BaseModel

Pydantic models are simply classes which inherit from BaseModel and define fields as annotated attributes.

pydantic.BaseModel

Usage Documentation

Models

A base class for creating Pydantic models.

Attributes:

Name	Type	Description
__class_vars__	set[str]	The names of the class variables defined on the model.
__private_attributes__	Dict[str, ModelPrivateAttr]	Metadata about the private attributes of the model.
__signature__	Signature	The synthesized __init__ Signature of the model.
__pydantic_complete__	bool	Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__	CoreSchema	The core schema of the model.
__pydantic_custom_init__	bool	Whether the model has a custom __init__ function.
__pydantic_decorators__	DecoratorInfos	Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
__pydantic_generic_metadata__	PydanticGenericMetadata	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.
__pydantic_parent_namespace__	Dict[str, Any] | None	Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__	None | Literal['model_post_init']	The name of the post-init method for the model, if defined.
__pydantic_root_model__	bool	Whether the model is a RootModel.
__pydantic_serializer__	SchemaSerializer	The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__	SchemaValidator | PluggableSchemaValidator	The pydantic-core SchemaValidator used to validate instances of the model.
__pydantic_fields__	Dict[str, FieldInfo]	A dictionary of field names and their corresponding FieldInfo objects.
__pydantic_computed_fields__	Dict[str, ComputedFieldInfo]	A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
__pydantic_extra__	dict[str, Any] | None	A dictionary containing extra values, if extra is set to 'allow'.
__pydantic_fields_set__	set[str]	The names of fields explicitly set during instantiation.
__pydantic_private__	dict[str, Any] | None	Values of private attributes set on the model instance.

Source code in pydantic/main.py

class BaseModel(metaclass=_model_construction.ModelMetaclass):
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/

    A base class for creating Pydantic models.

    Attributes:
        __class_vars__: The names of the class variables defined on the model.
        __private_attributes__: Metadata about the private attributes of the model.
        __signature__: The synthesized `__init__` [`Signature`][inspect.Signature] of the model.

        __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
        __pydantic_core_schema__: The core schema of the model.
        __pydantic_custom_init__: Whether the model has a custom `__init__` function.
        __pydantic_decorators__: Metadata containing the decorators defined on the model.
            This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
        __pydantic_generic_metadata__: 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.
        __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
        __pydantic_post_init__: The name of the post-init method for the model, if defined.
        __pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
        __pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
        __pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.

        __pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
        __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.

        __pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
            is set to `'allow'`.
        __pydantic_fields_set__: The names of fields explicitly set during instantiation.
        __pydantic_private__: Values of private attributes set on the model instance.
    """

    # Note: Many of the below class vars are defined in the metaclass, but we define them here for type checking purposes.

    model_config: ClassVar[ConfigDict] = ConfigDict()
    """
    Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
    """

    # Because `dict` is in the local namespace of the `BaseModel` class, we use `Dict` for annotations.
    # TODO v3 fallback to `dict` when the deprecated `dict` method gets removed.
    __class_vars__: ClassVar[set[str]]
    """The names of the class variables defined on the model."""

    __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]]  # noqa: UP006
    """Metadata about the private attributes of the model."""

    __signature__: ClassVar[Signature]
    """The synthesized `__init__` [`Signature`][inspect.Signature] of the model."""

    __pydantic_complete__: ClassVar[bool] = False
    """Whether model building is completed, or if there are still undefined fields."""

    __pydantic_core_schema__: ClassVar[CoreSchema]
    """The core schema of the model."""

    __pydantic_custom_init__: ClassVar[bool]
    """Whether the model has a custom `__init__` method."""

    # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation.
    __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = _decorators.DecoratorInfos()
    """Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1."""

    __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata]
    """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."""

    __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None  # noqa: UP006
    """Parent namespace of the model, used for automatic rebuilding of models."""

    __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
    """The name of the post-init method for the model, if defined."""

    __pydantic_root_model__: ClassVar[bool] = False
    """Whether the model is a [`RootModel`][pydantic.root_model.RootModel]."""

    __pydantic_serializer__: ClassVar[SchemaSerializer]
    """The `pydantic-core` `SchemaSerializer` used to dump instances of the model."""

    __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator]
    """The `pydantic-core` `SchemaValidator` used to validate instances of the model."""

    __pydantic_fields__: ClassVar[Dict[str, FieldInfo]]  # noqa: UP006
    """A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
    This replaces `Model.__fields__` from Pydantic V1.
    """

    __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]]  # noqa: UP006
    """A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects."""

    __pydantic_extra__: dict[str, Any] | None = _model_construction.NoInitField(init=False)
    """A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`."""

    __pydantic_fields_set__: set[str] = _model_construction.NoInitField(init=False)
    """The names of fields explicitly set during instantiation."""

    __pydantic_private__: dict[str, Any] | None = _model_construction.NoInitField(init=False)
    """Values of private attributes set on the model instance."""

    if not TYPE_CHECKING:
        # Prevent `BaseModel` from being instantiated directly
        # (defined in an `if not TYPE_CHECKING` block for clarity and to avoid type checking errors):
        __pydantic_core_schema__ = _mock_val_ser.MockCoreSchema(
            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
            code='base-model-instantiated',
        )
        __pydantic_validator__ = _mock_val_ser.MockValSer(
            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
            val_or_ser='validator',
            code='base-model-instantiated',
        )
        __pydantic_serializer__ = _mock_val_ser.MockValSer(
            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
            val_or_ser='serializer',
            code='base-model-instantiated',
        )

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

    def __init__(self, /, **data: Any) -> None:
        """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.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
        if self is not validated_self:
            warnings.warn(
                'A custom validator is returning a value other than `self`.\n'
                "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
                'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
                stacklevel=2,
            )

    # The following line sets a flag that we use to determine when `__init__` gets overridden by the user
    __init__.__pydantic_base_init__ = True  # pyright: ignore[reportFunctionMemberAccess]

    if TYPE_CHECKING:
        model_fields: ClassVar[dict[str, FieldInfo]]
        model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]]
    else:
        # TODO: V3 - remove `model_fields` and `model_computed_fields` properties from the `BaseModel` class - they should only
        # be accessible on the model class, not on instances. We have these purely for backwards compatibility with Pydantic <v2.10.
        # This is similar to the fact that we have __fields__ defined here (on `BaseModel`) and on `ModelMetaClass`.
        # Another problem here is that there's no great way to have class properties :(
        @property
        def model_fields(self) -> dict[str, FieldInfo]:
            """Get metadata about the fields defined on the model.

            Deprecation warning: you should be getting this information from the model class, not from an instance.
            In V3, this property will be removed from the `BaseModel` class.

            Returns:
                A mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects.
            """
            # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation, hence the default here.
            return getattr(self, '__pydantic_fields__', {})

        @property
        def model_computed_fields(self) -> dict[str, ComputedFieldInfo]:
            """Get metadata about the computed fields defined on the model.

            Deprecation warning: you should be getting this information from the model class, not from an instance.
            In V3, this property will be removed from the `BaseModel` class.

            Returns:
                A mapping of computed field names to [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.
            """
            # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation, hence the default here.
            return getattr(self, '__pydantic_computed_fields__', {})

    @property
    def model_extra(self) -> 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"`.
        """
        return self.__pydantic_extra__

    @property
    def model_fields_set(self) -> 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.
        """
        return self.__pydantic_fields_set__

    @classmethod
    def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: C901
        """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.

        Args:
            _fields_set: 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: Trusted or pre-validated data dictionary.

        Returns:
            A new instance of the `Model` class with validated data.
        """
        m = cls.__new__(cls)
        fields_values: dict[str, Any] = {}
        fields_set = set()

        for name, field in cls.__pydantic_fields__.items():
            if field.alias is not None and field.alias in values:
                fields_values[name] = values.pop(field.alias)
                fields_set.add(name)

            if (name not in fields_set) and (field.validation_alias is not None):
                validation_aliases: list[str | AliasPath] = (
                    field.validation_alias.choices
                    if isinstance(field.validation_alias, AliasChoices)
                    else [field.validation_alias]
                )

                for alias in validation_aliases:
                    if isinstance(alias, str) and alias in values:
                        fields_values[name] = values.pop(alias)
                        fields_set.add(name)
                        break
                    elif isinstance(alias, AliasPath):
                        value = alias.search_dict_for_path(values)
                        if value is not PydanticUndefined:
                            fields_values[name] = value
                            fields_set.add(name)
                            break

            if name not in fields_set:
                if name in values:
                    fields_values[name] = values.pop(name)
                    fields_set.add(name)
                elif not field.is_required():
                    fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values)
        if _fields_set is None:
            _fields_set = fields_set

        _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None
        _object_setattr(m, '__dict__', fields_values)
        _object_setattr(m, '__pydantic_fields_set__', _fields_set)
        if not cls.__pydantic_root_model__:
            _object_setattr(m, '__pydantic_extra__', _extra)

        if cls.__pydantic_post_init__:
            m.model_post_init(None)
            # update private attributes with values set
            if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
                for k, v in values.items():
                    if k in m.__private_attributes__:
                        m.__pydantic_private__[k] = v

        elif not cls.__pydantic_root_model__:
            # Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
            # Since it doesn't, that means that `__pydantic_private__` should be set to None
            _object_setattr(m, '__pydantic_private__', None)

        return m

    def model_copy(self, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self:
        """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

        Returns a copy of the model.

        Args:
            update: 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: Set to `True` to make a deep copy of the model.

        Returns:
            New model instance.
        """
        copied = self.__deepcopy__() if deep else self.__copy__()
        if update:
            if self.model_config.get('extra') == 'allow':
                for k, v in update.items():
                    if k in self.__pydantic_fields__:
                        copied.__dict__[k] = v
                    else:
                        if copied.__pydantic_extra__ is None:
                            copied.__pydantic_extra__ = {}
                        copied.__pydantic_extra__[k] = v
            else:
                copied.__dict__.update(update)
            copied.__pydantic_fields_set__.update(update.keys())
        return copied

    def model_dump(
        self,
        *,
        mode: Literal['json', 'python'] | str = 'python',
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        context: Any | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool | Literal['none', 'warn', 'error'] = True,
        serialize_as_any: bool = False,
    ) -> dict[str, Any]:
        """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

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

        Args:
            mode: 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: A set of fields to include in the output.
            exclude: A set of fields to exclude from the output.
            context: Additional context to pass to the serializer.
            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.
            exclude_none: Whether to exclude fields that have a value of `None`.
            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

        Returns:
            A dictionary representation of the model.
        """
        return self.__pydantic_serializer__.to_python(
            self,
            mode=mode,
            by_alias=by_alias,
            include=include,
            exclude=exclude,
            context=context,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
            serialize_as_any=serialize_as_any,
        )

    def model_dump_json(
        self,
        *,
        indent: int | None = None,
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        context: Any | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool | Literal['none', 'warn', 'error'] = True,
        serialize_as_any: bool = False,
    ) -> str:
        """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

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

        Args:
            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.
            exclude: Field(s) to exclude from the JSON output.
            context: Additional context to pass to the serializer.
            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 are set to their default value.
            exclude_none: Whether to exclude fields that have a value of `None`.
            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

        Returns:
            A JSON string representation of the model.
        """
        return self.__pydantic_serializer__.to_json(
            self,
            indent=indent,
            include=include,
            exclude=exclude,
            context=context,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
            serialize_as_any=serialize_as_any,
        ).decode()

    @classmethod
    def model_json_schema(
        cls,
        by_alias: bool = True,
        ref_template: str = DEFAULT_REF_TEMPLATE,
        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
        mode: JsonSchemaMode = 'validation',
    ) -> dict[str, Any]:
        """Generates a JSON schema for a model class.

        Args:
            by_alias: Whether to use attribute aliases or not.
            ref_template: The reference template.
            schema_generator: To override the logic used to generate the JSON schema, as a subclass of
                `GenerateJsonSchema` with your desired modifications
            mode: The mode in which to generate the schema.

        Returns:
            The JSON schema for the given model class.
        """
        return model_json_schema(
            cls, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator, mode=mode
        )

    @classmethod
    def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
        """Compute the class name for parametrizations of generic classes.

        This method can be overridden to achieve a custom naming scheme for generic BaseModels.

        Args:
            params: 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`.

        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.
        """
        if not issubclass(cls, typing.Generic):
            raise TypeError('Concrete names should only be generated for generic models.')

        # Any strings received should represent forward references, so we handle them specially below.
        # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
        # we may be able to remove this special case.
        param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
        params_component = ', '.join(param_names)
        return f'{cls.__name__}[{params_component}]'

    def model_post_init(self, __context: Any) -> None:
        """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.
        """
        pass

    @classmethod
    def model_rebuild(
        cls,
        *,
        force: bool = False,
        raise_errors: bool = True,
        _parent_namespace_depth: int = 2,
        _types_namespace: MappingNamespace | None = None,
    ) -> bool | 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.

        Args:
            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`.
        """
        if not force and cls.__pydantic_complete__:
            return None

        if '__pydantic_core_schema__' in cls.__dict__:
            delattr(cls, '__pydantic_core_schema__')  # delete cached value to ensure full rebuild happens

        if _types_namespace is not None:
            rebuild_ns = _types_namespace
        elif _parent_namespace_depth > 0:
            rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
        else:
            rebuild_ns = {}

        parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}

        ns_resolver = _namespace_utils.NsResolver(
            parent_namespace={**rebuild_ns, **parent_ns},
        )

        # manually override defer_build so complete_model_class doesn't skip building the model again
        config = {**cls.model_config, 'defer_build': False}
        return _model_construction.complete_model_class(
            cls,
            cls.__name__,
            _config.ConfigWrapper(config, check=False),
            raise_errors=raise_errors,
            ns_resolver=ns_resolver,
        )

    @classmethod
    def model_validate(
        cls,
        obj: Any,
        *,
        strict: bool | None = None,
        from_attributes: bool | None = None,
        context: Any | None = None,
    ) -> Self:
        """Validate a pydantic model instance.

        Args:
            obj: The object to validate.
            strict: Whether to enforce types strictly.
            from_attributes: Whether to extract data from object attributes.
            context: Additional context to pass to the validator.

        Raises:
            ValidationError: If the object could not be validated.

        Returns:
            The validated model instance.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        return cls.__pydantic_validator__.validate_python(
            obj, strict=strict, from_attributes=from_attributes, context=context
        )

    @classmethod
    def model_validate_json(
        cls,
        json_data: str | bytes | bytearray,
        *,
        strict: bool | None = None,
        context: Any | None = None,
    ) -> Self:
        """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

        Validate the given JSON data against the Pydantic model.

        Args:
            json_data: The JSON data to validate.
            strict: Whether to enforce types strictly.
            context: Extra variables to pass to the validator.

        Returns:
            The validated Pydantic model.

        Raises:
            ValidationError: If `json_data` is not a JSON string or the object could not be validated.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context)

    @classmethod
    def model_validate_strings(
        cls,
        obj: Any,
        *,
        strict: bool | None = None,
        context: Any | None = None,
    ) -> Self:
        """Validate the given object with string data against the Pydantic model.

        Args:
            obj: The object containing string data to validate.
            strict: Whether to enforce types strictly.
            context: Extra variables to pass to the validator.

        Returns:
            The validated Pydantic model.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        return cls.__pydantic_validator__.validate_strings(obj, strict=strict, context=context)

    @classmethod
    def __get_pydantic_core_schema__(cls, source: type[BaseModel], handler: GetCoreSchemaHandler, /) -> CoreSchema:
        """Hook into generating the model's CoreSchema.

        Args:
            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: A callable that calls into Pydantic's internal CoreSchema generation logic.

        Returns:
            A `pydantic-core` `CoreSchema`.
        """
        # Only use the cached value from this _exact_ class; we don't want one from a parent class
        # This is why we check `cls.__dict__` and don't use `cls.__pydantic_core_schema__` or similar.
        schema = cls.__dict__.get('__pydantic_core_schema__')
        if schema is not None and not isinstance(schema, _mock_val_ser.MockCoreSchema):
            # Due to the way generic classes are built, it's possible that an invalid schema may be temporarily
            # set on generic classes. I think we could resolve this to ensure that we get proper schema caching
            # for generics, but for simplicity for now, we just always rebuild if the class has a generic origin.
            if not cls.__pydantic_generic_metadata__['origin']:
                return cls.__pydantic_core_schema__

        return handler(source)

    @classmethod
    def __get_pydantic_json_schema__(
        cls,
        core_schema: CoreSchema,
        handler: GetJsonSchemaHandler,
        /,
    ) -> JsonSchemaValue:
        """Hook into generating the model's JSON schema.

        Args:
            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.

        Returns:
            A JSON schema, as a Python object.
        """
        return handler(core_schema)

    @classmethod
    def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
        """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.

        Args:
            **kwargs: Any keyword arguments passed to the class definition that aren't used internally
                by pydantic.
        """
        pass

    def __class_getitem__(
        cls, typevar_values: type[Any] | tuple[type[Any], ...]
    ) -> type[BaseModel] | _forward_ref.PydanticRecursiveRef:
        cached = _generics.get_cached_generic_type_early(cls, typevar_values)
        if cached is not None:
            return cached

        if cls is BaseModel:
            raise TypeError('Type parameters should be placed on typing.Generic, not BaseModel')
        if not hasattr(cls, '__parameters__'):
            raise TypeError(f'{cls} cannot be parametrized because it does not inherit from typing.Generic')
        if not cls.__pydantic_generic_metadata__['parameters'] and typing.Generic not in cls.__bases__:
            raise TypeError(f'{cls} is not a generic class')

        if not isinstance(typevar_values, tuple):
            typevar_values = (typevar_values,)
        _generics.check_parameters_count(cls, typevar_values)

        # Build map from generic typevars to passed params
        typevars_map: dict[TypeVar, type[Any]] = dict(
            zip(cls.__pydantic_generic_metadata__['parameters'], typevar_values)
        )

        if _utils.all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map:
            submodel = cls  # if arguments are equal to parameters it's the same object
            _generics.set_cached_generic_type(cls, typevar_values, submodel)
        else:
            parent_args = cls.__pydantic_generic_metadata__['args']
            if not parent_args:
                args = typevar_values
            else:
                args = tuple(_generics.replace_types(arg, typevars_map) for arg in parent_args)

            origin = cls.__pydantic_generic_metadata__['origin'] or cls
            model_name = origin.model_parametrized_name(args)
            params = tuple(
                {param: None for param in _generics.iter_contained_typevars(typevars_map.values())}
            )  # use dict as ordered set

            with _generics.generic_recursion_self_type(origin, args) as maybe_self_type:
                # Check cached first otherwise `mro` may return `PydanticRecursiveRef`
                cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args)
                if cached is not None:
                    return cached

                if maybe_self_type is not None:
                    return maybe_self_type

                # Attempt to rebuild the origin in case new types have been defined
                try:
                    # depth 2 gets you above this __class_getitem__ call.
                    # Note that we explicitly provide the parent ns, otherwise
                    # `model_rebuild` will use the parent ns no matter if it is the ns of a module.
                    # We don't want this here, as this has unexpected effects when a model
                    # is being parametrized during a forward annotation evaluation.
                    parent_ns = _typing_extra.parent_frame_namespace(parent_depth=2) or {}
                    origin.model_rebuild(_types_namespace=parent_ns)
                except PydanticUndefinedAnnotation:
                    # It's okay if it fails, it just means there are still undefined types
                    # that could be evaluated later.
                    pass

                submodel = _generics.create_generic_submodel(model_name, origin, args, params)

                # Update cache
                _generics.set_cached_generic_type(cls, typevar_values, submodel, origin, args)

        return submodel

    def __copy__(self) -> Self:
        """Returns a shallow copy of the model."""
        cls = type(self)
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', copy(self.__dict__))
        _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

        if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
            _object_setattr(m, '__pydantic_private__', None)
        else:
            _object_setattr(
                m,
                '__pydantic_private__',
                {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
            )

        return m

    def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
        """Returns a deep copy of the model."""
        cls = type(self)
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
        _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
        # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
        # and attempting a deepcopy would be marginally slower.
        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

        if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
            _object_setattr(m, '__pydantic_private__', None)
        else:
            _object_setattr(
                m,
                '__pydantic_private__',
                deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
            )

        return m

    if not TYPE_CHECKING:
        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
        # The same goes for __setattr__ and __delattr__, see: https://github.com/pydantic/pydantic/issues/8643

        def __getattr__(self, item: str) -> Any:
            private_attributes = object.__getattribute__(self, '__private_attributes__')
            if item in private_attributes:
                attribute = private_attributes[item]
                if hasattr(attribute, '__get__'):
                    return attribute.__get__(self, type(self))  # type: ignore

                try:
                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
                    return self.__pydantic_private__[item]  # type: ignore
                except KeyError as exc:
                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
            else:
                # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
                # See `BaseModel.__repr_args__` for more details
                try:
                    pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
                except AttributeError:
                    pydantic_extra = None

                if pydantic_extra:
                    try:
                        return pydantic_extra[item]
                    except KeyError as exc:
                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
                else:
                    if hasattr(self.__class__, item):
                        return super().__getattribute__(item)  # Raises AttributeError if appropriate
                    else:
                        # this is the current error
                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')

        def __setattr__(self, name: str, value: Any) -> None:
            if name in self.__class_vars__:
                raise AttributeError(
                    f'{name!r} is a ClassVar of `{self.__class__.__name__}` and cannot be set on an instance. '
                    f'If you want to set a value on the class, use `{self.__class__.__name__}.{name} = value`.'
                )
            elif not _fields.is_valid_field_name(name):
                if self.__pydantic_private__ is None or name not in self.__private_attributes__:
                    _object_setattr(self, name, value)
                else:
                    attribute = self.__private_attributes__[name]
                    if hasattr(attribute, '__set__'):
                        attribute.__set__(self, value)  # type: ignore
                    else:
                        self.__pydantic_private__[name] = value
                return

            self._check_frozen(name, value)

            attr = getattr(self.__class__, name, None)
            # NOTE: We currently special case properties and `cached_property`, but we might need
            # to generalize this to all data/non-data descriptors at some point. For non-data descriptors
            # (such as `cached_property`), it isn't obvious though. `cached_property` caches the value
            # to the instance's `__dict__`, but other non-data descriptors might do things differently.
            if isinstance(attr, property):
                attr.__set__(self, value)
            elif isinstance(attr, cached_property):
                self.__dict__[name] = value
            elif self.model_config.get('validate_assignment', None):
                self.__pydantic_validator__.validate_assignment(self, name, value)
            elif self.model_config.get('extra') != 'allow' and name not in self.__pydantic_fields__:
                # TODO - matching error
                raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
            elif self.model_config.get('extra') == 'allow' and name not in self.__pydantic_fields__:
                if self.model_extra and name in self.model_extra:
                    self.__pydantic_extra__[name] = value  # type: ignore
                else:
                    try:
                        getattr(self, name)
                    except AttributeError:
                        # attribute does not already exist on instance, so put it in extra
                        self.__pydantic_extra__[name] = value  # type: ignore
                    else:
                        # attribute _does_ already exist on instance, and was not in extra, so update it
                        _object_setattr(self, name, value)
            else:
                self.__dict__[name] = value
                self.__pydantic_fields_set__.add(name)

        def __delattr__(self, item: str) -> Any:
            if item in self.__private_attributes__:
                attribute = self.__private_attributes__[item]
                if hasattr(attribute, '__delete__'):
                    attribute.__delete__(self)  # type: ignore
                    return

                try:
                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
                    del self.__pydantic_private__[item]  # type: ignore
                    return
                except KeyError as exc:
                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc

            self._check_frozen(item, None)

            if item in self.__pydantic_fields__:
                object.__delattr__(self, item)
            elif self.__pydantic_extra__ is not None and item in self.__pydantic_extra__:
                del self.__pydantic_extra__[item]
            else:
                try:
                    object.__delattr__(self, item)
                except AttributeError:
                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')

        # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
        # type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
        def __replace__(self, **changes: Any) -> Self:
            return self.model_copy(update=changes)

    def _check_frozen(self, name: str, value: Any) -> None:
        if self.model_config.get('frozen', None):
            typ = 'frozen_instance'
        elif getattr(self.__pydantic_fields__.get(name), 'frozen', False):
            typ = 'frozen_field'
        else:
            return
        error: pydantic_core.InitErrorDetails = {
            'type': typ,
            'loc': (name,),
            'input': value,
        }
        raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])

    def __getstate__(self) -> dict[Any, Any]:
        private = self.__pydantic_private__
        if private:
            private = {k: v for k, v in private.items() if v is not PydanticUndefined}
        return {
            '__dict__': self.__dict__,
            '__pydantic_extra__': self.__pydantic_extra__,
            '__pydantic_fields_set__': self.__pydantic_fields_set__,
            '__pydantic_private__': private,
        }

    def __setstate__(self, state: dict[Any, Any]) -> None:
        _object_setattr(self, '__pydantic_fields_set__', state.get('__pydantic_fields_set__', {}))
        _object_setattr(self, '__pydantic_extra__', state.get('__pydantic_extra__', {}))
        _object_setattr(self, '__pydantic_private__', state.get('__pydantic_private__', {}))
        _object_setattr(self, '__dict__', state.get('__dict__', {}))

    if not TYPE_CHECKING:

        def __eq__(self, other: Any) -> bool:
            if isinstance(other, BaseModel):
                # When comparing instances of generic types for equality, as long as all field values are equal,
                # only require their generic origin types to be equal, rather than exact type equality.
                # This prevents headaches like MyGeneric(x=1) != MyGeneric[Any](x=1).
                self_type = self.__pydantic_generic_metadata__['origin'] or self.__class__
                other_type = other.__pydantic_generic_metadata__['origin'] or other.__class__

                # Perform common checks first
                if not (
                    self_type == other_type
                    and getattr(self, '__pydantic_private__', None) == getattr(other, '__pydantic_private__', None)
                    and self.__pydantic_extra__ == other.__pydantic_extra__
                ):
                    return False

                # We only want to compare pydantic fields but ignoring fields is costly.
                # We'll perform a fast check first, and fallback only when needed
                # See GH-7444 and GH-7825 for rationale and a performance benchmark

                # First, do the fast (and sometimes faulty) __dict__ comparison
                if self.__dict__ == other.__dict__:
                    # If the check above passes, then pydantic fields are equal, we can return early
                    return True

                # We don't want to trigger unnecessary costly filtering of __dict__ on all unequal objects, so we return
                # early if there are no keys to ignore (we would just return False later on anyway)
                model_fields = type(self).__pydantic_fields__.keys()
                if self.__dict__.keys() <= model_fields and other.__dict__.keys() <= model_fields:
                    return False

                # If we reach here, there are non-pydantic-fields keys, mapped to unequal values, that we need to ignore
                # Resort to costly filtering of the __dict__ objects
                # We use operator.itemgetter because it is much faster than dict comprehensions
                # NOTE: Contrary to standard python class and instances, when the Model class has a default value for an
                # attribute and the model instance doesn't have a corresponding attribute, accessing the missing attribute
                # raises an error in BaseModel.__getattr__ instead of returning the class attribute
                # So we can use operator.itemgetter() instead of operator.attrgetter()
                getter = operator.itemgetter(*model_fields) if model_fields else lambda _: _utils._SENTINEL
                try:
                    return getter(self.__dict__) == getter(other.__dict__)
                except KeyError:
                    # In rare cases (such as when using the deprecated BaseModel.copy() method),
                    # the __dict__ may not contain all model fields, which is how we can get here.
                    # getter(self.__dict__) is much faster than any 'safe' method that accounts
                    # for missing keys, and wrapping it in a `try` doesn't slow things down much
                    # in the common case.
                    self_fields_proxy = _utils.SafeGetItemProxy(self.__dict__)
                    other_fields_proxy = _utils.SafeGetItemProxy(other.__dict__)
                    return getter(self_fields_proxy) == getter(other_fields_proxy)

            # other instance is not a BaseModel
            else:
                return NotImplemented  # delegate to the other item in the comparison

    if TYPE_CHECKING:
        # We put `__init_subclass__` in a TYPE_CHECKING block because, even though we want the type-checking benefits
        # described in the signature of `__init_subclass__` below, we don't want to modify the default behavior of
        # subclass initialization.

        def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
            """This signature is included purely to help type-checkers check arguments to class declaration, which
            provides a way to conveniently set model_config key/value pairs.

            ```python
            from pydantic import BaseModel

            class MyModel(BaseModel, extra='allow'): ...
            ```

            However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
            of the config arguments, and will only receive any keyword arguments passed during class initialization
            that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)

            Args:
                **kwargs: Keyword arguments passed to the class definition, which set model_config

            Note:
                You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
                *after* the class is fully initialized.
            """

    def __iter__(self) -> TupleGenerator:
        """So `dict(model)` works."""
        yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')]
        extra = self.__pydantic_extra__
        if extra:
            yield from extra.items()

    def __repr__(self) -> str:
        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'

    def __repr_args__(self) -> _repr.ReprArgs:
        for k, v in self.__dict__.items():
            field = self.__pydantic_fields__.get(k)
            if field and field.repr:
                if v is not self:
                    yield k, v
                else:
                    yield k, self.__repr_recursion__(v)
        # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
        # This can happen if a `ValidationError` is raised during initialization and the instance's
        # repr is generated as part of the exception handling. Therefore, we use `getattr` here
        # with a fallback, even though the type hints indicate the attribute will always be present.
        try:
            pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
        except AttributeError:
            pydantic_extra = None

        if pydantic_extra is not None:
            yield from ((k, v) for k, v in pydantic_extra.items())
        yield from ((k, getattr(self, k)) for k, v in self.__pydantic_computed_fields__.items() if v.repr)

    # take logic from `_repr.Representation` without the side effects of inheritance, see #5740
    __repr_name__ = _repr.Representation.__repr_name__
    __repr_recursion__ = _repr.Representation.__repr_recursion__
    __repr_str__ = _repr.Representation.__repr_str__
    __pretty__ = _repr.Representation.__pretty__
    __rich_repr__ = _repr.Representation.__rich_repr__

    def __str__(self) -> str:
        return self.__repr_str__(' ')

    # ##### Deprecated methods from v1 #####
    @property
    @typing_extensions.deprecated(
        'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=None
    )
    def __fields__(self) -> dict[str, FieldInfo]:
        warnings.warn(
            'The `__fields__` attribute is deprecated, use `model_fields` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        return self.model_fields

    @property
    @typing_extensions.deprecated(
        'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
        category=None,
    )
    def __fields_set__(self) -> set[str]:
        warnings.warn(
            'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        return self.__pydantic_fields_set__

    @typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None)
    def dict(  # noqa: D102
        self,
        *,
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
    ) -> Dict[str, Any]:  # noqa UP006
        warnings.warn(
            'The `dict` method is deprecated; use `model_dump` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        return self.model_dump(
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )

    @typing_extensions.deprecated('The `json` method is deprecated; use `model_dump_json` instead.', category=None)
    def json(  # noqa: D102
        self,
        *,
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        encoder: Callable[[Any], Any] | None = PydanticUndefined,  # type: ignore[assignment]
        models_as_dict: bool = PydanticUndefined,  # type: ignore[assignment]
        **dumps_kwargs: Any,
    ) -> str:
        warnings.warn(
            'The `json` method is deprecated; use `model_dump_json` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        if encoder is not PydanticUndefined:
            raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.')
        if models_as_dict is not PydanticUndefined:
            raise TypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.')
        if dumps_kwargs:
            raise TypeError('`dumps_kwargs` keyword arguments are no longer supported.')
        return self.model_dump_json(
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )

    @classmethod
    @typing_extensions.deprecated('The `parse_obj` method is deprecated; use `model_validate` instead.', category=None)
    def parse_obj(cls, obj: Any) -> Self:  # noqa: D102
        warnings.warn(
            'The `parse_obj` method is deprecated; use `model_validate` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        return cls.model_validate(obj)

    @classmethod
    @typing_extensions.deprecated(
        'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
        'otherwise load the data then use `model_validate` instead.',
        category=None,
    )
    def parse_raw(  # noqa: D102
        cls,
        b: str | bytes,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: DeprecatedParseProtocol | None = None,
        allow_pickle: bool = False,
    ) -> Self:  # pragma: no cover
        warnings.warn(
            'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
            'otherwise load the data then use `model_validate` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        from .deprecated import parse

        try:
            obj = parse.load_str_bytes(
                b,
                proto=proto,
                content_type=content_type,
                encoding=encoding,
                allow_pickle=allow_pickle,
            )
        except (ValueError, TypeError) as exc:
            import json

            # try to match V1
            if isinstance(exc, UnicodeDecodeError):
                type_str = 'value_error.unicodedecode'
            elif isinstance(exc, json.JSONDecodeError):
                type_str = 'value_error.jsondecode'
            elif isinstance(exc, ValueError):
                type_str = 'value_error'
            else:
                type_str = 'type_error'

            # ctx is missing here, but since we've added `input` to the error, we're not pretending it's the same
            error: pydantic_core.InitErrorDetails = {
                # The type: ignore on the next line is to ignore the requirement of LiteralString
                'type': pydantic_core.PydanticCustomError(type_str, str(exc)),  # type: ignore
                'loc': ('__root__',),
                'input': b,
            }
            raise pydantic_core.ValidationError.from_exception_data(cls.__name__, [error])
        return cls.model_validate(obj)

    @classmethod
    @typing_extensions.deprecated(
        'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
        'use `model_validate_json`, otherwise `model_validate` instead.',
        category=None,
    )
    def parse_file(  # noqa: D102
        cls,
        path: str | Path,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: DeprecatedParseProtocol | None = None,
        allow_pickle: bool = False,
    ) -> Self:
        warnings.warn(
            'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
            'use `model_validate_json`, otherwise `model_validate` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        from .deprecated import parse

        obj = parse.load_file(
            path,
            proto=proto,
            content_type=content_type,
            encoding=encoding,
            allow_pickle=allow_pickle,
        )
        return cls.parse_obj(obj)

    @classmethod
    @typing_extensions.deprecated(
        'The `from_orm` method is deprecated; set '
        "`model_config['from_attributes']=True` and use `model_validate` instead.",
        category=None,
    )
    def from_orm(cls, obj: Any) -> Self:  # noqa: D102
        warnings.warn(
            'The `from_orm` method is deprecated; set '
            "`model_config['from_attributes']=True` and use `model_validate` instead.",
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        if not cls.model_config.get('from_attributes', None):
            raise PydanticUserError(
                'You must set the config attribute `from_attributes=True` to use from_orm', code=None
            )
        return cls.model_validate(obj)

    @classmethod
    @typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.', category=None)
    def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: D102
        warnings.warn(
            'The `construct` method is deprecated; use `model_construct` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        return cls.model_construct(_fields_set=_fields_set, **values)

    @typing_extensions.deprecated(
        'The `copy` method is deprecated; use `model_copy` instead. '
        'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
        category=None,
    )
    def copy(
        self,
        *,
        include: AbstractSetIntStr | MappingIntStrAny | None = None,
        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
        update: Dict[str, Any] | None = None,  # noqa UP006
        deep: bool = False,
    ) -> Self:  # pragma: no cover
        """Returns a copy of the model.

        !!! warning "Deprecated"
            This method is now deprecated; use `model_copy` instead.

        If you need `include` or `exclude`, use:

        ```python {test="skip" lint="skip"}
        data = self.model_dump(include=include, exclude=exclude, round_trip=True)
        data = {**data, **(update or {})}
        copied = self.model_validate(data)
        ```

        Args:
            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.
        """
        warnings.warn(
            'The `copy` method is deprecated; use `model_copy` instead. '
            'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        from .deprecated import copy_internals

        values = dict(
            copy_internals._iter(
                self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
            ),
            **(update or {}),
        )
        if self.__pydantic_private__ is None:
            private = None
        else:
            private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}

        if self.__pydantic_extra__ is None:
            extra: dict[str, Any] | None = None
        else:
            extra = self.__pydantic_extra__.copy()
            for k in list(self.__pydantic_extra__):
                if k not in values:  # k was in the exclude
                    extra.pop(k)
            for k in list(values):
                if k in self.__pydantic_extra__:  # k must have come from extra
                    extra[k] = values.pop(k)

        # new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
        if update:
            fields_set = self.__pydantic_fields_set__ | update.keys()
        else:
            fields_set = set(self.__pydantic_fields_set__)

        # removing excluded fields from `__pydantic_fields_set__`
        if exclude:
            fields_set -= set(exclude)

        return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)

    @classmethod
    @typing_extensions.deprecated('The `schema` method is deprecated; use `model_json_schema` instead.', category=None)
    def schema(  # noqa: D102
        cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
    ) -> Dict[str, Any]:  # noqa UP006
        warnings.warn(
            'The `schema` method is deprecated; use `model_json_schema` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template)

    @classmethod
    @typing_extensions.deprecated(
        'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
        category=None,
    )
    def schema_json(  # noqa: D102
        cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
    ) -> str:  # pragma: no cover
        warnings.warn(
            'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        import json

        from .deprecated.json import pydantic_encoder

        return json.dumps(
            cls.model_json_schema(by_alias=by_alias, ref_template=ref_template),
            default=pydantic_encoder,
            **dumps_kwargs,
        )

    @classmethod
    @typing_extensions.deprecated('The `validate` method is deprecated; use `model_validate` instead.', category=None)
    def validate(cls, value: Any) -> Self:  # noqa: D102
        warnings.warn(
            'The `validate` method is deprecated; use `model_validate` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        return cls.model_validate(value)

    @classmethod
    @typing_extensions.deprecated(
        'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
        category=None,
    )
    def update_forward_refs(cls, **localns: Any) -> None:  # noqa: D102
        warnings.warn(
            'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        if localns:  # pragma: no cover
            raise TypeError('`localns` arguments are not longer accepted.')
        cls.model_rebuild(force=True)

    @typing_extensions.deprecated(
        'The private method `_iter` will be removed and should no longer be used.', category=None
    )
    def _iter(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_iter` will be removed and should no longer be used.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        from .deprecated import copy_internals

        return copy_internals._iter(self, *args, **kwargs)

    @typing_extensions.deprecated(
        'The private method `_copy_and_set_values` will be removed and should no longer be used.',
        category=None,
    )
    def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_copy_and_set_values` will be removed and should no longer be used.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        from .deprecated import copy_internals

        return copy_internals._copy_and_set_values(self, *args, **kwargs)

    @classmethod
    @typing_extensions.deprecated(
        'The private method `_get_value` will be removed and should no longer be used.',
        category=None,
    )
    def _get_value(cls, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_get_value` will be removed and should no longer be used.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        from .deprecated import copy_internals

        return copy_internals._get_value(cls, *args, **kwargs)

    @typing_extensions.deprecated(
        'The private method `_calculate_keys` will be removed and should no longer be used.',
        category=None,
    )
    def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_calculate_keys` will be removed and should no longer be used.',
            category=PydanticDeprecatedSince20,
            stacklevel=2,
        )
        from .deprecated import copy_internals

        return copy_internals._calculate_keys(self, *args, **kwargs)

__init__

__init__(**data: Any) -> None

Raises 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.

Source code in pydantic/main.py

def __init__(self, /, **data: Any) -> None:
    """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.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True
    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
    if self is not validated_self:
        warnings.warn(
            'A custom validator is returning a value other than `self`.\n'
            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
            'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
            stacklevel=2,
        )

model_config class-attribute

model_config: ConfigDict = ConfigDict()

Configuration for the model, should be a dictionary conforming to ConfigDict.

model_computed_fields property

model_computed_fields: dict[str, ComputedFieldInfo]

Get metadata about the computed fields defined on the model.

Deprecation warning: you should be getting this information from the model class, not from an instance. In V3, this property will be removed from the BaseModel class.

Returns:

Type	Description
dict[str, ComputedFieldInfo]	A mapping of computed field names to ComputedFieldInfo objects.

model_extra property

model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

Type	Description
dict[str, Any] | None	A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields property

model_fields: dict[str, FieldInfo]

Get metadata about the fields defined on the model.

Deprecation warning: you should be getting this information from the model class, not from an instance. In V3, this property will be removed from the BaseModel class.

Returns:

Type	Description
dict[str, FieldInfo]	A mapping of field names to FieldInfo objects.

model_fields_set property

model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

Type	Description
set[str]	A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

__pydantic_core_schema__ class-attribute

__pydantic_core_schema__: CoreSchema

The core schema of the model.

model_construct classmethod

model_construct(
    _fields_set: set[str] | None = None, **values: Any
) -> Self

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:

Name	Type	Description	Default
_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 attribute. Otherwise, the field names from the values argument will be used.	None
values	Any	Trusted or pre-validated data dictionary.	{}

Returns:

Type	Description
Self	A new instance of the Model class with validated data.

Source code in pydantic/main.py

@classmethod
def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: C901
    """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.

    Args:
        _fields_set: 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: Trusted or pre-validated data dictionary.

    Returns:
        A new instance of the `Model` class with validated data.
    """
    m = cls.__new__(cls)
    fields_values: dict[str, Any] = {}
    fields_set = set()

    for name, field in cls.__pydantic_fields__.items():
        if field.alias is not None and field.alias in values:
            fields_values[name] = values.pop(field.alias)
            fields_set.add(name)

        if (name not in fields_set) and (field.validation_alias is not None):
            validation_aliases: list[str | AliasPath] = (
                field.validation_alias.choices
                if isinstance(field.validation_alias, AliasChoices)
                else [field.validation_alias]
            )

            for alias in validation_aliases:
                if isinstance(alias, str) and alias in values:
                    fields_values[name] = values.pop(alias)
                    fields_set.add(name)
                    break
                elif isinstance(alias, AliasPath):
                    value = alias.search_dict_for_path(values)
                    if value is not PydanticUndefined:
                        fields_values[name] = value
                        fields_set.add(name)
                        break

        if name not in fields_set:
            if name in values:
                fields_values[name] = values.pop(name)
                fields_set.add(name)
            elif not field.is_required():
                fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values)
    if _fields_set is None:
        _fields_set = fields_set

    _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None
    _object_setattr(m, '__dict__', fields_values)
    _object_setattr(m, '__pydantic_fields_set__', _fields_set)
    if not cls.__pydantic_root_model__:
        _object_setattr(m, '__pydantic_extra__', _extra)

    if cls.__pydantic_post_init__:
        m.model_post_init(None)
        # update private attributes with values set
        if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
            for k, v in values.items():
                if k in m.__private_attributes__:
                    m.__pydantic_private__[k] = v

    elif not cls.__pydantic_root_model__:
        # Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
        # Since it doesn't, that means that `__pydantic_private__` should be set to None
        _object_setattr(m, '__pydantic_private__', None)

    return m

model_copy

model_copy(
    *,
    update: Mapping[str, Any] | None = None,
    deep: bool = False
) -> Self

Usage Documentation

model_copy(...)

Returns a copy of the model.

Parameters:

Name	Type	Description	Default
update	Mapping[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.	None
deep	bool	Set to True to make a deep copy of the model.	False

Returns:

Type	Description
Self	New model instance.

Source code in pydantic/main.py

def model_copy(self, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

    Returns a copy of the model.

    Args:
        update: 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: Set to `True` to make a deep copy of the model.

    Returns:
        New model instance.
    """
    copied = self.__deepcopy__() if deep else self.__copy__()
    if update:
        if self.model_config.get('extra') == 'allow':
            for k, v in update.items():
                if k in self.__pydantic_fields__:
                    copied.__dict__[k] = v
                else:
                    if copied.__pydantic_extra__ is None:
                        copied.__pydantic_extra__ = {}
                    copied.__pydantic_extra__[k] = v
        else:
            copied.__dict__.update(update)
        copied.__pydantic_fields_set__.update(update.keys())
    return copied

model_dump

model_dump(
    *,
    mode: Literal["json", "python"] | str = "python",
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    context: Any | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: (
        bool | Literal["none", "warn", "error"]
    ) = True,
    serialize_as_any: bool = False
) -> dict[str, Any]

Usage Documentation

model.model_dump(...)

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

Parameters:

Name	Type	Description	Default
mode	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.	'python'
include	IncEx | None	A set of fields to include in the output.	None
exclude	IncEx | None	A set of fields to exclude from the output.	None
context	Any | None	Additional context to pass to the serializer.	None
by_alias	bool	Whether to use the field's alias in the dictionary key if defined.	False
exclude_unset	bool	Whether to exclude fields that have not been explicitly set.	False
exclude_defaults	bool	Whether to exclude fields that are set to their default value.	False
exclude_none	bool	Whether to exclude fields that have a value of None.	False
round_trip	bool	If True, dumped values should be valid as input for non-idempotent types such as Json[T].	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a PydanticSerializationError.	True
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False

Returns:

Type	Description
dict[str, Any]	A dictionary representation of the model.

Source code in pydantic/main.py

def model_dump(
    self,
    *,
    mode: Literal['json', 'python'] | str = 'python',
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    context: Any | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    serialize_as_any: bool = False,
) -> dict[str, Any]:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

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

    Args:
        mode: 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: A set of fields to include in the output.
        exclude: A set of fields to exclude from the output.
        context: Additional context to pass to the serializer.
        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.
        exclude_none: Whether to exclude fields that have a value of `None`.
        round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

    Returns:
        A dictionary representation of the model.
    """
    return self.__pydantic_serializer__.to_python(
        self,
        mode=mode,
        by_alias=by_alias,
        include=include,
        exclude=exclude,
        context=context,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        round_trip=round_trip,
        warnings=warnings,
        serialize_as_any=serialize_as_any,
    )

model_dump_json

model_dump_json(
    *,
    indent: int | None = None,
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    context: Any | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: (
        bool | Literal["none", "warn", "error"]
    ) = True,
    serialize_as_any: bool = False
) -> str

Usage Documentation

model.model_dump_json(...)

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

Parameters:

Name	Type	Description	Default
indent	int | None	Indentation to use in the JSON output. If None is passed, the output will be compact.	None
include	IncEx | None	Field(s) to include in the JSON output.	None
exclude	IncEx | None	Field(s) to exclude from the JSON output.	None
context	Any | None	Additional context to pass to the serializer.	None
by_alias	bool	Whether to serialize using field aliases.	False
exclude_unset	bool	Whether to exclude fields that have not been explicitly set.	False
exclude_defaults	bool	Whether to exclude fields that are set to their default value.	False
exclude_none	bool	Whether to exclude fields that have a value of None.	False
round_trip	bool	If True, dumped values should be valid as input for non-idempotent types such as Json[T].	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a PydanticSerializationError.	True
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False

Returns:

Type	Description
str	A JSON string representation of the model.

Source code in pydantic/main.py

def model_dump_json(
    self,
    *,
    indent: int | None = None,
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    context: Any | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    serialize_as_any: bool = False,
) -> str:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

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

    Args:
        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.
        exclude: Field(s) to exclude from the JSON output.
        context: Additional context to pass to the serializer.
        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 are set to their default value.
        exclude_none: Whether to exclude fields that have a value of `None`.
        round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

    Returns:
        A JSON string representation of the model.
    """
    return self.__pydantic_serializer__.to_json(
        self,
        indent=indent,
        include=include,
        exclude=exclude,
        context=context,
        by_alias=by_alias,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        round_trip=round_trip,
        warnings=warnings,
        serialize_as_any=serialize_as_any,
    ).decode()

model_json_schema classmethod

model_json_schema(
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[
        GenerateJsonSchema
    ] = GenerateJsonSchema,
    mode: JsonSchemaMode = "validation",
) -> dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

Name	Type	Description	Default
by_alias	bool	Whether to use attribute aliases or not.	True
ref_template	str	The reference template.	DEFAULT_REF_TEMPLATE
schema_generator	type[GenerateJsonSchema]	To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications	GenerateJsonSchema
mode	JsonSchemaMode	The mode in which to generate the schema.	'validation'

Returns:

Type	Description
dict[str, Any]	The JSON schema for the given model class.

Source code in pydantic/main.py

@classmethod
def model_json_schema(
    cls,
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
    mode: JsonSchemaMode = 'validation',
) -> dict[str, Any]:
    """Generates a JSON schema for a model class.

    Args:
        by_alias: Whether to use attribute aliases or not.
        ref_template: The reference template.
        schema_generator: To override the logic used to generate the JSON schema, as a subclass of
            `GenerateJsonSchema` with your desired modifications
        mode: The mode in which to generate the schema.

    Returns:
        The JSON schema for the given model class.
    """
    return model_json_schema(
        cls, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator, mode=mode
    )

model_parametrized_name classmethod

model_parametrized_name(
    params: tuple[type[Any], ...]
) -> str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

Name	Type	Description	Default
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.	required

Returns:

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

Raises:

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

Source code in pydantic/main.py

@classmethod
def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
    """Compute the class name for parametrizations of generic classes.

    This method can be overridden to achieve a custom naming scheme for generic BaseModels.

    Args:
        params: 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`.

    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.
    """
    if not issubclass(cls, typing.Generic):
        raise TypeError('Concrete names should only be generated for generic models.')

    # Any strings received should represent forward references, so we handle them specially below.
    # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
    # we may be able to remove this special case.
    param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
    params_component = ', '.join(param_names)
    return f'{cls.__name__}[{params_component}]'

model_post_init

model_post_init(__context: Any) -> None

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.

Source code in pydantic/main.py

def model_post_init(self, __context: Any) -> None:
    """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.
    """
    pass

model_rebuild classmethod

model_rebuild(
    *,
    force: bool = False,
    raise_errors: bool = True,
    _parent_namespace_depth: int = 2,
    _types_namespace: MappingNamespace | None = None
) -> bool | 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:

Name	Type	Description	Default
force	bool	Whether to force the rebuilding of the model schema, defaults to False.	False
raise_errors	bool	Whether to raise errors, defaults to True.	True
_parent_namespace_depth	int	The depth level of the parent namespace, defaults to 2.	2
_types_namespace	MappingNamespace | None	The types namespace, defaults to None.	None

Returns:

Type	Description
bool | None	Returns None if the schema is already "complete" and rebuilding was not required.
bool | None	If rebuilding was required, returns True if rebuilding was successful, otherwise False.

Source code in pydantic/main.py

@classmethod
def model_rebuild(
    cls,
    *,
    force: bool = False,
    raise_errors: bool = True,
    _parent_namespace_depth: int = 2,
    _types_namespace: MappingNamespace | None = None,
) -> bool | 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.

    Args:
        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`.
    """
    if not force and cls.__pydantic_complete__:
        return None

    if '__pydantic_core_schema__' in cls.__dict__:
        delattr(cls, '__pydantic_core_schema__')  # delete cached value to ensure full rebuild happens

    if _types_namespace is not None:
        rebuild_ns = _types_namespace
    elif _parent_namespace_depth > 0:
        rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
    else:
        rebuild_ns = {}

    parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}

    ns_resolver = _namespace_utils.NsResolver(
        parent_namespace={**rebuild_ns, **parent_ns},
    )

    # manually override defer_build so complete_model_class doesn't skip building the model again
    config = {**cls.model_config, 'defer_build': False}
    return _model_construction.complete_model_class(
        cls,
        cls.__name__,
        _config.ConfigWrapper(config, check=False),
        raise_errors=raise_errors,
        ns_resolver=ns_resolver,
    )

model_validate classmethod

model_validate(
    obj: Any,
    *,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    context: Any | None = None
) -> Self

Validate a pydantic model instance.

Parameters:

Name	Type	Description	Default
obj	Any	The object to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
from_attributes	bool | None	Whether to extract data from object attributes.	None
context	Any | None	Additional context to pass to the validator.	None

Raises:

Type	Description
ValidationError	If the object could not be validated.

Returns:

Type	Description
Self	The validated model instance.

Source code in pydantic/main.py

@classmethod
def model_validate(
    cls,
    obj: Any,
    *,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    context: Any | None = None,
) -> Self:
    """Validate a pydantic model instance.

    Args:
        obj: The object to validate.
        strict: Whether to enforce types strictly.
        from_attributes: Whether to extract data from object attributes.
        context: Additional context to pass to the validator.

    Raises:
        ValidationError: If the object could not be validated.

    Returns:
        The validated model instance.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True
    return cls.__pydantic_validator__.validate_python(
        obj, strict=strict, from_attributes=from_attributes, context=context
    )

model_validate_json classmethod

model_validate_json(
    json_data: str | bytes | bytearray,
    *,
    strict: bool | None = None,
    context: Any | None = None
) -> Self

Usage Documentation

Json Parsing

Validate the given JSON data against the Pydantic model.

Parameters:

Name	Type	Description	Default
json_data	str | bytes | bytearray	The JSON data to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
context	Any | None	Extra variables to pass to the validator.	None

Returns:

Type	Description
Self	The validated Pydantic model.

Raises:

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

Source code in pydantic/main.py

@classmethod
def model_validate_json(
    cls,
    json_data: str | bytes | bytearray,
    *,
    strict: bool | None = None,
    context: Any | None = None,
) -> Self:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

    Validate the given JSON data against the Pydantic model.

    Args:
        json_data: The JSON data to validate.
        strict: Whether to enforce types strictly.
        context: Extra variables to pass to the validator.

    Returns:
        The validated Pydantic model.

    Raises:
        ValidationError: If `json_data` is not a JSON string or the object could not be validated.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True
    return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context)

model_validate_strings classmethod

model_validate_strings(
    obj: Any,
    *,
    strict: bool | None = None,
    context: Any | None = None
) -> Self

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

Parameters:

Name	Type	Description	Default
obj	Any	The object containing string data to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
context	Any | None	Extra variables to pass to the validator.	None

Returns:

Type	Description
Self	The validated Pydantic model.

Source code in pydantic/main.py

@classmethod
def model_validate_strings(
    cls,
    obj: Any,
    *,
    strict: bool | None = None,
    context: Any | None = None,
) -> Self:
    """Validate the given object with string data against the Pydantic model.

    Args:
        obj: The object containing string data to validate.
        strict: Whether to enforce types strictly.
        context: Extra variables to pass to the validator.

    Returns:
        The validated Pydantic model.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True
    return cls.__pydantic_validator__.validate_strings(obj, strict=strict, context=context)

copy

copy(
    *,
    include: (
        AbstractSetIntStr | MappingIntStrAny | None
    ) = None,
    exclude: (
        AbstractSetIntStr | MappingIntStrAny | None
    ) = None,
    update: Dict[str, Any] | None = None,
    deep: bool = False
) -> Self

Returns a copy of the model.

Deprecated

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Parameters:

Name	Type	Description	Default
include	AbstractSetIntStr | MappingIntStrAny | None	Optional set or mapping specifying which fields to include in the copied model.	None
exclude	AbstractSetIntStr | MappingIntStrAny | None	Optional set or mapping specifying which fields to exclude in the copied model.	None
update	Dict[str, Any] | None	Optional dictionary of field-value pairs to override field values in the copied model.	None
deep	bool	If True, the values of fields that are Pydantic models will be deep-copied.	False

Returns:

Type	Description
Self	A copy of the model with included, excluded and updated fields as specified.

Source code in pydantic/main.py

@typing_extensions.deprecated(
    'The `copy` method is deprecated; use `model_copy` instead. '
    'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
    category=None,
)
def copy(
    self,
    *,
    include: AbstractSetIntStr | MappingIntStrAny | None = None,
    exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
    update: Dict[str, Any] | None = None,  # noqa UP006
    deep: bool = False,
) -> Self:  # pragma: no cover
    """Returns a copy of the model.

    !!! warning "Deprecated"
        This method is now deprecated; use `model_copy` instead.

    If you need `include` or `exclude`, use:

    ```python {test="skip" lint="skip"}
    data = self.model_dump(include=include, exclude=exclude, round_trip=True)
    data = {**data, **(update or {})}
    copied = self.model_validate(data)
    ```

    Args:
        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.
    """
    warnings.warn(
        'The `copy` method is deprecated; use `model_copy` instead. '
        'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
        category=PydanticDeprecatedSince20,
        stacklevel=2,
    )
    from .deprecated import copy_internals

    values = dict(
        copy_internals._iter(
            self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
        ),
        **(update or {}),
    )
    if self.__pydantic_private__ is None:
        private = None
    else:
        private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}

    if self.__pydantic_extra__ is None:
        extra: dict[str, Any] | None = None
    else:
        extra = self.__pydantic_extra__.copy()
        for k in list(self.__pydantic_extra__):
            if k not in values:  # k was in the exclude
                extra.pop(k)
        for k in list(values):
            if k in self.__pydantic_extra__:  # k must have come from extra
                extra[k] = values.pop(k)

    # new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
    if update:
        fields_set = self.__pydantic_fields_set__ | update.keys()
    else:
        fields_set = set(self.__pydantic_fields_set__)

    # removing excluded fields from `__pydantic_fields_set__`
    if exclude:
        fields_set -= set(exclude)

    return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)

pydantic.create_model

create_model(
    model_name: str,
    /,
    *,
    __config__: ConfigDict | None = None,
    __doc__: str | None = None,
    __base__: (
        type[ModelT] | tuple[type[ModelT], ...] | None
    ) = None,
    __module__: str | None = None,
    __validators__: (
        dict[str, Callable[..., Any]] | None
    ) = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    __slots__: tuple[str, ...] | None = None,
    **field_definitions: Any,
) -> type[ModelT]

Usage Documentation

Dynamic model creation

Dynamically creates and returns a new Pydantic model, in other words, create_model dynamically creates a subclass of BaseModel.

Parameters:

Name	Type	Description	Default
model_name	str	The name of the newly created model.	required
__config__	ConfigDict | None	The configuration of the new model.	None
__doc__	str | None	The docstring of the new model.	None
__base__	type[ModelT] | tuple[type[ModelT], ...] | None	The base class or classes for the new model.	None
__module__	str | None	The name of the module that the model belongs to; if None, the value is taken from sys._getframe(1)	None
__validators__	dict[str, Callable[..., Any]] | None	A dictionary of methods that validate fields. The keys are the names of the validation methods to be added to the model, and the values are the validation methods themselves. You can read more about functional validators here.	None
__cls_kwargs__	dict[str, Any] | None	A dictionary of keyword arguments for class creation, such as metaclass.	None
__slots__	tuple[str, ...] | None	Deprecated. Should not be passed to create_model.	None
**field_definitions	Any	Attributes of the new model. They should be passed in the format: <name>=(<type>, <default value>), <name>=(<type>, <FieldInfo>), or typing.Annotated[<type>, <FieldInfo>]. Any additional metadata in typing.Annotated[<type>, <FieldInfo>, ...] will be ignored. Note, FieldInfo instances should be created via pydantic.Field(...). Initializing FieldInfo instances directly is not supported.	{}

Returns:

Type	Description
type[ModelT]	The new model.

Raises:

Type	Description
PydanticUserError	If __base__ and __config__ are both passed.

Source code in pydantic/main.py

def create_model(  # noqa: C901
    model_name: str,
    /,
    *,
    __config__: ConfigDict | None = None,
    __doc__: str | None = None,
    __base__: type[ModelT] | tuple[type[ModelT], ...] | None = None,
    __module__: str | None = None,
    __validators__: dict[str, Callable[..., Any]] | None = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    __slots__: tuple[str, ...] | None = None,
    **field_definitions: Any,
) -> type[ModelT]:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/#dynamic-model-creation

    Dynamically creates and returns a new Pydantic model, in other words, `create_model` dynamically creates a
    subclass of [`BaseModel`][pydantic.BaseModel].

    Args:
        model_name: The name of the newly created model.
        __config__: The configuration of the new model.
        __doc__: The docstring of the new model.
        __base__: The base class or classes for the new model.
        __module__: The name of the module that the model belongs to;
            if `None`, the value is taken from `sys._getframe(1)`
        __validators__: A dictionary of methods that validate fields. The keys are the names of the validation methods to
            be added to the model, and the values are the validation methods themselves. You can read more about functional
            validators [here](https://docs.pydantic.dev/2.9/concepts/validators/#field-validators).
        __cls_kwargs__: A dictionary of keyword arguments for class creation, such as `metaclass`.
        __slots__: Deprecated. Should not be passed to `create_model`.
        **field_definitions: Attributes of the new model. They should be passed in the format:
            `<name>=(<type>, <default value>)`, `<name>=(<type>, <FieldInfo>)`, or `typing.Annotated[<type>, <FieldInfo>]`.
            Any additional metadata in `typing.Annotated[<type>, <FieldInfo>, ...]` will be ignored.
            Note, `FieldInfo` instances should be created via `pydantic.Field(...)`.
            Initializing `FieldInfo` instances directly is not supported.

    Returns:
        The new [model][pydantic.BaseModel].

    Raises:
        PydanticUserError: If `__base__` and `__config__` are both passed.
    """
    if __slots__ is not None:
        # __slots__ will be ignored from here on
        warnings.warn('__slots__ should not be passed to create_model', RuntimeWarning)

    if __base__ is not None:
        if __config__ is not None:
            raise PydanticUserError(
                'to avoid confusion `__config__` and `__base__` cannot be used together',
                code='create-model-config-base',
            )
        if not isinstance(__base__, tuple):
            __base__ = (__base__,)
    else:
        __base__ = (cast('type[ModelT]', BaseModel),)

    __cls_kwargs__ = __cls_kwargs__ or {}

    fields = {}
    annotations = {}

    for f_name, f_def in field_definitions.items():
        if not _fields.is_valid_field_name(f_name):
            warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
        if isinstance(f_def, tuple):
            f_def = cast('tuple[str, Any]', f_def)
            try:
                f_annotation, f_value = f_def
            except ValueError as e:
                raise PydanticUserError(
                    'Field definitions should be a `(<type>, <default>)`.',
                    code='create-model-field-definitions',
                ) from e

        elif _typing_extra.is_annotated(f_def):
            (f_annotation, f_value, *_) = typing_extensions.get_args(
                f_def
            )  # first two input are expected from Annotated, refer to https://docs.python.org/3/library/typing.html#typing.Annotated
            FieldInfo = _import_utils.import_cached_field_info()

            if not isinstance(f_value, FieldInfo):
                raise PydanticUserError(
                    'Field definitions should be a Annotated[<type>, <FieldInfo>]',
                    code='create-model-field-definitions',
                )

        else:
            f_annotation, f_value = None, f_def

        if f_annotation:
            annotations[f_name] = f_annotation
        fields[f_name] = f_value

    if __module__ is None:
        f = sys._getframe(1)
        __module__ = f.f_globals['__name__']

    namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__}
    if __doc__:
        namespace.update({'__doc__': __doc__})
    if __validators__:
        namespace.update(__validators__)
    namespace.update(fields)
    if __config__:
        namespace['model_config'] = _config.ConfigWrapper(__config__).config_dict
    resolved_bases = types.resolve_bases(__base__)
    meta, ns, kwds = types.prepare_class(model_name, resolved_bases, kwds=__cls_kwargs__)
    if resolved_bases is not __base__:
        ns['__orig_bases__'] = __base__
    namespace.update(ns)

    return meta(
        model_name,
        resolved_bases,
        namespace,
        __pydantic_reset_parent_namespace__=False,
        _create_model_module=__module__,
        **kwds,
    )