kittycad.models.input_format

Classes

OptionFbx(**data)	Autodesk Filmbox (FBX) format.
OptionGltf(**data)	Binary glTF 2.0.
OptionObj(**data)	Wavefront OBJ format.
OptionPly(**data)	The PLY Polygon File Format.
OptionSldprt(**data)	SolidWorks part (SLDPRT) format.
OptionStep(**data)	ISO 10303-21 (STEP) format.
OptionStl(**data)	*ST**ereo**L**ithography format.

class kittycad.models.input_format.OptionFbx(**data)

Autodesk Filmbox (FBX) format.

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.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})

__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['fbx']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

classmethod __get_pydantic_core_schema__(source, handler, /)

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 the cls argument 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-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

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]) – A pydantic-core CoreSchema. 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 a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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.

self is explicitly positional-only to allow self as a field name.

__iter__()

So dict(model) works.

Return type:

Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.input_format'

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.input_format.OptionFbx'>, 'config': {'title': 'OptionFbx'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionFbx'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionFbx'>>]}, 'ref': 'kittycad.models.input_format.OptionFbx:94734218748944', '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': 'fbx', 'schema': {'expected': ['fbx'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFbx', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x000056290774e410,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f83d7022580,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d32a1200,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "fbx",                                             },                                             expected_py: None,                                             name: "literal['fbx']",                                         },                                     ),                                 },                             ),                         ),                         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: "OptionFbx",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFbx", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "type",                         lookup_key: Simple {                             key: "type",                             py_key: Py(                                 0x00007f83d32a1ce0,                             ),                             path: LookupPath(                                 [                                     S(                                         "type",                                         Py(                                             0x00007f83d32a1bc0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d7022580,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d32a1200,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "fbx": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             values: [                                                 Py(                                                     0x00007f83d32a1200,                                                 ),                                             ],                                         },                                         expected_repr: "'fbx'",                                         name: "literal['fbx']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['fbx']]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionFbx",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x000056290774e410,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f83d4e0a330,         ),         name: "OptionFbx",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, type: Literal['fbx'] = 'fbx') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_check_frozen(name, value)

Return type:

None

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, 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)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class 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 the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_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 the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

• update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

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)

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 which to_python should 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 of None.

• 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:

dict[str, Any]

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

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 of None.

• 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:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['fbx'], required=False, default='fbx')}

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]

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')

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 of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

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 class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

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 to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

type: Literal['fbx']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.input_format.OptionGltf(**data)

Binary glTF 2.0. We refer to this as glTF since that is how our customers refer to it, but this can also import binary glTF (glb).

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.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})

__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['gltf']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

classmethod __get_pydantic_core_schema__(source, handler, /)

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 the cls argument 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-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

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]) – A pydantic-core CoreSchema. 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 a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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.

self is explicitly positional-only to allow self as a field name.

__iter__()

So dict(model) works.

Return type:

Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.input_format'

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.input_format.OptionGltf'>, 'config': {'title': 'OptionGltf'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionGltf'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionGltf'>>]}, 'ref': 'kittycad.models.input_format.OptionGltf:94734218754688', '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': 'gltf', 'schema': {'expected': ['gltf'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGltf', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x000056290774fa80,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f83d7022580,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d32a1440,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "gltf",                                             },                                             expected_py: None,                                             name: "literal['gltf']",                                         },                                     ),                                 },                             ),                         ),                         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: "OptionGltf",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGltf", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "type",                         lookup_key: Simple {                             key: "type",                             py_key: Py(                                 0x00007f83d32a24c0,                             ),                             path: LookupPath(                                 [                                     S(                                         "type",                                         Py(                                             0x00007f83d32a24f0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d7022580,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d32a1440,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "gltf": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             values: [                                                 Py(                                                     0x00007f83d32a1440,                                                 ),                                             ],                                         },                                         expected_repr: "'gltf'",                                         name: "literal['gltf']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['gltf']]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionGltf",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x000056290774fa80,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f83d4e0a330,         ),         name: "OptionGltf",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, type: Literal['gltf'] = 'gltf') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_check_frozen(name, value)

Return type:

None

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, 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)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class 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 the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_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 the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

• update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

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)

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 which to_python should 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 of None.

• 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:

dict[str, Any]

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

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 of None.

• 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:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['gltf'], required=False, default='gltf')}

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]

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')

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 of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

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 class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

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 to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

type: Literal['gltf']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.input_format.OptionObj(**data)

Wavefront OBJ format.

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.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})

__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]', 'coords': <class 'kittycad.models.system.System'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['obj'], 'units': <enum 'UnitLength'>}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

classmethod __get_pydantic_core_schema__(source, handler, /)

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 the cls argument 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-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

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]) – A pydantic-core CoreSchema. 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 a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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.

self is explicitly positional-only to allow self as a field name.

__iter__()

So dict(model) works.

Return type:

Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.input_format'

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, 'config': {'title': 'AxisDirectionPair'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>>]}, 'ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94734217731920', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'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.axis.Axis:94734218740272', 'type': 'definition-ref'}, 'type': 'model-field'}, 'direction': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.direction.Direction:94734217729360', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'AxisDirectionPair', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <enum 'Axis'>, 'members': [Axis.Y, Axis.Z], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.axis.Axis:94734218740272', 'sub_type': 'str', 'type': 'enum'}, {'cls': <enum 'Direction'>, 'members': [Direction.POSITIVE, Direction.NEGATIVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.direction.Direction:94734217729360', 'sub_type': 'str', 'type': 'enum'}], 'schema': {'cls': <class 'kittycad.models.input_format.OptionObj'>, 'config': {'title': 'OptionObj'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionObj'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionObj'>>]}, 'ref': 'kittycad.models.input_format.OptionObj:94734218761200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.system.System'>, 'config': {'title': 'System'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.system.System'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.system.System'>>]}, 'ref': 'kittycad.models.system.System:94734217741200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'forward': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94734217731920', '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.axis_direction_pair.AxisDirectionPair:94734217731920', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'System', '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': 'obj', 'schema': {'expected': ['obj'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'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:94734218746384', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'OptionObj', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x00005629077513f0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "units": SerField {                         key_py: Py(                             0x00007f83d5dcaa60,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Enum(                                 EnumSerializer {                                     class: Py(                                         0x000056290774da10,                                     ),                                     serializer: Some(                                         Str(                                             StrSerializer,                                         ),                                     ),                                 },                             ),                         ),                         required: true,                     },                     "coords": SerField {                         key_py: Py(                             0x00007f83d32a1e00,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Model(                                 ModelSerializer {                                     class: Py(                                         0x0000562907658390,                                     ),                                     serializer: Fields(                                         GeneralFieldsSerializer {                                             fields: {                                                 "forward": SerField {                                                     key_py: Py(                                                         0x00007f83d32a16b0,                                                     ),                                                     alias: None,                                                     alias_py: None,                                                     serializer: Some(                                                         Recursive(                                                             DefinitionRefSerializer {                                                                 definition: "...",                                                                 retry_with_lax_check: true,                                                             },                                                         ),                                                     ),                                                     required: true,                                                 },                                                 "up": SerField {                                                     key_py: Py(                                                         0x00007f83d4ff10b0,                                                     ),                                                     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: "System",                                 },                             ),                         ),                         required: true,                     },                     "type": SerField {                         key_py: Py(                             0x00007f83d7022580,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d7020740,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "obj",                                             },                                             expected_py: None,                                             name: "literal['obj']",                                         },                                     ),                                 },                             ),                         ),                         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: "OptionObj",     }, ), definitions=[Enum(EnumSerializer { class: Py(0x562907655550), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x562907655f50), serializer: Fields(GeneralFieldsSerializer { fields: {"direction": SerField { key_py: Py(0x7f83d58718b0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: false })), required: true }, "axis": SerField { key_py: Py(0x7f83d3672e20), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: 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: "AxisDirectionPair" }), Enum(EnumSerializer { class: Py(0x56290774c230), serializer: Some(Str(StrSerializer)) })])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionObj", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "coords",                         lookup_key: Simple {                             key: "coords",                             py_key: Py(                                 0x00007f83d32a2d30,                             ),                             path: LookupPath(                                 [                                     S(                                         "coords",                                         Py(                                             0x00007f83d32a2d90,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d32a1e00,                         ),                         validator: Model(                             ModelValidator {                                 revalidate: Never,                                 validator: ModelFields(                                     ModelFieldsValidator {                                         fields: [                                             Field {                                                 name: "forward",                                                 lookup_key: Simple {                                                     key: "forward",                                                     py_key: Py(                                                         0x00007f83d32a2c40,                                                     ),                                                     path: LookupPath(                                                         [                                                             S(                                                                 "forward",                                                                 Py(                                                                     0x00007f83d32a2ca0,                                                                 ),                                                             ),                                                         ],                                                     ),                                                 },                                                 name_py: Py(                                                     0x00007f83d32a16b0,                                                 ),                                                 validator: DefinitionRef(                                                     DefinitionRefValidator {                                                         definition: "...",                                                     },                                                 ),                                                 frozen: false,                                             },                                             Field {                                                 name: "up",                                                 lookup_key: Simple {                                                     key: "up",                                                     py_key: Py(                                                         0x00007f83d32a2d00,                                                     ),                                                     path: LookupPath(                                                         [                                                             S(                                                                 "up",                                                                 Py(                                                                     0x00007f83d32a2cd0,                                                                 ),                                                             ),                                                         ],                                                     ),                                                 },                                                 name_py: Py(                                                     0x00007f83d4ff10b0,                                                 ),                                                 validator: DefinitionRef(                                                     DefinitionRefValidator {                                                         definition: "...",                                                     },                                                 ),                                                 frozen: false,                                             },                                         ],                                         model_name: "System",                                         extra_behavior: Ignore,                                         extras_validator: None,                                         strict: false,                                         from_attributes: false,                                         loc_by_alias: true,                                     },                                 ),                                 class: Py(                                     0x0000562907658390,                                 ),                                 post_init: None,                                 frozen: false,                                 custom_init: false,                                 root_model: false,                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                                 name: "System",                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key: Simple {                             key: "type",                             py_key: Py(                                 0x00007f83d32a2df0,                             ),                             path: LookupPath(                                 [                                     S(                                         "type",                                         Py(                                             0x00007f83d32a2e20,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d7022580,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d7020740,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "obj": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             values: [                                                 Py(                                                     0x00007f83d7020740,                                                 ),                                             ],                                         },                                         expected_repr: "'obj'",                                         name: "literal['obj']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['obj']]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "units",                         lookup_key: Simple {                             key: "units",                             py_key: Py(                                 0x00007f83d329c720,                             ),                             path: LookupPath(                                 [                                     S(                                         "units",                                         Py(                                             0x00007f83d32a2e80,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d5dcaa60,                         ),                         validator: StrEnum(                             EnumValidator {                                 phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>,                                 class: Py(                                     0x000056290774da10,                                 ),                                 lookup: LiteralLookup {                                     expected_bool: None,                                     expected_int: None,                                     expected_str: Some(                                         {                                             "cm": 0,                                             "ft": 1,                                             "in": 2,                                             "m": 3,                                             "mm": 4,                                             "yd": 5,                                         },                                     ),                                     expected_py_dict: None,                                     expected_py_values: None,                                     values: [                                         Py(                                             0x00007f83d28b8b90,                                         ),                                         Py(                                             0x00007f83d28b9130,                                         ),                                         Py(                                             0x00007f83d28b91f0,                                         ),                                         Py(                                             0x00007f83d28b9250,                                         ),                                         Py(                                             0x00007f83d28b92b0,                                         ),                                         Py(                                             0x00007f83d28b9310,                                         ),                                     ],                                 },                                 missing: None,                                 expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'",                                 strict: false,                                 class_repr: "UnitLength",                                 name: "str-enum[UnitLength]",                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionObj",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x00005629077513f0,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f83d4e0a330,         ),         name: "OptionObj",     }, ), definitions=[StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x562907655550), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"negative": 1, "positive": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7f83d28b8f50), Py(0x7f83d28b8fb0)] }, missing: None, expected_repr: "'positive' or 'negative'", strict: false, class_repr: "Direction", name: "str-enum[Direction]" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x56290774c230), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"z": 1, "y": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7f83d28b8dd0), Py(0x7f83d28b8e30)] }, missing: None, expected_repr: "'y' or 'z'", strict: false, class_repr: "Axis", name: "str-enum[Axis]" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py(0x7f83d32a2b20), path: LookupPath([S("axis", Py(0x7f83d32a2c10))]) }, name_py: Py(0x7f83d3672e20), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }, Field { name: "direction", lookup_key: Simple { key: "direction", py_key: Py(0x7f83d33c6270), path: LookupPath([S("direction", Py(0x7f83d33c6230))]) }, name_py: Py(0x7f83d58718b0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }], model_name: "AxisDirectionPair", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x562907655f50), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f83d4e0a330), name: "AxisDirectionPair" })], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, coords: kittycad.models.system.System, type: Literal['obj'] = 'obj', units: kittycad.models.unit_length.UnitLength) -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_check_frozen(name, value)

Return type:

None

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

classmethod construct(_fields_set=None, **values)

Return type:

Self

coords: System

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, 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)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class 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 the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_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 the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

• update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

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)

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 which to_python should 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 of None.

• 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:

dict[str, Any]

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

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 of None.

• 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:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'coords': FieldInfo(annotation=System, required=True), 'type': FieldInfo(annotation=Literal['obj'], required=False, default='obj'), 'units': FieldInfo(annotation=UnitLength, required=True)}

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]

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')

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 of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

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 class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

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 to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

type: Literal['obj']

units: UnitLength

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.input_format.OptionPly(**data)

The PLY Polygon File Format.

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.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})

__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]', 'coords': <class 'kittycad.models.system.System'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['ply'], 'units': <enum 'UnitLength'>}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

classmethod __get_pydantic_core_schema__(source, handler, /)

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 the cls argument 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-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

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]) – A pydantic-core CoreSchema. 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 a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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.

self is explicitly positional-only to allow self as a field name.

__iter__()

So dict(model) works.

Return type:

Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.input_format'

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, 'config': {'title': 'AxisDirectionPair'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>>]}, 'ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94734217731920', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'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.axis.Axis:94734218740272', 'type': 'definition-ref'}, 'type': 'model-field'}, 'direction': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.direction.Direction:94734217729360', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'AxisDirectionPair', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <enum 'Axis'>, 'members': [Axis.Y, Axis.Z], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.axis.Axis:94734218740272', 'sub_type': 'str', 'type': 'enum'}, {'cls': <enum 'Direction'>, 'members': [Direction.POSITIVE, Direction.NEGATIVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.direction.Direction:94734217729360', 'sub_type': 'str', 'type': 'enum'}], 'schema': {'cls': <class 'kittycad.models.input_format.OptionPly'>, 'config': {'title': 'OptionPly'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionPly'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionPly'>>]}, 'ref': 'kittycad.models.input_format.OptionPly:94734217753216', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.system.System'>, 'config': {'title': 'System'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.system.System'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.system.System'>>]}, 'ref': 'kittycad.models.system.System:94734217741200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'forward': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94734217731920', '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.axis_direction_pair.AxisDirectionPair:94734217731920', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'System', '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': 'ply', 'schema': {'expected': ['ply'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'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:94734218746384', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'OptionPly', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x000056290765b280,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "coords": SerField {                         key_py: Py(                             0x00007f83d32a1e00,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Model(                                 ModelSerializer {                                     class: Py(                                         0x0000562907658390,                                     ),                                     serializer: Fields(                                         GeneralFieldsSerializer {                                             fields: {                                                 "up": SerField {                                                     key_py: Py(                                                         0x00007f83d4ff10b0,                                                     ),                                                     alias: None,                                                     alias_py: None,                                                     serializer: Some(                                                         Recursive(                                                             DefinitionRefSerializer {                                                                 definition: "...",                                                                 retry_with_lax_check: true,                                                             },                                                         ),                                                     ),                                                     required: true,                                                 },                                                 "forward": SerField {                                                     key_py: Py(                                                         0x00007f83d32a16b0,                                                     ),                                                     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: "System",                                 },                             ),                         ),                         required: true,                     },                     "type": SerField {                         key_py: Py(                             0x00007f83d7022580,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d32a15f0,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "ply",                                             },                                             expected_py: None,                                             name: "literal['ply']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                     },                     "units": SerField {                         key_py: Py(                             0x00007f83d5dcaa60,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Enum(                                 EnumSerializer {                                     class: Py(                                         0x000056290774da10,                                     ),                                     serializer: Some(                                         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: "OptionPly",     }, ), definitions=[Enum(EnumSerializer { class: Py(0x56290774c230), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x562907655f50), serializer: Fields(GeneralFieldsSerializer { fields: {"direction": SerField { key_py: Py(0x7f83d58718b0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: false })), required: true }, "axis": SerField { key_py: Py(0x7f83d3672e20), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: 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: "AxisDirectionPair" }), Enum(EnumSerializer { class: Py(0x562907655550), serializer: Some(Str(StrSerializer)) })])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPly", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "coords",                         lookup_key: Simple {                             key: "coords",                             py_key: Py(                                 0x00007f83d32a3690,                             ),                             path: LookupPath(                                 [                                     S(                                         "coords",                                         Py(                                             0x00007f83d32a36c0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d32a1e00,                         ),                         validator: Model(                             ModelValidator {                                 revalidate: Never,                                 validator: ModelFields(                                     ModelFieldsValidator {                                         fields: [                                             Field {                                                 name: "forward",                                                 lookup_key: Simple {                                                     key: "forward",                                                     py_key: Py(                                                         0x00007f83d32a35d0,                                                     ),                                                     path: LookupPath(                                                         [                                                             S(                                                                 "forward",                                                                 Py(                                                                     0x00007f83d32a3600,                                                                 ),                                                             ),                                                         ],                                                     ),                                                 },                                                 name_py: Py(                                                     0x00007f83d32a16b0,                                                 ),                                                 validator: DefinitionRef(                                                     DefinitionRefValidator {                                                         definition: "...",                                                     },                                                 ),                                                 frozen: false,                                             },                                             Field {                                                 name: "up",                                                 lookup_key: Simple {                                                     key: "up",                                                     py_key: Py(                                                         0x00007f83d32a3630,                                                     ),                                                     path: LookupPath(                                                         [                                                             S(                                                                 "up",                                                                 Py(                                                                     0x00007f83d32a3660,                                                                 ),                                                             ),                                                         ],                                                     ),                                                 },                                                 name_py: Py(                                                     0x00007f83d4ff10b0,                                                 ),                                                 validator: DefinitionRef(                                                     DefinitionRefValidator {                                                         definition: "...",                                                     },                                                 ),                                                 frozen: false,                                             },                                         ],                                         model_name: "System",                                         extra_behavior: Ignore,                                         extras_validator: None,                                         strict: false,                                         from_attributes: false,                                         loc_by_alias: true,                                     },                                 ),                                 class: Py(                                     0x0000562907658390,                                 ),                                 post_init: None,                                 frozen: false,                                 custom_init: false,                                 root_model: false,                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                                 name: "System",                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key: Simple {                             key: "type",                             py_key: Py(                                 0x00007f83d32a36f0,                             ),                             path: LookupPath(                                 [                                     S(                                         "type",                                         Py(                                             0x00007f83d32a3720,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d7022580,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d32a15f0,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "ply": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             values: [                                                 Py(                                                     0x00007f83d32a15f0,                                                 ),                                             ],                                         },                                         expected_repr: "'ply'",                                         name: "literal['ply']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['ply']]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "units",                         lookup_key: Simple {                             key: "units",                             py_key: Py(                                 0x00007f83d32a3750,                             ),                             path: LookupPath(                                 [                                     S(                                         "units",                                         Py(                                             0x00007f83d32a3780,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d5dcaa60,                         ),                         validator: StrEnum(                             EnumValidator {                                 phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>,                                 class: Py(                                     0x000056290774da10,                                 ),                                 lookup: LiteralLookup {                                     expected_bool: None,                                     expected_int: None,                                     expected_str: Some(                                         {                                             "in": 2,                                             "cm": 0,                                             "m": 3,                                             "mm": 4,                                             "ft": 1,                                             "yd": 5,                                         },                                     ),                                     expected_py_dict: None,                                     expected_py_values: None,                                     values: [                                         Py(                                             0x00007f83d28b8b90,                                         ),                                         Py(                                             0x00007f83d28b9130,                                         ),                                         Py(                                             0x00007f83d28b91f0,                                         ),                                         Py(                                             0x00007f83d28b9250,                                         ),                                         Py(                                             0x00007f83d28b92b0,                                         ),                                         Py(                                             0x00007f83d28b9310,                                         ),                                     ],                                 },                                 missing: None,                                 expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'",                                 strict: false,                                 class_repr: "UnitLength",                                 name: "str-enum[UnitLength]",                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionPly",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x000056290765b280,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f83d4e0a330,         ),         name: "OptionPly",     }, ), definitions=[StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x562907655550), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"positive": 0, "negative": 1}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7f83d28b8f50), Py(0x7f83d28b8fb0)] }, missing: None, expected_repr: "'positive' or 'negative'", strict: false, class_repr: "Direction", name: "str-enum[Direction]" }), Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py(0x7f83d32a3480), path: LookupPath([S("axis", Py(0x7f83d32a35a0))]) }, name_py: Py(0x7f83d3672e20), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }, Field { name: "direction", lookup_key: Simple { key: "direction", py_key: Py(0x7f83d33c4a70), path: LookupPath([S("direction", Py(0x7f83d33c5170))]) }, name_py: Py(0x7f83d58718b0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }], model_name: "AxisDirectionPair", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x562907655f50), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f83d4e0a330), name: "AxisDirectionPair" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x56290774c230), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"y": 0, "z": 1}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7f83d28b8dd0), Py(0x7f83d28b8e30)] }, missing: None, expected_repr: "'y' or 'z'", strict: false, class_repr: "Axis", name: "str-enum[Axis]" })], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, coords: kittycad.models.system.System, type: Literal['ply'] = 'ply', units: kittycad.models.unit_length.UnitLength) -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_check_frozen(name, value)

Return type:

None

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

classmethod construct(_fields_set=None, **values)

Return type:

Self

coords: System

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, 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)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class 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 the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_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 the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

• update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

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)

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 which to_python should 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 of None.

• 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:

dict[str, Any]

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

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 of None.

• 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:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'coords': FieldInfo(annotation=System, required=True), 'type': FieldInfo(annotation=Literal['ply'], required=False, default='ply'), 'units': FieldInfo(annotation=UnitLength, required=True)}

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]

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')

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 of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

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 class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

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 to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

type: Literal['ply']

units: UnitLength

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.input_format.OptionSldprt(**data)

SolidWorks part (SLDPRT) format.

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.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})

__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]]', 'split_closed_faces': <class 'bool'>, 'type': typing.Literal['sldprt']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

classmethod __get_pydantic_core_schema__(source, handler, /)

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 the cls argument 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-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

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]) – A pydantic-core CoreSchema. 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 a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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.

self is explicitly positional-only to allow self as a field name.

__iter__()

So dict(model) works.

Return type:

Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.input_format'

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.input_format.OptionSldprt'>, 'config': {'title': 'OptionSldprt'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionSldprt'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionSldprt'>>]}, 'ref': 'kittycad.models.input_format.OptionSldprt:94734217774560', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'split_closed_faces': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, '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': 'sldprt', 'schema': {'expected': ['sldprt'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSldprt', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x00005629076605e0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f83d7022580,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d32a1830,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "sldprt",                                             },                                             expected_py: None,                                             name: "literal['sldprt']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                     },                     "split_closed_faces": SerField {                         key_py: Py(                             0x00007f83d3230030,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d6f16d00,                                         ),                                     ),                                     serializer: Bool(                                         BoolSerializer,                                     ),                                 },                             ),                         ),                         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: "OptionSldprt",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSldprt", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "split_closed_faces",                         lookup_key: Simple {                             key: "split_closed_faces",                             py_key: Py(                                 0x00007f83d33c7b30,                             ),                             path: LookupPath(                                 [                                     S(                                         "split_closed_faces",                                         Py(                                             0x00007f83d33c7cb0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d3230030,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d6f16d00,                                     ),                                 ),                                 on_error: Raise,                                 validator: Bool(                                     BoolValidator {                                         strict: false,                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[bool]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key: Simple {                             key: "type",                             py_key: Py(                                 0x00007f83d32a3cf0,                             ),                             path: LookupPath(                                 [                                     S(                                         "type",                                         Py(                                             0x00007f83d32a3d20,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d7022580,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d32a1830,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "sldprt": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             values: [                                                 Py(                                                     0x00007f83d32a1830,                                                 ),                                             ],                                         },                                         expected_repr: "'sldprt'",                                         name: "literal['sldprt']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['sldprt']]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionSldprt",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x00005629076605e0,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f83d4e0a330,         ),         name: "OptionSldprt",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, split_closed_faces: bool = False, type: Literal['sldprt'] = 'sldprt') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_check_frozen(name, value)

Return type:

None

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, 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)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class 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 the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_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 the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

• update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

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)

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 which to_python should 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 of None.

• 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:

dict[str, Any]

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

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 of None.

• 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:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'split_closed_faces': FieldInfo(annotation=bool, required=False, default=False), 'type': FieldInfo(annotation=Literal['sldprt'], required=False, default='sldprt')}

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]

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')

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 of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

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 class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

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 to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

split_closed_faces: bool

type: Literal['sldprt']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.input_format.OptionStep(**data)

ISO 10303-21 (STEP) format.

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.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})

__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]]', 'split_closed_faces': <class 'bool'>, 'type': typing.Literal['step']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

classmethod __get_pydantic_core_schema__(source, handler, /)

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 the cls argument 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-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

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]) – A pydantic-core CoreSchema. 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 a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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.

self is explicitly positional-only to allow self as a field name.

__iter__()

So dict(model) works.

Return type:

Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.input_format'

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.input_format.OptionStep'>, 'config': {'title': 'OptionStep'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionStep'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionStep'>>]}, 'ref': 'kittycad.models.input_format.OptionStep:94734217784384', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'split_closed_faces': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, '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': 'step', 'schema': {'expected': ['step'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionStep', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000562907662c40,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "split_closed_faces": SerField {                         key_py: Py(                             0x00007f83d3230030,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d6f16d00,                                         ),                                     ),                                     serializer: Bool(                                         BoolSerializer,                                     ),                                 },                             ),                         ),                         required: true,                     },                     "type": SerField {                         key_py: Py(                             0x00007f83d7022580,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d7021df0,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "step",                                             },                                             expected_py: None,                                             name: "literal['step']",                                         },                                     ),                                 },                             ),                         ),                         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: "OptionStep",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionStep", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "split_closed_faces",                         lookup_key: Simple {                             key: "split_closed_faces",                             py_key: Py(                                 0x00007f83d33c7630,                             ),                             path: LookupPath(                                 [                                     S(                                         "split_closed_faces",                                         Py(                                             0x00007f83d33c55b0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d3230030,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d6f16d00,                                     ),                                 ),                                 on_error: Raise,                                 validator: Bool(                                     BoolValidator {                                         strict: false,                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[bool]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key: Simple {                             key: "type",                             py_key: Py(                                 0x00007f83d32a2880,                             ),                             path: LookupPath(                                 [                                     S(                                         "type",                                         Py(                                             0x00007f83d32a2850,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d7022580,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d7021df0,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "step": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             values: [                                                 Py(                                                     0x00007f83d7021df0,                                                 ),                                             ],                                         },                                         expected_repr: "'step'",                                         name: "literal['step']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['step']]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionStep",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x0000562907662c40,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f83d4e0a330,         ),         name: "OptionStep",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, split_closed_faces: bool = False, type: Literal['step'] = 'step') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_check_frozen(name, value)

Return type:

None

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, 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)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class 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 the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_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 the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

• update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

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)

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 which to_python should 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 of None.

• 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:

dict[str, Any]

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

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 of None.

• 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:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'split_closed_faces': FieldInfo(annotation=bool, required=False, default=False), 'type': FieldInfo(annotation=Literal['step'], required=False, default='step')}

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]

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')

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 of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

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 class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

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 to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

split_closed_faces: bool

type: Literal['step']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.input_format.OptionStl(**data)

*ST**ereo**L**ithography format.

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.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})

__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]', 'coords': <class 'kittycad.models.system.System'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['stl'], 'units': <enum 'UnitLength'>}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

classmethod __get_pydantic_core_schema__(source, handler, /)

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 the cls argument 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-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

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]) – A pydantic-core CoreSchema. 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 a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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.

self is explicitly positional-only to allow self as a field name.

__iter__()

So dict(model) works.

Return type:

Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.input_format'

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, 'config': {'title': 'AxisDirectionPair'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>>]}, 'ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94734217731920', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'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.axis.Axis:94734218740272', 'type': 'definition-ref'}, 'type': 'model-field'}, 'direction': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.direction.Direction:94734217729360', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'AxisDirectionPair', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <enum 'Axis'>, 'members': [Axis.Y, Axis.Z], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.axis.Axis:94734218740272', 'sub_type': 'str', 'type': 'enum'}, {'cls': <enum 'Direction'>, 'members': [Direction.POSITIVE, Direction.NEGATIVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.direction.Direction:94734217729360', 'sub_type': 'str', 'type': 'enum'}], 'schema': {'cls': <class 'kittycad.models.input_format.OptionStl'>, 'config': {'title': 'OptionStl'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.OptionStl'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.OptionStl'>>]}, 'ref': 'kittycad.models.input_format.OptionStl:94734217794400', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'coords': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.system.System'>, 'config': {'title': 'System'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.system.System'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.system.System'>>]}, 'ref': 'kittycad.models.system.System:94734217741200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'forward': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94734217731920', '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.axis_direction_pair.AxisDirectionPair:94734217731920', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'System', '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': 'stl', 'schema': {'expected': ['stl'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}, 'units': {'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:94734218746384', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'OptionStl', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000562907665360,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f83d7022580,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f83d32a17d0,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "stl",                                             },                                             expected_py: None,                                             name: "literal['stl']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                     },                     "coords": SerField {                         key_py: Py(                             0x00007f83d32a1e00,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Model(                                 ModelSerializer {                                     class: Py(                                         0x0000562907658390,                                     ),                                     serializer: Fields(                                         GeneralFieldsSerializer {                                             fields: {                                                 "forward": SerField {                                                     key_py: Py(                                                         0x00007f83d32a16b0,                                                     ),                                                     alias: None,                                                     alias_py: None,                                                     serializer: Some(                                                         Recursive(                                                             DefinitionRefSerializer {                                                                 definition: "...",                                                                 retry_with_lax_check: true,                                                             },                                                         ),                                                     ),                                                     required: true,                                                 },                                                 "up": SerField {                                                     key_py: Py(                                                         0x00007f83d4ff10b0,                                                     ),                                                     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: "System",                                 },                             ),                         ),                         required: true,                     },                     "units": SerField {                         key_py: Py(                             0x00007f83d5dcaa60,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Enum(                                 EnumSerializer {                                     class: Py(                                         0x000056290774da10,                                     ),                                     serializer: Some(                                         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: "OptionStl",     }, ), definitions=[Enum(EnumSerializer { class: Py(0x56290774c230), serializer: Some(Str(StrSerializer)) }), Model(ModelSerializer { class: Py(0x562907655f50), serializer: Fields(GeneralFieldsSerializer { fields: {"direction": SerField { key_py: Py(0x7f83d58718b0), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: false })), required: true }, "axis": SerField { key_py: Py(0x7f83d3672e20), alias: None, alias_py: None, serializer: Some(Recursive(DefinitionRefSerializer { definition: "...", retry_with_lax_check: 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: "AxisDirectionPair" }), Enum(EnumSerializer { class: Py(0x562907655550), serializer: Some(Str(StrSerializer)) })])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionStl", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "coords",                         lookup_key: Simple {                             key: "coords",                             py_key: Py(                                 0x00007f83d32a2130,                             ),                             path: LookupPath(                                 [                                     S(                                         "coords",                                         Py(                                             0x00007f83d32a20a0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d32a1e00,                         ),                         validator: Model(                             ModelValidator {                                 revalidate: Never,                                 validator: ModelFields(                                     ModelFieldsValidator {                                         fields: [                                             Field {                                                 name: "forward",                                                 lookup_key: Simple {                                                     key: "forward",                                                     py_key: Py(                                                         0x00007f83d32a06c0,                                                     ),                                                     path: LookupPath(                                                         [                                                             S(                                                                 "forward",                                                                 Py(                                                                     0x00007f83d32a15c0,                                                                 ),                                                             ),                                                         ],                                                     ),                                                 },                                                 name_py: Py(                                                     0x00007f83d32a16b0,                                                 ),                                                 validator: DefinitionRef(                                                     DefinitionRefValidator {                                                         definition: "...",                                                     },                                                 ),                                                 frozen: false,                                             },                                             Field {                                                 name: "up",                                                 lookup_key: Simple {                                                     key: "up",                                                     py_key: Py(                                                         0x00007f83d32a2220,                                                     ),                                                     path: LookupPath(                                                         [                                                             S(                                                                 "up",                                                                 Py(                                                                     0x00007f83d32a2100,                                                                 ),                                                             ),                                                         ],                                                     ),                                                 },                                                 name_py: Py(                                                     0x00007f83d4ff10b0,                                                 ),                                                 validator: DefinitionRef(                                                     DefinitionRefValidator {                                                         definition: "...",                                                     },                                                 ),                                                 frozen: false,                                             },                                         ],                                         model_name: "System",                                         extra_behavior: Ignore,                                         extras_validator: None,                                         strict: false,                                         from_attributes: false,                                         loc_by_alias: true,                                     },                                 ),                                 class: Py(                                     0x0000562907658390,                                 ),                                 post_init: None,                                 frozen: false,                                 custom_init: false,                                 root_model: false,                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                                 name: "System",                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key: Simple {                             key: "type",                             py_key: Py(                                 0x00007f83d32a19e0,                             ),                             path: LookupPath(                                 [                                     S(                                         "type",                                         Py(                                             0x00007f83d32a2f70,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d7022580,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f83d32a17d0,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "stl": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             values: [                                                 Py(                                                     0x00007f83d32a17d0,                                                 ),                                             ],                                         },                                         expected_repr: "'stl'",                                         name: "literal['stl']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['stl']]",                                 undefined: Py(                                     0x00007f83d4e0a330,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "units",                         lookup_key: Simple {                             key: "units",                             py_key: Py(                                 0x00007f83d32a2250,                             ),                             path: LookupPath(                                 [                                     S(                                         "units",                                         Py(                                             0x00007f83d32a3db0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f83d5dcaa60,                         ),                         validator: StrEnum(                             EnumValidator {                                 phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>,                                 class: Py(                                     0x000056290774da10,                                 ),                                 lookup: LiteralLookup {                                     expected_bool: None,                                     expected_int: None,                                     expected_str: Some(                                         {                                             "cm": 0,                                             "ft": 1,                                             "mm": 4,                                             "yd": 5,                                             "in": 2,                                             "m": 3,                                         },                                     ),                                     expected_py_dict: None,                                     expected_py_values: None,                                     values: [                                         Py(                                             0x00007f83d28b8b90,                                         ),                                         Py(                                             0x00007f83d28b9130,                                         ),                                         Py(                                             0x00007f83d28b91f0,                                         ),                                         Py(                                             0x00007f83d28b9250,                                         ),                                         Py(                                             0x00007f83d28b92b0,                                         ),                                         Py(                                             0x00007f83d28b9310,                                         ),                                     ],                                 },                                 missing: None,                                 expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'",                                 strict: false,                                 class_repr: "UnitLength",                                 name: "str-enum[UnitLength]",                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionStl",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x0000562907665360,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f83d4e0a330,         ),         name: "OptionStl",     }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "axis", lookup_key: Simple { key: "axis", py_key: Py(0x7f83d32a1da0), path: LookupPath([S("axis", Py(0x7f83d32a2160))]) }, name_py: Py(0x7f83d3672e20), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }, Field { name: "direction", lookup_key: Simple { key: "direction", py_key: Py(0x7f83d369aff0), path: LookupPath([S("direction", Py(0x7f83d369b7f0))]) }, name_py: Py(0x7f83d58718b0), validator: DefinitionRef(DefinitionRefValidator { definition: "..." }), frozen: false }], model_name: "AxisDirectionPair", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x562907655f50), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f83d4e0a330), name: "AxisDirectionPair" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x562907655550), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"negative": 1, "positive": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7f83d28b8f50), Py(0x7f83d28b8fb0)] }, missing: None, expected_repr: "'positive' or 'negative'", strict: false, class_repr: "Direction", name: "str-enum[Direction]" }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x56290774c230), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"z": 1, "y": 0}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7f83d28b8dd0), Py(0x7f83d28b8e30)] }, missing: None, expected_repr: "'y' or 'z'", strict: false, class_repr: "Axis", name: "str-enum[Axis]" })], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, coords: kittycad.models.system.System, type: Literal['stl'] = 'stl', units: kittycad.models.unit_length.UnitLength) -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_check_frozen(name, value)

Return type:

None

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

classmethod construct(_fields_set=None, **values)

Return type:

Self

coords: System

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, 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)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class 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 the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_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 the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

• update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

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)

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 which to_python should 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 of None.

• 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:

dict[str, Any]

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

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 of None.

• 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:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'coords': FieldInfo(annotation=System, required=True), 'type': FieldInfo(annotation=Literal['stl'], required=False, default='stl'), 'units': FieldInfo(annotation=UnitLength, required=True)}

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]

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')

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 of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

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 class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

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 to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

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)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

type: Literal['stl']

units: UnitLength

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self