kittycad.models.input_format.gltf

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

Bases: BaseModel

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.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

__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.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct(cls[, _fields_set])	rtype: Model
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])	rtype: typing.Dict[str, Any]
from_orm(cls, obj)	rtype: Model
json(*[, include, exclude, by_alias, ...])	rtype: str
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy
model_dump(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump
model_dump_json(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(_BaseModel__context)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing
model_validate_strings(obj, *[, strict, context])	Validate the given object contains string data against the Pydantic model.
parse_file(cls, path, *[, content_type, ...])	rtype: Model
parse_obj(cls, obj)	rtype: Model
parse_raw(cls, b, *[, content_type, ...])	rtype: Model
schema(cls[, by_alias, ref_template])	rtype: Dict[str, Any]
schema_json(cls, *[, by_alias, ref_template])	rtype: str
update_forward_refs(cls, **localns)	rtype: None
validate(cls, value)	rtype: Model

Attributes

model_computed_fields	Get the computed fields of this model instance.
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields	Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
type

__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]', '__signature__': 'ClassVar[Signature]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', 'type': typing.Literal['gltf']}

classmethod __class_getitem__(typevar_values)

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

__copy__()

Returns a shallow copy of the model.

Return type:

Model

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Model

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

__fields__ = {'type': FieldInfo(annotation=Literal['gltf'], required=False, default='gltf')}

property __fields_set__: set[str]

classmethod __get_pydantic_core_schema__(_BaseModel__source, _BaseModel__handler)

Hook into generating the model’s CoreSchema.

Parameters:

• __source – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.

• __handler – Call into Pydantic’s internal JSON schema generation. 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, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TuplePositionalSchema, TupleVariableSchema, 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]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(_BaseModel__core_schema, _BaseModel__handler)

Hook into generating the model’s JSON schema.

Parameters:

• __core_schema – 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 – 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__()

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.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

__iter__()

So dict(model) works.

Return type:

TupleGenerator

__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]] = {}

__pydantic_complete__: ClassVar[bool] = True

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.input_format.gltf'>, 'config': {'title': 'gltf'}, 'custom_init': False, 'metadata': {'pydantic.internal.needs_apply_discriminated_union': False, 'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.input_format.gltf'>), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.input_format.gltf'>>]}, 'ref': 'kittycad.models.input_format.gltf:93825014623968', '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'], 'metadata': {'pydantic.internal.needs_apply_discriminated_union': False}, 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'gltf', 'type': 'model-fields'}, 'type': 'model'}

__pydantic_custom_init__: ClassVar[bool] = False

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})

__pydantic_extra__: dict[str, Any] | None

__pydantic_fields_set__: set[str]

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

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] = {'Annotated': <pydantic._internal._model_construction._PydanticWeakRef object>, 'BaseModel': <pydantic._internal._model_construction._PydanticWeakRef object>, 'Field': <pydantic._internal._model_construction._PydanticWeakRef object>, 'Literal': <pydantic._internal._model_construction._PydanticWeakRef object>, 'RootModel': <pydantic._internal._model_construction._PydanticWeakRef object>, 'System': <pydantic._internal._model_construction._PydanticWeakRef object>, 'Union': <pydantic._internal._model_construction._PydanticWeakRef object>, 'UnitLength': <pydantic._internal._model_construction._PydanticWeakRef object>, '__builtins__': {'ArithmeticError': <class 'ArithmeticError'>, 'AssertionError': <class 'AssertionError'>, 'AttributeError': <class 'AttributeError'>, 'BaseException': <class 'BaseException'>, 'BlockingIOError': <class 'BlockingIOError'>, 'BrokenPipeError': <class 'BrokenPipeError'>, 'BufferError': <class 'BufferError'>, 'BytesWarning': <class 'BytesWarning'>, 'ChildProcessError': <class 'ChildProcessError'>, 'ConnectionAbortedError': <class 'ConnectionAbortedError'>, 'ConnectionError': <class 'ConnectionError'>, 'ConnectionRefusedError': <class 'ConnectionRefusedError'>, 'ConnectionResetError': <class 'ConnectionResetError'>, 'DeprecationWarning': <class 'DeprecationWarning'>, 'EOFError': <class 'EOFError'>, 'Ellipsis': Ellipsis, 'EnvironmentError': <class 'OSError'>, 'Exception': <class 'Exception'>, 'False': False, 'FileExistsError': <class 'FileExistsError'>, 'FileNotFoundError': <class 'FileNotFoundError'>, 'FloatingPointError': <class 'FloatingPointError'>, 'FutureWarning': <class 'FutureWarning'>, 'GeneratorExit': <class 'GeneratorExit'>, 'IOError': <class 'OSError'>, 'ImportError': <class 'ImportError'>, 'ImportWarning': <class 'ImportWarning'>, 'IndentationError': <class 'IndentationError'>, 'IndexError': <class 'IndexError'>, 'InterruptedError': <class 'InterruptedError'>, 'IsADirectoryError': <class 'IsADirectoryError'>, 'KeyError': <class 'KeyError'>, 'KeyboardInterrupt': <class 'KeyboardInterrupt'>, 'LookupError': <class 'LookupError'>, 'MemoryError': <class 'MemoryError'>, 'ModuleNotFoundError': <class 'ModuleNotFoundError'>, 'NameError': <class 'NameError'>, 'None': None, 'NotADirectoryError': <class 'NotADirectoryError'>, 'NotImplemented': NotImplemented, 'NotImplementedError': <class 'NotImplementedError'>, 'OSError': <class 'OSError'>, 'OverflowError': <class 'OverflowError'>, 'PendingDeprecationWarning': <class 'PendingDeprecationWarning'>, 'PermissionError': <class 'PermissionError'>, 'ProcessLookupError': <class 'ProcessLookupError'>, 'RecursionError': <class 'RecursionError'>, 'ReferenceError': <class 'ReferenceError'>, 'ResourceWarning': <class 'ResourceWarning'>, 'RuntimeError': <class 'RuntimeError'>, 'RuntimeWarning': <class 'RuntimeWarning'>, 'StopAsyncIteration': <class 'StopAsyncIteration'>, 'StopIteration': <class 'StopIteration'>, 'SyntaxError': <class 'SyntaxError'>, 'SyntaxWarning': <class 'SyntaxWarning'>, 'SystemError': <class 'SystemError'>, 'SystemExit': <class 'SystemExit'>, 'TabError': <class 'TabError'>, 'TimeoutError': <class 'TimeoutError'>, 'True': True, 'TypeError': <class 'TypeError'>, 'UnboundLocalError': <class 'UnboundLocalError'>, 'UnicodeDecodeError': <class 'UnicodeDecodeError'>, 'UnicodeEncodeError': <class 'UnicodeEncodeError'>, 'UnicodeError': <class 'UnicodeError'>, 'UnicodeTranslateError': <class 'UnicodeTranslateError'>, 'UnicodeWarning': <class 'UnicodeWarning'>, 'UserWarning': <class 'UserWarning'>, 'ValueError': <class 'ValueError'>, 'Warning': <class 'Warning'>, 'ZeroDivisionError': <class 'ZeroDivisionError'>, '__build_class__': <built-in function __build_class__>, '__debug__': True, '__doc__': "Built-in functions, exceptions, and other objects.\n\nNoteworthy: None is the `nil' object; Ellipsis represents `...' in slices.", '__import__': <built-in function __import__>, '__loader__': <class '_frozen_importlib.BuiltinImporter'>, '__name__': 'builtins', '__package__': '', '__spec__': ModuleSpec(name='builtins', loader=<class '_frozen_importlib.BuiltinImporter'>, origin='built-in'), 'abs': <built-in function abs>, 'all': <built-in function all>, 'any': <built-in function any>, 'ascii': <built-in function ascii>, 'bin': <built-in function bin>, 'bool': <class 'bool'>, 'breakpoint': <built-in function breakpoint>, 'bytearray': <class 'bytearray'>, 'bytes': <class 'bytes'>, 'callable': <built-in function callable>, 'chr': <built-in function chr>, 'classmethod': <class 'classmethod'>, 'compile': <built-in function compile>, 'complex': <class 'complex'>, 'copyright': Copyright (c) 2001-2023 Python Software Foundation. All Rights Reserved.  Copyright (c) 2000 BeOpen.com. All Rights Reserved.  Copyright (c) 1995-2001 Corporation for National Research Initiatives. All Rights Reserved.  Copyright (c) 1991-1995 Stichting Mathematisch Centrum, Amsterdam. All Rights Reserved., 'credits':     Thanks to CWI, CNRI, BeOpen.com, Zope Corporation and a cast of thousands     for supporting Python development.  See www.python.org for more information., 'delattr': <built-in function delattr>, 'dict': <class 'dict'>, 'dir': <built-in function dir>, 'divmod': <built-in function divmod>, 'enumerate': <class 'enumerate'>, 'eval': <built-in function eval>, 'exec': <built-in function exec>, 'exit': Use exit() or Ctrl-D (i.e. EOF) to exit, 'filter': <class 'filter'>, 'float': <class 'float'>, 'format': <built-in function format>, 'frozenset': <class 'frozenset'>, 'getattr': <built-in function getattr>, 'globals': <built-in function globals>, 'hasattr': <built-in function hasattr>, 'hash': <built-in function hash>, 'help': Type help() for interactive help, or help(object) for help about object., 'hex': <built-in function hex>, 'id': <built-in function id>, 'input': <built-in function input>, 'int': <class 'int'>, 'isinstance': <built-in function isinstance>, 'issubclass': <built-in function issubclass>, 'iter': <built-in function iter>, 'len': <built-in function len>, 'license': Type license() to see the full license text, 'list': <class 'list'>, 'locals': <built-in function locals>, 'map': <class 'map'>, 'max': <built-in function max>, 'memoryview': <class 'memoryview'>, 'min': <built-in function min>, 'next': <built-in function next>, 'object': <class 'object'>, 'oct': <built-in function oct>, 'open': <built-in function open>, 'ord': <built-in function ord>, 'pow': <built-in function pow>, 'print': <built-in function print>, 'property': <class 'property'>, 'quit': Use quit() or Ctrl-D (i.e. EOF) to exit, 'range': <class 'range'>, 'repr': <built-in function repr>, 'reversed': <class 'reversed'>, 'round': <built-in function round>, 'set': <class 'set'>, 'setattr': <built-in function setattr>, 'slice': <class 'slice'>, 'sorted': <built-in function sorted>, 'staticmethod': <class 'staticmethod'>, 'str': <class 'str'>, 'sum': <built-in function sum>, 'super': <class 'super'>, 'tuple': <class 'tuple'>, 'type': <class 'type'>, 'vars': <built-in function vars>, 'zip': <class 'zip'>}, '__cached__': '/home/user/src/kittycad/models/__pycache__/input_format.cpython-39.pyc', '__doc__': <pydantic._internal._model_construction._PydanticWeakRef object>, '__file__': '/home/user/src/kittycad/models/input_format.py', '__loader__': <pydantic._internal._model_construction._PydanticWeakRef object>, '__name__': 'kittycad.models.input_format', '__package__': 'kittycad.models', '__spec__': <pydantic._internal._model_construction._PydanticWeakRef object>, 'fbx': <pydantic._internal._model_construction._PydanticWeakRef object>}

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

__pydantic_private__: dict[str, Any] | None

__pydantic_root_model__: ClassVar[bool] = False

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000555556aaeee0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007fffff8ebef0,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007fffe137ba30,                                         ),                                     ),                                     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: "gltf",     }, ), definitions=[])

__pydantic_validator__: ClassVar[SchemaValidator] = SchemaValidator(title="gltf", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "type",                         lookup_key: Simple {                             key: "type",                             py_key: Py(                                 0x00007fffff8ebef0,                             ),                             path: LookupPath(                                 [                                     S(                                         "type",                                         Py(                                             0x00007fffff8ebef0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007fffff8ebef0,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007fffe137ba30,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "gltf": 0,                                                 },                                             ),                                             expected_py: None,                                             values: [                                                 Py(                                                     0x00007fffe137ba30,                                                 ),                                             ],                                         },                                         expected_repr: "'gltf'",                                         name: "literal['gltf']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['gltf']]",                             },                         ),                         frozen: false,                     },                 ],                 model_name: "gltf",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x0000555556aaeee0,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         name: "gltf",     }, ), definitions=[])

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

__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.

__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>

__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(cls, *args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

classmethod construct(cls, _fields_set=None, **values)

Return type:

Model

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 (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.

• exclude (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.

• update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.

• deep (bool) – If True, the values of fields that are Pydantic models will be deep copied.

Return type:

Model

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(cls, obj)

Return type:

Model

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

property model_computed_fields: dict[str, ComputedFieldInfo]

Get the computed fields of this model instance.

Returns:

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

model_config: ClassVar[ConfigDict] = {}

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. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters:

• _fields_set (set[str] | None) – The set of field names accepted for the Model instance.

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

Return type:

Model

Returns:

A new instance of the Model class with validated data.

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

Usage docs: https://docs.pydantic.dev/2.5/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:

Model

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True)

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode – The mode in which to_python should run. If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any Python objects.

• include – A list of fields to include in the output.

• exclude – A list of fields to exclude from the output.

• by_alias – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset – Whether to exclude fields that have not been explicitly set.

• exclude_defaults – Whether to exclude fields that are set to their default value from the output.

• exclude_none – Whether to exclude fields that have a value of None from the output.

• round_trip – Whether to enable serialization and deserialization round-trip support.

• warnings – Whether to log warnings when invalid fields are encountered.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True)

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json

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

Parameters:

• indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include – Field(s) to include in the JSON output. Can take either a string or set of strings.

• exclude – Field(s) to exclude from the JSON output. Can take either a string or set of strings.

• by_alias – Whether to serialize using field aliases.

• exclude_unset – Whether to exclude fields that have not been explicitly set.

• exclude_defaults – Whether to exclude fields that have the default value.

• exclude_none – Whether to exclude fields that have a value of None.

• round_trip – Whether to use serialization/deserialization between JSON and class instance.

• warnings – Whether to show any warnings that occurred during serialization.

Returns:

A JSON string representation of the model.

property model_extra

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].

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 – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

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

• _types_namespace – The types namespace, defaults to 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 raise an exception on invalid fields.

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

• context (dict[str, Any] | None) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Model

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)

Usage docs: https://docs.pydantic.dev/2.5/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 (dict[str, Any] | None) – Extra variables to pass to the validator.

Return type:

Model

Returns:

The validated Pydantic model.

Raises:

ValueError – If json_data is not a JSON string.

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

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

Parameters:

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

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

• context (dict[str, Any] | None) – Extra variables to pass to the validator.

Return type:

Model

Returns:

The validated Pydantic model.

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

Return type:

Model

classmethod parse_obj(cls, obj)

Return type:

Model

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

Return type:

Model

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['gltf']

classmethod update_forward_refs(cls, **localns)

Return type:

None

classmethod validate(cls, value)

Return type:

Model