kittycad.models.output_format.gltf

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

Bases: BaseModel

glTF 2.0. We refer to this as glTF since that is how our customers refer to it, although by default it will be in binary format and thus technically (glb). If you prefer ascii output, you can set that option for the export.

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.
presentation
storage
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]]', 'presentation': <enum 'GltfPresentation'>, 'storage': <enum 'GltfStorage'>, '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__ = {'presentation': FieldInfo(annotation=GltfPresentation, required=True), 'storage': FieldInfo(annotation=GltfStorage, required=True), '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.output_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.output_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.output_format.gltf'>), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.output_format.gltf'>>]}, 'ref': 'kittycad.models.output_format.gltf:93825014946976', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'presentation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'lax_schema': {'steps': [{'type': 'str'}, {'type': 'function-plain', 'function': {'type': 'no-info', 'function': <function get_enum_core_schema.<locals>.to_enum>}}], 'type': 'chain'}, 'metadata': {'pydantic.internal.needs_apply_discriminated_union': False, 'pydantic_js_functions': [<function get_enum_core_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.gltf_presentation.GltfPresentation:93825014855744', 'strict_schema': {'json_schema': {'function': {'function': <function get_enum_core_schema.<locals>.to_enum>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'python_schema': {'cls': <enum 'GltfPresentation'>, 'type': 'is-instance'}, 'type': 'json-or-python'}, 'type': 'lax-or-strict'}, 'type': 'model-field'}, 'storage': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'lax_schema': {'steps': [{'type': 'str'}, {'type': 'function-plain', 'function': {'type': 'no-info', 'function': <function get_enum_core_schema.<locals>.to_enum>}}], 'type': 'chain'}, 'metadata': {'pydantic.internal.needs_apply_discriminated_union': False, 'pydantic_js_functions': [<function get_enum_core_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.gltf_storage.GltfStorage:93825014856688', 'strict_schema': {'json_schema': {'function': {'function': <function get_enum_core_schema.<locals>.to_enum>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'python_schema': {'cls': <enum 'GltfStorage'>, 'type': 'is-instance'}, 'type': 'json-or-python'}, 'type': 'lax-or-strict'}, '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': '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>, 'FbxStorage': <pydantic._internal._model_construction._PydanticWeakRef object>, 'Field': <pydantic._internal._model_construction._PydanticWeakRef object>, 'GltfPresentation': <pydantic._internal._model_construction._PydanticWeakRef object>, 'GltfStorage': <pydantic._internal._model_construction._PydanticWeakRef object>, 'Literal': <pydantic._internal._model_construction._PydanticWeakRef object>, 'PlyStorage': <pydantic._internal._model_construction._PydanticWeakRef object>, 'RootModel': <pydantic._internal._model_construction._PydanticWeakRef object>, 'Selection': <pydantic._internal._model_construction._PydanticWeakRef object>, 'StlStorage': <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__/output_format.cpython-39.pyc', '__doc__': <pydantic._internal._model_construction._PydanticWeakRef object>, '__file__': '/home/user/src/kittycad/models/output_format.py', '__loader__': <pydantic._internal._model_construction._PydanticWeakRef object>, '__name__': 'kittycad.models.output_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(             0x0000555556afdca0,         ),         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,                     },                     "presentation": SerField {                         key_py: Py(                             0x00007fffe14c1eb0,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             JsonOrPython(                                 JsonOrPythonSerializer {                                     json: Str(                                         StrSerializer,                                     ),                                     python: Any(                                         AnySerializer,                                     ),                                     name: "json-or-python[json=str, python=any]",                                 },                             ),                         ),                         required: true,                     },                     "storage": SerField {                         key_py: Py(                             0x00007ffffd064cb0,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             JsonOrPython(                                 JsonOrPythonSerializer {                                     json: Str(                                         StrSerializer,                                     ),                                     python: Any(                                         AnySerializer,                                     ),                                     name: "json-or-python[json=str, python=any]",                                 },                             ),                         ),                         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: "gltf",     }, ), definitions=[])

__pydantic_validator__: ClassVar[SchemaValidator] = SchemaValidator(title="gltf", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "presentation",                         lookup_key: Simple {                             key: "presentation",                             py_key: Py(                                 0x00007fffe14c1eb0,                             ),                             path: LookupPath(                                 [                                     S(                                         "presentation",                                         Py(                                             0x00007fffe14c1eb0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007fffe14c1eb0,                         ),                         validator: LaxOrStrict(                             LaxOrStrictValidator {                                 strict: false,                                 lax_validator: Chain(                                     ChainValidator {                                         steps: [                                             Str(                                                 StrValidator {                                                     strict: false,                                                     coerce_numbers_to_str: false,                                                 },                                             ),                                             FunctionPlain(                                                 FunctionPlainValidator {                                                     func: Py(                                                         0x00007fffe139ea60,                                                     ),                                                     config: Py(                                                         0x00007fffe129f840,                                                     ),                                                     name: "function-plain[to_enum()]",                                                     field_name: None,                                                     info_arg: false,                                                 },                                             ),                                         ],                                         name: "chain[str,function-plain[to_enum()]]",                                     },                                 ),                                 strict_validator: JsonOrPython(                                     JsonOrPython {                                         json: FunctionAfter(                                             FunctionAfterValidator {                                                 validator: Str(                                                     StrValidator {                                                         strict: false,                                                         coerce_numbers_to_str: false,                                                     },                                                 ),                                                 func: Py(                                                     0x00007fffe139ea60,                                                 ),                                                 config: Py(                                                     0x00007fffe129f840,                                                 ),                                                 name: "function-after[to_enum(), str]",                                                 field_name: None,                                                 info_arg: false,                                             },                                         ),                                         python: IsInstance(                                             IsInstanceValidator {                                                 class: Py(                                                     0x0000555556ae7840,                                                 ),                                                 class_repr: "GltfPresentation",                                                 name: "is-instance[GltfPresentation]",                                             },                                         ),                                         name: "json-or-python[json=function-after[to_enum(), str],python=is-instance[GltfPresentation]]",                                     },                                 ),                                 name: "lax-or-strict[lax=chain[str,function-plain[to_enum()]],strict=json-or-python[json=function-after[to_enum(), str],python=is-instance[GltfPresentation]]]",                             },                         ),                         frozen: false,                     },                     Field {                         name: "storage",                         lookup_key: Simple {                             key: "storage",                             py_key: Py(                                 0x00007ffffd064cb0,                             ),                             path: LookupPath(                                 [                                     S(                                         "storage",                                         Py(                                             0x00007ffffd064cb0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007ffffd064cb0,                         ),                         validator: LaxOrStrict(                             LaxOrStrictValidator {                                 strict: false,                                 lax_validator: Chain(                                     ChainValidator {                                         steps: [                                             Str(                                                 StrValidator {                                                     strict: false,                                                     coerce_numbers_to_str: false,                                                 },                                             ),                                             FunctionPlain(                                                 FunctionPlainValidator {                                                     func: Py(                                                         0x00007fffe139e550,                                                     ),                                                     config: Py(                                                         0x00007fffe129f840,                                                     ),                                                     name: "function-plain[to_enum()]",                                                     field_name: None,                                                     info_arg: false,                                                 },                                             ),                                         ],                                         name: "chain[str,function-plain[to_enum()]]",                                     },                                 ),                                 strict_validator: JsonOrPython(                                     JsonOrPython {                                         json: FunctionAfter(                                             FunctionAfterValidator {                                                 validator: Str(                                                     StrValidator {                                                         strict: false,                                                         coerce_numbers_to_str: false,                                                     },                                                 ),                                                 func: Py(                                                     0x00007fffe139e550,                                                 ),                                                 config: Py(                                                     0x00007fffe129f840,                                                 ),                                                 name: "function-after[to_enum(), str]",                                                 field_name: None,                                                 info_arg: false,                                             },                                         ),                                         python: IsInstance(                                             IsInstanceValidator {                                                 class: Py(                                                     0x0000555556ae7bf0,                                                 ),                                                 class_repr: "GltfStorage",                                                 name: "is-instance[GltfStorage]",                                             },                                         ),                                         name: "json-or-python[json=function-after[to_enum(), str],python=is-instance[GltfStorage]]",                                     },                                 ),                                 name: "lax-or-strict[lax=chain[str,function-plain[to_enum()]],strict=json-or-python[json=function-after[to_enum(), str],python=is-instance[GltfStorage]]]",                             },                         ),                         frozen: false,                     },                     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(             0x0000555556afdca0,         ),         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 (*, presentation: kittycad.models.gltf_presentation.GltfPresentation, storage: kittycad.models.gltf_storage.GltfStorage, 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]] = {'presentation': FieldInfo(annotation=GltfPresentation, required=True), 'storage': FieldInfo(annotation=GltfStorage, required=True), '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

presentation: GltfPresentation

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

storage: GltfStorage

type: Literal['gltf']

classmethod update_forward_refs(cls, **localns)

Return type:

None

classmethod validate(cls, value)

Return type:

Model