketch_mode_plane", }, expected_py: None, name: "literal['get_sketch_mode_plane']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionGetSketchModePlane", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetSketchModePlane", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae6010, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae4510, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "origin", lookup_key: Simple { key: "origin", py_key: Py( 0x00007fa14aae6310, ), path: LookupPath( [ S( "origin", Py( 0x00007fa14aae6220, ), ), ], ), }, name_py: Py( 0x00007fa14fa3aa98, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "x_axis", lookup_key: Simple { key: "x_axis", py_key: Py( 0x00007fa14aae6280, ), path: LookupPath( [ S( "x_axis", Py( 0x00007fa14aae5f50, ), ), ], ), }, name_py: Py( 0x00007fa14b062f40, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "y_axis", lookup_key: Simple { key: "y_axis", py_key: Py( 0x00007fa14aae5fe0, ), path: LookupPath( [ S( "y_axis", Py( 0x00007fa14aae5e90, ), ), ], ), }, name_py: Py( 0x00007fa14b063030, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "z_axis", lookup_key: Simple { key: "z_axis", py_key: Py( 0x00007fa14aae5ec0, ), path: LookupPath( [ S( "z_axis", Py( 0x00007fa14aae5e30, ), ), ], ), }, name_py: Py( 0x00007fa14b063120, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "GetSketchModePlane", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038e0380, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "GetSketchModePlane", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae45d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae4270, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4f9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_sketch_mode_plane": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4f9b0, ), ], }, expected_repr: "'get_sketch_mode_plane'", name: "literal['get_sketch_mode_plane']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_sketch_mode_plane']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionGetSketchModePlane", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fe06e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionGetSketchModePlane", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.get_sketch_mode_plane.GetSketchModePlane, type: Literal['get_sketch_mode_plane'] = 'get_sketch_mode_plane') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=GetSketchModePlane, required=True), 'type': FieldInfo(annotation=Literal['get_sketch_mode_plane'], required=False, default='get_sketch_mode_plane')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragEnd(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.handle_mouse_drag_end.HandleMouseDragEnd'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['handle_mouse_drag_end']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragEnd'>, 'config': {'title': 'OptionHandleMouseDragEnd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragEnd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragEnd'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragEnd:94467871716320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.handle_mouse_drag_end.HandleMouseDragEnd'>, 'config': {'title': 'HandleMouseDragEnd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.handle_mouse_drag_end.HandleMouseDragEnd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.handle_mouse_drag_end.HandleMouseDragEnd'>>]}, 'ref': 'kittycad.models.handle_mouse_drag_end.HandleMouseDragEnd:94467865312464', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'HandleMouseDragEnd', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'handle_mouse_drag_end', 'schema': {'expected': ['handle_mouse_drag_end'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHandleMouseDragEnd', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03efb3e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038dfcd0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "HandleMouseDragEnd", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4ff70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "handle_mouse_drag_end", }, expected_py: None, name: "literal['handle_mouse_drag_end']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHandleMouseDragEnd", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHandleMouseDragEnd", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4c5d0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4c600, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "HandleMouseDragEnd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038dfcd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "HandleMouseDragEnd", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4c630, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4c660, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4ff70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "handle_mouse_drag_end": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4ff70, ), ], }, expected_repr: "'handle_mouse_drag_end'", name: "literal['handle_mouse_drag_end']", }, ), validate_default: false, copy_default: false, name: "default[literal['handle_mouse_drag_end']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionHandleMouseDragEnd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03efb3e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHandleMouseDragEnd", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.handle_mouse_drag_end.HandleMouseDragEnd, type: Literal['handle_mouse_drag_end'] = 'handle_mouse_drag_end') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=HandleMouseDragEnd, required=True), 'type': FieldInfo(annotation=Literal['handle_mouse_drag_end'], required=False, default='handle_mouse_drag_end')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragMove(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.handle_mouse_drag_move.HandleMouseDragMove'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['handle_mouse_drag_move']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragMove'>, 'config': {'title': 'OptionHandleMouseDragMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragMove'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragMove:94467871707056', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.handle_mouse_drag_move.HandleMouseDragMove'>, 'config': {'title': 'HandleMouseDragMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.handle_mouse_drag_move.HandleMouseDragMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.handle_mouse_drag_move.HandleMouseDragMove'>>]}, 'ref': 'kittycad.models.handle_mouse_drag_move.HandleMouseDragMove:94467865323760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'HandleMouseDragMove', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'handle_mouse_drag_move', 'schema': {'expected': ['handle_mouse_drag_move'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHandleMouseDragMove', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ef8fb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4fe30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "handle_mouse_drag_move", }, expected_py: None, name: "literal['handle_mouse_drag_move']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038e28f0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "HandleMouseDragMove", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHandleMouseDragMove", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHandleMouseDragMove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4c0c0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4c0f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "HandleMouseDragMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038e28f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "HandleMouseDragMove", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4c120, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4c150, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4fe30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "handle_mouse_drag_move": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4fe30, ), ], }, expected_repr: "'handle_mouse_drag_move'", name: "literal['handle_mouse_drag_move']", }, ), validate_default: false, copy_default: false, name: "default[literal['handle_mouse_drag_move']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionHandleMouseDragMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ef8fb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHandleMouseDragMove", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.handle_mouse_drag_move.HandleMouseDragMove, type: Literal['handle_mouse_drag_move'] = 'handle_mouse_drag_move') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=HandleMouseDragMove, required=True), 'type': FieldInfo(annotation=Literal['handle_mouse_drag_move'], required=False, default='handle_mouse_drag_move')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragStart(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.handle_mouse_drag_start.HandleMouseDragStart'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['handle_mouse_drag_start']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragStart'>, 'config': {'title': 'OptionHandleMouseDragStart'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragStart'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragStart'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionHandleMouseDragStart:94467871694336', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.handle_mouse_drag_start.HandleMouseDragStart'>, 'config': {'title': 'HandleMouseDragStart'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.handle_mouse_drag_start.HandleMouseDragStart'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.handle_mouse_drag_start.HandleMouseDragStart'>>]}, 'ref': 'kittycad.models.handle_mouse_drag_start.HandleMouseDragStart:94467865328176', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'HandleMouseDragStart', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'handle_mouse_drag_start', 'schema': {'expected': ['handle_mouse_drag_start'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHandleMouseDragStart', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ef5e00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be4fc70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "handle_mouse_drag_start", }, expected_py: None, name: "literal['handle_mouse_drag_start']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038e3a30, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "HandleMouseDragStart", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHandleMouseDragStart", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHandleMouseDragStart", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac97b70, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac97ba0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "HandleMouseDragStart", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038e3a30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "HandleMouseDragStart", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac97bd0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac97c00, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be4fc70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "handle_mouse_drag_start": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be4fc70, ), ], }, expected_repr: "'handle_mouse_drag_start'", name: "literal['handle_mouse_drag_start']", }, ), validate_default: false, copy_default: false, name: "default[literal['handle_mouse_drag_start']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionHandleMouseDragStart", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ef5e00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHandleMouseDragStart", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.handle_mouse_drag_start.HandleMouseDragStart, type: Literal['handle_mouse_drag_start'] = 'handle_mouse_drag_start') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=HandleMouseDragStart, required=True), 'type': FieldInfo(annotation=Literal['handle_mouse_drag_start'], required=False, default='handle_mouse_drag_start')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntities(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.highlight_set_entities.HighlightSetEntities'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['highlight_set_entities']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntities'>, 'config': {'title': 'OptionHighlightSetEntities'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntities'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntities'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntities:94467871099264', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.highlight_set_entities.HighlightSetEntities'>, 'config': {'title': 'HighlightSetEntities'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.highlight_set_entities.HighlightSetEntities'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.highlight_set_entities.HighlightSetEntities'>>]}, 'ref': 'kittycad.models.highlight_set_entities.HighlightSetEntities:94467865332592', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'HighlightSetEntities', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'highlight_set_entities', 'schema': {'expected': ['highlight_set_entities'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHighlightSetEntities', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e64980, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038e4b70, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "HighlightSetEntities", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be3a9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "highlight_set_entities", }, expected_py: None, name: "literal['highlight_set_entities']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHighlightSetEntities", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHighlightSetEntities", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac942a0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac942d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "HighlightSetEntities", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038e4b70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "HighlightSetEntities", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac94300, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac94330, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be3a9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "highlight_set_entities": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be3a9b0, ), ], }, expected_repr: "'highlight_set_entities'", name: "literal['highlight_set_entities']", }, ), validate_default: false, copy_default: false, name: "default[literal['highlight_set_entities']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionHighlightSetEntities", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e64980, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHighlightSetEntities", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.highlight_set_entities.HighlightSetEntities, type: Literal['highlight_set_entities'] = 'highlight_set_entities') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=HighlightSetEntities, required=True), 'type': FieldInfo(annotation=Literal['highlight_set_entities'], required=False, default='highlight_set_entities')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.highlight_set_entity.HighlightSetEntity'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['highlight_set_entity']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity'>, 'config': {'title': 'OptionHighlightSetEntity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity:94467871849216', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.highlight_set_entity.HighlightSetEntity'>, 'config': {'title': 'HighlightSetEntity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.highlight_set_entity.HighlightSetEntity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.highlight_set_entity.HighlightSetEntity'>>]}, 'ref': 'kittycad.models.highlight_set_entity.HighlightSetEntity:94467865337808', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'sequence': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'HighlightSetEntity', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'highlight_set_entity', 'schema': {'expected': ['highlight_set_entity'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHighlightSetEntity', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f1bb00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14be3a3b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "highlight_set_entity", }, expected_py: None, name: "literal['highlight_set_entity']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038e5fd0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "sequence": SerField { key_py: Py( 0x00007fa14fa3b790, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "HighlightSetEntity", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHighlightSetEntity", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHighlightSetEntity", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac94e40, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac95830, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14af721b0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14ad6fdb0, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "sequence", lookup_key: Simple { key: "sequence", py_key: Py( 0x00007fa14aa506b0, ), path: LookupPath( [ S( "sequence", Py( 0x00007fa14aef0df0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3b790, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "HighlightSetEntity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038e5fd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "HighlightSetEntity", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac948d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac97930, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14be3a3b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "highlight_set_entity": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14be3a3b0, ), ], }, expected_repr: "'highlight_set_entity'", name: "literal['highlight_set_entity']", }, ), validate_default: false, copy_default: false, name: "default[literal['highlight_set_entity']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionHighlightSetEntity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f1bb00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionHighlightSetEntity", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.highlight_set_entity.HighlightSetEntity, type: Literal['highlight_set_entity'] = 'highlight_set_entity') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=HighlightSetEntity, required=True), 'type': FieldInfo(annotation=Literal['highlight_set_entity'], required=False, default='highlight_set_entity')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionImportFiles(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.import_files.ImportFiles'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['import_files']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionImportFiles'>, 'config': {'title': 'OptionImportFiles'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionImportFiles'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionImportFiles'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionImportFiles:94467872560368', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.import_files.ImportFiles'>, 'config': {'title': 'ImportFiles'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.import_files.ImportFiles'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.import_files.ImportFiles'>>]}, 'ref': 'kittycad.models.import_files.ImportFiles:94467865410752', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ImportFiles', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'import_files', 'schema': {'expected': ['import_files'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionImportFiles', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fc94f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038f7cc0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007fa14d969d70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ImportFiles", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6b870, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "import_files", }, expected_py: None, name: "literal['import_files']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionImportFiles", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionImportFiles", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaabb40, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaab900, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007fa14aabdfb0, ), path: LookupPath( [ S( "object_id", Py( 0x00007fa14ac218f0, ), ), ], ), }, name_py: Py( 0x00007fa14d969d70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "ImportFiles", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038f7cc0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ImportFiles", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaab9f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaab930, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6b870, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "import_files": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6b870, ), ], }, expected_repr: "'import_files'", name: "literal['import_files']", }, ), validate_default: false, copy_default: false, name: "default[literal['import_files']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionImportFiles", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fc94f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionImportFiles", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.import_files.ImportFiles, type: Literal['import_files'] = 'import_files') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
ImportFiles[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ImportFiles, required=True), 'type': FieldInfo(annotation=Literal['import_files'], required=False, default='import_files')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.imported_geometry.ImportedGeometry'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['imported_geometry']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry'>, 'config': {'title': 'OptionImportedGeometry'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry:94467872578896', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.imported_geometry.ImportedGeometry'>, 'config': {'title': 'ImportedGeometry'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.imported_geometry.ImportedGeometry'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.imported_geometry.ImportedGeometry'>>]}, 'ref': 'kittycad.models.imported_geometry.ImportedGeometry:94467865427312', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'value': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'ImportedGeometry', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'imported_geometry', 'schema': {'expected': ['imported_geometry'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionImportedGeometry', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fcdd50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb038fbd70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "id": SerField { key_py: Py( 0x00007fa14fa390f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "value": SerField { key_py: Py( 0x00007fa14fa3c768, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "ImportedGeometry", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc6a4f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "imported_geometry", }, expected_py: None, name: "literal['imported_geometry']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionImportedGeometry", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionImportedGeometry", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae57a0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae5830, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "id", lookup_key: Simple { key: "id", py_key: Py( 0x00007fa14aae5b30, ), path: LookupPath( [ S( "id", Py( 0x00007fa14aae5ad0, ), ), ], ), }, name_py: Py( 0x00007fa14fa390f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "value", lookup_key: Simple { key: "value", py_key: Py( 0x00007fa14aae5aa0, ), path: LookupPath( [ S( "value", Py( 0x00007fa14aae5b00, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c768, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "ImportedGeometry", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb038fbd70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ImportedGeometry", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae56e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae5710, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc6a4f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "imported_geometry": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc6a4f0, ), ], }, expected_repr: "'imported_geometry'", name: "literal['imported_geometry']", }, ), validate_default: false, copy_default: false, name: "default[literal['imported_geometry']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionImportedGeometry", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fcdd50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionImportedGeometry", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.imported_geometry.ImportedGeometry, type: Literal['imported_geometry'] = 'imported_geometry') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ImportedGeometry, required=True), 'type': FieldInfo(annotation=Literal['imported_geometry'], required=False, default='imported_geometry')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionLoft(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.loft.Loft'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['loft']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionLoft'>, 'config': {'title': 'OptionLoft'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionLoft'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionLoft'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionLoft:94467871930256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.loft.Loft'>, 'config': {'title': 'Loft'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.loft.Loft'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.loft.Loft'>>]}, 'ref': 'kittycad.models.loft.Loft:94467865688640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'solid_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'Loft', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'loft', 'schema': {'expected': ['loft'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionLoft', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f2f790, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14c15eee0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "loft", }, expected_py: None, name: "literal['loft']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0393ba40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "solid_id": SerField { key_py: Py( 0x00007fa14af71570, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Loft", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionLoft", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionLoft", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac95590, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac957d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "solid_id", lookup_key: Simple { key: "solid_id", py_key: Py( 0x00007fa14aa4b070, ), path: LookupPath( [ S( "solid_id", Py( 0x00007fa14aa482b0, ), ), ], ), }, name_py: Py( 0x00007fa14af71570, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "Loft", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0393ba40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Loft", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac97240, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac97e40, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14c15eee0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "loft": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14c15eee0, ), ], }, expected_repr: "'loft'", name: "literal['loft']", }, ), validate_default: false, copy_default: false, name: "default[literal['loft']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionLoft", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f2f790, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionLoft", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.loft.Loft, type: Literal['loft'] = 'loft') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Loft, required=True), 'type': FieldInfo(annotation=Literal['loft'], required=False, default='loft')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionMakeAxesGizmo(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.make_axes_gizmo.MakeAxesGizmo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['make_axes_gizmo']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMakeAxesGizmo'>, 'config': {'title': 'OptionMakeAxesGizmo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMakeAxesGizmo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMakeAxesGizmo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMakeAxesGizmo:94467871336608', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.make_axes_gizmo.MakeAxesGizmo'>, 'config': {'title': 'MakeAxesGizmo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.make_axes_gizmo.MakeAxesGizmo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.make_axes_gizmo.MakeAxesGizmo'>>]}, 'ref': 'kittycad.models.make_axes_gizmo.MakeAxesGizmo:94467865708080', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'MakeAxesGizmo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'make_axes_gizmo', 'schema': {'expected': ['make_axes_gizmo'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMakeAxesGizmo', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e9e8a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc69c30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "make_axes_gizmo", }, expected_py: None, name: "literal['make_axes_gizmo']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03940630, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "MakeAxesGizmo", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMakeAxesGizmo", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMakeAxesGizmo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac940c0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac95fb0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "MakeAxesGizmo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03940630, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "MakeAxesGizmo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac94210, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac96370, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc69c30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "make_axes_gizmo": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc69c30, ), ], }, expected_repr: "'make_axes_gizmo'", name: "literal['make_axes_gizmo']", }, ), validate_default: false, copy_default: false, name: "default[literal['make_axes_gizmo']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMakeAxesGizmo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e9e8a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMakeAxesGizmo", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.make_axes_gizmo.MakeAxesGizmo, type: Literal['make_axes_gizmo'] = 'make_axes_gizmo') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
MakeAxesGizmo[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=MakeAxesGizmo, required=True), 'type': FieldInfo(annotation=Literal['make_axes_gizmo'], required=False, default='make_axes_gizmo')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionMakeOffsetPath(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.make_offset_path.MakeOffsetPath'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['make_offset_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMakeOffsetPath'>, 'config': {'title': 'OptionMakeOffsetPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMakeOffsetPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMakeOffsetPath'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMakeOffsetPath:94467872183456', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.make_offset_path.MakeOffsetPath'>, 'config': {'title': 'MakeOffsetPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.make_offset_path.MakeOffsetPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.make_offset_path.MakeOffsetPath'>>]}, 'ref': 'kittycad.models.make_offset_path.MakeOffsetPath:94467865692944', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'MakeOffsetPath', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'make_offset_path', 'schema': {'expected': ['make_offset_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMakeOffsetPath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f6d4a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0393cb10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "MakeOffsetPath", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc69f30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "make_offset_path", }, expected_py: None, name: "literal['make_offset_path']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMakeOffsetPath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMakeOffsetPath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa8ab0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa8ae0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14ad4beb0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14aaa0470, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "MakeOffsetPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0393cb10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "MakeOffsetPath", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa8b10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa8b40, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc69f30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "make_offset_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc69f30, ), ], }, expected_repr: "'make_offset_path'", name: "literal['make_offset_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['make_offset_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMakeOffsetPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f6d4a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMakeOffsetPath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.make_offset_path.MakeOffsetPath, type: Literal['make_offset_path'] = 'make_offset_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
MakeOffsetPath[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=MakeOffsetPath, required=True), 'type': FieldInfo(annotation=Literal['make_offset_path'], required=False, default='make_offset_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionMakePlane(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.make_plane.MakePlane'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['make_plane']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMakePlane'>, 'config': {'title': 'OptionMakePlane'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMakePlane'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMakePlane'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMakePlane:94467871211200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.make_plane.MakePlane'>, 'config': {'title': 'MakePlane'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.make_plane.MakePlane'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.make_plane.MakePlane'>>]}, 'ref': 'kittycad.models.make_plane.MakePlane:94467865697792', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'MakePlane', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'make_plane', 'schema': {'expected': ['make_plane'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMakePlane', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e7fec0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0393de00, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "MakePlane", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bc68d30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "make_plane", }, expected_py: None, name: "literal['make_plane']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMakePlane", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMakePlane", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac95c80, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac95cb0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "MakePlane", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0393de00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "MakePlane", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac95ce0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac95d10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bc68d30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "make_plane": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bc68d30, ), ], }, expected_repr: "'make_plane'", name: "literal['make_plane']", }, ), validate_default: false, copy_default: false, name: "default[literal['make_plane']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMakePlane", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e7fec0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMakePlane", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.make_plane.MakePlane, type: Literal['make_plane'] = 'make_plane') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=MakePlane, required=True), 'type': FieldInfo(annotation=Literal['make_plane'], required=False, default='make_plane')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionMass(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.mass.Mass'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['mass']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMass'>, 'config': {'title': 'OptionMass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMass'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMass:94467872590160', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.mass.Mass'>, 'config': {'title': 'Mass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.mass.Mass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.mass.Mass'>>]}, 'ref': 'kittycad.models.mass.Mass:94467865702208', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'mass': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitMass'>, 'members': [UnitMass.G, UnitMass.KG, UnitMass.LB], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_mass.UnitMass:94467863219280', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'Mass', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mass', 'schema': {'expected': ['mass'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMass', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fd0950, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0393ef40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036e0c50, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "mass": SerField { key_py: Py( 0x00007fa14c15f240, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Mass", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14c15f240, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "mass", }, expected_py: None, name: "literal['mass']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMass", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMass", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae41e0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae4150, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "mass", lookup_key: Simple { key: "mass", py_key: Py( 0x00007fa14aae4090, ), path: LookupPath( [ S( "mass", Py( 0x00007fa14aae40f0, ), ), ], ), }, name_py: Py( 0x00007fa14c15f240, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14aaf8070, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14aaf9470, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036e0c50, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "g": 0, "kg": 1, "lb": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7dd0, ), Py( 0x00007fa14b3c7e30, ), Py( 0x00007fa14b3c7e90, ), ], }, missing: None, expected_repr: "'g', 'kg' or 'lb'", strict: false, class_repr: "UnitMass", name: "str-enum[UnitMass]", }, ), frozen: false, }, ], model_name: "Mass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0393ef40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Mass", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae4300, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae4060, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14c15f240, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "mass": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14c15f240, ), ], }, expected_repr: "'mass'", name: "literal['mass']", }, ), validate_default: false, copy_default: false, name: "default[literal['mass']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fd0950, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMass", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.mass.Mass, type: Literal['mass'] = 'mass') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Mass, required=True), 'type': FieldInfo(annotation=Literal['mass'], required=False, default='mass')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionMouseClick(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.mouse_click.MouseClick'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['mouse_click']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMouseClick'>, 'config': {'title': 'OptionMouseClick'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMouseClick'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMouseClick'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMouseClick:94467872336064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.mouse_click.MouseClick'>, 'config': {'title': 'MouseClick'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.mouse_click.MouseClick'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.mouse_click.MouseClick'>>]}, 'ref': 'kittycad.models.mouse_click.MouseClick:94467870896320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entities_modified': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'entities_selected': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'MouseClick', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mouse_click', 'schema': {'expected': ['mouse_click'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMouseClick', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f928c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd79670, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "mouse_click", }, expected_py: None, name: "literal['mouse_click']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e330c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entities_modified": SerField { key_py: Py( 0x00007fa14ac334b0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "entities_selected": SerField { key_py: Py( 0x00007fa14ac315b0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "MouseClick", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMouseClick", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMouseClick", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa9c50, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa9d10, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entities_modified", lookup_key: Simple { key: "entities_modified", py_key: Py( 0x00007fa14aace6b0, ), path: LookupPath( [ S( "entities_modified", Py( 0x00007fa14aace670, ), ), ], ), }, name_py: Py( 0x00007fa14ac334b0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "entities_selected", lookup_key: Simple { key: "entities_selected", py_key: Py( 0x00007fa14aace730, ), path: LookupPath( [ S( "entities_selected", Py( 0x00007fa14aace6f0, ), ), ], ), }, name_py: Py( 0x00007fa14ac315b0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "MouseClick", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e330c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "MouseClick", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa9ef0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa9d70, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd79670, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "mouse_click": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd79670, ), ], }, expected_repr: "'mouse_click'", name: "literal['mouse_click']", }, ), validate_default: false, copy_default: false, name: "default[literal['mouse_click']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMouseClick", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f928c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMouseClick", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.mouse_click.MouseClick, type: Literal['mouse_click'] = 'mouse_click') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
MouseClick[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=MouseClick, required=True), 'type': FieldInfo(annotation=Literal['mouse_click'], required=False, default='mouse_click')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionMouseMove(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.mouse_move.MouseMove'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['mouse_move']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMouseMove'>, 'config': {'title': 'OptionMouseMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMouseMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMouseMove'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMouseMove:94467871238800', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.mouse_move.MouseMove'>, 'config': {'title': 'MouseMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.mouse_move.MouseMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.mouse_move.MouseMove'>>]}, 'ref': 'kittycad.models.mouse_move.MouseMove:94467870915024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'MouseMove', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mouse_move', 'schema': {'expected': ['mouse_move'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMouseMove', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e86a90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e379d0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "MouseMove", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd796f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "mouse_move", }, expected_py: None, name: "literal['mouse_move']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMouseMove", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMouseMove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac96760, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac96820, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "MouseMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e379d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "MouseMove", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac96850, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac968e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd796f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "mouse_move": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd796f0, ), ], }, expected_repr: "'mouse_move'", name: "literal['mouse_move']", }, ), validate_default: false, copy_default: false, name: "default[literal['mouse_move']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMouseMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e86a90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMouseMove", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.mouse_move.MouseMove, type: Literal['mouse_move'] = 'mouse_move') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=MouseMove, required=True), 'type': FieldInfo(annotation=Literal['mouse_move'], required=False, default='mouse_move')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionMovePathPen(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.move_path_pen.MovePathPen'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['move_path_pen']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMovePathPen'>, 'config': {'title': 'OptionMovePathPen'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMovePathPen'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMovePathPen'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMovePathPen:94467871478032', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.move_path_pen.MovePathPen'>, 'config': {'title': 'MovePathPen'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.move_path_pen.MovePathPen'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.move_path_pen.MovePathPen'>>]}, 'ref': 'kittycad.models.move_path_pen.MovePathPen:94467870918240', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'MovePathPen', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'move_path_pen', 'schema': {'expected': ['move_path_pen'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMovePathPen', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ec1110, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd79770, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "move_path_pen", }, expected_py: None, name: "literal['move_path_pen']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e38660, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "MovePathPen", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMovePathPen", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMovePathPen", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa34510, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa34210, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "MovePathPen", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e38660, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "MovePathPen", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa349f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa35350, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd79770, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "move_path_pen": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd79770, ), ], }, expected_repr: "'move_path_pen'", name: "literal['move_path_pen']", }, ), validate_default: false, copy_default: false, name: "default[literal['move_path_pen']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionMovePathPen", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ec1110, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionMovePathPen", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.move_path_pen.MovePathPen, type: Literal['move_path_pen'] = 'move_path_pen') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
MovePathPen[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=MovePathPen, required=True), 'type': FieldInfo(annotation=Literal['move_path_pen'], required=False, default='move_path_pen')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionNewAnnotation(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.new_annotation.NewAnnotation'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['new_annotation']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionNewAnnotation'>, 'config': {'title': 'OptionNewAnnotation'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionNewAnnotation'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionNewAnnotation'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionNewAnnotation:94467871108320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.new_annotation.NewAnnotation'>, 'config': {'title': 'NewAnnotation'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.new_annotation.NewAnnotation'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.new_annotation.NewAnnotation'>>]}, 'ref': 'kittycad.models.new_annotation.NewAnnotation:94467870922912', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'NewAnnotation', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'new_annotation', 'schema': {'expected': ['new_annotation'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionNewAnnotation', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e66ce0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e398a0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "NewAnnotation", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd797f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "new_annotation", }, expected_py: None, name: "literal['new_annotation']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionNewAnnotation", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionNewAnnotation", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac947b0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac947e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "NewAnnotation", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e398a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "NewAnnotation", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac94810, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac94840, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd797f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "new_annotation": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd797f0, ), ], }, expected_repr: "'new_annotation'", name: "literal['new_annotation']", }, ), validate_default: false, copy_default: false, name: "default[literal['new_annotation']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionNewAnnotation", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e66ce0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionNewAnnotation", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.new_annotation.NewAnnotation, type: Literal['new_annotation'] = 'new_annotation') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
NewAnnotation[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=NewAnnotation, required=True), 'type': FieldInfo(annotation=Literal['new_annotation'], required=False, default='new_annotation')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionObjectBringToFront(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.object_bring_to_front.ObjectBringToFront'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['object_bring_to_front']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionObjectBringToFront'>, 'config': {'title': 'OptionObjectBringToFront'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionObjectBringToFront'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionObjectBringToFront'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionObjectBringToFront:94467871145584', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.object_bring_to_front.ObjectBringToFront'>, 'config': {'title': 'ObjectBringToFront'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.object_bring_to_front.ObjectBringToFront'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.object_bring_to_front.ObjectBringToFront'>>]}, 'ref': 'kittycad.models.object_bring_to_front.ObjectBringToFront:94467870930528', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'ObjectBringToFront', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'object_bring_to_front', 'schema': {'expected': ['object_bring_to_front'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionObjectBringToFront', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e6fe70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd79a70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "object_bring_to_front", }, expected_py: None, name: "literal['object_bring_to_front']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e3b660, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "ObjectBringToFront", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionObjectBringToFront", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionObjectBringToFront", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac94c00, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac94c30, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "ObjectBringToFront", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e3b660, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ObjectBringToFront", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac94c60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac94c90, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd79a70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "object_bring_to_front": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd79a70, ), ], }, expected_repr: "'object_bring_to_front'", name: "literal['object_bring_to_front']", }, ), validate_default: false, copy_default: false, name: "default[literal['object_bring_to_front']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionObjectBringToFront", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e6fe70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionObjectBringToFront", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.object_bring_to_front.ObjectBringToFront, type: Literal['object_bring_to_front'] = 'object_bring_to_front') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ObjectBringToFront, required=True), 'type': FieldInfo(annotation=Literal['object_bring_to_front'], required=False, default='object_bring_to_front')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionObjectSetMaterialParamsPbr(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.object_set_material_params_pbr.ObjectSetMaterialParamsPbr'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['object_set_material_params_pbr']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionObjectSetMaterialParamsPbr'>, 'config': {'title': 'OptionObjectSetMaterialParamsPbr'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionObjectSetMaterialParamsPbr'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionObjectSetMaterialParamsPbr'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionObjectSetMaterialParamsPbr:94467871155104', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.object_set_material_params_pbr.ObjectSetMaterialParamsPbr'>, 'config': {'title': 'ObjectSetMaterialParamsPbr'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.object_set_material_params_pbr.ObjectSetMaterialParamsPbr'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.object_set_material_params_pbr.ObjectSetMaterialParamsPbr'>>]}, 'ref': 'kittycad.models.object_set_material_params_pbr.ObjectSetMaterialParamsPbr:94467870935728', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'ObjectSetMaterialParamsPbr', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'object_set_material_params_pbr', 'schema': {'expected': ['object_set_material_params_pbr'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionObjectSetMaterialParamsPbr', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e723a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd29ac0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "object_set_material_params_pbr", }, expected_py: None, name: "literal['object_set_material_params_pbr']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e3cab0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "ObjectSetMaterialParamsPbr", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionObjectSetMaterialParamsPbr", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionObjectSetMaterialParamsPbr", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac95110, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac95140, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "ObjectSetMaterialParamsPbr", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e3cab0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ObjectSetMaterialParamsPbr", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac95170, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac951a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd29ac0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "object_set_material_params_pbr": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd29ac0, ), ], }, expected_repr: "'object_set_material_params_pbr'", name: "literal['object_set_material_params_pbr']", }, ), validate_default: false, copy_default: false, name: "default[literal['object_set_material_params_pbr']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionObjectSetMaterialParamsPbr", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e723a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionObjectSetMaterialParamsPbr", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.object_set_material_params_pbr.ObjectSetMaterialParamsPbr, type: Literal['object_set_material_params_pbr'] = 'object_set_material_params_pbr') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ObjectSetMaterialParamsPbr, required=True), 'type': FieldInfo(annotation=Literal['object_set_material_params_pbr'], required=False, default='object_set_material_params_pbr')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionObjectVisible(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.object_visible.ObjectVisible'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['object_visible']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionObjectVisible'>, 'config': {'title': 'OptionObjectVisible'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionObjectVisible'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionObjectVisible'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionObjectVisible:94467871136512', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.object_visible.ObjectVisible'>, 'config': {'title': 'ObjectVisible'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.object_visible.ObjectVisible'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.object_visible.ObjectVisible'>>]}, 'ref': 'kittycad.models.object_visible.ObjectVisible:94467870938320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'ObjectVisible', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'object_visible', 'schema': {'expected': ['object_visible'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionObjectVisible', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e6db00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e3d4d0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "ObjectVisible", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd79bb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "object_visible", }, expected_py: None, name: "literal['object_visible']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionObjectVisible", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionObjectVisible", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac945d0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac94600, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "ObjectVisible", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e3d4d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ObjectVisible", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac94570, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac94750, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd79bb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "object_visible": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd79bb0, ), ], }, expected_repr: "'object_visible'", name: "literal['object_visible']", }, ), validate_default: false, copy_default: false, name: "default[literal['object_visible']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionObjectVisible", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e6db00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionObjectVisible", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.object_visible.ObjectVisible, type: Literal['object_visible'] = 'object_visible') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
ObjectVisible[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ObjectVisible, required=True), 'type': FieldInfo(annotation=Literal['object_visible'], required=False, default='object_visible')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_curve_uuid']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid'>, 'config': {'title': 'OptionPathGetCurveUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid:94467872412864', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid'>, 'config': {'title': 'PathGetCurveUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid'>>]}, 'ref': 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid:94467871044656', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'PathGetCurveUuid', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_curve_uuid', 'schema': {'expected': ['path_get_curve_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetCurveUuid', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fa54c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7a430, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_curve_uuid", }, expected_py: None, name: "literal['path_get_curve_uuid']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e57430, ), serializer: Fields( GeneralFieldsSerializer { fields: { "curve_id": SerField { key_py: Py( 0x00007fa14b09ad70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetCurveUuid", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetCurveUuid", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetCurveUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa93b0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa8d20, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007fa14aad6cb0, ), path: LookupPath( [ S( "curve_id", Py( 0x00007fa14aad6c30, ), ), ], ), }, name_py: Py( 0x00007fa14b09ad70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "PathGetCurveUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e57430, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PathGetCurveUuid", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa88d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa8900, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7a430, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_curve_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7a430, ), ], }, expected_repr: "'path_get_curve_uuid'", name: "literal['path_get_curve_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_curve_uuid']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetCurveUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fa54c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetCurveUuid", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_curve_uuid.PathGetCurveUuid, type: Literal['path_get_curve_uuid'] = 'path_get_curve_uuid') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetCurveUuid, required=True), 'type': FieldInfo(annotation=Literal['path_get_curve_uuid'], required=False, default='path_get_curve_uuid')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_curve_uuids_for_vertices']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices'>, 'config': {'title': 'OptionPathGetCurveUuidsForVertices'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices:94467872396720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices'>, 'config': {'title': 'PathGetCurveUuidsForVertices'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices'>>]}, 'ref': 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices:94467871047792', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'PathGetCurveUuidsForVertices', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_curve_uuids_for_vertices', 'schema': {'expected': ['path_get_curve_uuids_for_vertices'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetCurveUuidsForVertices', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fa15b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e58070, ), serializer: Fields( GeneralFieldsSerializer { fields: { "curve_ids": SerField { key_py: Py( 0x00007fa14abfb030, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetCurveUuidsForVertices", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd297f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_curve_uuids_for_vertices", }, expected_py: None, name: "literal['path_get_curve_uuids_for_vertices']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetCurveUuidsForVertices", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetCurveUuidsForVertices", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaab2d0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaab330, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_ids", lookup_key: Simple { key: "curve_ids", py_key: Py( 0x00007fa14aacf030, ), path: LookupPath( [ S( "curve_ids", Py( 0x00007fa14aacdaf0, ), ), ], ), }, name_py: Py( 0x00007fa14abfb030, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "PathGetCurveUuidsForVertices", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e58070, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PathGetCurveUuidsForVertices", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaab300, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaab450, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd297f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_curve_uuids_for_vertices": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd297f0, ), ], }, expected_repr: "'path_get_curve_uuids_for_vertices'", name: "literal['path_get_curve_uuids_for_vertices']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_curve_uuids_for_vertices']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetCurveUuidsForVertices", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fa15b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetCurveUuidsForVertices", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices, type: Literal['path_get_curve_uuids_for_vertices'] = 'path_get_curve_uuids_for_vertices') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetCurveUuidsForVertices, required=True), 'type': FieldInfo(annotation=Literal['path_get_curve_uuids_for_vertices'], required=False, default='path_get_curve_uuids_for_vertices')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_info.PathGetInfo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_info']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo'>, 'config': {'title': 'OptionPathGetInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo:94467872361536', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_info.PathGetInfo'>, 'config': {'title': 'PathGetInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_info.PathGetInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_info.PathGetInfo'>>]}, 'ref': 'kittycad.models.path_get_info.PathGetInfo:94467871321824', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'segments': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.path_segment_info.PathSegmentInfo'>, 'config': {'title': 'PathSegmentInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment_info.PathSegmentInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment_info.PathSegmentInfo'>>]}, 'ref': 'kittycad.models.path_segment_info.PathSegmentInfo:94467871056704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'command': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'PathCommand'>, 'members': [PathCommand.MOVE_TO, PathCommand.LINE_TO, PathCommand.BEZ_CURVE_TO, PathCommand.NURBS_CURVE_TO, PathCommand.ADD_ARC], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.path_command.PathCommand:94467871054144', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'command_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'relative': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}}, 'model_name': 'PathSegmentInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'PathGetInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_info', 'schema': {'expected': ['path_get_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetInfo', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f98c40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e9aee0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "segments": SerField { key_py: Py( 0x00007fa14e811b70, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055eb03e5a340, ), serializer: Fields( GeneralFieldsSerializer { fields: { "command": SerField { key_py: Py( 0x00007fa14fa37a48, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03e59940, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "relative": SerField { key_py: Py( 0x00007fa14e010bf0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "command_id": SerField { key_py: Py( 0x00007fa14ac004b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "PathSegmentInfo", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[PathSegmentInfo]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetInfo", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7a530, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_info", }, expected_py: None, name: "literal['path_get_info']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetInfo", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaab750, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaab780, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "segments", lookup_key: Simple { key: "segments", py_key: Py( 0x00007fa14aad5bb0, ), path: LookupPath( [ S( "segments", Py( 0x00007fa14aad5bf0, ), ), ], ), }, name_py: Py( 0x00007fa14e811b70, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "command", lookup_key: Simple { key: "command", py_key: Py( 0x00007fa14aaab6f0, ), path: LookupPath( [ S( "command", Py( 0x00007fa14aaab720, ), ), ], ), }, name_py: Py( 0x00007fa14fa37a48, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03e59940, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "add_arc": 4, "move_to": 0, "line_to": 1, "bez_curve_to": 2, "nurbs_curve_to": 3, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14afefc50, ), Py( 0x00007fa14afefcb0, ), Py( 0x00007fa14afefd10, ), Py( 0x00007fa14afefd70, ), Py( 0x00007fa14afefdd0, ), ], }, missing: None, expected_repr: "'move_to', 'line_to', 'bez_curve_to', 'nurbs_curve_to' or 'add_arc'", strict: false, class_repr: "PathCommand", name: "str-enum[PathCommand]", }, ), frozen: false, }, Field { name: "command_id", lookup_key: Simple { key: "command_id", py_key: Py( 0x00007fa14aad5af0, ), path: LookupPath( [ S( "command_id", Py( 0x00007fa14aad5ab0, ), ), ], ), }, name_py: Py( 0x00007fa14ac004b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055eb039991a0, ), config: Py( 0x00007fa14aad5780, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), name: "nullable[function-after[ModelingCmdId(), str]]", }, ), validate_default: false, copy_default: false, name: "default[nullable[function-after[ModelingCmdId(), str]]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "relative", lookup_key: Simple { key: "relative", py_key: Py( 0x00007fa14aad5b70, ), path: LookupPath( [ S( "relative", Py( 0x00007fa14aad5b30, ), ), ], ), }, name_py: Py( 0x00007fa14e010bf0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, ], model_name: "PathSegmentInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e5a340, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PathSegmentInfo", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "PathGetInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e9aee0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PathGetInfo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaab7b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaab7e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7a530, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7a530, ), ], }, expected_repr: "'path_get_info'", name: "literal['path_get_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_info']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f98c40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetInfo", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_info.PathGetInfo, type: Literal['path_get_info'] = 'path_get_info') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
PathGetInfo[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetInfo, required=True), 'type': FieldInfo(annotation=Literal['path_get_info'], required=False, default='path_get_info')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_sketch_target_uuid']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid'>, 'config': {'title': 'OptionPathGetSketchTargetUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid:94467872436768', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid'>, 'config': {'title': 'PathGetSketchTargetUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid'>>]}, 'ref': 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid:94467871340624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'target_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'PathGetSketchTargetUuid', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_sketch_target_uuid', 'schema': {'expected': ['path_get_sketch_target_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetSketchTargetUuid', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fab220, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e9f850, ), serializer: Fields( GeneralFieldsSerializer { fields: { "target_id": SerField { key_py: Py( 0x00007fa14fa29340, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetSketchTargetUuid", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2add0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_sketch_target_uuid", }, expected_py: None, name: "literal['path_get_sketch_target_uuid']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetSketchTargetUuid", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetSketchTargetUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae4390, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae43c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "target_id", lookup_key: Simple { key: "target_id", py_key: Py( 0x00007fa14aae1b30, ), path: LookupPath( [ S( "target_id", Py( 0x00007fa14aae1af0, ), ), ], ), }, name_py: Py( 0x00007fa14fa29340, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "PathGetSketchTargetUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e9f850, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PathGetSketchTargetUuid", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae43f0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae4420, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2add0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_sketch_target_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2add0, ), ], }, expected_repr: "'path_get_sketch_target_uuid'", name: "literal['path_get_sketch_target_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_sketch_target_uuid']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetSketchTargetUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fab220, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetSketchTargetUuid", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid, type: Literal['path_get_sketch_target_uuid'] = 'path_get_sketch_target_uuid') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetSketchTargetUuid, required=True), 'type': FieldInfo(annotation=Literal['path_get_sketch_target_uuid'], required=False, default='path_get_sketch_target_uuid')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_vertex_uuids']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids'>, 'config': {'title': 'OptionPathGetVertexUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids:94467872425488', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids'>, 'config': {'title': 'PathGetVertexUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids'>>]}, 'ref': 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids:94467871345952', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'vertex_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'PathGetVertexUuids', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_vertex_uuids', 'schema': {'expected': ['path_get_vertex_uuids'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetVertexUuids', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fa8610, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7a6b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_vertex_uuids", }, expected_py: None, name: "literal['path_get_vertex_uuids']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ea0d20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "vertex_ids": SerField { key_py: Py( 0x00007fa14b01fc70, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetVertexUuids", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetVertexUuids", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetVertexUuids", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaab360, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaab870, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "vertex_ids", lookup_key: Simple { key: "vertex_ids", py_key: Py( 0x00007fa14aae06b0, ), path: LookupPath( [ S( "vertex_ids", Py( 0x00007fa14aae0670, ), ), ], ), }, name_py: Py( 0x00007fa14b01fc70, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "PathGetVertexUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ea0d20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PathGetVertexUuids", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaabea0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaabed0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7a6b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_vertex_uuids": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7a6b0, ), ], }, expected_repr: "'path_get_vertex_uuids'", name: "literal['path_get_vertex_uuids']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_vertex_uuids']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathGetVertexUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fa8610, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathGetVertexUuids", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_vertex_uuids.PathGetVertexUuids, type: Literal['path_get_vertex_uuids'] = 'path_get_vertex_uuids') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetVertexUuids, required=True), 'type': FieldInfo(annotation=Literal['path_get_vertex_uuids'], required=False, default='path_get_vertex_uuids')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_segment_info.PathSegmentInfo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_segment_info']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo'>, 'config': {'title': 'OptionPathSegmentInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo:94467872376288', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_segment_info.PathSegmentInfo'>, 'config': {'title': 'PathSegmentInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment_info.PathSegmentInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment_info.PathSegmentInfo'>>]}, 'ref': 'kittycad.models.path_segment_info.PathSegmentInfo:94467871056704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'command': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'PathCommand'>, 'members': [PathCommand.MOVE_TO, PathCommand.LINE_TO, PathCommand.BEZ_CURVE_TO, PathCommand.NURBS_CURVE_TO, PathCommand.ADD_ARC], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.path_command.PathCommand:94467871054144', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'command_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'relative': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}}, 'model_name': 'PathSegmentInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_segment_info', 'schema': {'expected': ['path_segment_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathSegmentInfo', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f9c5e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e5a340, ), serializer: Fields( GeneralFieldsSerializer { fields: { "command": SerField { key_py: Py( 0x00007fa14fa37a48, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb03e59940, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "command_id": SerField { key_py: Py( 0x00007fa14ac004b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "relative": SerField { key_py: Py( 0x00007fa14e010bf0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "PathSegmentInfo", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7a830, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_segment_info", }, expected_py: None, name: "literal['path_segment_info']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathSegmentInfo", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathSegmentInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaabd80, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaabdb0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "command", lookup_key: Simple { key: "command", py_key: Py( 0x00007fa14aaabd20, ), path: LookupPath( [ S( "command", Py( 0x00007fa14aaabd50, ), ), ], ), }, name_py: Py( 0x00007fa14fa37a48, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb03e59940, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "bez_curve_to": 2, "line_to": 1, "move_to": 0, "nurbs_curve_to": 3, "add_arc": 4, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14afefc50, ), Py( 0x00007fa14afefcb0, ), Py( 0x00007fa14afefd10, ), Py( 0x00007fa14afefd70, ), Py( 0x00007fa14afefdd0, ), ], }, missing: None, expected_repr: "'move_to', 'line_to', 'bez_curve_to', 'nurbs_curve_to' or 'add_arc'", strict: false, class_repr: "PathCommand", name: "str-enum[PathCommand]", }, ), frozen: false, }, Field { name: "command_id", lookup_key: Simple { key: "command_id", py_key: Py( 0x00007fa14aad7370, ), path: LookupPath( [ S( "command_id", Py( 0x00007fa14aad7330, ), ), ], ), }, name_py: Py( 0x00007fa14ac004b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055eb039991a0, ), config: Py( 0x00007fa14aad7100, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), name: "nullable[function-after[ModelingCmdId(), str]]", }, ), validate_default: false, copy_default: false, name: "default[nullable[function-after[ModelingCmdId(), str]]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "relative", lookup_key: Simple { key: "relative", py_key: Py( 0x00007fa14aad73f0, ), path: LookupPath( [ S( "relative", Py( 0x00007fa14aad73b0, ), ), ], ), }, name_py: Py( 0x00007fa14e010bf0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, ], model_name: "PathSegmentInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e5a340, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PathSegmentInfo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaabde0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaabe10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7a830, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_segment_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7a830, ), ], }, expected_repr: "'path_segment_info'", name: "literal['path_segment_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_segment_info']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPathSegmentInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f9c5e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPathSegmentInfo", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_segment_info.PathSegmentInfo, type: Literal['path_segment_info'] = 'path_segment_info') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
PathSegmentInfo[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathSegmentInfo, required=True), 'type': FieldInfo(annotation=Literal['path_segment_info'], required=False, default='path_segment_info')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['plane_intersect_and_project']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject'>, 'config': {'title': 'OptionPlaneIntersectAndProject'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject:94467872540720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject'>, 'config': {'title': 'PlaneIntersectAndProject'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject'>>]}, 'ref': 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject:94467871396848', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'plane_coordinates': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94467866083888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'PlaneIntersectAndProject', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'plane_intersect_and_project', 'schema': {'expected': ['plane_intersect_and_project'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPlaneIntersectAndProject', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fc4830, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ead3f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "plane_coordinates": SerField { key_py: Py( 0x00007fa14aa31cb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055eb0399c230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PlaneIntersectAndProject", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2af10, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "plane_intersect_and_project", }, expected_py: None, name: "literal['plane_intersect_and_project']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPlaneIntersectAndProject", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPlaneIntersectAndProject", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae58c0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae58f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "plane_coordinates", lookup_key: Simple { key: "plane_coordinates", py_key: Py( 0x00007fa14aafa430, ), path: LookupPath( [ S( "plane_coordinates", Py( 0x00007fa14aafa3f0, ), ), ], ), }, name_py: Py( 0x00007fa14aa31cb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14aafa100, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055eb03768720, ), config: Py( 0x00007fa14aafa100, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0399c230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point2d", }, ), name: "nullable[Point2d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point2d]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "PlaneIntersectAndProject", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ead3f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PlaneIntersectAndProject", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae5920, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae5950, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2af10, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "plane_intersect_and_project": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2af10, ), ], }, expected_repr: "'plane_intersect_and_project'", name: "literal['plane_intersect_and_project']", }, ), validate_default: false, copy_default: false, name: "default[literal['plane_intersect_and_project']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPlaneIntersectAndProject", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fc4830, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPlaneIntersectAndProject", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject, type: Literal['plane_intersect_and_project'] = 'plane_intersect_and_project') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PlaneIntersectAndProject, required=True), 'type': FieldInfo(annotation=Literal['plane_intersect_and_project'], required=False, default='plane_intersect_and_project')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionPlaneSetColor(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.plane_set_color.PlaneSetColor'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['plane_set_color']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneSetColor'>, 'config': {'title': 'OptionPlaneSetColor'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneSetColor'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneSetColor'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPlaneSetColor:94467871220256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.plane_set_color.PlaneSetColor'>, 'config': {'title': 'PlaneSetColor'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.plane_set_color.PlaneSetColor'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.plane_set_color.PlaneSetColor'>>]}, 'ref': 'kittycad.models.plane_set_color.PlaneSetColor:94467871359472', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'PlaneSetColor', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'plane_set_color', 'schema': {'expected': ['plane_set_color'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPlaneSetColor', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e82220, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ea41f0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "PlaneSetColor", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7acf0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "plane_set_color", }, expected_py: None, name: "literal['plane_set_color']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPlaneSetColor", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPlaneSetColor", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac96160, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac96190, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "PlaneSetColor", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ea41f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "PlaneSetColor", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac961c0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac961f0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7acf0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "plane_set_color": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7acf0, ), ], }, expected_repr: "'plane_set_color'", name: "literal['plane_set_color']", }, ), validate_default: false, copy_default: false, name: "default[literal['plane_set_color']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionPlaneSetColor", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e82220, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionPlaneSetColor", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.plane_set_color.PlaneSetColor, type: Literal['plane_set_color'] = 'plane_set_color') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
PlaneSetColor[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PlaneSetColor, required=True), 'type': FieldInfo(annotation=Literal['plane_set_color'], required=False, default='plane_set_color')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionReconfigureStream(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.reconfigure_stream.ReconfigureStream'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['reconfigure_stream']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionReconfigureStream'>, 'config': {'title': 'OptionReconfigureStream'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionReconfigureStream'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionReconfigureStream'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionReconfigureStream:94467871735024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.reconfigure_stream.ReconfigureStream'>, 'config': {'title': 'ReconfigureStream'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.reconfigure_stream.ReconfigureStream'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.reconfigure_stream.ReconfigureStream'>>]}, 'ref': 'kittycad.models.reconfigure_stream.ReconfigureStream:94467871384560', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'ReconfigureStream', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'reconfigure_stream', 'schema': {'expected': ['reconfigure_stream'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionReconfigureStream', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03effcf0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03eaa3f0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "ReconfigureStream", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7afb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "reconfigure_stream", }, expected_py: None, name: "literal['reconfigure_stream']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionReconfigureStream", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionReconfigureStream", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac96ca0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac96df0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "ReconfigureStream", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eaa3f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ReconfigureStream", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac96f40, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac97210, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7afb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "reconfigure_stream": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7afb0, ), ], }, expected_repr: "'reconfigure_stream'", name: "literal['reconfigure_stream']", }, ), validate_default: false, copy_default: false, name: "default[literal['reconfigure_stream']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionReconfigureStream", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03effcf0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionReconfigureStream", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.reconfigure_stream.ReconfigureStream, type: Literal['reconfigure_stream'] = 'reconfigure_stream') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ReconfigureStream, required=True), 'type': FieldInfo(annotation=Literal['reconfigure_stream'], required=False, default='reconfigure_stream')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionRemoveSceneObjects(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.remove_scene_objects.RemoveSceneObjects'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['remove_scene_objects']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionRemoveSceneObjects'>, 'config': {'title': 'OptionRemoveSceneObjects'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionRemoveSceneObjects'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionRemoveSceneObjects'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionRemoveSceneObjects:94467871725952', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.remove_scene_objects.RemoveSceneObjects'>, 'config': {'title': 'RemoveSceneObjects'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.remove_scene_objects.RemoveSceneObjects'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.remove_scene_objects.RemoveSceneObjects'>>]}, 'ref': 'kittycad.models.remove_scene_objects.RemoveSceneObjects:94467871378928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'RemoveSceneObjects', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'remove_scene_objects', 'schema': {'expected': ['remove_scene_objects'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionRemoveSceneObjects', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03efd980, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b0b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "remove_scene_objects", }, expected_py: None, name: "literal['remove_scene_objects']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ea8df0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "RemoveSceneObjects", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionRemoveSceneObjects", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionRemoveSceneObjects", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac96460, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac962e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "RemoveSceneObjects", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ea8df0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "RemoveSceneObjects", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac962b0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac963d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b0b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "remove_scene_objects": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b0b0, ), ], }, expected_repr: "'remove_scene_objects'", name: "literal['remove_scene_objects']", }, ), validate_default: false, copy_default: false, name: "default[literal['remove_scene_objects']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionRemoveSceneObjects", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03efd980, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionRemoveSceneObjects", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.remove_scene_objects.RemoveSceneObjects, type: Literal['remove_scene_objects'] = 'remove_scene_objects') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=RemoveSceneObjects, required=True), 'type': FieldInfo(annotation=Literal['remove_scene_objects'], required=False, default='remove_scene_objects')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionRevolve(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.revolve.Revolve'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['revolve']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionRevolve'>, 'config': {'title': 'OptionRevolve'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionRevolve'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionRevolve'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionRevolve:94467871505696', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.revolve.Revolve'>, 'config': {'title': 'Revolve'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.revolve.Revolve'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.revolve.Revolve'>>]}, 'ref': 'kittycad.models.revolve.Revolve:94467871382144', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'Revolve', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'revolve', 'schema': {'expected': ['revolve'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionRevolve', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ec7d20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ea9a80, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "Revolve", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14c398f00, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "revolve", }, expected_py: None, name: "literal['revolve']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionRevolve", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionRevolve", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac16370, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac163a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "Revolve", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ea9a80, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Revolve", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac163d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac16400, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14c398f00, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "revolve": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14c398f00, ), ], }, expected_repr: "'revolve'", name: "literal['revolve']", }, ), validate_default: false, copy_default: false, name: "default[literal['revolve']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionRevolve", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ec7d20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionRevolve", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.revolve.Revolve, type: Literal['revolve'] = 'revolve') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Revolve, required=True), 'type': FieldInfo(annotation=Literal['revolve'], required=False, default='revolve')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionRevolveAboutEdge(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.revolve_about_edge.RevolveAboutEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['revolve_about_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionRevolveAboutEdge'>, 'config': {'title': 'OptionRevolveAboutEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionRevolveAboutEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionRevolveAboutEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionRevolveAboutEdge:94467871524176', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.revolve_about_edge.RevolveAboutEdge'>, 'config': {'title': 'RevolveAboutEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.revolve_about_edge.RevolveAboutEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.revolve_about_edge.RevolveAboutEdge'>>]}, 'ref': 'kittycad.models.revolve_about_edge.RevolveAboutEdge:94467871388736', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'RevolveAboutEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'revolve_about_edge', 'schema': {'expected': ['revolve_about_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionRevolveAboutEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ecc550, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03eab440, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "RevolveAboutEdge", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b1b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "revolve_about_edge", }, expected_py: None, name: "literal['revolve_about_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionRevolveAboutEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionRevolveAboutEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa35da0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa35a70, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "RevolveAboutEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eab440, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "RevolveAboutEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa35b60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa35d10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b1b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "revolve_about_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b1b0, ), ], }, expected_repr: "'revolve_about_edge'", name: "literal['revolve_about_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['revolve_about_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionRevolveAboutEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ecc550, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionRevolveAboutEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.revolve_about_edge.RevolveAboutEdge, type: Literal['revolve_about_edge'] = 'revolve_about_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=RevolveAboutEdge, required=True), 'type': FieldInfo(annotation=Literal['revolve_about_edge'], required=False, default='revolve_about_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSceneClearAll(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.scene_clear_all.SceneClearAll'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['scene_clear_all']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSceneClearAll'>, 'config': {'title': 'OptionSceneClearAll'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSceneClearAll'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSceneClearAll'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSceneClearAll:94467871080656', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.scene_clear_all.SceneClearAll'>, 'config': {'title': 'SceneClearAll'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.scene_clear_all.SceneClearAll'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.scene_clear_all.SceneClearAll'>>]}, 'ref': 'kittycad.models.scene_clear_all.SceneClearAll:94467871365456', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SceneClearAll', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'scene_clear_all', 'schema': {'expected': ['scene_clear_all'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSceneClearAll', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e600d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ea5950, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SceneClearAll", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b6b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "scene_clear_all", }, expected_py: None, name: "literal['scene_clear_all']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSceneClearAll", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSceneClearAll", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac16a60, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac16c40, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SceneClearAll", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ea5950, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SceneClearAll", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac162e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac161c0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b6b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "scene_clear_all": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b6b0, ), ], }, expected_repr: "'scene_clear_all'", name: "literal['scene_clear_all']", }, ), validate_default: false, copy_default: false, name: "default[literal['scene_clear_all']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSceneClearAll", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e600d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSceneClearAll", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.scene_clear_all.SceneClearAll, type: Literal['scene_clear_all'] = 'scene_clear_all') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
SceneClearAll[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SceneClearAll, required=True), 'type': FieldInfo(annotation=Literal['scene_clear_all'], required=False, default='scene_clear_all')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSelectAdd(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.select_add.SelectAdd'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_add']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectAdd'>, 'config': {'title': 'OptionSelectAdd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSelectAdd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectAdd'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSelectAdd:94467871589840', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.select_add.SelectAdd'>, 'config': {'title': 'SelectAdd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.select_add.SelectAdd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.select_add.SelectAdd'>>]}, 'ref': 'kittycad.models.select_add.SelectAdd:94467871368704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SelectAdd', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_add', 'schema': {'expected': ['select_add'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectAdd', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03edc5d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b8b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_add", }, expected_py: None, name: "literal['select_add']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ea6600, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SelectAdd", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectAdd", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectAdd", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa36d90, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa36e20, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SelectAdd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ea6600, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SelectAdd", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa36d30, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa36910, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b8b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_add": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b8b0, ), ], }, expected_repr: "'select_add'", name: "literal['select_add']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_add']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectAdd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03edc5d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectAdd", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.select_add.SelectAdd, type: Literal['select_add'] = 'select_add') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SelectAdd, required=True), 'type': FieldInfo(annotation=Literal['select_add'], required=False, default='select_add')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSelectClear(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.select_clear.SelectClear'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_clear']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectClear'>, 'config': {'title': 'OptionSelectClear'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSelectClear'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectClear'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSelectClear:94467871800784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.select_clear.SelectClear'>, 'config': {'title': 'SelectClear'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.select_clear.SelectClear'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.select_clear.SelectClear'>>]}, 'ref': 'kittycad.models.select_clear.SelectClear:94467871371952', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SelectClear', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_clear', 'schema': {'expected': ['select_clear'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectClear', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f0fdd0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b930, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_clear", }, expected_py: None, name: "literal['select_clear']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ea72b0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SelectClear", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectClear", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectClear", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4c900, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4c840, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SelectClear", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ea72b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SelectClear", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4c7e0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4c990, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b930, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_clear": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b930, ), ], }, expected_repr: "'select_clear'", name: "literal['select_clear']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_clear']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectClear", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f0fdd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectClear", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.select_clear.SelectClear, type: Literal['select_clear'] = 'select_clear') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
SelectClear[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SelectClear, required=True), 'type': FieldInfo(annotation=Literal['select_clear'], required=False, default='select_clear')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSelectGet(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.select_get.SelectGet'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_get']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectGet'>, 'config': {'title': 'OptionSelectGet'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSelectGet'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectGet'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSelectGet:94467872206720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.select_get.SelectGet'>, 'config': {'title': 'SelectGet'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.select_get.SelectGet'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.select_get.SelectGet'>>]}, 'ref': 'kittycad.models.select_get.SelectGet:94467871375200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'SelectGet', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_get', 'schema': {'expected': ['select_get'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectGet', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f72f80, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7b9b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_get", }, expected_py: None, name: "literal['select_get']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ea7f60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007fa14b038970, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "SelectGet", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectGet", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectGet", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa94d0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa9500, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007fa14aab13b0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007fa14aab1370, ), ), ], ), }, name_py: Py( 0x00007fa14b038970, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "SelectGet", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ea7f60, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SelectGet", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa9530, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa9560, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7b9b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_get": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7b9b0, ), ], }, expected_repr: "'select_get'", name: "literal['select_get']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_get']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectGet", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f72f80, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectGet", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.select_get.SelectGet, type: Literal['select_get'] = 'select_get') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SelectGet, required=True), 'type': FieldInfo(annotation=Literal['select_get'], required=False, default='select_get')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSelectRemove(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.select_remove.SelectRemove'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_remove']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectRemove'>, 'config': {'title': 'OptionSelectRemove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSelectRemove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectRemove'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSelectRemove:94467871071216', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.select_remove.SelectRemove'>, 'config': {'title': 'SelectRemove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.select_remove.SelectRemove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.select_remove.SelectRemove'>>]}, 'ref': 'kittycad.models.select_remove.SelectRemove:94467871599856', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SelectRemove', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_remove', 'schema': {'expected': ['select_remove'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectRemove', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e5dbf0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7ba30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_remove", }, expected_py: None, name: "literal['select_remove']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03edecf0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SelectRemove", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectRemove", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectRemove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac17450, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac17630, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SelectRemove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03edecf0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SelectRemove", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac172a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac17300, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7ba30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_remove": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7ba30, ), ], }, expected_repr: "'select_remove'", name: "literal['select_remove']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_remove']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectRemove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e5dbf0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectRemove", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.select_remove.SelectRemove, type: Literal['select_remove'] = 'select_remove') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
SelectRemove[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SelectRemove, required=True), 'type': FieldInfo(annotation=Literal['select_remove'], required=False, default='select_remove')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSelectReplace(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.select_replace.SelectReplace'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_replace']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectReplace'>, 'config': {'title': 'OptionSelectReplace'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSelectReplace'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectReplace'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSelectReplace:94467871089744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.select_replace.SelectReplace'>, 'config': {'title': 'SelectReplace'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.select_replace.SelectReplace'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.select_replace.SelectReplace'>>]}, 'ref': 'kittycad.models.select_replace.SelectReplace:94467871618816', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SelectReplace', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_replace', 'schema': {'expected': ['select_replace'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectReplace', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e62450, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7bab0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_replace", }, expected_py: None, name: "literal['select_replace']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee3700, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SelectReplace", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectReplace", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectReplace", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac17d80, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac17db0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SelectReplace", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee3700, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SelectReplace", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac17de0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac17e10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7bab0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_replace": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7bab0, ), ], }, expected_repr: "'select_replace'", name: "literal['select_replace']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_replace']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectReplace", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e62450, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectReplace", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.select_replace.SelectReplace, type: Literal['select_replace'] = 'select_replace') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
SelectReplace[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SelectReplace, required=True), 'type': FieldInfo(annotation=Literal['select_replace'], required=False, default='select_replace')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.select_with_point.SelectWithPoint'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_with_point']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint'>, 'config': {'title': 'OptionSelectWithPoint'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint:94467871829248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.select_with_point.SelectWithPoint'>, 'config': {'title': 'SelectWithPoint'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.select_with_point.SelectWithPoint'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.select_with_point.SelectWithPoint'>>]}, 'ref': 'kittycad.models.select_with_point.SelectWithPoint:94467871622064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'SelectWithPoint', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_with_point', 'schema': {'expected': ['select_with_point'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectWithPoint', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f16d00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7bb70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_with_point", }, expected_py: None, name: "literal['select_with_point']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee43b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007fa14b0459f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "SelectWithPoint", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectWithPoint", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectWithPoint", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4e0d0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4e100, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007fa14aa44070, ), path: LookupPath( [ S( "entity_id", Py( 0x00007fa14aa440b0, ), ), ], ), }, name_py: Py( 0x00007fa14b0459f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "SelectWithPoint", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee43b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SelectWithPoint", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4e130, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4e160, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7bb70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_with_point": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7bb70, ), ], }, expected_repr: "'select_with_point'", name: "literal['select_with_point']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_with_point']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSelectWithPoint", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f16d00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSelectWithPoint", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.select_with_point.SelectWithPoint, type: Literal['select_with_point'] = 'select_with_point') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
SelectWithPoint[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SelectWithPoint, required=True), 'type': FieldInfo(annotation=Literal['select_with_point'], required=False, default='select_with_point')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSendObject(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.send_object.SendObject'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['send_object']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSendObject'>, 'config': {'title': 'OptionSendObject'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSendObject'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSendObject'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSendObject:94467871183040', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.send_object.SendObject'>, 'config': {'title': 'SendObject'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.send_object.SendObject'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.send_object.SendObject'>>]}, 'ref': 'kittycad.models.send_object.SendObject:94467871623120', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SendObject', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'send_object', 'schema': {'expected': ['send_object'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSendObject', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e790c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee47d0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SendObject", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7bc70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "send_object", }, expected_py: None, name: "literal['send_object']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSendObject", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSendObject", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac17d20, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac17f30, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SendObject", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee47d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SendObject", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac17ed0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac17fc0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7bc70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "send_object": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7bc70, ), ], }, expected_repr: "'send_object'", name: "literal['send_object']", }, ), validate_default: false, copy_default: false, name: "default[literal['send_object']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSendObject", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e790c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSendObject", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.send_object.SendObject, type: Literal['send_object'] = 'send_object') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
SendObject[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SendObject, required=True), 'type': FieldInfo(annotation=Literal['send_object'], required=False, default='send_object')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSetBackgroundColor(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.set_background_color.SetBackgroundColor'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_background_color']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSetBackgroundColor'>, 'config': {'title': 'OptionSetBackgroundColor'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSetBackgroundColor'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSetBackgroundColor'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSetBackgroundColor:94467871294544', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.set_background_color.SetBackgroundColor'>, 'config': {'title': 'SetBackgroundColor'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.set_background_color.SetBackgroundColor'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.set_background_color.SetBackgroundColor'>>]}, 'ref': 'kittycad.models.set_background_color.SetBackgroundColor:94467871628320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SetBackgroundColor', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_background_color', 'schema': {'expected': ['set_background_color'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetBackgroundColor', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e94450, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee5c20, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SetBackgroundColor", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7bfb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_background_color", }, expected_py: None, name: "literal['set_background_color']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetBackgroundColor", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetBackgroundColor", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac17f00, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac16f70, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SetBackgroundColor", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee5c20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SetBackgroundColor", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac17930, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac17ea0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7bfb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_background_color": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7bfb0, ), ], }, expected_repr: "'set_background_color'", name: "literal['set_background_color']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_background_color']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetBackgroundColor", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e94450, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetBackgroundColor", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.set_background_color.SetBackgroundColor, type: Literal['set_background_color'] = 'set_background_color') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SetBackgroundColor, required=True), 'type': FieldInfo(annotation=Literal['set_background_color'], required=False, default='set_background_color')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSetCurrentToolProperties(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.set_current_tool_properties.SetCurrentToolProperties'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_current_tool_properties']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSetCurrentToolProperties'>, 'config': {'title': 'OptionSetCurrentToolProperties'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSetCurrentToolProperties'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSetCurrentToolProperties'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSetCurrentToolProperties:94467871304080', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.set_current_tool_properties.SetCurrentToolProperties'>, 'config': {'title': 'SetCurrentToolProperties'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.set_current_tool_properties.SetCurrentToolProperties'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.set_current_tool_properties.SetCurrentToolProperties'>>]}, 'ref': 'kittycad.models.set_current_tool_properties.SetCurrentToolProperties:94467871632048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SetCurrentToolProperties', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_current_tool_properties', 'schema': {'expected': ['set_current_tool_properties'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetCurrentToolProperties', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e96990, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b0f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_current_tool_properties", }, expected_py: None, name: "literal['set_current_tool_properties']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee6ab0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SetCurrentToolProperties", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetCurrentToolProperties", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetCurrentToolProperties", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac97300, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac97330, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SetCurrentToolProperties", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee6ab0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SetCurrentToolProperties", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac972a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac97480, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b0f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_current_tool_properties": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b0f0, ), ], }, expected_repr: "'set_current_tool_properties'", name: "literal['set_current_tool_properties']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_current_tool_properties']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetCurrentToolProperties", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e96990, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetCurrentToolProperties", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.set_current_tool_properties.SetCurrentToolProperties, type: Literal['set_current_tool_properties'] = 'set_current_tool_properties') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SetCurrentToolProperties, required=True), 'type': FieldInfo(annotation=Literal['set_current_tool_properties'], required=False, default='set_current_tool_properties')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSetDefaultSystemProperties(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.set_default_system_properties.SetDefaultSystemProperties'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_default_system_properties']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSetDefaultSystemProperties'>, 'config': {'title': 'OptionSetDefaultSystemProperties'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSetDefaultSystemProperties'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSetDefaultSystemProperties'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSetDefaultSystemProperties:94467871672128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.set_default_system_properties.SetDefaultSystemProperties'>, 'config': {'title': 'SetDefaultSystemProperties'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.set_default_system_properties.SetDefaultSystemProperties'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.set_default_system_properties.SetDefaultSystemProperties'>>]}, 'ref': 'kittycad.models.set_default_system_properties.SetDefaultSystemProperties:94467871635296', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SetDefaultSystemProperties', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_default_system_properties', 'schema': {'expected': ['set_default_system_properties'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetDefaultSystemProperties', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ef0740, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee7760, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SetDefaultSystemProperties", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b190, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_default_system_properties", }, expected_py: None, name: "literal['set_default_system_properties']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetDefaultSystemProperties", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetDefaultSystemProperties", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac969a0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac96a30, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SetDefaultSystemProperties", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee7760, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SetDefaultSystemProperties", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac95a10, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac96910, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b190, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_default_system_properties": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b190, ), ], }, expected_repr: "'set_default_system_properties'", name: "literal['set_default_system_properties']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_default_system_properties']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetDefaultSystemProperties", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ef0740, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetDefaultSystemProperties", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.set_default_system_properties.SetDefaultSystemProperties, type: Literal['set_default_system_properties'] = 'set_default_system_properties') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SetDefaultSystemProperties, required=True), 'type': FieldInfo(annotation=Literal['set_default_system_properties'], required=False, default='set_default_system_properties')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSetSceneUnits(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.set_scene_units.SetSceneUnits'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_scene_units']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSetSceneUnits'>, 'config': {'title': 'OptionSetSceneUnits'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSetSceneUnits'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSetSceneUnits'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSetSceneUnits:94467871740368', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.set_scene_units.SetSceneUnits'>, 'config': {'title': 'SetSceneUnits'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.set_scene_units.SetSceneUnits'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.set_scene_units.SetSceneUnits'>>]}, 'ref': 'kittycad.models.set_scene_units.SetSceneUnits:94467871610512', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SetSceneUnits', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_scene_units', 'schema': {'expected': ['set_scene_units'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetSceneUnits', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f011d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c1b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_scene_units", }, expected_py: None, name: "literal['set_scene_units']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee1690, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SetSceneUnits", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetSceneUnits", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetSceneUnits", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4c420, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4c390, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SetSceneUnits", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee1690, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SetSceneUnits", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4c570, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4c1e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c1b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_scene_units": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c1b0, ), ], }, expected_repr: "'set_scene_units'", name: "literal['set_scene_units']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_scene_units']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetSceneUnits", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f011d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetSceneUnits", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.set_scene_units.SetSceneUnits, type: Literal['set_scene_units'] = 'set_scene_units') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
SetSceneUnits[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SetSceneUnits, required=True), 'type': FieldInfo(annotation=Literal['set_scene_units'], required=False, default='set_scene_units')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSetSelectionFilter(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.set_selection_filter.SetSelectionFilter'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_selection_filter']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSetSelectionFilter'>, 'config': {'title': 'OptionSetSelectionFilter'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSetSelectionFilter'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSetSelectionFilter'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSetSelectionFilter:94467871763136', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.set_selection_filter.SetSelectionFilter'>, 'config': {'title': 'SetSelectionFilter'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.set_selection_filter.SetSelectionFilter'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.set_selection_filter.SetSelectionFilter'>>]}, 'ref': 'kittycad.models.set_selection_filter.SetSelectionFilter:94467871613760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SetSelectionFilter', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_selection_filter', 'schema': {'expected': ['set_selection_filter'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetSelectionFilter', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f06ac0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee2340, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SetSelectionFilter", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c270, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_selection_filter", }, expected_py: None, name: "literal['set_selection_filter']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetSelectionFilter", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetSelectionFilter", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4cf00, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4cf30, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SetSelectionFilter", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee2340, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SetSelectionFilter", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4cf60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4cf90, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c270, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_selection_filter": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c270, ), ], }, expected_repr: "'set_selection_filter'", name: "literal['set_selection_filter']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_selection_filter']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetSelectionFilter", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f06ac0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetSelectionFilter", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.set_selection_filter.SetSelectionFilter, type: Literal['set_selection_filter'] = 'set_selection_filter') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SetSelectionFilter, required=True), 'type': FieldInfo(annotation=Literal['set_selection_filter'], required=False, default='set_selection_filter')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSetSelectionType(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.set_selection_type.SetSelectionType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_selection_type']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSetSelectionType'>, 'config': {'title': 'OptionSetSelectionType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSetSelectionType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSetSelectionType'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSetSelectionType:94467871744352', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.set_selection_type.SetSelectionType'>, 'config': {'title': 'SetSelectionType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.set_selection_type.SetSelectionType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.set_selection_type.SetSelectionType'>>]}, 'ref': 'kittycad.models.set_selection_type.SetSelectionType:94467871655536', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SetSelectionType', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_selection_type', 'schema': {'expected': ['set_selection_type'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetSelectionType', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f02160, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c370, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_selection_type", }, expected_py: None, name: "literal['set_selection_type']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03eec670, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SetSelectionType", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetSelectionType", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetSelectionType", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4c9f0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4ca20, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SetSelectionType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eec670, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SetSelectionType", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4ca50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4ca80, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c370, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_selection_type": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c370, ), ], }, expected_repr: "'set_selection_type'", name: "literal['set_selection_type']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_selection_type']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetSelectionType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f02160, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetSelectionType", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.set_selection_type.SetSelectionType, type: Literal['set_selection_type'] = 'set_selection_type') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SetSelectionType, required=True), 'type': FieldInfo(annotation=Literal['set_selection_type'], required=False, default='set_selection_type')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSetTool(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.set_tool.SetTool'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['set_tool']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSetTool'>, 'config': {'title': 'OptionSetTool'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSetTool'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSetTool'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSetTool:94467871229744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.set_tool.SetTool'>, 'config': {'title': 'SetTool'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.set_tool.SetTool'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.set_tool.SetTool'>>]}, 'ref': 'kittycad.models.set_tool.SetTool:94467871658784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SetTool', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'set_tool', 'schema': {'expected': ['set_tool'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSetTool', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e84730, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c430, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "set_tool", }, expected_py: None, name: "literal['set_tool']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03eed320, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SetTool", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSetTool", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSetTool", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac96640, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac96670, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SetTool", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eed320, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SetTool", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac966a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac966d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c430, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "set_tool": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c430, ), ], }, expected_repr: "'set_tool'", name: "literal['set_tool']", }, ), validate_default: false, copy_default: false, name: "default[literal['set_tool']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSetTool", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e84730, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSetTool", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.set_tool.SetTool, type: Literal['set_tool'] = 'set_tool') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SetTool, required=True), 'type': FieldInfo(annotation=Literal['set_tool'], required=False, default='set_tool')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSketchModeDisable(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.sketch_mode_disable.SketchModeDisable'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['sketch_mode_disable']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSketchModeDisable'>, 'config': {'title': 'OptionSketchModeDisable'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSketchModeDisable'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSketchModeDisable'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSketchModeDisable:94467871248272', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.sketch_mode_disable.SketchModeDisable'>, 'config': {'title': 'SketchModeDisable'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.sketch_mode_disable.SketchModeDisable'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.sketch_mode_disable.SketchModeDisable'>>]}, 'ref': 'kittycad.models.sketch_mode_disable.SketchModeDisable:94467871662032', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'SketchModeDisable', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'sketch_mode_disable', 'schema': {'expected': ['sketch_mode_disable'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSketchModeDisable', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e88f90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c630, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "sketch_mode_disable", }, expected_py: None, name: "literal['sketch_mode_disable']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03eedfd0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "SketchModeDisable", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSketchModeDisable", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSketchModeDisable", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac95ef0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac95ec0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "SketchModeDisable", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eedfd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SketchModeDisable", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac95e60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac95e30, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c630, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "sketch_mode_disable": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c630, ), ], }, expected_repr: "'sketch_mode_disable'", name: "literal['sketch_mode_disable']", }, ), validate_default: false, copy_default: false, name: "default[literal['sketch_mode_disable']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSketchModeDisable", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e88f90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSketchModeDisable", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.sketch_mode_disable.SketchModeDisable, type: Literal['sketch_mode_disable'] = 'sketch_mode_disable') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SketchModeDisable, required=True), 'type': FieldInfo(annotation=Literal['sketch_mode_disable'], required=False, default='sketch_mode_disable')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid2DAddHole(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid2d_add_hole.Solid2dAddHole'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid2d_add_hole']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid2DAddHole'>, 'config': {'title': 'OptionSolid2DAddHole'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid2DAddHole'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid2DAddHole'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid2DAddHole:94467871164208', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid2d_add_hole.Solid2dAddHole'>, 'config': {'title': 'Solid2dAddHole'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid2d_add_hole.Solid2dAddHole'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid2d_add_hole.Solid2dAddHole'>>]}, 'ref': 'kittycad.models.solid2d_add_hole.Solid2dAddHole:94467865622624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'Solid2dAddHole', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid2d_add_hole', 'schema': {'expected': ['solid2d_add_hole'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid2DAddHole', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e74730, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0392b860, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "Solid2dAddHole", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c730, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid2d_add_hole", }, expected_py: None, name: "literal['solid2d_add_hole']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid2DAddHole", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid2DAddHole", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac95620, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac95650, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "Solid2dAddHole", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0392b860, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid2dAddHole", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac95680, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac956b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c730, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid2d_add_hole": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c730, ), ], }, expected_repr: "'solid2d_add_hole'", name: "literal['solid2d_add_hole']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid2d_add_hole']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid2DAddHole", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e74730, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid2DAddHole", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid2d_add_hole.Solid2dAddHole, type: Literal['solid2d_add_hole'] = 'solid2d_add_hole') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
Solid2dAddHole[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid2dAddHole, required=True), 'type': FieldInfo(annotation=Literal['solid2d_add_hole'], required=False, default='solid2d_add_hole')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DFilletEdge(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_fillet_edge.Solid3dFilletEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_fillet_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DFilletEdge'>, 'config': {'title': 'OptionSolid3DFilletEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DFilletEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DFilletEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DFilletEdge:94467871173888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_fillet_edge.Solid3dFilletEdge'>, 'config': {'title': 'Solid3dFilletEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_fillet_edge.Solid3dFilletEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_fillet_edge.Solid3dFilletEdge'>>]}, 'ref': 'kittycad.models.solid3d_fillet_edge.Solid3dFilletEdge:94467865625872', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'Solid3dFilletEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_fillet_edge', 'schema': {'expected': ['solid3d_fillet_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DFilletEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e76d00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7c7f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_fillet_edge", }, expected_py: None, name: "literal['solid3d_fillet_edge']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0392c510, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "Solid3dFilletEdge", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DFilletEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DFilletEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa34fc0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa353b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "Solid3dFilletEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0392c510, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dFilletEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa369d0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa35290, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7c7f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_fillet_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7c7f0, ), ], }, expected_repr: "'solid3d_fillet_edge'", name: "literal['solid3d_fillet_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_fillet_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DFilletEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e76d00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DFilletEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_fillet_edge.Solid3dFilletEdge, type: Literal['solid3d_fillet_edge'] = 'solid3d_fillet_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dFilletEdge, required=True), 'type': FieldInfo(annotation=Literal['solid3d_fillet_edge'], required=False, default='solid3d_fillet_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_all_edge_faces']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces'>, 'config': {'title': 'OptionSolid3DGetAllEdgeFaces'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces:94467872215968', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces'>, 'config': {'title': 'Solid3dGetAllEdgeFaces'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces'>>]}, 'ref': 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces:94467865627648', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'faces': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetAllEdgeFaces', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_all_edge_faces', 'schema': {'expected': ['solid3d_get_all_edge_faces'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetAllEdgeFaces', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f753a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b230, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_all_edge_faces", }, expected_py: None, name: "literal['solid3d_get_all_edge_faces']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0392cc00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "faces": SerField { key_py: Py( 0x00007fa14aa348a0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetAllEdgeFaces", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetAllEdgeFaces", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetAllEdgeFaces", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa9a40, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa9a70, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "faces", lookup_key: Simple { key: "faces", py_key: Py( 0x00007fa14aaa99e0, ), path: LookupPath( [ S( "faces", Py( 0x00007fa14aaa9a10, ), ), ], ), }, name_py: Py( 0x00007fa14aa348a0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "Solid3dGetAllEdgeFaces", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0392cc00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dGetAllEdgeFaces", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa9aa0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa9ad0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b230, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_all_edge_faces": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b230, ), ], }, expected_repr: "'solid3d_get_all_edge_faces'", name: "literal['solid3d_get_all_edge_faces']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_all_edge_faces']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetAllEdgeFaces", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f753a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetAllEdgeFaces", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces, type: Literal['solid3d_get_all_edge_faces'] = 'solid3d_get_all_edge_faces') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetAllEdgeFaces, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_all_edge_faces'], required=False, default='solid3d_get_all_edge_faces')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_all_opposite_edges']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges'>, 'config': {'title': 'OptionSolid3DGetAllOppositeEdges'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges:94467872228384', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges'>, 'config': {'title': 'Solid3dGetAllOppositeEdges'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges'>>]}, 'ref': 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges:94467870967392', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edges': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetAllOppositeEdges', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_all_opposite_edges', 'schema': {'expected': ['solid3d_get_all_opposite_edges'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetAllOppositeEdges', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f78420, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b280, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_all_opposite_edges", }, expected_py: None, name: "literal['solid3d_get_all_opposite_edges']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e44660, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edges": SerField { key_py: Py( 0x00007fa14fa29280, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetAllOppositeEdges", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetAllOppositeEdges", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetAllOppositeEdges", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa9fb0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa9fe0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edges", lookup_key: Simple { key: "edges", py_key: Py( 0x00007fa14aaa9f50, ), path: LookupPath( [ S( "edges", Py( 0x00007fa14aaa9f80, ), ), ], ), }, name_py: Py( 0x00007fa14fa29280, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "Solid3dGetAllOppositeEdges", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e44660, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dGetAllOppositeEdges", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaaa010, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaaa040, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b280, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_all_opposite_edges": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b280, ), ], }, expected_repr: "'solid3d_get_all_opposite_edges'", name: "literal['solid3d_get_all_opposite_edges']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_all_opposite_edges']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetAllOppositeEdges", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f78420, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetAllOppositeEdges", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges, type: Literal['solid3d_get_all_opposite_edges'] = 'solid3d_get_all_opposite_edges') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetAllOppositeEdges, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_all_opposite_edges'], required=False, default='solid3d_get_all_opposite_edges')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetCommonEdge(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_common_edge.Solid3dGetCommonEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_common_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetCommonEdge'>, 'config': {'title': 'OptionSolid3DGetCommonEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetCommonEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetCommonEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetCommonEdge:94467872279584', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_common_edge.Solid3dGetCommonEdge'>, 'config': {'title': 'Solid3dGetCommonEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_common_edge.Solid3dGetCommonEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_common_edge.Solid3dGetCommonEdge'>>]}, 'ref': 'kittycad.models.solid3d_get_common_edge.Solid3dGetCommonEdge:94467870972256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetCommonEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_common_edge', 'schema': {'expected': ['solid3d_get_common_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetCommonEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f84c20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7ca30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_common_edge", }, expected_py: None, name: "literal['solid3d_get_common_edge']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e45960, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge": SerField { key_py: Py( 0x00007fa14b0330f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetCommonEdge", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetCommonEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetCommonEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaaa5b0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaaa5e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge", lookup_key: Simple { key: "edge", py_key: Py( 0x00007fa14aaaa550, ), path: LookupPath( [ S( "edge", Py( 0x00007fa14aaaa580, ), ), ], ), }, name_py: Py( 0x00007fa14b0330f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "Solid3dGetCommonEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e45960, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dGetCommonEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaaa610, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaaa640, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7ca30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_common_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7ca30, ), ], }, expected_repr: "'solid3d_get_common_edge'", name: "literal['solid3d_get_common_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_common_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetCommonEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f84c20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetCommonEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_common_edge.Solid3dGetCommonEdge, type: Literal['solid3d_get_common_edge'] = 'solid3d_get_common_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetCommonEdge, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_common_edge'], required=False, default='solid3d_get_common_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_extrusion_face_info']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo'>, 'config': {'title': 'OptionSolid3DGetExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo:94467872733568', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo'>, 'config': {'title': 'Solid3dGetExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo:94467871640720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'faces': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, 'config': {'title': 'ExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo:94467864852048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'cap': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'ExtrusionFaceCapType'>, 'members': [ExtrusionFaceCapType.NONE, ExtrusionFaceCapType.TOP, ExtrusionFaceCapType.BOTTOM], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.extrusion_face_cap_type.ExtrusionFaceCapType:94467864849104', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_extrusion_face_info', 'schema': {'expected': ['solid3d_get_extrusion_face_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ff3980, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b320, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_extrusion_face_info", }, expected_py: None, name: "literal['solid3d_get_extrusion_face_info']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03ee8c90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "faces": SerField { key_py: Py( 0x00007fa14aa348a0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055eb0386f650, ), serializer: Fields( GeneralFieldsSerializer { fields: { "face_id": SerField { key_py: Py( 0x00007fa14b32c8a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "cap": SerField { key_py: Py( 0x00007fa14d7b6b50, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb0386ead0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "curve_id": SerField { key_py: Py( 0x00007fa14b09ad70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "ExtrusionFaceInfo", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[ExtrusionFaceInfo]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetExtrusionFaceInfo", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetExtrusionFaceInfo", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetExtrusionFaceInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae7840, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae74b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "faces", lookup_key: Simple { key: "faces", py_key: Py( 0x00007fa14aae76f0, ), path: LookupPath( [ S( "faces", Py( 0x00007fa14aae7660, ), ), ], ), }, name_py: Py( 0x00007fa14aa348a0, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "cap", lookup_key: Simple { key: "cap", py_key: Py( 0x00007fa14aae7ae0, ), path: LookupPath( [ S( "cap", Py( 0x00007fa14aae7780, ), ), ], ), }, name_py: Py( 0x00007fa14d7b6b50, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb0386ead0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "bottom": 2, "top": 1, "none": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b0423f0, ), Py( 0x00007fa14b042450, ), Py( 0x00007fa14b0424b0, ), ], }, missing: None, expected_repr: "'none', 'top' or 'bottom'", strict: false, class_repr: "ExtrusionFaceCapType", name: "str-enum[ExtrusionFaceCapType]", }, ), frozen: false, }, Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007fa14ab08330, ), path: LookupPath( [ S( "curve_id", Py( 0x00007fa14ab08bb0, ), ), ], ), }, name_py: Py( 0x00007fa14b09ad70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007fa14aae7810, ), path: LookupPath( [ S( "face_id", Py( 0x00007fa14aae76c0, ), ), ], ), }, name_py: Py( 0x00007fa14b32c8a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "ExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0386f650, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ExtrusionFaceInfo", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "Solid3dGetExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ee8c90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dGetExtrusionFaceInfo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae7510, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae74e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b320, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_extrusion_face_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b320, ), ], }, expected_repr: "'solid3d_get_extrusion_face_info'", name: "literal['solid3d_get_extrusion_face_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_extrusion_face_info']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ff3980, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetExtrusionFaceInfo", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo, type: Literal['solid3d_get_extrusion_face_info'] = 'solid3d_get_extrusion_face_info') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetExtrusionFaceInfo, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_extrusion_face_info'], required=False, default='solid3d_get_extrusion_face_info')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_next_adjacent_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge'>, 'config': {'title': 'OptionSolid3DGetNextAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge:94467872253744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge'>, 'config': {'title': 'Solid3dGetNextAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge'>>]}, 'ref': 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge:94467871646720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetNextAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_next_adjacent_edge', 'schema': {'expected': ['solid3d_get_next_adjacent_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetNextAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f7e730, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03eea400, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge": SerField { key_py: Py( 0x00007fa14b0330f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetNextAdjacentEdge", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b3c0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_next_adjacent_edge", }, expected_py: None, name: "literal['solid3d_get_next_adjacent_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetNextAdjacentEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetNextAdjacentEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa8e10, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa8d80, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge", lookup_key: Simple { key: "edge", py_key: Py( 0x00007fa14aaa8f30, ), path: LookupPath( [ S( "edge", Py( 0x00007fa14aaa8de0, ), ), ], ), }, name_py: Py( 0x00007fa14b0330f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "Solid3dGetNextAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eea400, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dGetNextAdjacentEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa8f60, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa8bd0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b3c0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_next_adjacent_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b3c0, ), ], }, expected_repr: "'solid3d_get_next_adjacent_edge'", name: "literal['solid3d_get_next_adjacent_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_next_adjacent_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetNextAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f7e730, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetNextAdjacentEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge, type: Literal['solid3d_get_next_adjacent_edge'] = 'solid3d_get_next_adjacent_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetNextAdjacentEdge, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_next_adjacent_edge'], required=False, default='solid3d_get_next_adjacent_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_opposite_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge'>, 'config': {'title': 'OptionSolid3DGetOppositeEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge:94467872241024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge'>, 'config': {'title': 'Solid3dGetOppositeEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge'>>]}, 'ref': 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge:94467870995488', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetOppositeEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_opposite_edge', 'schema': {'expected': ['solid3d_get_opposite_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetOppositeEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f7b580, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b460, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_opposite_edge", }, expected_py: None, name: "literal['solid3d_get_opposite_edge']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e4b420, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge": SerField { key_py: Py( 0x00007fa14b0330f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetOppositeEdge", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetOppositeEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetOppositeEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa9800, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa9830, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge", lookup_key: Simple { key: "edge", py_key: Py( 0x00007fa14aaa98c0, ), path: LookupPath( [ S( "edge", Py( 0x00007fa14aaa9950, ), ), ], ), }, name_py: Py( 0x00007fa14b0330f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "Solid3dGetOppositeEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e4b420, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dGetOppositeEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa97a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa9980, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b460, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_opposite_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b460, ), ], }, expected_repr: "'solid3d_get_opposite_edge'", name: "literal['solid3d_get_opposite_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_opposite_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetOppositeEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f7b580, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetOppositeEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge, type: Literal['solid3d_get_opposite_edge'] = 'solid3d_get_opposite_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetOppositeEdge, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_opposite_edge'], required=False, default='solid3d_get_opposite_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_prev_adjacent_edge']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge'>, 'config': {'title': 'OptionSolid3DGetPrevAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge:94467872266112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge'>, 'config': {'title': 'Solid3dGetPrevAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge'>>]}, 'ref': 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge:94467871032704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetPrevAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_prev_adjacent_edge', 'schema': {'expected': ['solid3d_get_prev_adjacent_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetPrevAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f81780, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e54580, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge": SerField { key_py: Py( 0x00007fa14b0330f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetPrevAdjacentEdge", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd2b4b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_prev_adjacent_edge", }, expected_py: None, name: "literal['solid3d_get_prev_adjacent_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetPrevAdjacentEdge", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetPrevAdjacentEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa86f0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaaa070, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge", lookup_key: Simple { key: "edge", py_key: Py( 0x00007fa14aaa8630, ), path: LookupPath( [ S( "edge", Py( 0x00007fa14aaa8510, ), ), ], ), }, name_py: Py( 0x00007fa14b0330f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "Solid3dGetPrevAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e54580, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dGetPrevAdjacentEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaaa0a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaaa0d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd2b4b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_prev_adjacent_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd2b4b0, ), ], }, expected_repr: "'solid3d_get_prev_adjacent_edge'", name: "literal['solid3d_get_prev_adjacent_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_prev_adjacent_edge']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetPrevAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f81780, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DGetPrevAdjacentEdge", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge, type: Literal['solid3d_get_prev_adjacent_edge'] = 'solid3d_get_prev_adjacent_edge') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetPrevAdjacentEdge, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_prev_adjacent_edge'], required=False, default='solid3d_get_prev_adjacent_edge')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSolid3DShellFace(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_shell_face.Solid3dShellFace'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_shell_face']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DShellFace'>, 'config': {'title': 'OptionSolid3DShellFace'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DShellFace'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DShellFace'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DShellFace:94467871515168', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_shell_face.Solid3dShellFace'>, 'config': {'title': 'Solid3dShellFace'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_shell_face.Solid3dShellFace'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_shell_face.Solid3dShellFace'>>]}, 'ref': 'kittycad.models.solid3d_shell_face.Solid3dShellFace:94467870999248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'Solid3dShellFace', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_shell_face', 'schema': {'expected': ['solid3d_shell_face'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DShellFace', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03eca220, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7cd70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_shell_face", }, expected_py: None, name: "literal['solid3d_shell_face']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e4c2d0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "Solid3dShellFace", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DShellFace", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DShellFace", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac16820, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac16850, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "Solid3dShellFace", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e4c2d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Solid3dShellFace", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac16880, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac168b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7cd70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_shell_face": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7cd70, ), ], }, expected_repr: "'solid3d_shell_face'", name: "literal['solid3d_shell_face']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_shell_face']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DShellFace", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eca220, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSolid3DShellFace", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_shell_face.Solid3dShellFace, type: Literal['solid3d_shell_face'] = 'solid3d_shell_face') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dShellFace, required=True), 'type': FieldInfo(annotation=Literal['solid3d_shell_face'], required=False, default='solid3d_shell_face')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionStartPath(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.start_path.StartPath'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['start_path']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionStartPath'>, 'config': {'title': 'OptionStartPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionStartPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionStartPath'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionStartPath:94467871462656', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.start_path.StartPath'>, 'config': {'title': 'StartPath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.start_path.StartPath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.start_path.StartPath'>>]}, 'ref': 'kittycad.models.start_path.StartPath:94467871003280', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'StartPath', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'start_path', 'schema': {'expected': ['start_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionStartPath', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03ebd500, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7d030, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "start_path", }, expected_py: None, name: "literal['start_path']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e4d290, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "StartPath", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionStartPath", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionStartPath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa36370, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa36340, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "StartPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e4d290, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "StartPath", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa363a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa36070, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7d030, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "start_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7d030, ), ], }, expected_repr: "'start_path'", name: "literal['start_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['start_path']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionStartPath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03ebd500, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionStartPath", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.start_path.StartPath, type: Literal['start_path'] = 'start_path') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=StartPath, required=True), 'type': FieldInfo(annotation=Literal['start_path'], required=False, default='start_path')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.surface_area.SurfaceArea'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['surface_area']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea'>, 'config': {'title': 'OptionSurfaceArea'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea:94467872629024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.surface_area.SurfaceArea'>, 'config': {'title': 'SurfaceArea'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.surface_area.SurfaceArea'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.surface_area.SurfaceArea'>>]}, 'ref': 'kittycad.models.surface_area.SurfaceArea:94467871007824', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitArea'>, 'members': [UnitArea.CM2, UnitArea.DM2, UnitArea.FT2, UnitArea.IN2, UnitArea.KM2, UnitArea.M2, UnitArea.MM2, UnitArea.YD2], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_area.UnitArea:94467863212352', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'surface_area': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'SurfaceArea', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'surface_area', 'schema': {'expected': ['surface_area'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSurfaceArea', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fda120, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e4e450, ), serializer: Fields( GeneralFieldsSerializer { fields: { "surface_area": SerField { key_py: Py( 0x00007fa14bd7d5f0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036df140, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "SurfaceArea", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7d5f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "surface_area", }, expected_py: None, name: "literal['surface_area']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSurfaceArea", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSurfaceArea", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae6af0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae6b20, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14ab03570, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14ab03530, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036df140, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "cm2": 0, "ft2": 2, "mm2": 6, "m2": 5, "dm2": 1, "yd2": 7, "km2": 4, "in2": 3, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7950, ), Py( 0x00007fa14b3c79b0, ), Py( 0x00007fa14b3c7a10, ), Py( 0x00007fa14b3c7a70, ), Py( 0x00007fa14b3c7ad0, ), Py( 0x00007fa14b3c7b30, ), Py( 0x00007fa14b3c7b90, ), Py( 0x00007fa14b3c7bf0, ), ], }, missing: None, expected_repr: "'cm2', 'dm2', 'ft2', 'in2', 'km2', 'm2', 'mm2' or 'yd2'", strict: false, class_repr: "UnitArea", name: "str-enum[UnitArea]", }, ), frozen: false, }, Field { name: "surface_area", lookup_key: Simple { key: "surface_area", py_key: Py( 0x00007fa14ab035f0, ), path: LookupPath( [ S( "surface_area", Py( 0x00007fa14ab035b0, ), ), ], ), }, name_py: Py( 0x00007fa14bd7d5f0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "SurfaceArea", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e4e450, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "SurfaceArea", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae6b50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae6b80, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7d5f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "surface_area": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7d5f0, ), ], }, expected_repr: "'surface_area'", name: "literal['surface_area']", }, ), validate_default: false, copy_default: false, name: "default[literal['surface_area']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionSurfaceArea", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fda120, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionSurfaceArea", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.surface_area.SurfaceArea, type: Literal['surface_area'] = 'surface_area') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
SurfaceArea[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SurfaceArea, required=True), 'type': FieldInfo(annotation=Literal['surface_area'], required=False, default='surface_area')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.take_snapshot.TakeSnapshot'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['take_snapshot']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot'>, 'config': {'title': 'OptionTakeSnapshot'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot:94467872347488', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.take_snapshot.TakeSnapshot'>, 'config': {'title': 'TakeSnapshot'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.take_snapshot.TakeSnapshot'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.take_snapshot.TakeSnapshot'>>]}, 'ref': 'kittycad.models.take_snapshot.TakeSnapshot:94467871014704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'contents': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <bound method Base64Data.validate of <class 'kittycad.models.base64data.Base64Data'>>, 'type': 'no-info'}, 'schema': {'choices': [{'type': 'str'}, {'type': 'bytes'}], 'type': 'union'}, 'serialization': {'function': <bound method Base64Data.serialize of <class 'kittycad.models.base64data.Base64Data'>>, 'type': 'function-plain'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'TakeSnapshot', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'take_snapshot', 'schema': {'expected': ['take_snapshot'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionTakeSnapshot', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f95560, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7d670, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "take_snapshot", }, expected_py: None, name: "literal['take_snapshot']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e4ff30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "contents": SerField { key_py: Py( 0x00007fa14f9f4760, ), alias: None, alias_py: None, serializer: Some( Function( FunctionPlainSerializer { func: Py( 0x00007fa14ad2e380, ), name: "plain_function[serialize]", function_name: "serialize", return_serializer: Any( AnySerializer, ), fallback_serializer: None, when_used: Always, is_field_serializer: false, info_arg: false, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "TakeSnapshot", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionTakeSnapshot", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionTakeSnapshot", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaab1b0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaab1e0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "contents", lookup_key: Simple { key: "contents", py_key: Py( 0x00007fa14aacfd30, ), path: LookupPath( [ S( "contents", Py( 0x00007fa14aacfcf0, ), ), ], ), }, name_py: Py( 0x00007fa14f9f4760, ), validator: FunctionAfter( FunctionAfterValidator { validator: Union( UnionValidator { mode: Smart, choices: [ ( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), None, ), ( Bytes( BytesValidator { strict: false, bytes_mode: ValBytesMode { ser: Utf8, }, }, ), None, ), ], custom_error: None, strict: false, name: "union[str,bytes]", }, ), func: Py( 0x00007fa14acb4500, ), config: Py( 0x00007fa14aacfa80, ), name: "function-after[validate(), union[str,bytes]]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "TakeSnapshot", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e4ff30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "TakeSnapshot", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaab210, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaab240, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7d670, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "take_snapshot": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7d670, ), ], }, expected_repr: "'take_snapshot'", name: "literal['take_snapshot']", }, ), validate_default: false, copy_default: false, name: "default[literal['take_snapshot']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionTakeSnapshot", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f95560, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionTakeSnapshot", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.take_snapshot.TakeSnapshot, type: Literal['take_snapshot'] = 'take_snapshot') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
TakeSnapshot[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=TakeSnapshot, required=True), 'type': FieldInfo(annotation=Literal['take_snapshot'], required=False, default='take_snapshot')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionUpdateAnnotation(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.update_annotation.UpdateAnnotation'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['update_annotation']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionUpdateAnnotation'>, 'config': {'title': 'OptionUpdateAnnotation'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionUpdateAnnotation'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionUpdateAnnotation'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionUpdateAnnotation:94467871117936', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.update_annotation.UpdateAnnotation'>, 'config': {'title': 'UpdateAnnotation'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.update_annotation.UpdateAnnotation'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.update_annotation.UpdateAnnotation'>>]}, 'ref': 'kittycad.models.update_annotation.UpdateAnnotation:94467870992032', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'UpdateAnnotation', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'update_annotation', 'schema': {'expected': ['update_annotation'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionUpdateAnnotation', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03e69270, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e4a6a0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "UpdateAnnotation", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd7eff0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "update_annotation", }, expected_py: None, name: "literal['update_annotation']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionUpdateAnnotation", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionUpdateAnnotation", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14ac16670, ), path: LookupPath( [ S( "data", Py( 0x00007fa14ac16970, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "UpdateAnnotation", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e4a6a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "UpdateAnnotation", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14ac16dc0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14ac16c10, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd7eff0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "update_annotation": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd7eff0, ), ], }, expected_repr: "'update_annotation'", name: "literal['update_annotation']", }, ), validate_default: false, copy_default: false, name: "default[literal['update_annotation']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionUpdateAnnotation", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e69270, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionUpdateAnnotation", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.update_annotation.UpdateAnnotation, type: Literal['update_annotation'] = 'update_annotation') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=UpdateAnnotation, required=True), 'type': FieldInfo(annotation=Literal['update_annotation'], required=False, default='update_annotation')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionViewIsometric(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.view_isometric.ViewIsometric'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['view_isometric']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionViewIsometric'>, 'config': {'title': 'OptionViewIsometric'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionViewIsometric'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionViewIsometric'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionViewIsometric:94467872108960', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.view_isometric.ViewIsometric'>, 'config': {'title': 'ViewIsometric'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.view_isometric.ViewIsometric'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.view_isometric.ViewIsometric'>>]}, 'ref': 'kittycad.models.view_isometric.ViewIsometric:94467870989328', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94467863769344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94467863725024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'ViewIsometric', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'view_isometric', 'schema': {'expected': ['view_isometric'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionViewIsometric', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f5b1a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03e49c10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007fa14e7f0370, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03767100, ), serializer: Fields( GeneralFieldsSerializer { fields: { "ortho_scale": SerField { key_py: Py( 0x00007fa14b31b1b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007fa14bbea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007fa14b31b1f0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0375c3e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "w": SerField { key_py: Py( 0x00007fa14fa3e110, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007fa14d9ed0b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007fa14bbea7c0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ViewIsometric", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd9f870, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "view_isometric", }, expected_py: None, name: "literal['view_isometric']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionViewIsometric", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionViewIsometric", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aa4fd20, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aa4fcf0, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007fa14aa3b0b0, ), path: LookupPath( [ S( "settings", Py( 0x00007fa14aa3af70, ), ), ], ), }, name_py: Py( 0x00007fa14e7f0370, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14aa4ff60, ), path: LookupPath( [ S( "center", Py( 0x00007fa14aa4fe40, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007fa14aa4fe70, ), path: LookupPath( [ S( "fov_y", Py( 0x00007fa14aa4fde0, ), ), ], ), }, name_py: Py( 0x00007fa14bbea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007fa14ad2d5b0, ), path: LookupPath( [ S( "orientation", Py( 0x00007fa14acbc630, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1f0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007fa14fa3e110, ), path: LookupPath( [ S( "w", Py( 0x00007fa14fa3e110, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e110, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0375c3e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007fa14aa4ff90, ), path: LookupPath( [ S( "ortho", Py( 0x00007fa14aa4fc30, ), ), ], ), }, name_py: Py( 0x00007fa14bbea7c0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007fa14ad6f670, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007fa14ae06730, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14aa4fc90, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14aa4fc60, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007fa14aa4fdb0, ), path: LookupPath( [ S( "up", Py( 0x00007fa14aa4fd80, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed0b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03767100, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "ViewIsometric", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03e49c10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ViewIsometric", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aa4fd50, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aa4e9d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd9f870, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "view_isometric": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd9f870, ), ], }, expected_repr: "'view_isometric'", name: "literal['view_isometric']", }, ), validate_default: false, copy_default: false, name: "default[literal['view_isometric']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionViewIsometric", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f5b1a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionViewIsometric", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.view_isometric.ViewIsometric, type: Literal['view_isometric'] = 'view_isometric') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
-
data:
ViewIsometric[source]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ViewIsometric, required=True), 'type': FieldInfo(annotation=Literal['view_isometric'], required=False, default='view_isometric')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionVolume(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.volume.Volume'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['volume']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionVolume'>, 'config': {'title': 'OptionVolume'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionVolume'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionVolume'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionVolume:94467872603040', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.volume.Volume'>, 'config': {'title': 'Volume'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.volume.Volume'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.volume.Volume'>>]}, 'ref': 'kittycad.models.volume.Volume:94467871415808', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitVolume'>, 'members': [UnitVolume.CM3, UnitVolume.FT3, UnitVolume.IN3, UnitVolume.M3, UnitVolume.YD3, UnitVolume.USFLOZ, UnitVolume.USGAL, UnitVolume.L, UnitVolume.ML], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_volume.UnitVolume:94467863223008', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'volume': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Volume', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'volume', 'schema': {'expected': ['volume'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionVolume', 'type': 'model-fields'}, 'type': 'model'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03fd3ba0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03eb1e00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "output_unit": SerField { key_py: Py( 0x00007fa14bb4f470, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055eb036e1ae0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "volume": SerField { key_py: Py( 0x00007fa14fa3c7f8, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Volume", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14fa3c7f8, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "volume", }, expected_py: None, name: "literal['volume']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionVolume", }, ), definitions=[])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionVolume", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aae60d0, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aae6100, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007fa14ab00ab0, ), path: LookupPath( [ S( "output_unit", Py( 0x00007fa14ab00a70, ), ), ], ), }, name_py: Py( 0x00007fa14bb4f470, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055eb036e1ae0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "usgal": 6, "yd3": 4, "ft3": 1, "l": 7, "ml": 8, "m3": 3, "usfloz": 5, "cm3": 0, "in3": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14b3c7f50, ), Py( 0x00007fa14b3c7fb0, ), Py( 0x00007fa14b38c050, ), Py( 0x00007fa14b38c0b0, ), Py( 0x00007fa14b38c110, ), Py( 0x00007fa14b38c170, ), Py( 0x00007fa14b38c1d0, ), Py( 0x00007fa14b38c230, ), Py( 0x00007fa14b38c290, ), ], }, missing: None, expected_repr: "'cm3', 'ft3', 'in3', 'm3', 'yd3', 'usfloz', 'usgal', 'l' or 'ml'", strict: false, class_repr: "UnitVolume", name: "str-enum[UnitVolume]", }, ), frozen: false, }, Field { name: "volume", lookup_key: Simple { key: "volume", py_key: Py( 0x00007fa14aae6070, ), path: LookupPath( [ S( "volume", Py( 0x00007fa14aae60a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c7f8, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Volume", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eb1e00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Volume", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aae6130, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aae6160, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14fa3c7f8, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "volume": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14fa3c7f8, ), ], }, expected_repr: "'volume'", name: "literal['volume']", }, ), validate_default: false, copy_default: false, name: "default[literal['volume']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionVolume", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03fd3ba0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionVolume", }, ), definitions=[], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.volume.Volume, type: Literal['volume'] = 'volume') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Volume, required=True), 'type': FieldInfo(annotation=Literal['volume'], required=False, default='volume')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- class kittycad.models.ok_modeling_cmd_response.OptionZoomToFit(**data)[source][source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.zoom_to_fit.ZoomToFit'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['zoom_to_fit']}[source]
- classmethod __class_getitem__(typevar_values)[source]
- Return type:
type[BaseModel] |PydanticRecursiveRef
- __class_vars__: ClassVar[set[str]] = {}[source]
The names of the class variables defined on the model.
- classmethod __get_pydantic_core_schema__(source, handler, /)[source]
Hook into generating the model’s CoreSchema.
- Parameters:
source (
type[BaseModel]) – The class we are generating a schema for. This will generally be the same as theclsargument if this is a classmethod.handler (
GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Return type:
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]- Returns:
A
pydantic-coreCoreSchema.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – Apydantic-coreCoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise apydantic.errors.PydanticInvalidForJsonSchemaif JSON schema generation fails. Since this gets called byBaseModel.model_json_schemayou can override theschema_generatorargument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.
- __pretty__(fmt, **kwargs)[source]
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True[source]
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94467859212112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionZoomToFit'>, 'config': {'title': 'OptionZoomToFit'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionZoomToFit'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionZoomToFit'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionZoomToFit:94467872071664', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.zoom_to_fit.ZoomToFit'>, 'config': {'title': 'ZoomToFit'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.zoom_to_fit.ZoomToFit'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.zoom_to_fit.ZoomToFit'>>]}, 'ref': 'kittycad.models.zoom_to_fit.ZoomToFit:94467871436128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94467863769344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94467863725024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94467859212112', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'ZoomToFit', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'zoom_to_fit', 'schema': {'expected': ['zoom_to_fit'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionZoomToFit', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False[source]
Whether the model has a custom
__init__method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]
Metadata containing the decorators defined on the model. This replaces
Model.__validators__andModel.__root_validators__from Pydantic V1.
- __pydantic_extra__: dict[str, Any] | None[source]
A dictionary containing extra values, if [
extra][pydantic.config.ConfigDict.extra] is set to'allow'.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- classmethod __pydantic_init_subclass__(**kwargs)[source]
This is intended to behave just like
__init_subclass__, but is called byModelMetaclassonly after the class is actually fully initialized. In particular, attributes likemodel_fieldswill be present when this is called.This is necessary because
__init_subclass__will always be called bytype.__new__, and it would require a prohibitively large refactor to theModelMetaclassto ensure thattype.__new__was called in such a manner that the class would already be sufficiently initialized.This will receive the same
kwargsthat would be passed to the standard__init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]
The name of the post-init method for the model, if defined.
- __pydantic_private__: dict[str, Any] | None[source]
Values of private attributes set on the model instance.
- __pydantic_root_model__: ClassVar[bool] = False[source]
Whether the model is a [
RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055eb03f51ff0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007fa14fa37cd8, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03eb6d60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007fa14e7f0370, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb03767100, ), serializer: Fields( GeneralFieldsSerializer { fields: { "fov_y": SerField { key_py: Py( 0x00007fa14bbea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007fa14d9ed0b0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007fa14ef08150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007fa14b31b1f0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055eb0375c3e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "z": SerField { key_py: Py( 0x00007fa14fa3e1a0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007fa14fa3e170, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "w": SerField { key_py: Py( 0x00007fa14fa3e110, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007fa14fa3e140, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007fa14bbea7c0, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007fa14b31b1b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14f947100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007fa14fa3adf8, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ZoomToFit", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007fa14fa3c5a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fa14bd9fcb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "zoom_to_fit", }, expected_py: None, name: "literal['zoom_to_fit']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionZoomToFit", }, ), definitions=[Model(ModelSerializer { class: Py(0x55eb0330e750), serializer: Fields(GeneralFieldsSerializer { fields: {"y": SerField { key_py: Py(0x7fa14fa3e170), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7fa14fa3e1a0), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7fa14fa3e140), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]
The
pydantic-coreSchemaSerializerused to dump instances of the model.
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionZoomToFit", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007fa14aaa8240, ), path: LookupPath( [ S( "data", Py( 0x00007fa14aaa8270, ), ), ], ), }, name_py: Py( 0x00007fa14fa37cd8, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007fa14aaa1c30, ), path: LookupPath( [ S( "settings", Py( 0x00007fa14aaa1c70, ), ), ], ), }, name_py: Py( 0x00007fa14e7f0370, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007fa14aaa8060, ), path: LookupPath( [ S( "center", Py( 0x00007fa14aaa8090, ), ), ], ), }, name_py: Py( 0x00007fa14ef08150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007fa14aaa80c0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007fa14aaa80f0, ), ), ], ), }, name_py: Py( 0x00007fa14bbea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007fa14ad4baf0, ), path: LookupPath( [ S( "orientation", Py( 0x00007fa14aaa1b70, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1f0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007fa14fa3e110, ), path: LookupPath( [ S( "w", Py( 0x00007fa14fa3e110, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e110, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007fa14fa3e140, ), path: LookupPath( [ S( "x", Py( 0x00007fa14fa3e140, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e140, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007fa14fa3e170, ), path: LookupPath( [ S( "y", Py( 0x00007fa14fa3e170, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e170, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007fa14fa3e1a0, ), path: LookupPath( [ S( "z", Py( 0x00007fa14fa3e1a0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3e1a0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb0375c3e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007fa14aaa8120, ), path: LookupPath( [ S( "ortho", Py( 0x00007fa14aaa8150, ), ), ], ), }, name_py: Py( 0x00007fa14bbea7c0, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007fa14aaa1bf0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007fa14aaa1bb0, ), ), ], ), }, name_py: Py( 0x00007fa14b31b1b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14f947100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007fa14aaa8180, ), path: LookupPath( [ S( "pos", Py( 0x00007fa14aaa81b0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3adf8, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007fa14aaa81e0, ), path: LookupPath( [ S( "up", Py( 0x00007fa14aaa8210, ), ), ], ), }, name_py: Py( 0x00007fa14d9ed0b0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03767100, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "ZoomToFit", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03eb6d60, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "ZoomToFit", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007fa14aaa82a0, ), path: LookupPath( [ S( "type", Py( 0x00007fa14aaa82d0, ), ), ], ), }, name_py: Py( 0x00007fa14fa3c5a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fa14bd9fcb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "zoom_to_fit": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007fa14bd9fcb0, ), ], }, expected_repr: "'zoom_to_fit'", name: "literal['zoom_to_fit']", }, ), validate_default: false, copy_default: false, name: "default[literal['zoom_to_fit']]", undefined: Py( 0x00007fa14d802350, ), }, ), frozen: false, }, ], model_name: "OptionZoomToFit", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055eb03f51ff0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fa14d802350, ), name: "OptionZoomToFit", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7fa14fa3e140), path: LookupPath([S("x", Py(0x7fa14fa3e140))]) }, name_py: Py(0x7fa14fa3e140), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7fa14fa3e170), path: LookupPath([S("y", Py(0x7fa14fa3e170))]) }, name_py: Py(0x7fa14fa3e170), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7fa14fa3e1a0), path: LookupPath([S("z", Py(0x7fa14fa3e1a0))]) }, name_py: Py(0x7fa14fa3e1a0), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55eb0330e750), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7fa14d802350), name: "Point3d" })], cache_strings=True)[source]
The
pydantic-coreSchemaValidatorused to validate instances of the model.
- __rich_repr__()[source]
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.zoom_to_fit.ZoomToFit, type: Literal['zoom_to_fit'] = 'zoom_to_fit') -> None>[source]
The synthesized
__init__[Signature][inspect.Signature] of the model.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]
- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copyinstead.
If you need
includeorexclude, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]
A dictionary of computed field names and their corresponding
ComputedFieldInfoobjects.
- model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
Creates a new instance of the
Modelclass with validated data.Creates a new model setting
__dict__and__pydantic_fields_set__from trusted or pre-validated data. Default values are respected, but no other validation is performed.- !!! note
model_construct()generally respects themodel_config.extrasetting on the provided model. That is, ifmodel_config.extra == 'allow', then all extra passed values are added to the model instance’s__dict__and__pydantic_extra__fields. Ifmodel_config.extra == 'ignore'(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct(), havingmodel_config.extra == 'forbid'does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevaluesargument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the
Modelclass with validated data.
- model_copy(*, update=None, deep=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in whichto_pythonshould run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to include in the output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_jsonmethod.- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to include in the JSON output.exclude (
Union[Set[int],Set[str],Mapping[int,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],Mapping[str,Union[Set[int],Set[str],Mapping[int,Union[IncEx,Literal[True]]],Mapping[str,Union[IncEx,Literal[True]]],Literal[True]]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value ofNone.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
Noneifconfig.extrais not set to"allow".
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ZoomToFit, required=True), 'type': FieldInfo(annotation=Literal['zoom_to_fit'], required=False, default='zoom_to_fit')}[source]
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo][pydantic.fields.FieldInfo] objects.This replaces
Model.__fields__from Pydantic V1.
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchemawith your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic classModelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.- Return type:
- Returns:
String representing the new class where
paramsare passed toclsas type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__context)[source]
Override this method to perform additional initialization after
__init__andmodel_construct. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults toFalse.raise_errors (
bool) – Whether to raise errors, defaults toTrue._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
dict[str,Any] |None) – The types namespace, defaults toNone.
- Return type:
- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]
Validate a pydantic model instance.
- Parameters:
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None)[source]
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_datais not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None)[source]
Validate the given object with string data against the Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self