Загрузить файлы в «venv/Lib/site-packages/pydantic»

venv/Lib/site-packages/pydantic/root_model.py

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+"""RootModel class and type definitions."""
+
+from __future__ import annotations as _annotations
+
+from copy import copy, deepcopy
+from typing import TYPE_CHECKING, Any, Generic, Literal, TypeVar
+
+from pydantic_core import PydanticUndefined
+from typing_extensions import Self, dataclass_transform
+
+from . import PydanticUserError
+from ._internal import _model_construction, _repr
+from .main import BaseModel, _object_setattr
+
+if TYPE_CHECKING:
+    from .fields import Field as PydanticModelField
+    from .fields import PrivateAttr as PydanticModelPrivateAttr
+
+    # dataclass_transform could be applied to RootModel directly, but `ModelMetaclass`'s dataclass_transform
+    # takes priority (at least with pyright). We trick type checkers into thinking we apply dataclass_transform
+    # on a new metaclass.
+    @dataclass_transform(kw_only_default=False, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr))
+    class _RootModelMetaclass(_model_construction.ModelMetaclass): ...
+else:
+    _RootModelMetaclass = _model_construction.ModelMetaclass
+
+__all__ = ('RootModel',)
+
+RootModelRootType = TypeVar('RootModelRootType')
+
+
+class RootModel(BaseModel, Generic[RootModelRootType], metaclass=_RootModelMetaclass):
+    """!!! abstract "Usage Documentation"
+        [`RootModel` and Custom Root Types](../concepts/models.md#rootmodel-and-custom-root-types)
+
+    A Pydantic `BaseModel` for the root object of the model.
+
+    Attributes:
+        root: The root object of the model.
+        __pydantic_root_model__: Whether the model is a RootModel.
+        __pydantic_private__: Private fields in the model.
+        __pydantic_extra__: Extra fields in the model.
+
+    """
+
+    __pydantic_root_model__ = True
+    __pydantic_private__ = None
+    __pydantic_extra__ = None
+
+    root: RootModelRootType
+
+    def __init_subclass__(cls, **kwargs):
+        extra = cls.model_config.get('extra')
+        if extra is not None:
+            raise PydanticUserError(
+                "`RootModel` does not support setting `model_config['extra']`", code='root-model-extra'
+            )
+        super().__init_subclass__(**kwargs)
+
+    def __init__(self, /, root: RootModelRootType = PydanticUndefined, **data) -> None:  # type: ignore
+        __tracebackhide__ = True
+        if data:
+            if root is not PydanticUndefined:
+                raise ValueError(
+                    '"RootModel.__init__" accepts either a single positional argument or arbitrary keyword arguments'
+                )
+            root = data  # type: ignore
+        self.__pydantic_validator__.validate_python(root, self_instance=self)
+
+    __init__.__pydantic_base_init__ = True  # pyright: ignore[reportFunctionMemberAccess]
+
+    @classmethod
+    def model_construct(cls, root: RootModelRootType, _fields_set: set[str] | None = None) -> Self:  # type: ignore
+        """Create a new model using the provided root object and update fields set.
+
+        Args:
+            root: The root object of the model.
+            _fields_set: The set of fields to be updated.
+
+        Returns:
+            The new model.
+
+        Raises:
+            NotImplemented: If the model is not a subclass of `RootModel`.
+        """
+        return super().model_construct(root=root, _fields_set=_fields_set)
+
+    def __getstate__(self) -> dict[Any, Any]:
+        return {
+            '__dict__': self.__dict__,
+            '__pydantic_fields_set__': self.__pydantic_fields_set__,
+        }
+
+    def __setstate__(self, state: dict[Any, Any]) -> None:
+        _object_setattr(self, '__pydantic_fields_set__', state['__pydantic_fields_set__'])
+        _object_setattr(self, '__dict__', state['__dict__'])
+
+    def __copy__(self) -> Self:
+        """Returns a shallow copy of the model."""
+        cls = type(self)
+        m = cls.__new__(cls)
+        new_dict = copy(self.__dict__)
+        new_dict['root'] = copy(self.__dict__['root'])
+        _object_setattr(m, '__dict__', new_dict)
+        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
+        return m
+
+    def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
+        """Returns a deep copy of the model."""
+        cls = type(self)
+        m = cls.__new__(cls)
+        _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
+        # 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__))
+        return m
+
+    if TYPE_CHECKING:
+
+        def model_dump(  # type: ignore
+            self,
+            *,
+            mode: Literal['json', 'python'] | str = 'python',
+            include: Any = None,
+            exclude: Any = None,
+            context: dict[str, Any] | None = None,
+            by_alias: bool | None = None,
+            exclude_unset: bool = False,
+            exclude_defaults: bool = False,
+            exclude_none: bool = False,
+            exclude_computed_fields: bool = False,
+            round_trip: bool = False,
+            warnings: bool | Literal['none', 'warn', 'error'] = True,
+            serialize_as_any: bool = False,
+        ) -> Any:
+            """This method is included just to get a more accurate return type for type checkers.
+            It is included in this `if TYPE_CHECKING:` block since no override is actually necessary.
+
+            See the documentation of `BaseModel.model_dump` for more details about the arguments.
+
+            Generally, this method will have a return type of `RootModelRootType`, assuming that `RootModelRootType` is
+            not a `BaseModel` subclass. If `RootModelRootType` is a `BaseModel` subclass, then the return
+            type will likely be `dict[str, Any]`, as `model_dump` calls are recursive. The return type could
+            even be something different, in the case of a custom serializer.
+            Thus, `Any` is used here to catch all of these cases.
+            """
+            ...
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, RootModel):
+            return NotImplemented
+        return self.__pydantic_fields__['root'].annotation == other.__pydantic_fields__[
+            'root'
+        ].annotation and super().__eq__(other)
+
+    def __repr_args__(self) -> _repr.ReprArgs:
+        yield 'root', self.root

venv/Lib/site-packages/pydantic/schema.py

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+"""The `schema` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

venv/Lib/site-packages/pydantic/tools.py

@@ -0,0 +1,5 @@
+"""The `tools` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

venv/Lib/site-packages/pydantic/type_adapter.py

@@ -0,0 +1,801 @@
+"""Type adapter specification."""
+
+from __future__ import annotations as _annotations
+
+import sys
+import types
+from collections.abc import Callable, Iterable
+from dataclasses import is_dataclass
+from types import FrameType
+from typing import (
+    Any,
+    Generic,
+    Literal,
+    TypeVar,
+    cast,
+    final,
+    overload,
+)
+
+from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator, Some
+from typing_extensions import ParamSpec, is_typeddict
+
+from pydantic.errors import PydanticUserError
+from pydantic.main import BaseModel, IncEx
+
+from ._internal import _config, _generate_schema, _mock_val_ser, _namespace_utils, _repr, _typing_extra, _utils
+from .config import ConfigDict, ExtraValues
+from .errors import PydanticUndefinedAnnotation
+from .json_schema import (
+    DEFAULT_REF_TEMPLATE,
+    GenerateJsonSchema,
+    JsonSchemaKeyT,
+    JsonSchemaMode,
+    JsonSchemaValue,
+)
+from .plugin._schema_validator import PluggableSchemaValidator, create_schema_validator
+
+T = TypeVar('T')
+R = TypeVar('R')
+P = ParamSpec('P')
+TypeAdapterT = TypeVar('TypeAdapterT', bound='TypeAdapter')
+
+
+def _getattr_no_parents(obj: Any, attribute: str) -> Any:
+    """Returns the attribute value without attempting to look up attributes from parent types."""
+    if hasattr(obj, '__dict__'):
+        try:
+            return obj.__dict__[attribute]
+        except KeyError:
+            pass
+
+    slots = getattr(obj, '__slots__', None)
+    if slots is not None and attribute in slots:
+        return getattr(obj, attribute)
+    else:
+        raise AttributeError(attribute)
+
+
+def _type_has_config(type_: Any) -> bool:
+    """Returns whether the type has config."""
+    type_ = _typing_extra.annotated_type(type_) or type_
+    try:
+        return issubclass(type_, BaseModel) or is_dataclass(type_) or is_typeddict(type_)
+    except TypeError:
+        # type is not a class
+        return False
+
+
+@final
+class TypeAdapter(Generic[T]):
+    """!!! abstract "Usage Documentation"
+        [`TypeAdapter`](../concepts/type_adapter.md)
+
+    Type adapters provide a flexible way to perform validation and serialization based on a Python type.
+
+    A `TypeAdapter` instance exposes some of the functionality from `BaseModel` instance methods
+    for types that do not have such methods (such as dataclasses, primitive types, and more).
+
+    **Note:** `TypeAdapter` instances are not types, and cannot be used as type annotations for fields.
+
+    Args:
+        type: The type associated with the `TypeAdapter`.
+        config: Configuration for the `TypeAdapter`, should be a dictionary conforming to
+            [`ConfigDict`][pydantic.config.ConfigDict].
+
+            !!! note
+                You cannot provide a configuration when instantiating a `TypeAdapter` if the type you're using
+                has its own config that cannot be overridden (ex: `BaseModel`, `TypedDict`, and `dataclass`). A
+                [`type-adapter-config-unused`](../errors/usage_errors.md#type-adapter-config-unused) error will
+                be raised in this case.
+        _parent_depth: Depth at which to search for the [parent frame][frame-objects]. This frame is used when
+            resolving forward annotations during schema building, by looking for the globals and locals of this
+            frame. Defaults to 2, which will result in the frame where the `TypeAdapter` was instantiated.
+
+            !!! note
+                This parameter is named with an underscore to suggest its private nature and discourage use.
+                It may be deprecated in a minor version, so we only recommend using it if you're comfortable
+                with potential change in behavior/support. It's default value is 2 because internally,
+                the `TypeAdapter` class makes another call to fetch the frame.
+        module: The module that passes to plugin if provided.
+
+    Attributes:
+        core_schema: The core schema for the type.
+        validator: The schema validator for the type.
+        serializer: The schema serializer for the type.
+        pydantic_complete: Whether the core schema for the type is successfully built.
+
+    ??? tip "Compatibility with `mypy`"
+        Depending on the type used, `mypy` might raise an error when instantiating a `TypeAdapter`. As a workaround, you can explicitly
+        annotate your variable:
+
+        ```py
+        from typing import Union
+
+        from pydantic import TypeAdapter
+
+        ta: TypeAdapter[Union[str, int]] = TypeAdapter(Union[str, int])  # type: ignore[arg-type]
+        ```
+
+    ??? info "Namespace management nuances and implementation details"
+
+        Here, we collect some notes on namespace management, and subtle differences from `BaseModel`:
+
+        `BaseModel` uses its own `__module__` to find out where it was defined
+        and then looks for symbols to resolve forward references in those globals.
+        On the other hand, `TypeAdapter` can be initialized with arbitrary objects,
+        which may not be types and thus do not have a `__module__` available.
+        So instead we look at the globals in our parent stack frame.
+
+        It is expected that the `ns_resolver` passed to this function will have the correct
+        namespace for the type we're adapting. See the source code for `TypeAdapter.__init__`
+        and `TypeAdapter.rebuild` for various ways to construct this namespace.
+
+        This works for the case where this function is called in a module that
+        has the target of forward references in its scope, but
+        does not always work for more complex cases.
+
+        For example, take the following:
+
+        ```python {title="a.py"}
+        IntList = list[int]
+        OuterDict = dict[str, 'IntList']
+        ```
+
+        ```python {test="skip" title="b.py"}
+        from a import OuterDict
+
+        from pydantic import TypeAdapter
+
+        IntList = int  # replaces the symbol the forward reference is looking for
+        v = TypeAdapter(OuterDict)
+        v({'x': 1})  # should fail but doesn't
+        ```
+
+        If `OuterDict` were a `BaseModel`, this would work because it would resolve
+        the forward reference within the `a.py` namespace.
+        But `TypeAdapter(OuterDict)` can't determine what module `OuterDict` came from.
+
+        In other words, the assumption that _all_ forward references exist in the
+        module we are being called from is not technically always true.
+        Although most of the time it is and it works fine for recursive models and such,
+        `BaseModel`'s behavior isn't perfect either and _can_ break in similar ways,
+        so there is no right or wrong between the two.
+
+        But at the very least this behavior is _subtly_ different from `BaseModel`'s.
+    """
+
+    core_schema: CoreSchema
+    validator: SchemaValidator | PluggableSchemaValidator
+    serializer: SchemaSerializer
+    pydantic_complete: bool
+
+    @overload
+    def __init__(
+        self,
+        type: type[T],
+        *,
+        config: ConfigDict | None = ...,
+        _parent_depth: int = ...,
+        module: str | None = ...,
+    ) -> None: ...
+
+    # This second overload is for unsupported special forms (such as Annotated, Union, etc.)
+    # Currently there is no way to type this correctly
+    # See https://github.com/python/typing/pull/1618
+    @overload
+    def __init__(
+        self,
+        type: Any,
+        *,
+        config: ConfigDict | None = ...,
+        _parent_depth: int = ...,
+        module: str | None = ...,
+    ) -> None: ...
+
+    def __init__(
+        self,
+        type: Any,
+        *,
+        config: ConfigDict | None = None,
+        _parent_depth: int = 2,
+        module: str | None = None,
+    ) -> None:
+        if _type_has_config(type) and config is not None:
+            raise PydanticUserError(
+                'Cannot use `config` when the type is a BaseModel, dataclass or TypedDict.'
+                ' These types can have their own config and setting the config via the `config`'
+                ' parameter to TypeAdapter will not override it, thus the `config` you passed to'
+                ' TypeAdapter becomes meaningless, which is probably not what you want.',
+                code='type-adapter-config-unused',
+            )
+
+        self._type = type
+        self._config = config
+        self._parent_depth = _parent_depth
+        self.pydantic_complete = False
+
+        parent_frame = self._fetch_parent_frame()
+        if isinstance(type, types.FunctionType):
+            # Special case functions, which are *not* pushed to the `NsResolver` stack and without this special case
+            # would only have access to the parent namespace where the `TypeAdapter` was instantiated (if the function is defined
+            # in another module, we need to look at that module's globals).
+            if parent_frame is not None:
+                # `f_locals` is the namespace where the type adapter was instantiated (~ to `f_globals` if at the module level):
+                parent_ns = parent_frame.f_locals
+            else:  # pragma: no cover
+                parent_ns = None
+            globalns, localns = _namespace_utils.ns_for_function(
+                type,
+                parent_namespace=parent_ns,
+            )
+            parent_namespace = None
+        else:
+            if parent_frame is not None:
+                globalns = parent_frame.f_globals
+                # Do not provide a local ns if the type adapter happens to be instantiated at the module level:
+                localns = parent_frame.f_locals if parent_frame.f_locals is not globalns else {}
+            else:  # pragma: no cover
+                globalns = {}
+                localns = {}
+            parent_namespace = localns
+
+        self._module_name = module or cast(str, globalns.get('__name__', ''))
+        self._init_core_attrs(
+            ns_resolver=_namespace_utils.NsResolver(
+                namespaces_tuple=_namespace_utils.NamespacesTuple(locals=localns, globals=globalns),
+                parent_namespace=parent_namespace,
+            ),
+            force=False,
+        )
+
+    def _fetch_parent_frame(self) -> FrameType | None:
+        frame = sys._getframe(self._parent_depth)
+        if frame.f_globals.get('__name__') == 'typing':
+            # Because `TypeAdapter` is generic, explicitly parametrizing the class results
+            # in a `typing._GenericAlias` instance, which proxies instantiation calls to the
+            # "real" `TypeAdapter` class and thus adding an extra frame to the call. To avoid
+            # pulling anything from the `typing` module, use the correct frame (the one before):
+            return frame.f_back
+
+        return frame
+
+    def _init_core_attrs(
+        self, ns_resolver: _namespace_utils.NsResolver, force: bool, raise_errors: bool = False
+    ) -> bool:
+        """Initialize the core schema, validator, and serializer for the type.
+
+        Args:
+            ns_resolver: The namespace resolver to use when building the core schema for the adapted type.
+            force: Whether to force the construction of the core schema, validator, and serializer.
+                If `force` is set to `False` and `_defer_build` is `True`, the core schema, validator, and serializer will be set to mocks.
+            raise_errors: Whether to raise errors if initializing any of the core attrs fails.
+
+        Returns:
+            `True` if the core schema, validator, and serializer were successfully initialized, otherwise `False`.
+
+        Raises:
+            PydanticUndefinedAnnotation: If `PydanticUndefinedAnnotation` occurs in`__get_pydantic_core_schema__`
+                and `raise_errors=True`.
+        """
+        if not force and self._defer_build:
+            _mock_val_ser.set_type_adapter_mocks(self)
+            self.pydantic_complete = False
+            return False
+
+        try:
+            self.core_schema = _getattr_no_parents(self._type, '__pydantic_core_schema__')
+            self.validator = _getattr_no_parents(self._type, '__pydantic_validator__')
+            self.serializer = _getattr_no_parents(self._type, '__pydantic_serializer__')
+
+            # TODO: we don't go through the rebuild logic here directly because we don't want
+            # to repeat all of the namespace fetching logic that we've already done
+            # so we simply skip to the block below that does the actual schema generation
+            if (
+                isinstance(self.core_schema, _mock_val_ser.MockCoreSchema)
+                or isinstance(self.validator, _mock_val_ser.MockValSer)
+                or isinstance(self.serializer, _mock_val_ser.MockValSer)
+            ):
+                raise AttributeError()
+        except AttributeError:
+            config_wrapper = _config.ConfigWrapper(self._config)
+
+            schema_generator = _generate_schema.GenerateSchema(config_wrapper, ns_resolver=ns_resolver)
+
+            try:
+                core_schema = schema_generator.generate_schema(self._type)
+            except PydanticUndefinedAnnotation:
+                if raise_errors:
+                    raise
+                _mock_val_ser.set_type_adapter_mocks(self)
+                return False
+
+            try:
+                self.core_schema = schema_generator.clean_schema(core_schema)
+            except _generate_schema.InvalidSchemaError:
+                _mock_val_ser.set_type_adapter_mocks(self)
+                return False
+
+            core_config = config_wrapper.core_config(None)
+
+            self.validator = create_schema_validator(
+                schema=self.core_schema,
+                schema_type=self._type,
+                schema_type_module=self._module_name,
+                schema_type_name=str(self._type),
+                schema_kind='TypeAdapter',
+                config=core_config,
+                plugin_settings=config_wrapper.plugin_settings,
+            )
+            self.serializer = SchemaSerializer(self.core_schema, core_config)
+
+        self.pydantic_complete = True
+        return True
+
+    @property
+    def _defer_build(self) -> bool:
+        config = self._config if self._config is not None else self._model_config
+        if config:
+            return config.get('defer_build') is True
+        return False
+
+    @property
+    def _model_config(self) -> ConfigDict | None:
+        type_: Any = _typing_extra.annotated_type(self._type) or self._type  # Eg FastAPI heavily uses Annotated
+        if _utils.lenient_issubclass(type_, BaseModel):
+            return type_.model_config
+        return getattr(type_, '__pydantic_config__', None)
+
+    def __repr__(self) -> str:
+        return f'TypeAdapter({_repr.display_as_type(self._type)})'
+
+    def rebuild(
+        self,
+        *,
+        force: bool = False,
+        raise_errors: bool = True,
+        _parent_namespace_depth: int = 2,
+        _types_namespace: _namespace_utils.MappingNamespace | None = None,
+    ) -> bool | None:
+        """Try to rebuild the pydantic-core schema for the adapter's type.
+
+        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 type adapter's schema, defaults to `False`.
+            raise_errors: Whether to raise errors, defaults to `True`.
+            _parent_namespace_depth: Depth at which to search for the [parent frame][frame-objects]. This
+                frame is used when resolving forward annotations during schema rebuilding, by looking for
+                the locals of this frame. Defaults to 2, which will result in the frame where the method
+                was called.
+            _types_namespace: An explicit types namespace to use, instead of using the local namespace
+                from the parent frame. 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 self.pydantic_complete:
+            return None
+
+        if _types_namespace is not None:
+            rebuild_ns = _types_namespace
+        elif _parent_namespace_depth > 0:
+            rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
+        else:
+            rebuild_ns = {}
+
+        # we have to manually fetch globals here because there's no type on the stack of the NsResolver
+        # and so we skip the globalns = get_module_ns_of(typ) call that would normally happen
+        globalns = sys._getframe(max(_parent_namespace_depth - 1, 1)).f_globals
+        ns_resolver = _namespace_utils.NsResolver(
+            namespaces_tuple=_namespace_utils.NamespacesTuple(locals=rebuild_ns, globals=globalns),
+            parent_namespace=rebuild_ns,
+        )
+        return self._init_core_attrs(ns_resolver=ns_resolver, force=True, raise_errors=raise_errors)
+
+    def validate_python(
+        self,
+        object: Any,
+        /,
+        *,
+        strict: bool | None = None,
+        extra: ExtraValues | None = None,
+        from_attributes: bool | None = None,
+        context: Any | None = None,
+        experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
+        by_alias: bool | None = None,
+        by_name: bool | None = None,
+    ) -> T:
+        """Validate a Python object against the model.
+
+        Args:
+            object: The Python object to validate against the model.
+            strict: Whether to strictly check types.
+            extra: Whether to ignore, allow, or forbid extra data during model validation.
+                See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
+            from_attributes: Whether to extract data from object attributes.
+            context: Additional context to pass to the validator.
+            experimental_allow_partial: **Experimental** whether to enable
+                [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
+                * False / 'off': Default behavior, no partial validation.
+                * True / 'on': Enable partial validation.
+                * 'trailing-strings': Enable partial validation and allow trailing strings in the input.
+            by_alias: Whether to use the field's alias when validating against the provided input data.
+            by_name: Whether to use the field's name when validating against the provided input data.
+
+        !!! note
+            When using `TypeAdapter` with a Pydantic `dataclass`, the use of the `from_attributes`
+            argument is not supported.
+
+        Returns:
+            The validated object.
+        """
+        if by_alias is False and by_name is not True:
+            raise PydanticUserError(
+                'At least one of `by_alias` or `by_name` must be set to True.',
+                code='validate-by-alias-and-name-false',
+            )
+
+        return self.validator.validate_python(
+            object,
+            strict=strict,
+            extra=extra,
+            from_attributes=from_attributes,
+            context=context,
+            allow_partial=experimental_allow_partial,
+            by_alias=by_alias,
+            by_name=by_name,
+        )
+
+    def validate_json(
+        self,
+        data: str | bytes | bytearray,
+        /,
+        *,
+        strict: bool | None = None,
+        extra: ExtraValues | None = None,
+        context: Any | None = None,
+        experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
+        by_alias: bool | None = None,
+        by_name: bool | None = None,
+    ) -> T:
+        """!!! abstract "Usage Documentation"
+            [JSON Parsing](../concepts/json.md#json-parsing)
+
+        Validate a JSON string or bytes against the model.
+
+        Args:
+            data: The JSON data to validate against the model.
+            strict: Whether to strictly check types.
+            extra: Whether to ignore, allow, or forbid extra data during model validation.
+                See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
+            context: Additional context to use during validation.
+            experimental_allow_partial: **Experimental** whether to enable
+                [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
+                * False / 'off': Default behavior, no partial validation.
+                * True / 'on': Enable partial validation.
+                * 'trailing-strings': Enable partial validation and allow trailing strings in the input.
+            by_alias: Whether to use the field's alias when validating against the provided input data.
+            by_name: Whether to use the field's name when validating against the provided input data.
+
+        Returns:
+            The validated object.
+        """
+        if by_alias is False and by_name is not True:
+            raise PydanticUserError(
+                'At least one of `by_alias` or `by_name` must be set to True.',
+                code='validate-by-alias-and-name-false',
+            )
+
+        return self.validator.validate_json(
+            data,
+            strict=strict,
+            extra=extra,
+            context=context,
+            allow_partial=experimental_allow_partial,
+            by_alias=by_alias,
+            by_name=by_name,
+        )
+
+    def validate_strings(
+        self,
+        obj: Any,
+        /,
+        *,
+        strict: bool | None = None,
+        extra: ExtraValues | None = None,
+        context: Any | None = None,
+        experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
+        by_alias: bool | None = None,
+        by_name: bool | None = None,
+    ) -> T:
+        """Validate object contains string data against the model.
+
+        Args:
+            obj: The object contains string data to validate.
+            strict: Whether to strictly check types.
+            extra: Whether to ignore, allow, or forbid extra data during model validation.
+                See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
+            context: Additional context to use during validation.
+            experimental_allow_partial: **Experimental** whether to enable
+                [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
+                * False / 'off': Default behavior, no partial validation.
+                * True / 'on': Enable partial validation.
+                * 'trailing-strings': Enable partial validation and allow trailing strings in the input.
+            by_alias: Whether to use the field's alias when validating against the provided input data.
+            by_name: Whether to use the field's name when validating against the provided input data.
+
+        Returns:
+            The validated object.
+        """
+        if by_alias is False and by_name is not True:
+            raise PydanticUserError(
+                'At least one of `by_alias` or `by_name` must be set to True.',
+                code='validate-by-alias-and-name-false',
+            )
+
+        return self.validator.validate_strings(
+            obj,
+            strict=strict,
+            extra=extra,
+            context=context,
+            allow_partial=experimental_allow_partial,
+            by_alias=by_alias,
+            by_name=by_name,
+        )
+
+    def get_default_value(self, *, strict: bool | None = None, context: Any | None = None) -> Some[T] | None:
+        """Get the default value for the wrapped type.
+
+        Args:
+            strict: Whether to strictly check types.
+            context: Additional context to pass to the validator.
+
+        Returns:
+            The default value wrapped in a `Some` if there is one or None if not.
+        """
+        return self.validator.get_default_value(strict=strict, context=context)
+
+    def dump_python(
+        self,
+        instance: T,
+        /,
+        *,
+        mode: Literal['json', 'python'] = 'python',
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        by_alias: bool | None = None,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        exclude_computed_fields: bool = False,
+        round_trip: bool = False,
+        warnings: bool | Literal['none', 'warn', 'error'] = True,
+        fallback: Callable[[Any], Any] | None = None,
+        serialize_as_any: bool = False,
+        polymorphic_serialization: bool | None = None,
+        context: Any | None = None,
+    ) -> Any:
+        """Dump an instance of the adapted type to a Python object.
+
+        Args:
+            instance: The Python object to serialize.
+            mode: The output format.
+            include: Fields to include in the output.
+            exclude: Fields to exclude from the output.
+            by_alias: Whether to use alias names for field names.
+            exclude_unset: Whether to exclude unset fields.
+            exclude_defaults: Whether to exclude fields with default values.
+            exclude_none: Whether to exclude fields with None values.
+            exclude_computed_fields: Whether to exclude computed fields.
+                While this can be useful for round-tripping, it is usually recommended to use the dedicated
+                `round_trip` parameter instead.
+            round_trip: Whether to output the serialized data in a way that is compatible with deserialization.
+            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
+                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
+            fallback: A function to call when an unknown value is encountered. If not provided,
+                a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
+            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
+            polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
+            context: Additional context to pass to the serializer.
+
+        Returns:
+            The serialized object.
+        """
+        return self.serializer.to_python(
+            instance,
+            mode=mode,
+            by_alias=by_alias,
+            include=include,
+            exclude=exclude,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            exclude_computed_fields=exclude_computed_fields,
+            round_trip=round_trip,
+            warnings=warnings,
+            fallback=fallback,
+            serialize_as_any=serialize_as_any,
+            polymorphic_serialization=polymorphic_serialization,
+            context=context,
+        )
+
+    def dump_json(
+        self,
+        instance: T,
+        /,
+        *,
+        indent: int | None = None,
+        ensure_ascii: bool = False,
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        by_alias: bool | None = None,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        exclude_computed_fields: bool = False,
+        round_trip: bool = False,
+        warnings: bool | Literal['none', 'warn', 'error'] = True,
+        fallback: Callable[[Any], Any] | None = None,
+        serialize_as_any: bool = False,
+        polymorphic_serialization: bool | None = None,
+        context: Any | None = None,
+    ) -> bytes:
+        """!!! abstract "Usage Documentation"
+            [JSON Serialization](../concepts/json.md#json-serialization)
+
+        Serialize an instance of the adapted type to JSON.
+
+        Args:
+            instance: The instance to be serialized.
+            indent: Number of spaces for JSON indentation.
+            ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped.
+                If `False` (the default), these characters will be output as-is.
+            include: Fields to include.
+            exclude: Fields to exclude.
+            by_alias: Whether to use alias names for field names.
+            exclude_unset: Whether to exclude unset fields.
+            exclude_defaults: Whether to exclude fields with default values.
+            exclude_none: Whether to exclude fields with a value of `None`.
+            exclude_computed_fields: Whether to exclude computed fields.
+                While this can be useful for round-tripping, it is usually recommended to use the dedicated
+                `round_trip` parameter instead.
+            round_trip: Whether to serialize and deserialize the instance to ensure round-tripping.
+            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
+                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
+            fallback: A function to call when an unknown value is encountered. If not provided,
+                a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
+            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
+            polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
+            context: Additional context to pass to the serializer.
+
+        Returns:
+            The JSON representation of the given instance as bytes.
+        """
+        return self.serializer.to_json(
+            instance,
+            indent=indent,
+            ensure_ascii=ensure_ascii,
+            include=include,
+            exclude=exclude,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            exclude_computed_fields=exclude_computed_fields,
+            round_trip=round_trip,
+            warnings=warnings,
+            fallback=fallback,
+            serialize_as_any=serialize_as_any,
+            polymorphic_serialization=polymorphic_serialization,
+            context=context,
+        )
+
+    def json_schema(
+        self,
+        *,
+        by_alias: bool = True,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+        union_format: Literal['any_of', 'primitive_type_array'] = 'any_of',
+        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+        mode: JsonSchemaMode = 'validation',
+    ) -> dict[str, Any]:
+        """Generate a JSON schema for the adapted type.
+
+        Args:
+            by_alias: Whether to use alias names for field names.
+            ref_template: The format string used for generating $ref strings.
+            union_format: The format to use when combining schemas from unions together. Can be one of:
+
+                - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
+                keyword to combine schemas (the default).
+                - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type)
+                keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive
+                type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to
+                `any_of`.
+            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.
+            schema_generator: The generator class used for creating the schema.
+            mode: The mode to use for schema generation.
+
+        Returns:
+            The JSON schema for the model as a dictionary.
+        """
+        schema_generator_instance = schema_generator(
+            by_alias=by_alias, ref_template=ref_template, union_format=union_format
+        )
+        if isinstance(self.core_schema, _mock_val_ser.MockCoreSchema):
+            self.core_schema.rebuild()
+            assert not isinstance(self.core_schema, _mock_val_ser.MockCoreSchema), 'this is a bug! please report it'
+        return schema_generator_instance.generate(self.core_schema, mode=mode)
+
+    @staticmethod
+    def json_schemas(
+        inputs: Iterable[tuple[JsonSchemaKeyT, JsonSchemaMode, TypeAdapter[Any]]],
+        /,
+        *,
+        by_alias: bool = True,
+        title: str | None = None,
+        description: str | None = None,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+        union_format: Literal['any_of', 'primitive_type_array'] = 'any_of',
+        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+    ) -> tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]:
+        """Generate a JSON schema including definitions from multiple type adapters.
+
+        Args:
+            inputs: Inputs to schema generation. The first two items will form the keys of the (first)
+                output mapping; the type adapters will provide the core schemas that get converted into
+                definitions in the output JSON schema.
+            by_alias: Whether to use alias names.
+            title: The title for the schema.
+            description: The description for the schema.
+            ref_template: The format string used for generating $ref strings.
+            union_format: The format to use when combining schemas from unions together. Can be one of:
+
+                - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
+                keyword to combine schemas (the default).
+                - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type)
+                keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive
+                type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to
+                `any_of`.
+            schema_generator: The generator class used for creating the schema.
+
+        Returns:
+            A tuple where:
+
+                - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and
+                    whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have
+                    JsonRef references to definitions that are defined in the second returned element.)
+                - The second element is a JSON schema containing all definitions referenced in the first returned
+                    element, along with the optional title and description keys.
+
+        """
+        schema_generator_instance = schema_generator(
+            by_alias=by_alias, ref_template=ref_template, union_format=union_format
+        )
+
+        inputs_ = []
+        for key, mode, adapter in inputs:
+            # This is the same pattern we follow for model json schemas - we attempt a core schema rebuild if we detect a mock
+            if isinstance(adapter.core_schema, _mock_val_ser.MockCoreSchema):
+                adapter.core_schema.rebuild()
+                assert not isinstance(adapter.core_schema, _mock_val_ser.MockCoreSchema), (
+                    'this is a bug! please report it'
+                )
+            inputs_.append((key, mode, adapter.core_schema))
+
+        json_schemas_map, definitions = schema_generator_instance.generate_definitions(inputs_)
+
+        json_schema: dict[str, Any] = {}
+        if definitions:
+            json_schema['$defs'] = definitions
+        if title:
+            json_schema['title'] = title
+        if description:
+            json_schema['description'] = description
+
+        return json_schemas_map, json_schema