<!-- 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
"""
You may have types that are not `BaseModel`s that you want to validate data against.
Or you may want to validate a `List[SomeModel]`, or dump it to JSON.

For use cases like this, Pydantic provides [`TypeAdapter`][pydantic.type_adapter.TypeAdapter],
which can be used for type validation, serialization, and JSON schema generation without creating a
[`BaseModel`][pydantic.main.BaseModel].

A [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] instance exposes some of the functionality from
[`BaseModel`][pydantic.main.BaseModel] instance methods for types that do not have such methods
(such as dataclasses, primitive types, and more):

```py
from typing import List

from typing_extensions import TypedDict

from pydantic import TypeAdapter, ValidationError

class User(TypedDict):
    name: str
    id: int

UserListValidator = TypeAdapter(List[User])
print(repr(UserListValidator.validate_python([{'name': 'Fred', 'id': '3'}])))
#> [{'name': 'Fred', 'id': 3}]

try:
    UserListValidator.validate_python(
        [{'name': 'Fred', 'id': 'wrong', 'other': 'no'}]
    )
except ValidationError as e:
    print(e)
    '''
    1 validation error for list[typed-dict]
    0.id
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='wrong', input_type=str]
    '''
```

Note:
    Despite some overlap in use cases with [`RootModel`][pydantic.root_model.RootModel],
    [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] should not be used as a type annotation for
    specifying fields of a `BaseModel`, etc.

## Parsing data into a specified type

[`TypeAdapter`][pydantic.type_adapter.TypeAdapter] can be used to apply the parsing logic to populate Pydantic models
in a more ad-hoc way. This function behaves similarly to
[`BaseModel.model_validate`][pydantic.main.BaseModel.model_validate],
but works with arbitrary Pydantic-compatible types.

This is especially useful when you want to parse results into a type that is not a direct subclass of
[`BaseModel`][pydantic.main.BaseModel]. For example:

```py
from typing import List

from pydantic import BaseModel, TypeAdapter

class Item(BaseModel):
    id: int
    name: str

# `item_data` could come from an API call, eg., via something like:
# item_data = requests.get('https://my-api.com/items').json()
item_data = [{'id': 1, 'name': 'My Item'}]

items = TypeAdapter(List[Item]).validate_python(item_data)
print(items)
#> [Item(id=1, name='My Item')]
```

[`TypeAdapter`][pydantic.type_adapter.TypeAdapter] is capable of parsing data into any of the types Pydantic can
handle as fields of a [`BaseModel`][pydantic.main.BaseModel].
"""  # noqa: D212
from __future__ import annotations as _annotations

import sys
from dataclasses import is_dataclass
from typing import TYPE_CHECKING, Any, Dict, Generic, Iterable, Set, TypeVar, Union, overload

from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator, Some
from typing_extensions import Literal, is_typeddict

from pydantic.errors import PydanticUserError
from pydantic.main import BaseModel

from ._internal import _config, _core_utils, _discriminated_union, _generate_schema, _typing_extra
from .config import ConfigDict
from .json_schema import (
    DEFAULT_REF_TEMPLATE,
    GenerateJsonSchema,
    JsonSchemaKeyT,
    JsonSchemaMode,
    JsonSchemaValue,
)
from .plugin._schema_validator import create_schema_validator

T = TypeVar('T')

if TYPE_CHECKING:
    # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
    IncEx = Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]]


def _get_schema(type_: Any, config_wrapper: _config.ConfigWrapper, parent_depth: int) -> CoreSchema:
    """`BaseModel` uses its own `__module__` to find out where it was defined
    and then look for symbols to resolve forward references in those globals.
    On the other hand this function can be called with arbitrary objects,
    including type aliases where `__module__` (always `typing.py`) is not useful.
    So instead we look at the globals in our parent stack frame.

    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 work for more complex cases.

    For example, take the following:

    a.py
    ```python
    from typing import Dict, List

    IntList = List[int]
    OuterDict = Dict[str, 'IntList']
    ```

    b.py
    ```python test="skip"
    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 know 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.
    """
    local_ns = _typing_extra.parent_frame_namespace(parent_depth=parent_depth)
    global_ns = sys._getframe(max(parent_depth - 1, 1)).f_globals.copy()
    global_ns.update(local_ns or {})
    gen = _generate_schema.GenerateSchema(config_wrapper, types_namespace=global_ns, typevars_map={})
    schema = gen.generate_schema(type_)
    schema = gen.collect_definitions(schema)
    return schema


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)


class TypeAdapter(Generic[T]):
    """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 that `TypeAdapter` is not an actual type, so you cannot use it in type annotations.

    Attributes:
        core_schema: The core schema for the type.
        validator (SchemaValidator): The schema validator for the type.
        serializer: The schema serializer for the type.
    """

    if TYPE_CHECKING:

        @overload
        def __new__(cls, __type: type[T], *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
            ...

        # this overload is for non-type things like Union[int, str]
        # Pyright currently handles this "correctly", but MyPy understands this as TypeAdapter[object]
        # so an explicit type cast is needed
        @overload
        def __new__(cls, __type: T, *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
            ...

        def __new__(cls, __type: Any, *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
            """A class representing the type adapter."""
            raise NotImplementedError

        @overload
        def __init__(self, type: type[T], *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
            ...

        # this overload is for non-type things like Union[int, str]
        # Pyright currently handles this "correctly", but MyPy understands this as TypeAdapter[object]
        # so an explicit type cast is needed
        @overload
        def __init__(self, type: T, *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
            ...

    def __init__(self, type: Any, *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
        """Initializes the TypeAdapter object."""
        config_wrapper = _config.ConfigWrapper(config)

        try:
            type_has_config = issubclass(type, BaseModel) or is_dataclass(type) or is_typeddict(type)
        except TypeError:
            # type is not a class
            type_has_config = False

        if type_has_config 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',
            )

        core_schema: CoreSchema
        try:
            core_schema = _getattr_no_parents(type, '__pydantic_core_schema__')
        except AttributeError:
            core_schema = _get_schema(type, config_wrapper, parent_depth=_parent_depth + 1)

        core_schema = _discriminated_union.apply_discriminators(_core_utils.simplify_schema_references(core_schema))

        core_schema = _core_utils.validate_core_schema(core_schema)

        core_config = config_wrapper.core_config(None)
        validator: SchemaValidator
        try:
            validator = _getattr_no_parents(type, '__pydantic_validator__')
        except AttributeError:
            validator = create_schema_validator(core_schema, core_config, config_wrapper.plugin_settings)

        serializer: SchemaSerializer
        try:
            serializer = _getattr_no_parents(type, '__pydantic_serializer__')
        except AttributeError:
            serializer = SchemaSerializer(core_schema, core_config)

        self.core_schema = core_schema
        self.validator = validator
        self.serializer = serializer

    def validate_python(
        self,
        __object: Any,
        *,
        strict: bool | None = None,
        from_attributes: bool | None = None,
        context: dict[str, Any] | 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.
            from_attributes: Whether to extract data from object attributes.
            context: Additional context to pass to the validator.

        Returns:
            The validated object.
        """
        return self.validator.validate_python(__object, strict=strict, from_attributes=from_attributes, context=context)

    def validate_json(
        self, __data: str | bytes, *, strict: bool | None = None, context: dict[str, Any] | None = None
    ) -> T:
        """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.
            context: Additional context to use during validation.

        Returns:
            The validated object.
        """
        return self.validator.validate_json(__data, strict=strict, context=context)

    def validate_strings(self, __obj: Any, *, strict: bool | None = None, context: dict[str, Any] | 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.
            context: Additional context to use during validation.

        Returns:
            The validated object.
        """
        return self.validator.validate_strings(__obj, strict=strict, context=context)

    def get_default_value(self, *, strict: bool | None = None, context: dict[str, 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 = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> 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.
            round_trip: Whether to output the serialized data in a way that is compatible with deserialization.
            warnings: Whether to display serialization warnings.

        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,
            round_trip=round_trip,
            warnings=warnings,
        )

    def dump_json(
        self,
        __instance: T,
        *,
        indent: int | None = None,
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> bytes:
        """Serialize an instance of the adapted type to JSON.

        Args:
            __instance: The instance to be serialized.
            indent: Number of spaces for JSON indentation.
            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`.
            round_trip: Whether to serialize and deserialize the instance to ensure round-tripping.
            warnings: Whether to emit serialization warnings.

        Returns:
            The JSON representation of the given instance as bytes.
        """
        return self.serializer.to_json(
            __instance,
            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,
        )

    def json_schema(
        self,
        *,
        by_alias: bool = True,
        ref_template: str = DEFAULT_REF_TEMPLATE,
        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.
            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)
        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,
        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.
            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)

        inputs = [(key, mode, adapter.core_schema) for key, mode, adapter in __inputs]

        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

"""
You may have types that are not `BaseModel`s that you want to validate data against.
Or you may want to validate a `List[SomeModel]`, or dump it to JSON.

For use cases like this, Pydantic provides [`TypeAdapter`][pydantic.type_adapter.TypeAdapter],
which can be used for type validation, serialization, and JSON schema generation without creating a
[`BaseModel`][pydantic.main.BaseModel].

A [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] instance exposes some of the functionality from
[`BaseModel`][pydantic.main.BaseModel] instance methods for types that do not have such methods
(such as dataclasses, primitive types, and more):

```py
from typing import List

from typing_extensions import TypedDict

from pydantic import TypeAdapter, ValidationError

class User(TypedDict):
    name: str
    id: int

UserListValidator = TypeAdapter(List[User])
print(repr(UserListValidator.validate_python([{'name': 'Fred', 'id': '3'}])))
#> [{'name': 'Fred', 'id': 3}]

try:
    UserListValidator.validate_python(
        [{'name': 'Fred', 'id': 'wrong', 'other': 'no'}]
    )
except ValidationError as e:
    print(e)
    '''
    1 validation error for list[typed-dict]
    0.id
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='wrong', input_type=str]
    '''
```

Note:
    Despite some overlap in use cases with [`RootModel`][pydantic.root_model.RootModel],
    [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] should not be used as a type annotation for
    specifying fields of a `BaseModel`, etc.

## Parsing data into a specified type

[`TypeAdapter`][pydantic.type_adapter.TypeAdapter] can be used to apply the parsing logic to populate Pydantic models
in a more ad-hoc way. This function behaves similarly to
[`BaseModel.model_validate`][pydantic.main.BaseModel.model_validate],
but works with arbitrary Pydantic-compatible types.

This is especially useful when you want to parse results into a type that is not a direct subclass of
[`BaseModel`][pydantic.main.BaseModel]. For example:

```py
from typing import List

from pydantic import BaseModel, TypeAdapter

class Item(BaseModel):
    id: int
    name: str

# `item_data` could come from an API call, eg., via something like:
# item_data = requests.get('https://my-api.com/items').json()
item_data = [{'id': 1, 'name': 'My Item'}]

items = TypeAdapter(List[Item]).validate_python(item_data)
print(items)
#> [Item(id=1, name='My Item')]
```

[`TypeAdapter`][pydantic.type_adapter.TypeAdapter] is capable of parsing data into any of the types Pydantic can
handle as fields of a [`BaseModel`][pydantic.main.BaseModel].
"""  # noqa: D212
from __future__ import annotations as _annotations

import sys
from dataclasses import is_dataclass
from typing import TYPE_CHECKING, Any, Dict, Generic, Iterable, Set, TypeVar, Union, overload

from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator, Some
from typing_extensions import Literal, is_typeddict

from pydantic.errors import PydanticUserError
from pydantic.main import BaseModel

from ._internal import _config, _generate_schema, _typing_extra
from .config import ConfigDict
from .json_schema import (
    DEFAULT_REF_TEMPLATE,
    GenerateJsonSchema,
    JsonSchemaKeyT,
    JsonSchemaMode,
    JsonSchemaValue,
)
from .plugin._schema_validator import create_schema_validator

T = TypeVar('T')

if TYPE_CHECKING:
    # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
    IncEx = Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]]


def _get_schema(type_: Any, config_wrapper: _config.ConfigWrapper, parent_depth: int) -> CoreSchema:
    """`BaseModel` uses its own `__module__` to find out where it was defined
    and then look for symbols to resolve forward references in those globals.
    On the other hand this function can be called with arbitrary objects,
    including type aliases where `__module__` (always `typing.py`) is not useful.
    So instead we look at the globals in our parent stack frame.

    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 work for more complex cases.

    For example, take the following:

    a.py
    ```python
    from typing import Dict, List

    IntList = List[int]
    OuterDict = Dict[str, 'IntList']
    ```

    b.py
    ```python test="skip"
    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 know 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.
    """
    local_ns = _typing_extra.parent_frame_namespace(parent_depth=parent_depth)
    global_ns = sys._getframe(max(parent_depth - 1, 1)).f_globals.copy()
    global_ns.update(local_ns or {})
    gen = _generate_schema.GenerateSchema(config_wrapper, types_namespace=global_ns, typevars_map={})
    schema = gen.generate_schema(type_)
    schema = gen.clean_schema(schema)
    return schema


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)


class TypeAdapter(Generic[T]):
    """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 that `TypeAdapter` is not an actual type, so you cannot use it in type annotations.

    Attributes:
        core_schema: The core schema for the type.
        validator (SchemaValidator): The schema validator for the type.
        serializer: The schema serializer for the type.
    """

    if TYPE_CHECKING:

        @overload
        def __new__(cls, __type: type[T], *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
            ...

        # this overload is for non-type things like Union[int, str]
        # Pyright currently handles this "correctly", but MyPy understands this as TypeAdapter[object]
        # so an explicit type cast is needed
        @overload
        def __new__(cls, __type: T, *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
            ...

        def __new__(cls, __type: Any, *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
            """A class representing the type adapter."""
            raise NotImplementedError

        @overload
        def __init__(self, type: type[T], *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
            ...

        # this overload is for non-type things like Union[int, str]
        # Pyright currently handles this "correctly", but MyPy understands this as TypeAdapter[object]
        # so an explicit type cast is needed
        @overload
        def __init__(self, type: T, *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
            ...

    def __init__(self, type: Any, *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
        """Initializes the TypeAdapter object."""
        config_wrapper = _config.ConfigWrapper(config)

        try:
            type_has_config = issubclass(type, BaseModel) or is_dataclass(type) or is_typeddict(type)
        except TypeError:
            # type is not a class
            type_has_config = False

        if type_has_config 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',
            )

        core_schema: CoreSchema
        try:
            core_schema = _getattr_no_parents(type, '__pydantic_core_schema__')
        except AttributeError:
            core_schema = _get_schema(type, config_wrapper, parent_depth=_parent_depth + 1)

        core_config = config_wrapper.core_config(None)
        validator: SchemaValidator
        try:
            validator = _getattr_no_parents(type, '__pydantic_validator__')
        except AttributeError:
            validator = create_schema_validator(core_schema, core_config, config_wrapper.plugin_settings)

        serializer: SchemaSerializer
        try:
            serializer = _getattr_no_parents(type, '__pydantic_serializer__')
        except AttributeError:
            serializer = SchemaSerializer(core_schema, core_config)

        self.core_schema = core_schema
        self.validator = validator
        self.serializer = serializer

    def validate_python(
        self,
        __object: Any,
        *,
        strict: bool | None = None,
        from_attributes: bool | None = None,
        context: dict[str, Any] | 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.
            from_attributes: Whether to extract data from object attributes.
            context: Additional context to pass to the validator.

        Returns:
            The validated object.
        """
        return self.validator.validate_python(__object, strict=strict, from_attributes=from_attributes, context=context)

    def validate_json(
        self, __data: str | bytes, *, strict: bool | None = None, context: dict[str, Any] | None = None
    ) -> T:
        """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.
            context: Additional context to use during validation.

        Returns:
            The validated object.
        """
        return self.validator.validate_json(__data, strict=strict, context=context)

    def validate_strings(self, __obj: Any, *, strict: bool | None = None, context: dict[str, Any] | 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.
            context: Additional context to use during validation.

        Returns:
            The validated object.
        """
        return self.validator.validate_strings(__obj, strict=strict, context=context)

    def get_default_value(self, *, strict: bool | None = None, context: dict[str, 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 = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> 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.
            round_trip: Whether to output the serialized data in a way that is compatible with deserialization.
            warnings: Whether to display serialization warnings.

        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,
            round_trip=round_trip,
            warnings=warnings,
        )

    def dump_json(
        self,
        __instance: T,
        *,
        indent: int | None = None,
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> bytes:
        """Serialize an instance of the adapted type to JSON.

        Args:
            __instance: The instance to be serialized.
            indent: Number of spaces for JSON indentation.
            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`.
            round_trip: Whether to serialize and deserialize the instance to ensure round-tripping.
            warnings: Whether to emit serialization warnings.

        Returns:
            The JSON representation of the given instance as bytes.
        """
        return self.serializer.to_json(
            __instance,
            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,
        )

    def json_schema(
        self,
        *,
        by_alias: bool = True,
        ref_template: str = DEFAULT_REF_TEMPLATE,
        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.
            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)
        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,
        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.
            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)

        inputs = [(key, mode, adapter.core_schema) for key, mode, adapter in __inputs]

        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
