<!-- Thank you for your contribution! -->
<!-- Unless your change is trivial, please create an issue to discuss the change before creating a PR -->

## Change Summary

I noticed similar or identical logic in several places and extracted a couple of methods to reduce duplication.

The two commits are fairly independent and it may be easier to view their diffs separately to understand the changes. The first is probably a bit more compelling.

Note that the original code replaced by `clean_schema` wasn't always exactly the same, but the differences didn't really strike me as intentional, and either way it seemed good to draw attention to those differences and consider discussing them or adding comments to clarify.

## Related issue number

There's no issue because this isn't fixing/adding anything notable, it's just refactoring. I thought it made more sense to discuss the changes here rather than in a separate issue.

## Checklist

* [x] The pull request title is a good summary of the changes - it will be used in the changelog
* [x] Unit tests for the changes exist
* [x] Tests pass on CI
* [x] Documentation reflects the changes where applicable
* [x] My PR is ready to review, **please add a comment including the phrase "please review" to assign reviewers**


Selected Reviewer: @adriangb
Refactoring schema-related code. <!-- Thank you for your contribution! -->
<!-- Unless your change is trivial, please create an issue to discuss the change before creating a PR -->

## Change Summary

I noticed similar or identical logic in several places and extracted a couple of methods to reduce duplication.

The two commits are fairly independent and it may be easier to view their diffs separately to understand the changes. The first is probably a bit more compelling.

Note that the original code replaced by `clean_schema` wasn't always exactly the same, but the differences didn't really strike me as intentional, and either way it seemed good to draw attention to those differences and consider discussing them or adding comments to clarify.

## Related issue number

There's no issue because this isn't fixing/adding anything notable, it's just refactoring. I thought it made more sense to discuss the changes here rather than in a separate issue.

## Checklist

* [x] The pull request title is a good summary of the changes - it will be used in the changelog
* [x] Unit tests for the changes exist
* [x] Tests pass on CI
* [x] Documentation reflects the changes where applicable
* [x] My PR is ready to review, **please add a comment including the phrase "please review" to assign reviewers**


Selected Reviewer: @adriangb
"""Logic for creating models."""
from __future__ import annotations as _annotations

import types
import typing
import warnings
from copy import copy, deepcopy
from typing import Any, ClassVar

import pydantic_core
import typing_extensions
from pydantic_core import PydanticUndefined

from ._internal import (
    _config,
    _decorators,
    _fields,
    _forward_ref,
    _generics,
    _mock_val_ser,
    _model_construction,
    _repr,
    _typing_extra,
    _utils,
)
from ._migration import getattr_migration
from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
from .config import ConfigDict
from .errors import PydanticUndefinedAnnotation, PydanticUserError
from .fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr
from .json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema, JsonSchemaMode, JsonSchemaValue, model_json_schema
from .warnings import PydanticDeprecatedSince20

if typing.TYPE_CHECKING:
    from inspect import Signature
    from pathlib import Path

    from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator
    from typing_extensions import Literal, Unpack

    from ._internal._utils import AbstractSetIntStr, MappingIntStrAny
    from .deprecated.parse import Protocol as DeprecatedParseProtocol
    from .fields import Field as _Field

    AnyClassMethod = classmethod[Any, Any, Any]
    TupleGenerator = typing.Generator[typing.Tuple[str, Any], None, None]
    Model = typing.TypeVar('Model', bound='BaseModel')
    # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
    IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'
else:
    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
    # and https://youtrack.jetbrains.com/issue/PY-51428
    DeprecationWarning = PydanticDeprecatedSince20

__all__ = 'BaseModel', 'create_model'

_object_setattr = _model_construction.object_setattr


class BaseModel(metaclass=_model_construction.ModelMetaclass):
    """Usage docs: https://docs.pydantic.dev/2.4/concepts/models/

    A base class for creating Pydantic models.

    Attributes:
        __class_vars__: The names of classvars defined on the model.
        __private_attributes__: Metadata about the private attributes of the model.
        __signature__: The signature for instantiating the model.

        __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
        __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
        __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_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_extra__: An instance attribute with the values of extra fields from validation when
            `model_config['extra'] == 'allow'`.
        __pydantic_fields_set__: An instance attribute with the names of fields explicitly specified during validation.
        __pydantic_private__: Instance attribute with the values of private attributes set on the model instance.
    """

    if typing.TYPE_CHECKING:
        # Here we provide annotations for the attributes of BaseModel.
        # Many of these are populated by the metaclass, which is why this section is in a `TYPE_CHECKING` block.
        # However, for the sake of easy review, we have included type annotations of all class and instance attributes
        # of `BaseModel` here:

        # Class attributes
        model_config: ClassVar[ConfigDict]
        """
        Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
        """

        model_fields: ClassVar[dict[str, FieldInfo]]
        """
        Metadata about the fields defined on the model,
        mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo].

        This replaces `Model.__fields__` from Pydantic V1.
        """

        __class_vars__: ClassVar[set[str]]
        __private_attributes__: ClassVar[dict[str, ModelPrivateAttr]]
        __signature__: ClassVar[Signature]

        __pydantic_complete__: ClassVar[bool]
        __pydantic_core_schema__: ClassVar[CoreSchema]
        __pydantic_custom_init__: ClassVar[bool]
        __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
        __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata]
        __pydantic_parent_namespace__: ClassVar[dict[str, Any] | None]
        __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
        __pydantic_root_model__: ClassVar[bool]
        __pydantic_serializer__: ClassVar[SchemaSerializer]
        __pydantic_validator__: ClassVar[SchemaValidator]

        # Instance attributes
        # Note: we use the non-existent kwarg `init=False` in pydantic.fields.Field below so that @dataclass_transform
        # doesn't think these are valid as keyword arguments to the class initializer.
        __pydantic_extra__: dict[str, Any] | None = _Field(init=False)  # type: ignore
        __pydantic_fields_set__: set[str] = _Field(init=False)  # type: ignore
        __pydantic_private__: dict[str, Any] | None = _Field(init=False)  # type: ignore
    else:
        # `model_fields` and `__pydantic_decorators__` must be set for
        # pydantic._internal._generate_schema.GenerateSchema.model_schema to work for a plain BaseModel annotation
        model_fields = {}
        __pydantic_decorators__ = _decorators.DecoratorInfos()
        # Prevent `BaseModel` from being instantiated directly:
        __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__'

    model_config = ConfigDict()
    __pydantic_complete__ = False
    __pydantic_root_model__ = False

    def __init__(__pydantic_self__, **data: Any) -> None:  # type: ignore
        """Create a new model by parsing and validating input data from keyword arguments.

        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
        validated to form a valid model.

        `__init__` uses `__pydantic_self__` instead of the more common `self` for the first arg to
        allow `self` as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)

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

    @property
    def model_computed_fields(self) -> dict[str, ComputedFieldInfo]:
        """Get the computed fields of this model instance.

        Returns:
            A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects.
        """
        return {k: v.info for k, v in self.__pydantic_decorators__.computed_fields.items()}

    @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 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: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:
        """Creates a new instance of the `Model` class with validated data.

        Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
        Default values are respected, but no other validation is performed.
        Behaves as if `Config.extra = 'allow'` was set since it adds all passed values

        Args:
            _fields_set: The set of field names accepted for the Model instance.
            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] = {}
        defaults: dict[str, Any] = {}  # keeping this separate from `fields_values` helps us compute `_fields_set`
        for name, field in cls.model_fields.items():
            if field.alias and field.alias in values:
                fields_values[name] = values.pop(field.alias)
            elif name in values:
                fields_values[name] = values.pop(name)
            elif not field.is_required():
                defaults[name] = field.get_default(call_default_factory=True)
        if _fields_set is None:
            _fields_set = set(fields_values.keys())
        fields_values.update(defaults)

        _extra: dict[str, Any] | None = None
        if cls.model_config.get('extra') == 'allow':
            _extra = {}
            for k, v in values.items():
                _extra[k] = v
        else:
            fields_values.update(values)
        _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)
        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: Model, *, update: dict[str, Any] | None = None, deep: bool = False) -> Model:
        """Usage docs: https://docs.pydantic.dev/2.4/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.model_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,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> dict[str, Any]:
        """Usage docs: https://docs.pydantic.dev/2.4/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 dictionary will only contain JSON serializable types.
                If mode is 'python', the dictionary may contain any Python objects.
            include: A list of fields to include in the output.
            exclude: A list of fields to exclude from the output.
            by_alias: Whether to use the field's alias in the dictionary key if defined.
            exclude_unset: Whether to exclude fields that are unset or None from the output.
            exclude_defaults: Whether to exclude fields that are set to their default value from the output.
            exclude_none: Whether to exclude fields that have a value of `None` from the output.
            round_trip: Whether to enable serialization and deserialization round-trip support.
            warnings: Whether to log warnings when invalid fields are encountered.

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

    def model_dump_json(
        self,
        *,
        indent: int | None = None,
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> str:
        """Usage docs: https://docs.pydantic.dev/2.4/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. Can take either a string or set of strings.
            exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings.
            by_alias: Whether to serialize using field aliases.
            exclude_unset: Whether to exclude fields that have not been explicitly set.
            exclude_defaults: Whether to exclude fields that have the default value.
            exclude_none: Whether to exclude fields that have a value of `None`.
            round_trip: Whether to use serialization/deserialization between JSON and class instance.
            warnings: Whether to show any warnings that occurred during serialization.

        Returns:
            A JSON string representation of the model.
        """
        return self.__pydantic_serializer__.to_json(
            self,
            indent=indent,
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
        ).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: dict[str, Any] | 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
        else:
            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:
                types_namespace: dict[str, Any] | None = _types_namespace.copy()
            else:
                if _parent_namespace_depth > 0:
                    frame_parent_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth) or {}
                    cls_parent_ns = (
                        _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}
                    )
                    types_namespace = {**cls_parent_ns, **frame_parent_ns}
                    cls.__pydantic_parent_namespace__ = _model_construction.build_lenient_weakvaluedict(types_namespace)
                else:
                    types_namespace = _model_construction.unpack_lenient_weakvaluedict(
                        cls.__pydantic_parent_namespace__
                    )

                types_namespace = _typing_extra.get_cls_types_namespace(cls, types_namespace)

            # 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,
                types_namespace=types_namespace,
            )

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

        Args:
            obj: The object to validate.
            strict: Whether to raise an exception on invalid fields.
            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: type[Model],
        json_data: str | bytes | bytearray,
        *,
        strict: bool | None = None,
        context: dict[str, Any] | None = None,
    ) -> Model:
        """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:
            ValueError: If `json_data` is not a JSON string.
        """
        # `__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: type[Model],
        obj: Any,
        *,
        strict: bool | None = None,
        context: dict[str, Any] | None = None,
    ) -> Model:
        """Validate the given object contains string data against the Pydantic model.

        Args:
            obj: The object contains 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: Call into Pydantic's internal JSON schema generation.
                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.
        if '__pydantic_core_schema__' in cls.__dict__:
            # 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[_typing_extra.TypeVarType, 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:
                if maybe_self_type is not None:
                    return maybe_self_type

                cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args)
                if cached is not None:
                    return cached

                # Attempt to rebuild the origin in case new types have been defined
                try:
                    # depth 3 gets you above this __class_getitem__ call
                    origin.model_rebuild(_parent_namespace_depth=3)
                except PydanticUndefinedAnnotation:
                    # It's okay if it fails, it just means there are still undefined types
                    # that could be evaluated later.
                    # TODO: Make sure validation fails if there are still undefined types, perhaps using MockValidator
                    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: Model) -> Model:
        """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 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: Model, memo: dict[int, Any] | None = None) -> Model:
        """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 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 typing.TYPE_CHECKING:
        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access

        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 is not None:
                    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
        elif self.model_config.get('frozen', None):
            error: pydantic_core.InitErrorDetails = {
                'type': 'frozen_instance',
                'loc': (name,),
                'input': value,
            }
            raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])
        elif getattr(self.model_fields.get(name), 'frozen', False):
            error: pydantic_core.InitErrorDetails = {
                'type': 'frozen_field',
                'loc': (name,),
                'input': value,
            }
            raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])

        attr = getattr(self.__class__, name, None)
        if isinstance(attr, property):
            attr.__set__(self, 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.model_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.model_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
            except KeyError as exc:
                raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
        elif item in self.model_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}')

    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['__pydantic_fields_set__'])
        _object_setattr(self, '__pydantic_extra__', state['__pydantic_extra__'])
        _object_setattr(self, '__pydantic_private__', state['__pydantic_private__'])
        _object_setattr(self, '__dict__', state['__dict__'])

    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__

            return (
                self_type == other_type
                and self.__dict__ == other.__dict__
                and self.__pydantic_private__ == other.__pydantic_private__
                and self.__pydantic_extra__ == other.__pydantic_extra__
            )
        else:
            return NotImplemented  # delegate to the other item in the comparison

    if typing.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.

            ```py
            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.model_fields.get(k)
            if field and field.repr:
                yield k, 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.model_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_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=PydanticDeprecatedSince20
    )
    def __fields__(self) -> dict[str, FieldInfo]:
        warnings.warn('The `__fields__` attribute is deprecated, use `model_fields` instead.', DeprecationWarning)
        return self.model_fields

    @property
    @typing_extensions.deprecated(
        'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
        category=PydanticDeprecatedSince20,
    )
    def __fields_set__(self) -> set[str]:
        warnings.warn(
            'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', DeprecationWarning
        )
        return self.__pydantic_fields_set__

    @typing_extensions.deprecated(
        'The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20
    )
    def dict(  # noqa: D102
        self,
        *,
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
    ) -> typing.Dict[str, Any]:  # noqa UP006
        warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', DeprecationWarning)
        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=PydanticDeprecatedSince20
    )
    def json(  # noqa: D102
        self,
        *,
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        encoder: typing.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.', DeprecationWarning)
        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=PydanticDeprecatedSince20
    )
    def parse_obj(cls: type[Model], obj: Any) -> Model:  # noqa: D102
        warnings.warn('The `parse_obj` method is deprecated; use `model_validate` instead.', DeprecationWarning)
        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=PydanticDeprecatedSince20,
    )
    def parse_raw(  # noqa: D102
        cls: type[Model],
        b: str | bytes,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: DeprecatedParseProtocol | None = None,
        allow_pickle: bool = False,
    ) -> Model:  # 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.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20,
    )
    def parse_file(  # noqa: D102
        cls: type[Model],
        path: str | Path,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: DeprecatedParseProtocol | None = None,
        allow_pickle: bool = False,
    ) -> Model:
        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.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20,
    )
    def from_orm(cls: type[Model], obj: Any) -> Model:  # noqa: D102
        warnings.warn(
            'The `from_orm` method is deprecated; set `model_config["from_attributes"]=True` '
            'and use `model_validate` instead.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20
    )
    def construct(cls: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:  # noqa: D102
        warnings.warn('The `construct` method is deprecated; use `model_construct` instead.', DeprecationWarning)
        return cls.model_construct(_fields_set=_fields_set, **values)

    @typing_extensions.deprecated(
        'The copy method is deprecated; use `model_copy` instead.', category=PydanticDeprecatedSince20
    )
    def copy(
        self: Model,
        *,
        include: AbstractSetIntStr | MappingIntStrAny | None = None,
        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
        update: typing.Dict[str, Any] | None = None,  # noqa UP006
        deep: bool = False,
    ) -> Model:  # 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:

        ```py
        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`.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20
    )
    def schema(  # noqa: D102
        cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
    ) -> typing.Dict[str, Any]:  # noqa UP006
        warnings.warn('The `schema` method is deprecated; use `model_json_schema` instead.', DeprecationWarning)
        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=PydanticDeprecatedSince20,
    )
    def schema_json(  # noqa: D102
        cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
    ) -> str:  # pragma: no cover
        import json

        warnings.warn(
            'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20
    )
    def validate(cls: type[Model], value: Any) -> Model:  # noqa: D102
        warnings.warn('The `validate` method is deprecated; use `model_validate` instead.', DeprecationWarning)
        return cls.model_validate(value)

    @classmethod
    @typing_extensions.deprecated(
        'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
        category=PydanticDeprecatedSince20,
    )
    def update_forward_refs(cls, **localns: Any) -> None:  # noqa: D102
        warnings.warn(
            'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', DeprecationWarning
        )
        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=PydanticDeprecatedSince20
    )
    def _iter(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn('The private method `_iter` will be removed and should no longer be used.', DeprecationWarning)

        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=PydanticDeprecatedSince20,
    )
    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.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20,
    )
    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.', DeprecationWarning
        )

        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=PydanticDeprecatedSince20,
    )
    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.', DeprecationWarning
        )

        from .deprecated import copy_internals

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


@typing.overload
def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | None = None,
    __base__: None = None,
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] | None = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    **field_definitions: Any,
) -> type[BaseModel]:
    ...


@typing.overload
def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | None = None,
    __base__: type[Model] | tuple[type[Model], ...],
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] | None = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    **field_definitions: Any,
) -> type[Model]:
    ...


def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | None = None,
    __base__: type[Model] | tuple[type[Model], ...] | None = None,
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] | None = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    __slots__: tuple[str, ...] | None = None,
    **field_definitions: Any,
) -> type[Model]:
    """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.
        __base__: The base class for the new model.
        __module__: The name of the module that the model belongs to.
        __validators__: A dictionary of methods that validate
            fields.
        __cls_kwargs__: A dictionary of keyword arguments for class creation.
        __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>)` or `<name>=(<type>, <FieldInfo>)`.

    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__ = (typing.cast(typing.Type['Model'], 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 = typing.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
        else:
            f_annotation, f_value = None, f_def

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

    namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__}
    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, **kwds)


__getattr__ = getattr_migration(__name__)

"""Logic for creating models."""
from __future__ import annotations as _annotations

import types
import typing
import warnings
from copy import copy, deepcopy
from typing import Any, ClassVar

import pydantic_core
import typing_extensions
from pydantic_core import PydanticUndefined

from ._internal import (
    _config,
    _decorators,
    _fields,
    _forward_ref,
    _generics,
    _mock_val_ser,
    _model_construction,
    _repr,
    _typing_extra,
    _utils,
)
from ._migration import getattr_migration
from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
from .config import ConfigDict
from .errors import PydanticUndefinedAnnotation, PydanticUserError
from .fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr
from .json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema, JsonSchemaMode, JsonSchemaValue, model_json_schema
from .warnings import PydanticDeprecatedSince20

if typing.TYPE_CHECKING:
    from inspect import Signature
    from pathlib import Path

    from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator
    from typing_extensions import Literal, Unpack

    from ._internal._utils import AbstractSetIntStr, MappingIntStrAny
    from .deprecated.parse import Protocol as DeprecatedParseProtocol
    from .fields import Field as _Field

    AnyClassMethod = classmethod[Any, Any, Any]
    TupleGenerator = typing.Generator[typing.Tuple[str, Any], None, None]
    Model = typing.TypeVar('Model', bound='BaseModel')
    # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
    IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'
else:
    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
    # and https://youtrack.jetbrains.com/issue/PY-51428
    DeprecationWarning = PydanticDeprecatedSince20

__all__ = 'BaseModel', 'create_model'

_object_setattr = _model_construction.object_setattr


class BaseModel(metaclass=_model_construction.ModelMetaclass):
    """Usage docs: https://docs.pydantic.dev/2.4/concepts/models/

    A base class for creating Pydantic models.

    Attributes:
        __class_vars__: The names of classvars defined on the model.
        __private_attributes__: Metadata about the private attributes of the model.
        __signature__: The signature for instantiating the model.

        __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
        __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
        __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_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_extra__: An instance attribute with the values of extra fields from validation when
            `model_config['extra'] == 'allow'`.
        __pydantic_fields_set__: An instance attribute with the names of fields explicitly specified during validation.
        __pydantic_private__: Instance attribute with the values of private attributes set on the model instance.
    """

    if typing.TYPE_CHECKING:
        # Here we provide annotations for the attributes of BaseModel.
        # Many of these are populated by the metaclass, which is why this section is in a `TYPE_CHECKING` block.
        # However, for the sake of easy review, we have included type annotations of all class and instance attributes
        # of `BaseModel` here:

        # Class attributes
        model_config: ClassVar[ConfigDict]
        """
        Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
        """

        model_fields: ClassVar[dict[str, FieldInfo]]
        """
        Metadata about the fields defined on the model,
        mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo].

        This replaces `Model.__fields__` from Pydantic V1.
        """

        __class_vars__: ClassVar[set[str]]
        __private_attributes__: ClassVar[dict[str, ModelPrivateAttr]]
        __signature__: ClassVar[Signature]

        __pydantic_complete__: ClassVar[bool]
        __pydantic_core_schema__: ClassVar[CoreSchema]
        __pydantic_custom_init__: ClassVar[bool]
        __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
        __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata]
        __pydantic_parent_namespace__: ClassVar[dict[str, Any] | None]
        __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
        __pydantic_root_model__: ClassVar[bool]
        __pydantic_serializer__: ClassVar[SchemaSerializer]
        __pydantic_validator__: ClassVar[SchemaValidator]

        # Instance attributes
        # Note: we use the non-existent kwarg `init=False` in pydantic.fields.Field below so that @dataclass_transform
        # doesn't think these are valid as keyword arguments to the class initializer.
        __pydantic_extra__: dict[str, Any] | None = _Field(init=False)  # type: ignore
        __pydantic_fields_set__: set[str] = _Field(init=False)  # type: ignore
        __pydantic_private__: dict[str, Any] | None = _Field(init=False)  # type: ignore
    else:
        # `model_fields` and `__pydantic_decorators__` must be set for
        # pydantic._internal._generate_schema.GenerateSchema.model_schema to work for a plain BaseModel annotation
        model_fields = {}
        __pydantic_decorators__ = _decorators.DecoratorInfos()
        # Prevent `BaseModel` from being instantiated directly:
        __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__'

    model_config = ConfigDict()
    __pydantic_complete__ = False
    __pydantic_root_model__ = False

    def __init__(__pydantic_self__, **data: Any) -> None:  # type: ignore
        """Create a new model by parsing and validating input data from keyword arguments.

        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
        validated to form a valid model.

        `__init__` uses `__pydantic_self__` instead of the more common `self` for the first arg to
        allow `self` as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)

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

    @property
    def model_computed_fields(self) -> dict[str, ComputedFieldInfo]:
        """Get the computed fields of this model instance.

        Returns:
            A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects.
        """
        return {k: v.info for k, v in self.__pydantic_decorators__.computed_fields.items()}

    @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 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: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:
        """Creates a new instance of the `Model` class with validated data.

        Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
        Default values are respected, but no other validation is performed.
        Behaves as if `Config.extra = 'allow'` was set since it adds all passed values

        Args:
            _fields_set: The set of field names accepted for the Model instance.
            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] = {}
        defaults: dict[str, Any] = {}  # keeping this separate from `fields_values` helps us compute `_fields_set`
        for name, field in cls.model_fields.items():
            if field.alias and field.alias in values:
                fields_values[name] = values.pop(field.alias)
            elif name in values:
                fields_values[name] = values.pop(name)
            elif not field.is_required():
                defaults[name] = field.get_default(call_default_factory=True)
        if _fields_set is None:
            _fields_set = set(fields_values.keys())
        fields_values.update(defaults)

        _extra: dict[str, Any] | None = None
        if cls.model_config.get('extra') == 'allow':
            _extra = {}
            for k, v in values.items():
                _extra[k] = v
        else:
            fields_values.update(values)
        _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)
        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: Model, *, update: dict[str, Any] | None = None, deep: bool = False) -> Model:
        """Usage docs: https://docs.pydantic.dev/2.4/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.model_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,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> dict[str, Any]:
        """Usage docs: https://docs.pydantic.dev/2.4/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 dictionary will only contain JSON serializable types.
                If mode is 'python', the dictionary may contain any Python objects.
            include: A list of fields to include in the output.
            exclude: A list of fields to exclude from the output.
            by_alias: Whether to use the field's alias in the dictionary key if defined.
            exclude_unset: Whether to exclude fields that are unset or None from the output.
            exclude_defaults: Whether to exclude fields that are set to their default value from the output.
            exclude_none: Whether to exclude fields that have a value of `None` from the output.
            round_trip: Whether to enable serialization and deserialization round-trip support.
            warnings: Whether to log warnings when invalid fields are encountered.

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

    def model_dump_json(
        self,
        *,
        indent: int | None = None,
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> str:
        """Usage docs: https://docs.pydantic.dev/2.4/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. Can take either a string or set of strings.
            exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings.
            by_alias: Whether to serialize using field aliases.
            exclude_unset: Whether to exclude fields that have not been explicitly set.
            exclude_defaults: Whether to exclude fields that have the default value.
            exclude_none: Whether to exclude fields that have a value of `None`.
            round_trip: Whether to use serialization/deserialization between JSON and class instance.
            warnings: Whether to show any warnings that occurred during serialization.

        Returns:
            A JSON string representation of the model.
        """
        return self.__pydantic_serializer__.to_json(
            self,
            indent=indent,
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
        ).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: dict[str, Any] | 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
        else:
            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:
                types_namespace: dict[str, Any] | None = _types_namespace.copy()
            else:
                if _parent_namespace_depth > 0:
                    frame_parent_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth) or {}
                    cls_parent_ns = (
                        _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}
                    )
                    types_namespace = {**cls_parent_ns, **frame_parent_ns}
                    cls.__pydantic_parent_namespace__ = _model_construction.build_lenient_weakvaluedict(types_namespace)
                else:
                    types_namespace = _model_construction.unpack_lenient_weakvaluedict(
                        cls.__pydantic_parent_namespace__
                    )

                types_namespace = _typing_extra.get_cls_types_namespace(cls, types_namespace)

            # 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,
                types_namespace=types_namespace,
            )

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

        Args:
            obj: The object to validate.
            strict: Whether to raise an exception on invalid fields.
            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: type[Model],
        json_data: str | bytes | bytearray,
        *,
        strict: bool | None = None,
        context: dict[str, Any] | None = None,
    ) -> Model:
        """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:
            ValueError: If `json_data` is not a JSON string.
        """
        # `__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: type[Model],
        obj: Any,
        *,
        strict: bool | None = None,
        context: dict[str, Any] | None = None,
    ) -> Model:
        """Validate the given object contains string data against the Pydantic model.

        Args:
            obj: The object contains 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: Call into Pydantic's internal JSON schema generation.
                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.
        if '__pydantic_core_schema__' in cls.__dict__:
            # 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[_typing_extra.TypeVarType, 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:
                if maybe_self_type is not None:
                    return maybe_self_type

                cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args)
                if cached is not None:
                    return cached

                # Attempt to rebuild the origin in case new types have been defined
                try:
                    # depth 3 gets you above this __class_getitem__ call
                    origin.model_rebuild(_parent_namespace_depth=3)
                except PydanticUndefinedAnnotation:
                    # It's okay if it fails, it just means there are still undefined types
                    # that could be evaluated later.
                    # TODO: Make sure validation fails if there are still undefined types, perhaps using MockValidator
                    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: Model) -> Model:
        """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 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: Model, memo: dict[int, Any] | None = None) -> Model:
        """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 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 typing.TYPE_CHECKING:
        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access

        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 is not None:
                    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
        elif self.model_config.get('frozen', None):
            error: pydantic_core.InitErrorDetails = {
                'type': 'frozen_instance',
                'loc': (name,),
                'input': value,
            }
            raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])
        elif getattr(self.model_fields.get(name), 'frozen', False):
            error: pydantic_core.InitErrorDetails = {
                'type': 'frozen_field',
                'loc': (name,),
                'input': value,
            }
            raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])

        attr = getattr(self.__class__, name, None)
        if isinstance(attr, property):
            attr.__set__(self, 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.model_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.model_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
            except KeyError as exc:
                raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
        elif item in self.model_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}')

    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['__pydantic_fields_set__'])
        _object_setattr(self, '__pydantic_extra__', state['__pydantic_extra__'])
        _object_setattr(self, '__pydantic_private__', state['__pydantic_private__'])
        _object_setattr(self, '__dict__', state['__dict__'])

    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__

            return (
                self_type == other_type
                and self.__dict__ == other.__dict__
                and self.__pydantic_private__ == other.__pydantic_private__
                and self.__pydantic_extra__ == other.__pydantic_extra__
            )
        else:
            return NotImplemented  # delegate to the other item in the comparison

    if typing.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.

            ```py
            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.model_fields.get(k)
            if field and field.repr:
                yield k, 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.model_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_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=PydanticDeprecatedSince20
    )
    def __fields__(self) -> dict[str, FieldInfo]:
        warnings.warn('The `__fields__` attribute is deprecated, use `model_fields` instead.', DeprecationWarning)
        return self.model_fields

    @property
    @typing_extensions.deprecated(
        'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
        category=PydanticDeprecatedSince20,
    )
    def __fields_set__(self) -> set[str]:
        warnings.warn(
            'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', DeprecationWarning
        )
        return self.__pydantic_fields_set__

    @typing_extensions.deprecated(
        'The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20
    )
    def dict(  # noqa: D102
        self,
        *,
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
    ) -> typing.Dict[str, Any]:  # noqa UP006
        warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', DeprecationWarning)
        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=PydanticDeprecatedSince20
    )
    def json(  # noqa: D102
        self,
        *,
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        encoder: typing.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.', DeprecationWarning)
        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=PydanticDeprecatedSince20
    )
    def parse_obj(cls: type[Model], obj: Any) -> Model:  # noqa: D102
        warnings.warn('The `parse_obj` method is deprecated; use `model_validate` instead.', DeprecationWarning)
        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=PydanticDeprecatedSince20,
    )
    def parse_raw(  # noqa: D102
        cls: type[Model],
        b: str | bytes,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: DeprecatedParseProtocol | None = None,
        allow_pickle: bool = False,
    ) -> Model:  # 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.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20,
    )
    def parse_file(  # noqa: D102
        cls: type[Model],
        path: str | Path,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: DeprecatedParseProtocol | None = None,
        allow_pickle: bool = False,
    ) -> Model:
        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.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20,
    )
    def from_orm(cls: type[Model], obj: Any) -> Model:  # noqa: D102
        warnings.warn(
            'The `from_orm` method is deprecated; set `model_config["from_attributes"]=True` '
            'and use `model_validate` instead.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20
    )
    def construct(cls: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:  # noqa: D102
        warnings.warn('The `construct` method is deprecated; use `model_construct` instead.', DeprecationWarning)
        return cls.model_construct(_fields_set=_fields_set, **values)

    @typing_extensions.deprecated(
        'The copy method is deprecated; use `model_copy` instead.', category=PydanticDeprecatedSince20
    )
    def copy(
        self: Model,
        *,
        include: AbstractSetIntStr | MappingIntStrAny | None = None,
        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
        update: typing.Dict[str, Any] | None = None,  # noqa UP006
        deep: bool = False,
    ) -> Model:  # 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:

        ```py
        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`.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20
    )
    def schema(  # noqa: D102
        cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
    ) -> typing.Dict[str, Any]:  # noqa UP006
        warnings.warn('The `schema` method is deprecated; use `model_json_schema` instead.', DeprecationWarning)
        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=PydanticDeprecatedSince20,
    )
    def schema_json(  # noqa: D102
        cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
    ) -> str:  # pragma: no cover
        import json

        warnings.warn(
            'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20
    )
    def validate(cls: type[Model], value: Any) -> Model:  # noqa: D102
        warnings.warn('The `validate` method is deprecated; use `model_validate` instead.', DeprecationWarning)
        return cls.model_validate(value)

    @classmethod
    @typing_extensions.deprecated(
        'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
        category=PydanticDeprecatedSince20,
    )
    def update_forward_refs(cls, **localns: Any) -> None:  # noqa: D102
        warnings.warn(
            'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', DeprecationWarning
        )
        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=PydanticDeprecatedSince20
    )
    def _iter(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn('The private method `_iter` will be removed and should no longer be used.', DeprecationWarning)

        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=PydanticDeprecatedSince20,
    )
    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.',
            DeprecationWarning,
        )
        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=PydanticDeprecatedSince20,
    )
    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.', DeprecationWarning
        )

        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=PydanticDeprecatedSince20,
    )
    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.', DeprecationWarning
        )

        from .deprecated import copy_internals

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


@typing.overload
def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | None = None,
    __base__: None = None,
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] | None = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    **field_definitions: Any,
) -> type[BaseModel]:
    ...


@typing.overload
def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | None = None,
    __base__: type[Model] | tuple[type[Model], ...],
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] | None = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    **field_definitions: Any,
) -> type[Model]:
    ...


def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | None = None,
    __base__: type[Model] | tuple[type[Model], ...] | None = None,
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] | None = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    __slots__: tuple[str, ...] | None = None,
    **field_definitions: Any,
) -> type[Model]:
    """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.
        __base__: The base class for the new model.
        __module__: The name of the module that the model belongs to.
        __validators__: A dictionary of methods that validate
            fields.
        __cls_kwargs__: A dictionary of keyword arguments for class creation.
        __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>)` or `<name>=(<type>, <FieldInfo>)`.

    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__ = (typing.cast(typing.Type['Model'], 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 = typing.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
        else:
            f_annotation, f_value = None, f_def

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

    namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__}
    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, **kwds)


__getattr__ = getattr_migration(__name__)
