<!-- 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
import sys
from typing import TYPE_CHECKING, Any, Dict, FrozenSet, NamedTuple, Type

from .fields import Required
from .main import BaseModel, create_model
from .typing import is_typeddict, is_typeddict_special

if TYPE_CHECKING:
    from typing_extensions import TypedDict

if sys.version_info < (3, 11):

    def is_legacy_typeddict(typeddict_cls: Type['TypedDict']) -> bool:  # type: ignore[valid-type]
        return is_typeddict(typeddict_cls) and type(typeddict_cls).__module__ == 'typing'

else:

    def is_legacy_typeddict(_: Any) -> Any:
        return False


def create_model_from_typeddict(
    # Mypy bug: `Type[TypedDict]` is resolved as `Any` https://github.com/python/mypy/issues/11030
    typeddict_cls: Type['TypedDict'],  # type: ignore[valid-type]
    **kwargs: Any,
) -> Type['BaseModel']:
    """
    Create a `BaseModel` based on the fields of a `TypedDict`.
    Since `typing.TypedDict` in Python 3.8 does not store runtime information about optional keys,
    we raise an error if this happens (see https://bugs.python.org/issue38834).
    """
    field_definitions: Dict[str, Any]

    # Best case scenario: with python 3.9+ or when `TypedDict` is imported from `typing_extensions`
    if not hasattr(typeddict_cls, '__required_keys__'):
        raise TypeError(
            'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.9.2. '
            'Without it, there is no way to differentiate required and optional fields when subclassed.'
        )

    if is_legacy_typeddict(typeddict_cls) and any(
        is_typeddict_special(t) for t in typeddict_cls.__annotations__.values()
    ):
        raise TypeError(
            'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.11. '
            'Without it, there is no way to reflect Required/NotRequired keys.'
        )

    required_keys: FrozenSet[str] = typeddict_cls.__required_keys__  # type: ignore[attr-defined]
    field_definitions = {
        field_name: (field_type, Required if field_name in required_keys else None)
        for field_name, field_type in typeddict_cls.__annotations__.items()
    }

    return create_model(typeddict_cls.__name__, **kwargs, **field_definitions)


def create_model_from_namedtuple(namedtuple_cls: Type['NamedTuple'], **kwargs: Any) -> Type['BaseModel']:
    """
    Create a `BaseModel` based on the fields of a named tuple.
    A named tuple can be created with `typing.NamedTuple` and declared annotations
    but also with `collections.namedtuple`, in this case we consider all fields
    to have type `Any`.
    """
    # With python 3.10+, `__annotations__` always exists but can be empty hence the `getattr... or...` logic
    namedtuple_annotations: Dict[str, Type[Any]] = getattr(namedtuple_cls, '__annotations__', None) or {
        k: Any for k in namedtuple_cls._fields
    }
    field_definitions: Dict[str, Any] = {
        field_name: (field_type, Required) for field_name, field_type in namedtuple_annotations.items()
    }
    return create_model(namedtuple_cls.__name__, **kwargs, **field_definitions)

import sys
from typing import TYPE_CHECKING, Any, Dict, FrozenSet, NamedTuple, Type

from .fields import Required
from .main import BaseModel, create_model
from .typing import is_typeddict, is_typeddict_special

if TYPE_CHECKING:
    from typing_extensions import TypedDict

if sys.version_info < (3, 11):

    def is_legacy_typeddict(typeddict_cls: Type['TypedDict']) -> bool:  # type: ignore[valid-type]
        return is_typeddict(typeddict_cls) and type(typeddict_cls).__module__ == 'typing'

else:

    def is_legacy_typeddict(_: Any) -> Any:
        return False


def create_model_from_typeddict(
    # Mypy bug: `Type[TypedDict]` is resolved as `Any` https://github.com/python/mypy/issues/11030
    typeddict_cls: Type['TypedDict'],  # type: ignore[valid-type]
    **kwargs: Any,
) -> Type['BaseModel']:
    """
    Create a `BaseModel` based on the fields of a `TypedDict`.
    Since `typing.TypedDict` in Python 3.8 does not store runtime information about optional keys,
    we raise an error if this happens (see https://bugs.python.org/issue38834).
    """
    field_definitions: Dict[str, Any]

    # Best case scenario: with python 3.9+ or when `TypedDict` is imported from `typing_extensions`
    if not hasattr(typeddict_cls, '__required_keys__'):
        raise TypeError(
            'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.9.2. '
            'Without it, there is no way to differentiate required and optional fields when subclassed.'
        )

    if is_legacy_typeddict(typeddict_cls) and any(
        is_typeddict_special(t) for t in typeddict_cls.__annotations__.values()
    ):
        raise TypeError(
            'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.11. '
            'Without it, there is no way to reflect Required/NotRequired keys.'
        )

    required_keys: FrozenSet[str] = typeddict_cls.__required_keys__  # type: ignore[attr-defined]
    field_definitions = {
        field_name: (field_type, Required if field_name in required_keys else None)
        for field_name, field_type in typeddict_cls.__annotations__.items()
    }

    return create_model(typeddict_cls.__name__, **kwargs, **field_definitions)


def create_model_from_namedtuple(namedtuple_cls: Type['NamedTuple'], **kwargs: Any) -> Type['BaseModel']:
    """
    Create a `BaseModel` based on the fields of a named tuple.
    A named tuple can be created with `typing.NamedTuple` and declared annotations
    but also with `collections.namedtuple`, in this case we consider all fields
    to have type `Any`.
    """
    # With python 3.10+, `__annotations__` always exists but can be empty hence the `getattr... or...` logic
    namedtuple_annotations: Dict[str, Type[Any]] = getattr(namedtuple_cls, '__annotations__', None) or {
        k: Any for k in namedtuple_cls._fields
    }
    field_definitions: Dict[str, Any] = {
        field_name: (field_type, Required) for field_name, field_type in namedtuple_annotations.items()
    }
    return create_model(namedtuple_cls.__name__, **kwargs, **field_definitions)
