# `typing` --- 型ヒントのサポート

Added in version 3.5.

ソースコード: Lib/typing.py

注釈

Python ランタイムは、関数や変数の型アノテーションを強制しません。型アノテーションは、 type checkers や IDE、linterなどのサードパーティーツールで使われます。

このモジュールは型ヒントの実行時サポートを提供します。

以下の関数を例に考えてみます:

    def surface_area_of_cube(edge_length: float) -> str:
        return f"The surface area of the cube is {6 * edge_length ** 2}."

`surface_area_of_cube` 関数は、`edge_length: float` という type hint で示されているように、引数が `float` のインスタンスであるという前提です。 そして、`-> str` というヒントにある通り、この関数は `str` のインスタンスを返すことになっています。

型ヒントは `float` や `str` のような単純な型を指定することもできますし、もっと複雑な型も指定できます。 `typing` モジュールには、より高度な型ヒントを表現するためのいろいろな型が含まれています。

`typing` モジュールには新しい機能が頻繁に追加されます。 typing_extensions パッケージを使うことで、古い Python バージョンからもその新機能を使うことができます。

参考

Typing cheat sheet

  型ヒントの簡単な概要 (mypy ドキュメンテーション)

Type System Reference section of the mypy docs

  Python の型システムの規格は PEP によって定められているので、このレファレンスはほとんどの Python 型チェッカーに適用できるはずです。(mypy のみに適用される部分もあるかもしれません。)

Static Typing with Python

  コミュニティーによって書かれた、型システムの機能や便利な型関連のツール、型に関するベストプラクティスを詳しく説明している、特定の型チェッカーに依らないドキュメンテーション。

## Python 型システムの仕様

最新の正式な Python 型システムの使用は Specification for the Python type system を参照してください。

## 型エイリアス

型エイリアスは `type` 文を用いて定義され、これにより `TypeAliasType` インスタンスが生成されます。 以下の例では、 `Vector` と `list[float]` は静的型チェッカーによって同じ型とみなされます:

    type Vector = list[float]

    def scale(scalar: float, vector: Vector) -> Vector:
        return [scalar * num for num in vector]

    # floatのlistはVectorとして要件を満たすため、型チェックを通過する。
    new_vector = scale(2.0, [1.0, -4.2, 5.4])

型エイリアスは複雑な型シグネチャを単純化するのに有用です。例えば:

    from collections.abc import Sequence

    type ConnectionOptions = dict[str, str]
    type Address = tuple[str, int]
    type Server = tuple[Address, ConnectionOptions]

    def broadcast_message(message: str, servers: Sequence[Server]) -> None:
        ...

    # 静的型チェッカーでは、上記の型アノテーションと
    # 以下のアノテーションを同等のものとして扱う。
    def broadcast_message(
        message: str,
        servers: Sequence[tuple[tuple[str, int], dict[str, str]]]
    ) -> None:
        ...

`type` 文はPython3.12で新しく導入されました。後方互換性のために、単に代入によって型エイリアスを作成することもできます：

    Vector = list[float]

あるいは、 `TypeAlias` でマークすることで、これが通常の変数代入ではなく、型エイリアスであることを明示できます

    from typing import TypeAlias

    Vector: TypeAlias = list[float]

## NewType

異なる型を作るためには `NewType` ヘルパークラスを使います:

    from typing import NewType

    UserId = NewType('UserId', int)
    some_id = UserId(524313)

静的型チェッカーは新しい型を元々の型のサブクラスのように扱います。この振る舞いは論理的な誤りを見つける手助けとして役に立ちます。

    def get_user_name(user_id: UserId) -> str:
        ...

    # 型チェックを通過する
    user_a = get_user_name(UserId(42351))

    # 型チェックに失敗する。int型はUserId型ではないため。
    user_b = get_user_name(-1)

`UserId` 型の変数も `int` の全ての演算が行えますが、その結果は常に `int` 型になります。 この振る舞いにより、 `int` が期待されるところに `UserId` を渡せますが、不正な方法で `UserId` を作ってしまうことを防ぎます。

    # 'output' は'int'型となる。'UserId'型にはならない。
    output = UserId(23413) + UserId(54341)

これらのチェックは静的型チェッカーのみによって強制されるということに注意してください。 実行時に `Derived = NewType('Derived', Base)` という文は渡された仮引数をただちに返す `Derived` callable を作ります。 つまり `Derived(some_value)` という式は新しいクラスを作ることはなく、通常の関数呼び出しより多くのオーバーヘッドがないということを意味します。

より正確に言うと、式 `some_value is Derived(some_value)` は実行時に常に真を返します。

`Derived` のサブタイプを作成することはできません

    from typing import NewType

    UserId = NewType('UserId', int)

    # 実行時にエラーとなり、型チェックも通らない
    class AdminUserId(UserId): pass

しかし、 'derived' である `NewType` をもとにした `NewType` は作ることが出来ます:

    from typing import NewType

    UserId = NewType('UserId', int)

    ProUserId = NewType('ProUserId', UserId)

そして `ProUserId` に対する型検査は期待通りに動作します。

より詳しくは PEP 484 を参照してください。

注釈

先述の通り、型エイリアスは、2つの型が互いに等価であることを宣言します。 `type Alias = Original` とすると、静的型チェッカーは `Alias` を `Original` と"完全に等価な"ものとして扱います。これは、複雑な型シグネチャを単純化したい場合に便利です。

これに対し、 `NewType` はある型をもう一方の型の サブタイプ として宣言します。 `Derived = NewType('Derived', Original)` とすると静的型チェッカーは `Derived` を `Original` の サブクラス として扱います。つまり `Original` 型の値は `Derived` 型の値が期待される場所で使うことが出来ないということです。これにより、実行パフォーマンスをほとんど犠牲にせずに論理エラーを防ぐことができます。

Added in version 3.5.2.

バージョン 3.10 で変更: `NewType` は関数ではなくクラスになりました。その結果、通常の関数よりも `NewType` を呼び出す際に多少の実行時コストが追加されます。

バージョン 3.11 で変更: `NewType` 呼び出し時のパフォーマンスが Python 3.9 のレベルに戻りました

## 呼び出し可能オブジェクトのアノテーション

関数、または他の callable なオブジェクトは、 `collections.abc.Callable` または `typing.Callable` を使ってアノテーションすることができます。 `Callable[[int], str]` は、 `int` 型のパラメータを1つ受け取り、 `str` を返す関数を意味します。

例えば:

    from collections.abc import Callable, Awaitable

    def feeder(get_next_item: Callable[[], str]) -> None:
        ...  # Body

    def async_query(on_success: Callable[[int], None],
                    on_error: Callable[[int, Exception], None]) -> None:
        ...  # Body

    async def on_update(value: str) -> None:
        ...  # Body

    callback: Callable[[str], Awaitable[None]] = on_update

添字表記には必ず２つの値、すなわち引数のリストと返り値の型を指定する必要があります。 引数のリストは型のリスト、`ParamSpec`、`Concatenate`、ellipsis (`...`) のいずれかでなければなりません。返り値の型は単一の型でなければなりません。

もしellipsisリテラル `...` が引数リストとして与えられた場合、それは任意のパラメータリストを持つ呼び出し可能オブジェクトを受け入れることを示します。

    def concat(x: str, y: str) -> str:
        return x + y

    x: Callable[..., str]
    x = str     # OK
    x = concat  # これもOK

`Callable` は可変引数、オーバーロード関数、キーワード専用引数などを含む複雑な型シグネチャを表現することはできません。 しかし、`Protocol` クラスとその `__call__()` メソッドを定義すれば表現することができます:

    from collections.abc import Iterable
    from typing import Protocol

    class Combiner(Protocol):
        def __call__(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ...

    def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes:
        for item in data:
            ...

    def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]:
        ...
    def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]:
        ...

    batch_proc([], good_cb)  # OK
    batch_proc([], bad_cb)   # エラー! 第二実引数の名前と(引数としての)種類が
                             # 一致しません

callable が別の callable を引数に取る場合は、`ParamSpec` を使えば両者のパラメータ引数の依存関係を表現することができます。 さらに、ある callable が別の callable から引数を追加したり削除したりする場合は、 `Concatenate` 演算子を使うことで表現できます。 その場合、callable の型は `Callable[ParamSpecVariable, ReturnType]` と `Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]` という形になります。

バージョン 3.10 で変更: `Callable` は `ParamSpec` と `Concatenate` をサポートしました。詳細は PEP 612 を参照してください。

参考

`ParamSpec` と `Concatenate` のドキュメントに、`Callable` での使用例が記載されています。

## ジェネリクス

コンテナに含まれるオブジェクトに関する型情報は、一般的な方法で静的に推論することができないため、標準ライブラリの多くのコンテナクラスは、要素に期待する型を示す添字表記をサポートしています

    from collections.abc import Mapping, Sequence

    class Employee: ...

    # Sequence[Employee] はシーケンスの要素がすべて "Employee" の
    # インスタンスでなければならないということを表します。
    # Mapping[str, str] は mapping に含まれるキーとバリューが
    # すべて文字列である必要があることを表します。
    def notify_by_email(employees: Sequence[Employee],
                        overrides: Mapping[str, str]) -> None: ...

ジェネリック関数およびクラスは以下の通り、 型引数構文 を使うことでパラメータ化できます:

    from collections.abc import Sequence

    def first[T](l: Sequence[T]) -> T:  # この関数は型変数 "T" に関してジェネリックです
        return l[0]

もしくは、 `TypeVar` のコンストラクタを使うこともできます:

    from collections.abc import Sequence
    from typing import TypeVar

    U = TypeVar('U')                  # 型引数 "U" を宣言

    def second(l: Sequence[U]) -> U:  # 型引数 "U" に関してジェネリックな関数
        return l[1]

バージョン 3.12 で変更: ジェネリックが構文としてサポートされたのは Python 3.12 が初めてです。

## タプルのアノテーション

Python のコンテナのほとんどは、すべての要素が同じ型であると型システムが想定するようになっています。例えば:

    from collections.abc import Mapping

    # ``x`` の要素がすべて int であると型チェッカーが推定する
    x: list[int] = []

    # 型チェッカーエラー： ``list`` は一つの引数しか受け付けない
    y: list[int, str] = [1, 'foo']

    # ``z`` のキーはすべて文字列であり、バリューは文字列か int のどちらか
    # であると型チェッカーが推定する
    z: Mapping[str, str | int] = {}

`list` は引数に1つの型のみ受け入れるので、型チェッカーは上記の `y` への代入でエラーを出力します。同様に、`Mapping` は引数に2つの型のみ受け入れます。1つ目はキーの型を示し、2つ目は値の型を示します。

しかし、他の多くのPythonのコンテナとは異なり、タプルがすべて同じ型ではない要素を持つことは、慣用的なPythonコードでは一般的です。このため、タプルはPythonの型システムの中でも特殊です。`tuple` は任意の数の引数を受け入れます

    # OK: ``x`` に代入されるのは長さ1のタプルで、要素は int
    x: tuple[int] = (5,)

    # OK: ``y`` に代入されるのは長さ2のタプルで、
    # 第１要素は int、第２要素は文字列
    y: tuple[int, str] = (5, "foo")

    # エラー: 型アノテーションでは長さ1のタプルとなっているが、
    # ``z`` には長さ3のタプルが代入されている
    z: tuple[int] = (1, 2, 3)

タプルの長さが可変で、すべての要素が同じ型 `T` であるということを表すには、リテラルの ellipsis `...` を使って `tuple[T, ...]` と表現します。空のタプルを表すには `tuple[()]` を使います。型アノテーションとして `tuple` だけを指定すると `tuple[Any, ...]` と同じ意味になります:

    x: tuple[int, ...] = (1, 2)
    # 以下の再代入は OK: ``tuple[int, ...]`` は x の長さが任意であることを表す
    x = (1, 2, 3)
    x = ()
    # 以下の再代入はエラー: ``x`` のすべての要素が int である必要がある
    x = ("foo", "bar")

    # ``y`` に代入できるのは空のタプルのみ
    y: tuple[()] = ()

    z: tuple = ("foo", "bar")
    # 以下の再代入は OK: ``tuple`` は ``tuple[Any, ...]`` と等価
    z = (1, 2, 3)
    z = ()

## クラスオブジェクトの型

A variable annotated with `C` may accept a value of type `C`. In contrast, a variable annotated with `type[C]` (or deprecated `typing.Type[C]`) may accept values that are classes themselves -- specifically, it will accept the class object of `C`. For example:

    a = 3         # Has type ``int``
    b = int       # Has type ``type[int]``
    c = type(a)   # Also has type ``type[int]``

Note that `type[C]` is covariant:

    class User: ...
    class ProUser(User): ...
    class TeamUser(User): ...

    def make_new_user(user_class: type[User]) -> User:
        # ...
        return user_class()

    make_new_user(User)      # OK
    make_new_user(ProUser)   # Also OK: ``type[ProUser]`` is a subtype of ``type[User]``
    make_new_user(TeamUser)  # Still fine
    make_new_user(User())    # Error: expected ``type[User]`` but got ``User``
    make_new_user(int)       # Error: ``type[int]`` is not a subtype of ``type[User]``

The only legal parameters for `type` are classes, `Any`, type variables, and unions of any of these types. For example:

    def new_non_team_user(user_class: type[BasicUser | ProUser]): ...

    new_non_team_user(BasicUser)  # OK
    new_non_team_user(ProUser)    # OK
    new_non_team_user(TeamUser)   # Error: ``type[TeamUser]`` is not a subtype
                                  # of ``type[BasicUser | ProUser]``
    new_non_team_user(User)       # Also an error

`type[Any]` is equivalent to `type`, which is the root of Python's metaclass hierarchy.

## Annotating generators and coroutines

A generator can be annotated using the generic type `Generator[YieldType, SendType, ReturnType]`. For example:

    def echo_round() -> Generator[int, float, str]:
        sent = yield 0
        while sent >= 0:
            sent = yield round(sent)
        return 'Done'

Note that unlike many other generic classes in the standard library, the `SendType` of `Generator` behaves contravariantly, not covariantly or invariantly.

The `SendType` and `ReturnType` parameters default to `None`:

    def infinite_stream(start: int) -> Generator[int]:
        while True:
            yield start
            start += 1

It is also possible to set these types explicitly:

    def infinite_stream(start: int) -> Generator[int, None, None]:
        while True:
            yield start
            start += 1

Simple generators that only ever yield values can also be annotated as having a return type of either `Iterable[YieldType]` or `Iterator[YieldType]`:

    def infinite_stream(start: int) -> Iterator[int]:
        while True:
            yield start
            start += 1

Async generators are handled in a similar fashion, but don't expect a `ReturnType` type argument (`AsyncGenerator[YieldType, SendType]`). The `SendType` argument defaults to `None`, so the following definitions are equivalent:

    async def infinite_stream(start: int) -> AsyncGenerator[int]:
        while True:
            yield start
            start = await increment(start)

    async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
        while True:
            yield start
            start = await increment(start)

As in the synchronous case, `AsyncIterable[YieldType]` and `AsyncIterator[YieldType]` are available as well:

    async def infinite_stream(start: int) -> AsyncIterator[int]:
        while True:
            yield start
            start = await increment(start)

Coroutines can be annotated using `Coroutine[YieldType, SendType, ReturnType]`. Generic arguments correspond to those of `Generator`, for example:

    from collections.abc import Coroutine
    c: Coroutine[list[str], str, int]  # Some coroutine defined elsewhere
    x = c.send('hi')                   # Inferred type of 'x' is list[str]
    async def bar() -> None:
        y = await c                    # Inferred type of 'y' is int

## ユーザー定義のジェネリック型

ユーザー定義のクラスを、ジェネリッククラスとして定義できます。

    from logging import Logger

    class LoggedVar[T]:
        def __init__(self, value: T, name: str, logger: Logger) -> None:
            self.name = name
            self.logger = logger
            self.value = value

        def set(self, new: T) -> None:
            self.log('Set ' + repr(self.value))
            self.value = new

        def get(self) -> T:
            self.log('Get ' + repr(self.value))
            return self.value

        def log(self, message: str) -> None:
            self.logger.info('%s: %s', self.name, message)

This syntax indicates that the class `LoggedVar` is parameterised around a single type variable `T` . This also makes `T` valid as a type within the class body.

Generic classes implicitly inherit from `Generic`. For compatibility with Python 3.11 and lower, it is also possible to inherit explicitly from `Generic` to indicate a generic class:

    from typing import TypeVar, Generic

    T = TypeVar('T')

    class LoggedVar(Generic[T]):
        ...

Generic classes have `__class_getitem__()` methods, meaning they can be parameterised at runtime (e.g. `LoggedVar[int]` below):

    from collections.abc import Iterable

    def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
        for var in vars:
            var.set(0)

A generic type can have any number of type variables. All varieties of `TypeVar` are permissible as parameters for a generic type:

    from typing import TypeVar, Generic, Sequence

    class WeirdTrio[T, B: Sequence[bytes], S: (int, str)]:
        ...

    OldT = TypeVar('OldT', contravariant=True)
    OldB = TypeVar('OldB', bound=Sequence[bytes], covariant=True)
    OldS = TypeVar('OldS', int, str)

    class OldWeirdTrio(Generic[OldT, OldB, OldS]):
        ...

`Generic` の引数のそれぞれの型変数は別のものでなければなりません。このため次のクラス定義は無効です:

    from typing import TypeVar, Generic
    ...

    class Pair[M, M]:  # SyntaxError
        ...

    T = TypeVar('T')

    class Pair(Generic[T, T]):   # INVALID
        ...

Generic classes can also inherit from other classes:

    from collections.abc import Sized

    class LinkedList[T](Sized):
        ...

When inheriting from generic classes, some type parameters could be fixed:

    from collections.abc import Mapping

    class MyDict[T](Mapping[str, T]):
        ...

この場合では `MyDict` は仮引数 `T` を 1 つとります。

Using a generic class without specifying type parameters assumes `Any` for each position. In the following example, `MyIterable` is not generic but implicitly inherits from `Iterable[Any]`:

    from collections.abc import Iterable

    class MyIterable(Iterable): # Same as Iterable[Any]
        ...

User-defined generic type aliases are also supported. Examples:

    from collections.abc import Iterable

    type Response[S] = Iterable[S] | int

    # Return type here is same as Iterable[str] | int
    def response(query: str) -> Response[str]:
        ...

    type Vec[T] = Iterable[tuple[T, T]]

    def inproduct[T: (int, float, complex)](v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
        return sum(x*y for x, y in v)

For backward compatibility, generic type aliases can also be created through a simple assignment:

    from collections.abc import Iterable
    from typing import TypeVar

    S = TypeVar("S")
    Response = Iterable[S] | int

バージョン 3.7 で変更: `Generic` にあった独自のメタクラスは無くなりました。

バージョン 3.12 で変更: Syntactic support for generics and type aliases is new in version 3.12. Previously, generic classes had to explicitly inherit from `Generic` or contain a type variable in one of their bases.

User-defined generics for parameter expressions are also supported via parameter specification variables in the form `[**P]`. The behavior is consistent with type variables' described above as parameter specification variables are treated by the `typing` module as a specialized type variable. The one exception to this is that a list of types can be used to substitute a `ParamSpec`:

    >>> class Z[T, **P]: ...  # T is a TypeVar; P is a ParamSpec
    ...
    >>> Z[int, [dict, float]]
    __main__.Z[int, [dict, float]]

Classes generic over a `ParamSpec` can also be created using explicit inheritance from `Generic`. In this case, `**` is not used:

    from typing import ParamSpec, Generic

    P = ParamSpec('P')

    class Z(Generic[P]):
        ...

Another difference between `TypeVar` and `ParamSpec` is that a generic with only one parameter specification variable will accept parameter lists in the forms `X[[Type1, Type2, ...]]` and also `X[Type1, Type2, ...]` for aesthetic reasons. Internally, the latter is converted to the former, so the following are equivalent:

    >>> class X[**P]: ...
    ...
    >>> X[int, str]
    __main__.X[[int, str]]
    >>> X[[int, str]]
    __main__.X[[int, str]]

Note that generics with `ParamSpec` may not have correct `__parameters__` after substitution in some cases because they are intended primarily for static type checking.

バージョン 3.10 で変更: `Generic` can now be parameterized over parameter expressions. See `ParamSpec` and PEP 612 for more details.

A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the `typing` module are hashable and comparable for equality.

## `Any` 型

A special kind of type is `Any`. A static type checker will treat every type as assignable to `Any` and `Any` as assignable to every type.

これは、 `Any` 型の値では、任意の演算やメソッドの呼び出しが行えることを意味します:

    from typing import Any

    a: Any = None
    a = []          # OK
    a = 2           # OK

    s: str = ''
    s = a           # OK

    def foo(item: Any) -> int:
        # Passes type checking; 'item' could be any type,
        # and that type might have a 'bar' method
        item.bar()
        ...

`Any` 型の値をより詳細な型に代入する時に型検査が行われないことに注意してください。例えば、静的型検査器は `a` を `s` に代入する時、`s` が `str` 型として宣言されていて実行時に `int` の値を受け取るとしても、エラーを報告しません。

さらに、返り値や引数の型のないすべての関数は暗黙的に `Any` を使用します。

    def legacy_parser(text):
        ...
        return data

    # A static type checker will treat the above
    # as having the same signature as:
    def legacy_parser(text: Any) -> Any:
        ...
        return data

この挙動により、動的型付けと静的型付けが混在したコードを書かなければならない時に `Any` を 非常口 として使用することができます。

`Any` の挙動と `object` の挙動を対比しましょう。 `Any` と同様に、すべての型は `object` のサブタイプです。しかしながら、 `Any` と異なり、逆は成り立ちません: `object` はすべての他の型のサブタイプでは ありません。

これは、ある値の型が `object` のとき、型検査器はこれについてのほとんどすべての操作を拒否し、これをより特殊化された変数に代入する (または返り値として利用する) ことは型エラーになることを意味します。例えば:

    def hash_a(item: object) -> int:
        # Fails type checking; an object does not have a 'magic' method.
        item.magic()
        ...

    def hash_b(item: Any) -> int:
        # Passes type checking
        item.magic()
        ...

    # Passes type checking, since ints and strs are subclasses of object
    hash_a(42)
    hash_a("foo")

    # Passes type checking, since Any is assignable to all types
    hash_b(42)
    hash_b("foo")

`object` は、ある値が型安全な方法で任意の型として使えることを示すために使用します。 `Any` はある値が動的に型付けられることを示すために使用します。

## 名前的部分型 vs 構造的部分型

初めは PEP 484 は Python の静的型システムを 名前的部分型 を使って定義していました。 名前的部分型とは、クラス `B` が期待されているところにクラス `A` が許容されるのは `A` が `B` のサブクラスの場合かつその場合に限る、ということです。

前出の必要条件は、`Iterable` などの抽象基底クラスにも当て嵌まります。 この型付け手法の問題は、この手法をサポートするためにクラスに明確な型付けを行う必要があることで、これは pythonic ではなく、普段行っている 慣用的な Python コードへの動的型付けとは似ていません。 例えば、次のコードは PEP 484 に従ったものです

    from collections.abc import Sized, Iterable, Iterator

    class Bucket(Sized, Iterable[int]):
        ...
        def __len__(self) -> int: ...
        def __iter__(self) -> Iterator[int]: ...

PEP 544 solves this problem by allowing users to write the above code without explicit base classes in the class definition, allowing `Bucket` to be implicitly considered a subtype of both `Sized` and `Iterable[int]` by static type checkers. This is known as structural subtyping (or static duck-typing):

    from collections.abc import Iterator, Iterable

    class Bucket:  # Note: no base classes
        ...
        def __len__(self) -> int: ...
        def __iter__(self) -> Iterator[int]: ...

    def collect(items: Iterable[int]) -> int: ...
    result = collect(Bucket())  # Passes type check

さらに、特別なクラス `Protocol` のサブクラスを作ることで、新しい独自のプロトコルを作って構造的部分型というものを満喫できます。

## モジュールの内容

The `typing` module defines the following classes, functions and decorators.

### 特殊型付けプリミティブ

#### 特殊型

These can be used as types in annotations. They do not support subscription using `[]`.

typing.Any

  制約のない型であることを示す特別な型です。

  - Every type is assignable to `Any`.

  - `Any` is assignable to every type.

  バージョン 3.11 で変更: `Any` can now be used as a base class. This can be useful for avoiding type checker errors with classes that can duck type anywhere or are highly dynamic.

typing.AnyStr

  A constrained type variable.

  Definition:

      AnyStr = TypeVar('AnyStr', str, bytes)

  `AnyStr` is meant to be used for functions that may accept `str` or `bytes` arguments but cannot allow the two to mix.

  例えば:

      def concat(a: AnyStr, b: AnyStr) -> AnyStr:
          return a + b

      concat("foo", "bar")    # OK, output has type 'str'
      concat(b"foo", b"bar")  # OK, output has type 'bytes'
      concat("foo", b"bar")   # Error, cannot mix str and bytes

  Note that, despite its name, `AnyStr` has nothing to do with the `Any` type, nor does it mean "any string". In particular, `AnyStr` and `str | bytes` are different from each other and have different use cases:

      # Invalid use of AnyStr:
      # The type variable is used only once in the function signature,
      # so cannot be "solved" by the type checker
      def greet_bad(cond: bool) -> AnyStr:
          return "hi there!" if cond else b"greetings!"

      # The better way of annotating this function:
      def greet_proper(cond: bool) -> str | bytes:
          return "hi there!" if cond else b"greetings!"

  バージョン 3.13 で非推奨、バージョン 3.18 で削除予定: Deprecated in favor of the new type parameter syntax. Use `class A[T: (str, bytes)]: ...` instead of importing `AnyStr`. See PEP 695 for more details.

  In Python 3.16, `AnyStr` will be removed from `typing.__all__`, and deprecation warnings will be emitted at runtime when it is accessed or imported from `typing`. `AnyStr` will be removed from `typing` in Python 3.18.

typing.LiteralString

  Special type that includes only literal strings.

  Any string literal is compatible with `LiteralString`, as is another `LiteralString`. However, an object typed as just `str` is not. A string created by composing `LiteralString`-typed objects is also acceptable as a `LiteralString`.

  例:

      def run_query(sql: LiteralString) -> None:
          ...

      def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
          run_query("SELECT * FROM students")  # OK
          run_query(literal_string)  # OK
          run_query("SELECT * FROM " + literal_string)  # OK
          run_query(arbitrary_string)  # type checker error
          run_query(  # type checker error
              f"SELECT * FROM students WHERE name = {arbitrary_string}"
          )

  `LiteralString` is useful for sensitive APIs where arbitrary user-generated strings could generate problems. For example, the two cases above that generate type checker errors could be vulnerable to an SQL injection attack.

  より詳しくは PEP 675 を参照してください。

  Added in version 3.11.

typing.Never
typing.NoReturn

  `Never` and `NoReturn` represent the bottom type, a type that has no members.

  They can be used to indicate that a function never returns, such as `sys.exit()`:

      from typing import Never  # or NoReturn

      def stop() -> Never:
          raise RuntimeError('no way')

  Or to define a function that should never be called, as there are no valid arguments, such as `assert_never()`:

      from typing import Never  # or NoReturn

      def never_call_me(arg: Never) -> None:
          pass

      def int_or_str(arg: int | str) -> None:
          never_call_me(arg)  # type checker error
          match arg:
              case int():
                  print("It's an int")
              case str():
                  print("It's a str")
              case _:
                  never_call_me(arg)  # OK, arg is of type Never (or NoReturn)

  `Never` and `NoReturn` have the same meaning in the type system and static type checkers treat both equivalently.

  Added in version 3.6.2: Added `NoReturn`.

  Added in version 3.11: Added `Never`.

typing.Self

  Special type to represent the current enclosed class.

  例えば:

      from typing import Self, reveal_type

      class Foo:
          def return_self(self) -> Self:
              ...
              return self

      class SubclassOfFoo(Foo): pass

      reveal_type(Foo().return_self())  # Revealed type is "Foo"
      reveal_type(SubclassOfFoo().return_self())  # Revealed type is "SubclassOfFoo"

  This annotation is semantically equivalent to the following, albeit in a more succinct fashion:

      from typing import TypeVar

      Self = TypeVar("Self", bound="Foo")

      class Foo:
          def return_self(self: Self) -> Self:
              ...
              return self

  In general, if something returns `self`, as in the above examples, you should use `Self` as the return annotation. If `Foo.return_self` was annotated as returning `"Foo"`, then the type checker would infer the object returned from `SubclassOfFoo.return_self` as being of type `Foo` rather than `SubclassOfFoo`.

  Other common use cases include:

  - `classmethod`s that are used as alternative constructors and return instances of the `cls` parameter.

  - Annotating an `__enter__()` method which returns self.

  You should not use `Self` as the return annotation if the method is not guaranteed to return an instance of a subclass when the class is subclassed:

      class Eggs:
          # Self would be an incorrect return annotation here,
          # as the object returned is always an instance of Eggs,
          # even in subclasses
          def returns_eggs(self) -> "Eggs":
              return Eggs()

  より詳しくは PEP 673 を参照してください。

  Added in version 3.11.

typing.TypeAlias

  Special annotation for explicitly declaring a type alias.

  例えば:

      from typing import TypeAlias

      Factors: TypeAlias = list[int]

  `TypeAlias` is particularly useful on older Python versions for annotating aliases that make use of forward references, as it can be hard for type checkers to distinguish these from normal variable assignments:

      from typing import Generic, TypeAlias, TypeVar

      T = TypeVar("T")

      # "Box" does not exist yet,
      # so we have to use quotes for the forward reference on Python <3.12.
      # Using ``TypeAlias`` tells the type checker that this is a type alias declaration,
      # not a variable assignment to a string.
      BoxOfStrings: TypeAlias = "Box[str]"

      class Box(Generic[T]):
          @classmethod
          def make_box_of_strings(cls) -> BoxOfStrings: ...

  より詳しくは、 PEP 613 をご覧ください。

  Added in version 3.10.

  バージョン 3.12 で非推奨: `TypeAlias` is deprecated in favor of the `type` statement, which creates instances of `TypeAliasType` and which natively supports forward references. Note that while `TypeAlias` and `TypeAliasType` serve similar purposes and have similar names, they are distinct and the latter is not the type of the former. Removal of `TypeAlias` is not currently planned, but users are encouraged to migrate to `type` statements.

#### 特殊形式

これらはアノテーションの型として使用できます。これらは全て `[]` を使用した添字表記をサポートしますが、それぞれ固有の構文があります。

class typing.Union

  ユニオン型; `Union[X, Y]` は `X | Y` と等価で X または Y を表します。

  To define a union, use e.g. `Union[int, str]` or the shorthand `int | str`. Using that shorthand is recommended. Details:

  - 引数は型でなければならず、少なくとも一つ必要です。

  - ユニオン型のユニオン型は平滑化されます。例えば:

        Union[Union[int, str], float] == Union[int, str, float]

    However, this does not apply to unions referenced through a type alias, to avoid forcing evaluation of the underlying `TypeAliasType`:

        type A = Union[int, str]
        Union[A, float] != Union[int, str, float]

  - 引数が一つのユニオン型は消えます。例えば:

        Union[int] == int  # The constructor actually returns int

  - 冗長な実引数は飛ばされます。例えば:

        Union[int, str, int] == Union[int, str] == int | str

  - ユニオン型を比較するとき引数の順序は無視されます。例えば:

        Union[int, str] == Union[str, int]

  - `Union` のサブクラスを作成したり、インスタンスを作成することは出来ません。

  - `Union[X][Y]` と書くことは出来ません。

  バージョン 3.7 で変更: 明示的に書かれているサブクラスを、実行時に直和型から取り除かなくなりました。

  バージョン 3.10 で変更: ユニオン型は `X | Y` のように書けるようになりました。 union型の表現 を参照ください。

  バージョン 3.14 で変更: `types.UnionType` is now an alias for `Union`, and both `Union[int, str]` and `int | str` create instances of the same class. To check whether an object is a `Union` at runtime, use `isinstance(obj, Union)`. For compatibility with earlier versions of Python, use `get_origin(obj) is typing.Union or get_origin(obj) is types.UnionType`.

typing.Optional

  `Optional[X]` は `X | None` (や `Union[X, None]`) と同等です。

  これがデフォルト値を持つオプション引数とは同じ概念ではないということに注意してください。 デフォルト値を持つオプション引数はオプション引数であるために、型アノテーションに `Optional` 修飾子は必要ありません。 例えば次のようになります:

      def foo(arg: int = 0) -> None:
          ...

  それとは逆に、 `None` という値が許されていることが明示されている場合は、引数がオプションであろうとなかろうと、 `Optional` を使うのが好ましいです。 例えば次のようになります:

      def foo(arg: Optional[int] = None) -> None:
          ...

  バージョン 3.10 で変更: Optionalは `X | None` のように書けるようになりました。 ref:union型の表現 <types-union> を参照ください。

typing.Concatenate

  Special form for annotating higher-order functions.

  `Concatenate` can be used in conjunction with Callable and `ParamSpec` to annotate a higher-order callable which adds, removes, or transforms parameters of another callable. Usage is in the form `Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]`. `Concatenate` is valid when used in Callable type hints and when instantiating user-defined generic classes with `ParamSpec` parameters. The last parameter to `Concatenate` must be a `ParamSpec` or ellipsis (`...`).

  For example, to annotate a decorator `with_lock` which provides a `threading.Lock` to the decorated function, `Concatenate` can be used to indicate that `with_lock` expects a callable which takes in a `Lock` as the first argument, and returns a callable with a different type signature. In this case, the `ParamSpec` indicates that the returned callable's parameter types are dependent on the parameter types of the callable being passed in:

      from collections.abc import Callable
      from threading import Lock
      from typing import Concatenate

      # Use this lock to ensure that only one thread is executing a function
      # at any time.
      my_lock = Lock()

      def with_lock[**P, R](f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
          '''A type-safe decorator which provides a lock.'''
          def inner(*args: P.args, **kwargs: P.kwargs) -> R:
              # Provide the lock as the first argument.
              return f(my_lock, *args, **kwargs)
          return inner

      @with_lock
      def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
          '''Add a list of numbers together in a thread-safe manner.'''
          with lock:
              return sum(numbers)

      # We don't need to pass in the lock ourselves thanks to the decorator.
      sum_threadsafe([1.1, 2.2, 3.3])

  Added in version 3.10.

  参考

  - PEP 612 -- Parameter Specification Variables (the PEP which introduced `ParamSpec` and `Concatenate`)

  - `ParamSpec`

  - 呼び出し可能オブジェクトのアノテーション

typing.Literal

  Special typing form to define "literal types".

  `Literal` can be used to indicate to type checkers that the annotated object has a value equivalent to one of the provided literals.

  例えば:

      def validate_simple(data: Any) -> Literal[True]:  # always returns True
          ...

      type Mode = Literal['r', 'rb', 'w', 'wb']
      def open_helper(file: str, mode: Mode) -> str:
          ...

      open_helper('/some/path', 'r')      # Passes type check
      open_helper('/other/path', 'typo')  # Error in type checker

  `Literal[...]` はサブクラスにはできません。 実行時に、任意の値が `Literal[...]` の型引数として使えますが、型チェッカーが制約を課すことがあります。 リテラル型についてより詳しいことは PEP 586 を参照してください。

  Additional details:

  - The arguments must be literal values and there must be at least one.

  - Nested `Literal` types are flattened, e.g.:

        assert Literal[Literal[1, 2], 3] == Literal[1, 2, 3]

    However, this does not apply to `Literal` types referenced through a type alias, to avoid forcing evaluation of the underlying `TypeAliasType`:

        type A = Literal[1, 2]
        assert Literal[A, 3] != Literal[1, 2, 3]

  - 冗長な実引数は飛ばされます。例えば:

        assert Literal[1, 2, 1] == Literal[1, 2]

  - When comparing literals, the argument order is ignored, e.g.:

        assert Literal[1, 2] == Literal[2, 1]

  - You cannot subclass or instantiate a `Literal`.

  - You cannot write `Literal[X][Y]`.

  Added in version 3.8.

  バージョン 3.9.1 で変更: `Literal` ではパラメータの重複を解消するようになりました。`Literal` オブジェクトの等値比較は順序に依存しないようになりました。`Literal` オブジェクトは、等値比較する際に、パラメータのうち 1 つでも hashable でない場合は `TypeError` を送出するようになりました。

typing.ClassVar

  クラス変数であることを示す特別な型構築子です。

  PEP 526 で導入された通り、 ClassVar でラップされた変数アノテーションによって、ある属性はクラス変数として使うつもりであり、そのクラスのインスタンスから設定すべきではないということを示せます。使い方は次のようになります:

      class Starship:
          stats: ClassVar[dict[str, int]] = {} # class variable
          damage: int = 10                     # instance variable

  `ClassVar` は型のみを受け入れ、それ以外は受け付けられません。

  `ClassVar` is not a class itself, and cannot be used with `isinstance()` or `issubclass()`. `ClassVar` does not change Python runtime behavior, but it can be used by static type checkers. For example, a type checker might flag the following code as an error:

      enterprise_d = Starship(3000)
      enterprise_d.stats = {} # Error, setting class variable on instance
      Starship.stats = {}     # This is OK

  Added in version 3.5.3.

  バージョン 3.13 で変更: `ClassVar` can now be nested in `Final` and vice versa.

typing.Final

  Special typing construct to indicate final names to type checkers.

  Final names cannot be reassigned in any scope. Final names declared in class scopes cannot be overridden in subclasses.

  例えば:

      MAX_SIZE: Final = 9000
      MAX_SIZE += 1  # Error reported by type checker

      class Connection:
          TIMEOUT: Final[int] = 10

      class FastConnector(Connection):
          TIMEOUT = 1  # Error reported by type checker

  この機能は実行時には検査されません。詳細については PEP 591 を参照してください。

  Added in version 3.8.

  バージョン 3.13 で変更: `Final` can now be nested in `ClassVar` and vice versa.

typing.Required

  Special typing construct to mark a `TypedDict` key as required.

  This is mainly useful for `total=False` TypedDicts. See `TypedDict` and PEP 655 for more details.

  Added in version 3.11.

typing.NotRequired

  Special typing construct to mark a `TypedDict` key as potentially missing.

  より詳しくは、 `TypedDict` と PEP 655 を参照してください。

  Added in version 3.11.

typing.ReadOnly

  A special typing construct to mark an item of a `TypedDict` as read-only.

  例えば:

      class Movie(TypedDict):
         title: ReadOnly[str]
         year: int

      def mutate_movie(m: Movie) -> None:
         m["year"] = 1999  # allowed
         m["title"] = "The Matrix"  # type checker error

  There is no runtime checking for this property.

  See `TypedDict` and PEP 705 for more details.

  Added in version 3.13.

typing.Annotated

  Special typing form to add context-specific metadata to an annotation.

  Add metadata `x` to a given type `T` by using the annotation `Annotated[T, x]`. Metadata added using `Annotated` can be used by static analysis tools or at runtime. At runtime, the metadata is stored in a `__metadata__` attribute.

  If a library or tool encounters an annotation `Annotated[T, x]` and has no special logic for the metadata, it should ignore the metadata and simply treat the annotation as `T`. As such, `Annotated` can be useful for code that wants to use annotations for purposes outside Python's static typing system.

  Using `Annotated[T, x]` as an annotation still allows for static typechecking of `T`, as type checkers will simply ignore the metadata `x`. In this way, `Annotated` differs from the `@no_type_check` decorator, which can also be used for adding annotations outside the scope of the typing system, but completely disables typechecking for a function or class.

  The responsibility of how to interpret the metadata lies with the tool or library encountering an `Annotated` annotation. A tool or library encountering an `Annotated` type can scan through the metadata elements to determine if they are of interest (e.g., using `isinstance()`).

  Annotated[<type>, <metadata>]

  Here is an example of how you might use `Annotated` to add metadata to type annotations if you were doing range analysis:

      @dataclass
      class ValueRange:
          lo: int
          hi: int

      T1 = Annotated[int, ValueRange(-10, 5)]
      T2 = Annotated[T1, ValueRange(-20, 3)]

  The first argument to `Annotated` must be a valid type. Multiple metadata elements can be supplied as `Annotated` supports variadic arguments. The order of the metadata elements is preserved and matters for equality checks:

      @dataclass
      class ctype:
           kind: str

      a1 = Annotated[int, ValueRange(3, 10), ctype("char")]
      a2 = Annotated[int, ctype("char"), ValueRange(3, 10)]

      assert a1 != a2  # Order matters

  It is up to the tool consuming the annotations to decide whether the client is allowed to add multiple metadata elements to one annotation and how to merge those annotations.

  Nested `Annotated` types are flattened. The order of the metadata elements starts with the innermost annotation:

      assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
          int, ValueRange(3, 10), ctype("char")
      ]

  However, this does not apply to `Annotated` types referenced through a type alias, to avoid forcing evaluation of the underlying `TypeAliasType`:

      type From3To10[T] = Annotated[T, ValueRange(3, 10)]
      assert Annotated[From3To10[int], ctype("char")] != Annotated[
         int, ValueRange(3, 10), ctype("char")
      ]

  Duplicated metadata elements are not removed:

      assert Annotated[int, ValueRange(3, 10)] != Annotated[
          int, ValueRange(3, 10), ValueRange(3, 10)
      ]

  `Annotated` can be used with nested and generic aliases:

  >     @dataclass
  >     class MaxLen:
  >         value: int

  >     type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)]

  >     # When used in a type annotation, a type checker will treat "V" the same as
  >     # ``Annotated[list[tuple[int, int]], MaxLen(10)]``:
  >     type V = Vec[int]

  `Annotated` cannot be used with an unpacked `TypeVarTuple`:

      type Variadic[*Ts] = Annotated[*Ts, Ann1] = Annotated[T1, T2, T3, ..., Ann1]  # NOT valid

  where `T1`, `T2`, ... are `TypeVars`. This is invalid as only one type should be passed to Annotated.

  By default, `get_type_hints()` strips the metadata from annotations. Pass `include_extras=True` to have the metadata preserved:

  >     >>> from typing import Annotated, get_type_hints
  >     >>> def func(x: Annotated[int, "metadata"]) -> None: pass
  >     ...
  >     >>> get_type_hints(func)
  >     {'x': <class 'int'>, 'return': <class 'NoneType'>}
  >     >>> get_type_hints(func, include_extras=True)
  >     {'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}

  At runtime, the metadata associated with an `Annotated` type can be retrieved via the `__metadata__` attribute:

  >     >>> from typing import Annotated
  >     >>> X = Annotated[int, "very", "important", "metadata"]
  >     >>> X
  >     typing.Annotated[int, 'very', 'important', 'metadata']
  >     >>> X.__metadata__
  >     ('very', 'important', 'metadata')

  If you want to retrieve the original type wrapped by `Annotated`, use the `__origin__` attribute:

  >     >>> from typing import Annotated, get_origin
  >     >>> Password = Annotated[str, "secret"]
  >     >>> Password.__origin__
  >     <class 'str'>

  Note that using `get_origin()` will return `Annotated` itself:

  >     >>> get_origin(Password)
  >     typing.Annotated

  参考

  PEP 593 - Flexible function and variable annotations

    The PEP introducing `Annotated` to the standard library.

  Added in version 3.9.

typing.TypeForm

  A special form representing the value that results from evaluating a type expression.

  This value encodes the information supplied in the type expression, and it represents the type described by that type expression.

  When used in a type expression, `TypeForm` describes a set of type form objects. It accepts a single type argument, which must be a valid type expression. `TypeForm[T]` describes the set of all type form objects that represent the type `T` or types assignable to `T`.

  `TypeForm(obj)` simply returns `obj` unchanged. This is useful for explicitly marking a value as a type form for static type checkers.

  以下はプログラム例です:

      from typing import Any, TypeForm

      def cast[T](typ: TypeForm[T], value: Any) -> T: ...

      reveal_type(cast(int, "x"))  # Revealed type is "int"

  See PEP 747 for details.

  Added in version 3.15.

typing.TypeIs

  Special typing construct for marking user-defined type predicate functions.

  `TypeIs` can be used to annotate the return type of a user-defined type predicate function. `TypeIs` only accepts a single type argument. At runtime, functions marked this way should return a boolean and take at least one positional argument.

  `TypeIs` aims to benefit type narrowing -- a technique used by static type checkers to determine a more precise type of an expression within a program's code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a "type predicate":

      def is_str(val: str | float):
          # "isinstance" type predicate
          if isinstance(val, str):
              # Type of ``val`` is narrowed to ``str``
              ...
          else:
              # Else, type of ``val`` is narrowed to ``float``.
              ...

  Sometimes it would be convenient to use a user-defined boolean function as a type predicate. Such a function should use `TypeIs[...]` or `TypeGuard` as its return type to alert static type checkers to this intention. `TypeIs` usually has more intuitive behavior than `TypeGuard`, but it cannot be used when the input and output types are incompatible (e.g., `list[object]` to `list[int]`) or when the function does not return `True` for all instances of the narrowed type.

  Using `-> TypeIs[NarrowedType]` tells the static type checker that for a given function:

  1. The return value is a boolean.

  2. If the return value is `True`, the type of its argument is the intersection of the argument's original type and `NarrowedType`.

  3. If the return value is `False`, the type of its argument is narrowed to exclude `NarrowedType`.

  例えば:

      from typing import assert_type, final, TypeIs

      class Parent: pass
      class Child(Parent): pass
      @final
      class Unrelated: pass

      def is_parent(val: object) -> TypeIs[Parent]:
          return isinstance(val, Parent)

      def run(arg: Child | Unrelated):
          if is_parent(arg):
              # Type of ``arg`` is narrowed to the intersection
              # of ``Parent`` and ``Child``, which is equivalent to
              # ``Child``.
              assert_type(arg, Child)
          else:
              # Type of ``arg`` is narrowed to exclude ``Parent``,
              # so only ``Unrelated`` is left.
              assert_type(arg, Unrelated)

  The type inside `TypeIs` must be consistent with the type of the function's argument; if it is not, static type checkers will raise an error. An incorrectly written `TypeIs` function can lead to unsound behavior in the type system; it is the user's responsibility to write such functions in a type-safe manner.

  If a `TypeIs` function is a class or instance method, then the type in `TypeIs` maps to the type of the second parameter (after `cls` or `self`).

  In short, the form `def foo(arg: TypeA) -> TypeIs[TypeB]: ...`, means that if `foo(arg)` returns `True`, then `arg` is an instance of `TypeB`, and if it returns `False`, it is not an instance of `TypeB`.

  `TypeIs` also works with type variables. For more information, see PEP 742 (Narrowing types with `TypeIs`).

  Added in version 3.13.

typing.TypeGuard

  Special typing construct for marking user-defined type predicate functions.

  Type predicate functions are user-defined functions that return whether their argument is an instance of a particular type. `TypeGuard` works similarly to `TypeIs`, but has subtly different effects on type checking behavior (see below).

  Using `-> TypeGuard` tells the static type checker that for a given function:

  1. The return value is a boolean.

  2. If the return value is `True`, the type of its argument is the type inside `TypeGuard`.

  `TypeGuard` also works with type variables. See PEP 647 for more details.

  例えば:

      def is_str_list(val: list[object]) -> TypeGuard[list[str]]:
          '''Determines whether all objects in the list are strings'''
          return all(isinstance(x, str) for x in val)

      def func1(val: list[object]):
          if is_str_list(val):
              # Type of ``val`` is narrowed to ``list[str]``.
              print(" ".join(val))
          else:
              # Type of ``val`` remains as ``list[object]``.
              print("Not a list of strings!")

  `TypeIs` and `TypeGuard` differ in the following ways:

  - `TypeIs` requires the narrowed type to be a subtype of the input type, while `TypeGuard` does not. The main reason is to allow for things like narrowing `list[object]` to `list[str]` even though the latter is not a subtype of the former, since `list` is invariant.

  - When a `TypeGuard` function returns `True`, type checkers narrow the type of the variable to exactly the `TypeGuard` type. When a `TypeIs` function returns `True`, type checkers can infer a more precise type combining the previously known type of the variable with the `TypeIs` type. (Technically, this is known as an intersection type.)

  - When a `TypeGuard` function returns `False`, type checkers cannot narrow the type of the variable at all. When a `TypeIs` function returns `False`, type checkers can narrow the type of the variable to exclude the `TypeIs` type.

  Added in version 3.10.

typing.Unpack

  Typing operator to conceptually mark an object as having been unpacked.

  For example, using the unpack operator `*` on a type variable tuple is equivalent to using `Unpack` to mark the type variable tuple as having been unpacked:

      Ts = TypeVarTuple('Ts')
      tup: tuple[*Ts]
      # Effectively does:
      tup: tuple[Unpack[Ts]]

  In fact, `Unpack` can be used interchangeably with `*` in the context of `typing.TypeVarTuple` and `builtins.tuple` types. You might see `Unpack` being used explicitly in older versions of Python, where `*` couldn't be used in certain places:

      # In older versions of Python, TypeVarTuple and Unpack
      # are located in the `typing_extensions` backports package.
      from typing_extensions import TypeVarTuple, Unpack

      Ts = TypeVarTuple('Ts')
      tup: tuple[*Ts]         # Syntax error on Python <= 3.10!
      tup: tuple[Unpack[Ts]]  # Semantically equivalent, and backwards-compatible

  `Unpack` can also be used along with `typing.TypedDict` for typing `**kwargs` in a function signature:

      from typing import TypedDict, Unpack

      class Movie(TypedDict):
          name: str
          year: int

      # This function expects two keyword arguments - `name` of type `str`
      # and `year` of type `int`.
      def foo(**kwargs: Unpack[Movie]): ...

  See PEP 692 for more details on using `Unpack` for `**kwargs` typing.

  Added in version 3.11.

#### Building generic types and type aliases

The following classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating generic types and type aliases.

These objects can be created through special syntax (type parameter lists and the `type` statement). For compatibility with Python 3.11 and earlier, they can also be created without the dedicated syntax, as documented below.

class typing.Generic

  ジェネリック型のための抽象基底クラスです。

  A generic type is typically declared by adding a list of type parameters after the class name:

      class Mapping[KT, VT]:
          def __getitem__(self, key: KT) -> VT:
              ...
              # Etc.

  Such a class implicitly inherits from `Generic`. The runtime semantics of this syntax are discussed in the Language Reference.

  このクラスは次のように使用することが出来ます:

      def lookup_name[X, Y](mapping: Mapping[X, Y], key: X, default: Y) -> Y:
          try:
              return mapping[key]
          except KeyError:
              return default

  Here the brackets after the function name indicate a generic function.

  For backwards compatibility, generic classes can also be declared by explicitly inheriting from `Generic`. In this case, the type parameters must be declared separately:

      KT = TypeVar('KT')
      VT = TypeVar('VT')

      class Mapping(Generic[KT, VT]):
          def __getitem__(self, key: KT) -> VT:
              ...
              # Etc.

class typing.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)

  型変数です。

  The preferred way to construct a type variable is via the dedicated syntax for generic functions, generic classes, and generic type aliases:

      class Sequence[T]:  # T is a TypeVar
          ...

  This syntax can also be used to create bounded and constrained type variables:

      class StrSequence[S: str]:  # S is a TypeVar with a `str` upper bound;
          ...                     # we can say that S is "bounded by `str`"

      class StrOrBytesSequence[A: (str, bytes)]:  # A is a TypeVar constrained to str or bytes
          ...

  However, if desired, reusable type variables can also be constructed manually, like so:

      T = TypeVar('T')  # Can be anything
      S = TypeVar('S', bound=str)  # Can be any subtype of str
      A = TypeVar('A', str, bytes)  # Must be exactly str or bytes

  Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function and type alias definitions. See `Generic` for more information on generic types. Generic functions work as follows:

      def repeat[T](x: T, n: int) -> Sequence[T]:
          """Return a list containing n references to x."""
          return [x]*n

      def print_capitalized[S: str](x: S) -> S:
          """Print x capitalized, and return x."""
          print(x.capitalize())
          return x

      def concatenate[A: (str, bytes)](x: A, y: A) -> A:
          """Add two strings or bytes objects together."""
          return x + y

  Note that type variables can be bounded, constrained, or neither, but cannot be both bounded and constrained.

  The variance of type variables is inferred by type checkers when they are created through the type parameter syntax or when `infer_variance=True` is passed. Manually created type variables may be explicitly marked covariant or contravariant by passing `covariant=True` or `contravariant=True`. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.

  Bounded type variables and constrained type variables have different semantics in several important ways. Using a bounded type variable means that the `TypeVar` will be solved using the most specific type possible:

      x = print_capitalized('a string')
      reveal_type(x)  # revealed type is str

      class StringSubclass(str):
          pass

      y = print_capitalized(StringSubclass('another string'))
      reveal_type(y)  # revealed type is StringSubclass

      z = print_capitalized(45)  # error: int is not a subtype of str

  The upper bound of a type variable can be a concrete type, abstract type (ABC or Protocol), or even a union of types:

      # Can be anything with an __abs__ method
      def print_abs[T: SupportsAbs](arg: T) -> None:
          print("Absolute value:", abs(arg))

      U = TypeVar('U', bound=str|bytes)  # Can be any subtype of the union str|bytes
      V = TypeVar('V', bound=SupportsAbs)  # Can be anything with an __abs__ method

  Using a constrained type variable, however, means that the `TypeVar` can only ever be solved as being exactly one of the constraints given:

      a = concatenate('one', 'two')
      reveal_type(a)  # revealed type is str

      b = concatenate(StringSubclass('one'), StringSubclass('two'))
      reveal_type(b)  # revealed type is str, despite StringSubclass being passed in

      c = concatenate('one', b'two')  # error: type variable 'A' can be either str or bytes in a function call, but not both

  At runtime, `isinstance(x, T)` will raise `TypeError`.

  __name__

    The name of the type variable.

  __covariant__

    Whether the type var has been explicitly marked as covariant.

  __contravariant__

    Whether the type var has been explicitly marked as contravariant.

  __infer_variance__

    Whether the type variable's variance should be inferred by type checkers.

    Added in version 3.12.

  __bound__

    The upper bound of the type variable, if any.

    バージョン 3.12 で変更: For type variables created through type parameter syntax, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

  evaluate_bound()

    An evaluate function corresponding to the `__bound__` attribute. When called directly, this method supports only the `VALUE` format, which is equivalent to accessing the `__bound__` attribute directly, but the method object can be passed to `annotationlib.call_evaluate_function()` to evaluate the value in a different format.

    Added in version 3.14.

  __constraints__

    A tuple containing the constraints of the type variable, if any.

    バージョン 3.12 で変更: For type variables created through type parameter syntax, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

  evaluate_constraints()

    An evaluate function corresponding to the `__constraints__` attribute. When called directly, this method supports only the `VALUE` format, which is equivalent to accessing the `__constraints__` attribute directly, but the method object can be passed to `annotationlib.call_evaluate_function()` to evaluate the value in a different format.

    Added in version 3.14.

  __default__

    The default value of the type variable, or `typing.NoDefault` if it has no default.

    Added in version 3.13.

  evaluate_default()

    An evaluate function corresponding to the `__default__` attribute. When called directly, this method supports only the `VALUE` format, which is equivalent to accessing the `__default__` attribute directly, but the method object can be passed to `annotationlib.call_evaluate_function()` to evaluate the value in a different format.

    Added in version 3.14.

  has_default()

    Return whether or not the type variable has a default value. This is equivalent to checking whether `__default__` is not the `typing.NoDefault` singleton, except that it does not force evaluation of the lazily evaluated default value.

    Added in version 3.13.

  バージョン 3.12 で変更: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The `infer_variance` parameter was added.

  バージョン 3.13 で変更: Support for default values was added.

class typing.TypeVarTuple(name, *, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)

  Type variable tuple. A specialized form of type variable that enables variadic generics.

  Type variable tuples can be declared in type parameter lists using a single asterisk (`*`) before the name:

      def move_first_element_to_last[T, *Ts](tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
          return (*tup[1:], tup[0])

  Or by explicitly invoking the `TypeVarTuple` constructor:

      T = TypeVar("T")
      Ts = TypeVarTuple("Ts")

      def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
          return (*tup[1:], tup[0])

  A normal type variable enables parameterization with a single type. A type variable tuple, in contrast, allows parameterization with an arbitrary number of types by acting like an arbitrary number of type variables wrapped in a tuple. For example:

      # T is bound to int, Ts is bound to ()
      # Return value is (1,), which has type tuple[int]
      move_first_element_to_last(tup=(1,))

      # T is bound to int, Ts is bound to (str,)
      # Return value is ('spam', 1), which has type tuple[str, int]
      move_first_element_to_last(tup=(1, 'spam'))

      # T is bound to int, Ts is bound to (str, float)
      # Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]
      move_first_element_to_last(tup=(1, 'spam', 3.0))

      # This fails to type check (and fails at runtime)
      # because tuple[()] is not compatible with tuple[T, *Ts]
      # (at least one element is required)
      move_first_element_to_last(tup=())

  Note the use of the unpacking operator `*` in `tuple[T, *Ts]`. Conceptually, you can think of `Ts` as a tuple of type variables `(T1, T2, ...)`. `tuple[T, *Ts]` would then become `tuple[T, *(T1, T2, ...)]`, which is equivalent to `tuple[T, T1, T2, ...]`. (Note that in older versions of Python, you might see this written using `Unpack` instead, as `Unpack[Ts]`.)

  Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables:

      x: Ts          # Not valid
      x: tuple[Ts]   # Not valid
      x: tuple[*Ts]  # The correct way to do it

  Type variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types:

      class Array[*Shape]:
          def __getitem__(self, key: tuple[*Shape]) -> float: ...
          def __abs__(self) -> "Array[*Shape]": ...
          def get_shape(self) -> tuple[*Shape]: ...

  Type variable tuples can be happily combined with normal type variables:

      class Array[DType, *Shape]:  # This is fine
          pass

      class Array2[*Shape, DType]:  # This would also be fine
          pass

      class Height: ...
      class Width: ...

      float_array_1d: Array[float, Height] = Array()     # Totally fine
      int_array_2d: Array[int, Height, Width] = Array()  # Yup, fine too

  However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters:

      x: tuple[*Ts, *Ts]            # Not valid
      class Array[*Shape, *Shape]:  # Not valid
          pass

  Finally, an unpacked type variable tuple can be used as the type annotation of `*args`:

      def call_soon[*Ts](
          callback: Callable[[*Ts], None],
          *args: *Ts
      ) -> None:
          ...
          callback(*args)

  In contrast to non-unpacked annotations of `*args` - e.g. `*args: int`, which would specify that all arguments are `int` - `*args: *Ts` enables reference to the types of the individual arguments in `*args`. Here, this allows us to ensure the types of the `*args` passed to `call_soon` match the types of the (positional) arguments of `callback`.

  See PEP 646 for more details on type variable tuples.

  __name__

    The name of the type variable tuple.

  __covariant__

    Whether the type variable tuple has been explicitly marked as covariant.

    Added in version 3.15.

  __contravariant__

    Whether the type variable tuple has been explicitly marked as contravariant.

    Added in version 3.15.

  __infer_variance__

    Whether the type variable tuple's variance should be inferred by type checkers.

    Added in version 3.15.

  __default__

    The default value of the type variable tuple, or `typing.NoDefault` if it has no default.

    Added in version 3.13.

  evaluate_default()

    An evaluate function corresponding to the `__default__` attribute. When called directly, this method supports only the `VALUE` format, which is equivalent to accessing the `__default__` attribute directly, but the method object can be passed to `annotationlib.call_evaluate_function()` to evaluate the value in a different format.

    Added in version 3.14.

  has_default()

    Return whether or not the type variable tuple has a default value. This is equivalent to checking whether `__default__` is not the `typing.NoDefault` singleton, except that it does not force evaluation of the lazily evaluated default value.

    Added in version 3.13.

  Type variable tuples created with `covariant=True` or `contravariant=True` can be used to declare covariant or contravariant generic types. The `bound` argument is also accepted, similar to `TypeVar`, but its actual semantics are yet to be decided.

  Added in version 3.11.

  バージョン 3.12 で変更: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

  バージョン 3.13 で変更: Support for default values was added.

  バージョン 3.15 で変更: Added support for the `bound`, `covariant`, `contravariant`, and `infer_variance` parameters.

class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)

  Parameter specification variable. A specialized version of type variables.

  In type parameter lists, parameter specifications can be declared with two asterisks (`**`):

      type IntFunc[**P] = Callable[P, int]

  For compatibility with Python 3.11 and earlier, `ParamSpec` objects can also be created as follows:

      P = ParamSpec('P')

  Parameter specification variables exist primarily for the benefit of static type checkers. They are used to forward the parameter types of one callable to another callable -- a pattern commonly found in higher order functions and decorators. They are only valid when used in `Concatenate`, or as the first argument to `Callable`, or as parameters for user-defined Generics. See `Generic` for more information on generic types.

  For example, to add basic logging to a function, one can create a decorator `add_logging` to log function calls. The parameter specification variable tells the type checker that the callable passed into the decorator and the new callable returned by it have inter-dependent type parameters:

      from collections.abc import Callable
      import logging

      def add_logging[T, **P](f: Callable[P, T]) -> Callable[P, T]:
          '''A type-safe decorator to add logging to a function.'''
          def inner(*args: P.args, **kwargs: P.kwargs) -> T:
              logging.info(f'{f.__name__} was called')
              return f(*args, **kwargs)
          return inner

      @add_logging
      def add_two(x: float, y: float) -> float:
          '''Add two numbers together.'''
          return x + y

  Without `ParamSpec`, the simplest way to annotate this previously was to use a `TypeVar` with upper bound `Callable[..., Any]`. However this causes two problems:

  1. The type checker can't type check the `inner` function because `*args` and `**kwargs` have to be typed `Any`.

  2. `cast()` may be required in the body of the `add_logging` decorator when returning the `inner` function, or the static type checker must be told to ignore the `return inner`.

  args
  kwargs

    Since `ParamSpec` captures both positional and keyword parameters, `P.args` and `P.kwargs` can be used to split a `ParamSpec` into its components. `P.args` represents the tuple of positional parameters in a given call and should only be used to annotate `*args`. `P.kwargs` represents the mapping of keyword parameters to their values in a given call, and should be only be used to annotate `**kwargs`. Both attributes require the annotated parameter to be in scope. At runtime, `P.args` and `P.kwargs` are instances respectively of `ParamSpecArgs` and `ParamSpecKwargs`.

  __name__

    The name of the parameter specification.

  __covariant__

    Whether the parameter specification has been explicitly marked as covariant.

  __contravariant__

    Whether the parameter specification has been explicitly marked as contravariant.

  __infer_variance__

    Whether the parameter specification's variance should be inferred by type checkers.

    Added in version 3.12.

  __default__

    The default value of the parameter specification, or `typing.NoDefault` if it has no default.

    Added in version 3.13.

  evaluate_default()

    An evaluate function corresponding to the `__default__` attribute. When called directly, this method supports only the `VALUE` format, which is equivalent to accessing the `__default__` attribute directly, but the method object can be passed to `annotationlib.call_evaluate_function()` to evaluate the value in a different format.

    Added in version 3.14.

  has_default()

    Return whether or not the parameter specification has a default value. This is equivalent to checking whether `__default__` is not the `typing.NoDefault` singleton, except that it does not force evaluation of the lazily evaluated default value.

    Added in version 3.13.

  Parameter specification variables created with `covariant=True` or `contravariant=True` can be used to declare covariant or contravariant generic types. The `bound` argument is also accepted, similar to `TypeVar`. However the actual semantics of these keywords are yet to be decided.

  Added in version 3.10.

  バージョン 3.12 で変更: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

  バージョン 3.13 で変更: Support for default values was added.

  注釈

  Only parameter specification variables defined in global scope can be pickled.

  参考

  - PEP 612 -- Parameter Specification Variables (the PEP which introduced `ParamSpec` and `Concatenate`)

  - `Concatenate`

  - 呼び出し可能オブジェクトのアノテーション

typing.ParamSpecArgs
typing.ParamSpecKwargs

  Arguments and keyword arguments attributes of a `ParamSpec`. The `P.args` attribute of a `ParamSpec` is an instance of `ParamSpecArgs`, and `P.kwargs` is an instance of `ParamSpecKwargs`. They are intended for runtime introspection and have no special meaning to static type checkers.

  Calling `get_origin()` on either of these objects will return the original `ParamSpec`:

      >>> from typing import ParamSpec, get_origin
      >>> P = ParamSpec("P")
      >>> get_origin(P.args) is P
      True
      >>> get_origin(P.kwargs) is P
      True

  Added in version 3.10.

class typing.TypeAliasType(name, value, *, type_params=(), qualname=None)

  The type of type aliases created through the `type` statement.

  例:

      >>> type Alias = int
      >>> type(Alias)
      <class 'typing.TypeAliasType'>

  Added in version 3.12.

  __name__

    The name of the type alias:

        >>> type Alias = int
        >>> Alias.__name__
        'Alias'

  __qualname__

    The qualified name of the type alias:

        >>> class Class:
        ...     type Alias = int
        ...
        >>> Class.Alias.__qualname__
        'Class.Alias'

    Added in version 3.15.

  __module__

    The name of the module in which the type alias was defined:

        >>> type Alias = int
        >>> Alias.__module__
        '__main__'

    This attribute is writable.

    バージョン 3.15 で変更: The attribute is now writable.

  __type_params__

    The type parameters of the type alias, or an empty tuple if the alias is not generic:

        >>> type ListOrSet[T] = list[T] | set[T]
        >>> ListOrSet.__type_params__
        (T,)
        >>> type NotGeneric = int
        >>> NotGeneric.__type_params__
        ()

  __value__

    The type alias's value. This is lazily evaluated, so names used in the definition of the alias are not resolved until the `__value__` attribute is accessed:

        >>> type Mutually = Recursive
        >>> type Recursive = Mutually
        >>> Mutually
        Mutually
        >>> Recursive
        Recursive
        >>> Mutually.__value__
        Recursive
        >>> Recursive.__value__
        Mutually

  evaluate_value()

    An evaluate function corresponding to the `__value__` attribute. When called directly, this method supports only the `VALUE` format, which is equivalent to accessing the `__value__` attribute directly, but the method object can be passed to `annotationlib.call_evaluate_function()` to evaluate the value in a different format:

        >>> type Alias = undefined
        >>> Alias.__value__
        Traceback (most recent call last):
        ...
        NameError: name 'undefined' is not defined
        >>> from annotationlib import Format, call_evaluate_function
        >>> Alias.evaluate_value(Format.VALUE)
        Traceback (most recent call last):
        ...
        NameError: name 'undefined' is not defined
        >>> call_evaluate_function(Alias.evaluate_value, Format.FORWARDREF)
        ForwardRef('undefined')

    Added in version 3.14.

  展開

  Type aliases support star unpacking using the `*Alias` syntax. This is equivalent to using `Unpack[Alias]` directly:

      >>> type Alias = tuple[int, str]
      >>> type Unpacked = tuple[bool, *Alias]
      >>> Unpacked.__value__
      tuple[bool, typing.Unpack[Alias]]

  Added in version 3.14.

#### Other special directives

These functions and classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating and declaring types.

class typing.NamedTuple

  `collections.namedtuple()` の型付き版です。

  使い方:

      class Employee(NamedTuple):
          name: str
          id: int

  これは次と等価です:

      Employee = collections.namedtuple('Employee', ['name', 'id'])

  フィールドにデフォルト値を与えるにはクラス本体で代入してください:

      class Employee(NamedTuple):
          name: str
          id: int = 3

      employee = Employee('Guido')
      assert employee.id == 3

  デフォルト値のあるフィールドはデフォルト値のないフィールドの後でなければなりません。

  The types for each field name can be retrieved by calling `annotationlib.get_annotations()` on the resulting class. (The field names are in the `_fields` attribute and the default values are in the `_field_defaults` attribute, both of which are part of the `namedtuple()` API.)

  `NamedTuple` のサブクラスは docstring やメソッドも持てます:

      class Employee(NamedTuple):
          """Represents an employee."""
          name: str
          id: int = 3

          def __repr__(self) -> str:
              return f'<Employee {self.name}, id={self.id}>'

  `NamedTuple` subclasses can be generic:

      class Group[T](NamedTuple):
          key: T
          group: list[T]

  後方互換な使用法:

      # For creating a generic NamedTuple on Python 3.11
      T = TypeVar("T")

      class Group(NamedTuple, Generic[T]):
          key: T
          group: list[T]

      # A functional syntax is also supported
      Employee = NamedTuple('Employee', [('name', str), ('id', int)])

  バージョン 3.6 で変更: PEP 526 変数アノテーションのシンタックスが追加されました。

  バージョン 3.6.1 で変更: デフォルト値、メソッド、ドキュメンテーション文字列への対応が追加されました。

  バージョン 3.8 で変更: `_field_types` 属性および `__annotations__` 属性は `OrderedDict` インスタンスではなく普通の辞書になりました。

  バージョン 3.9 で変更: `_field_types` 属性は削除されました。代わりに同じ情報を持つより標準的な `__annotations__` 属性を使ってください。

  バージョン 3.9 で変更: `NamedTuple` is now a function rather than a class. It can still be used as a class base, as described above.

  バージョン 3.11 で変更: Added support for generic namedtuples.

  バージョン 3.14 で変更: Using `super()` (and the `__class__` closure variable) in methods of `NamedTuple` subclasses is unsupported and causes a `TypeError`.

class typing.NewType(name, tp)

  Helper class to create low-overhead distinct types.

  A `NewType` is considered a distinct type by a type checker. At runtime, however, calling a `NewType` returns its argument unchanged.

  使い方:

      UserId = NewType('UserId', int)  # Declare the NewType "UserId"
      first_user = UserId(1)  # "UserId" returns the argument unchanged at runtime

  __module__

    The name of the module in which the new type is defined.

  __name__

    The name of the new type.

  __supertype__

    The type that the new type is based on.

  Added in version 3.5.2.

  バージョン 3.10 で変更: `NewType` is now a class rather than a function.

class typing.Protocol(Generic)

  Base class for protocol classes.

  Protocol classes are defined like this:

      class Proto(Protocol):
          def meth(self) -> int:
              ...

  このようなクラスは主に構造的部分型 (静的ダックタイピング) を認識する静的型チェッカーが使います。例えば:

      class C:
          def meth(self) -> int:
              return 0

      def func(x: Proto) -> int:
          return x.meth()

      func(C())  # Passes static type check

  See PEP 544 for more details. Protocol classes decorated with `runtime_checkable()` (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes without this decorator cannot be used as the second argument to `isinstance()` or `issubclass()`.

  プロトコルクラスはジェネリックにもできます。例えば:

      class GenProto[T](Protocol):
          def meth(self) -> T:
              ...

  In code that needs to be compatible with Python 3.11 or older, generic Protocols can be written as follows:

      T = TypeVar("T")

      class GenProto(Protocol[T]):
          def meth(self) -> T:
              ...

  Added in version 3.8.

  バージョン 3.15 で非推奨、バージョン 3.20 で削除予定: It is deprecated to call `isinstance()` and `issubclass()` checks on protocol classes that were not explicitly decorated with `runtime_checkable()` but that inherit from a runtime-checkable protocol class. This will throw a `TypeError` in Python 3.20.

@typing.runtime_checkable

  Mark a protocol class as a runtime protocol.

  Such a protocol can be used with `isinstance()` and `issubclass()`. This allows a simple-minded structural check, very similar to "one-trick ponies" in `collections.abc` such as `Iterable`. For example:

      @runtime_checkable
      class Closable(Protocol):
          def close(self): ...

      assert isinstance(open('/some/file'), Closable)

      @runtime_checkable
      class Named(Protocol):
          name: str

      import threading
      assert isinstance(threading.Thread(name='Bob'), Named)

  Runtime checkability of protocols is not inherited. A subclass of a runtime-checkable protocol is only runtime-checkable if it is explicitly marked as such, regardless of class hierarchy:

      @runtime_checkable
      class Iterable(Protocol):
          def __iter__(self): ...

      # Without @runtime_checkable, Reversible would no longer be runtime-checkable.
      @runtime_checkable
      class Reversible(Iterable, Protocol):
          def __reversed__(self): ...

  This decorator raises `TypeError` when applied to a non-protocol class.

  注釈

  `runtime_checkable()` will check only the presence of the required methods or attributes, not their type signatures or types. For example, `ssl.SSLObject` is a class, therefore it passes an `issubclass()` check against Callable. However, the `ssl.SSLObject.__init__` method exists only to raise a `TypeError` with a more informative message, therefore making it impossible to call (instantiate) `ssl.SSLObject`.

  注釈

  An `isinstance()` check against a runtime-checkable protocol can be surprisingly slow compared to an `isinstance()` check against a non-protocol class. Consider using alternative idioms such as `hasattr()` calls for structural checks in performance-sensitive code.

  Added in version 3.8.

  バージョン 3.12 で変更: The internal implementation of `isinstance()` checks against runtime-checkable protocols now uses `inspect.getattr_static()` to look up attributes (previously, `hasattr()` was used). As a result, some objects which used to be considered instances of a runtime-checkable protocol may no longer be considered instances of that protocol on Python 3.12+, and vice versa. Most users are unlikely to be affected by this change.

  バージョン 3.12 で変更: The members of a runtime-checkable protocol are now considered "frozen" at runtime as soon as the class has been created. Monkey-patching attributes onto a runtime-checkable protocol will still work, but will have no impact on `isinstance()` checks comparing objects to the protocol. See What's new in Python 3.12 for more details.

  バージョン 3.15 で非推奨、バージョン 3.20 で削除予定: It is deprecated to call `isinstance()` and `issubclass()` checks on protocol classes that were not explicitly decorated with `runtime_checkable()` but that inherit from a runtime-checkable protocol class. This will throw a `TypeError` in Python 3.20.

class typing.TypedDict(dict)

  Special construct to add type hints to a dictionary. At runtime "`TypedDict` instances" are simply `dicts`.

  `TypedDict` は、その全てのインスタンスにおいてキーの集合が固定されていて、各キーに対応する値が全てのインスタンスで同じ型を持つことが期待される辞書型を宣言します。 この期待は実行時にはチェックされず、型チェッカーでのみ強制されます。 使用方法は次の通りです:

      class Point2D(TypedDict):
          x: int
          y: int
          label: str

      a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
      b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check

      assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')

  An alternative way to create a `TypedDict` is by using function-call syntax. The second argument must be a literal `dict`:

      Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})

  This functional syntax allows defining keys which are not valid identifiers, for example because they are keywords or contain hyphens, or when key names must not be mangled like regular private names:

      # raises SyntaxError
      class Point2D(TypedDict):
          in: int  # 'in' is a keyword
          x-y: int  # name with hyphens

      class Definition(TypedDict):
          __schema: str  # mangled to `_Definition__schema`

      # OK, functional syntax
      Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})
      Definition = TypedDict('Definition', {'__schema': str})  # not mangled

  By default, all keys must be present in a `TypedDict`. It is possible to mark individual keys as non-required using `NotRequired`:

      class Point2D(TypedDict):
          x: int
          y: int
          label: NotRequired[str]

      # Alternative syntax
      Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': NotRequired[str]})

  This means that a `Point2D` `TypedDict` can have the `label` key omitted.

  It is also possible to mark all keys as non-required by default by specifying a totality of `False`:

      class Point2D(TypedDict, total=False):
          x: int
          y: int

      # Alternative syntax
      Point2D = TypedDict('Point2D', {'x': int, 'y': int}, total=False)

  This means that a `Point2D` `TypedDict` can have any of the keys omitted. A type checker is only expected to support a literal `False` or `True` as the value of the `total` argument. `True` is the default, and makes all items defined in the class body required.

  Individual keys of a `total=False` `TypedDict` can be marked as required using `Required`:

      class Point2D(TypedDict, total=False):
          x: Required[int]
          y: Required[int]
          label: str

      # Alternative syntax
      Point2D = TypedDict('Point2D', {
          'x': Required[int],
          'y': Required[int],
          'label': str
      }, total=False)

  It is possible for a `TypedDict` type to inherit from one or more other `TypedDict` types using the class-based syntax. Usage:

      class Point3D(Point2D):
          z: int

  `Point3D` has three items: `x`, `y` and `z`. It is equivalent to this definition:

      class Point3D(TypedDict):
          x: int
          y: int
          z: int

  By default, a `TypedDict` is open, meaning that it may contain additional keys at runtime beyond those defined in the class body. The closed class argument can be used to control this; if `closed=True`, the `TypedDict` cannot contain additional keys.

      class ClosedPoint(TypedDict, closed=True):
          x: int
          y: int

      class ClosedPoint3D(ClosedPoint):  # type checker error: cannot add keys to a closed TypedDict
          z: int

  Setting `closed=False` explicitly requests the default open behavior. If the argument is not passed, this state is inherited from the parent class.

  In addition to being open or closed, a `TypedDict` can also be configured to have extra items. If the extra_items class argument is set to a type, the `TypedDict` can contain arbitrary additional keys, but the values of those keys must be of the specified type.

      class ExtraItemsPoint(TypedDict, extra_items=int):
          x: int
          y: int

      point: ExtraItemsPoint = {'x': 1, 'y': 2, 'anything': 3}  # OK

  The extra_items argument is also inherited through subclassing. It is unset by default, and it may not be used together with the closed argument.

  A `TypedDict` cannot inherit from a non-`TypedDict` class, except for `Generic`. For example:

      class X(TypedDict):
          x: int

      class Y(TypedDict):
          y: int

      class Z(object): pass  # A non-TypedDict class

      class XY(X, Y): pass  # OK

      class XZ(X, Z): pass  # raises TypeError

  A `TypedDict` can be generic:

      class Group[T](TypedDict):
          key: T
          group: list[T]

  To create a generic `TypedDict` that is compatible with Python 3.11 or lower, inherit from `Generic` explicitly:

      T = TypeVar("T")

      class Group(TypedDict, Generic[T]):
          key: T
          group: list[T]

  A `TypedDict` can be introspected via `annotationlib.get_annotations()` (see アノテーションのベストプラクティス for more information on annotations best practices) and the following attributes:

  __total__

    `Point2D.__total__` gives the value of the `total` argument. Example:

        >>> from typing import TypedDict
        >>> class Point2D(TypedDict): pass
        >>> Point2D.__total__
        True
        >>> class Point2D(TypedDict, total=False): pass
        >>> Point2D.__total__
        False
        >>> class Point3D(Point2D): pass
        >>> Point3D.__total__
        True

    This attribute reflects only the value of the `total` argument to the current `TypedDict` class, not whether the class is semantically total. For example, a `TypedDict` with `__total__` set to `True` may have keys marked with `NotRequired`, or it may inherit from another `TypedDict` with `total=False`. Therefore, it is generally better to use `__required_keys__` and `__optional_keys__` for introspection.

  __required_keys__

    Added in version 3.9.

  __optional_keys__

    `Point2D.__required_keys__` and `Point2D.__optional_keys__` return `frozenset` objects containing required and non-required keys, respectively.

    Keys marked with `Required` will always appear in `__required_keys__` and keys marked with `NotRequired` will always appear in `__optional_keys__`.

    For backwards compatibility with Python 3.10 and below, it is also possible to use inheritance to declare both required and non-required keys in the same `TypedDict`. This is done by declaring a `TypedDict` with one value for the `total` argument and then inheriting from it in another `TypedDict` with a different value for `total`:

        >>> class Point2D(TypedDict, total=False):
        ...     x: int
        ...     y: int
        ...
        >>> class Point3D(Point2D):
        ...     z: int
        ...
        >>> Point3D.__required_keys__ == frozenset({'z'})
        True
        >>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
        True

    Added in version 3.9.

    注釈

    If `from __future__ import annotations` is used or if annotations are given as strings, annotations are not evaluated when the `TypedDict` is defined. Therefore, the runtime introspection that `__required_keys__` and `__optional_keys__` rely on may not work properly, and the values of the attributes may be incorrect.

  __readonly_keys__

    A `frozenset` containing the names of all read-only keys. Keys are read-only if they carry the `ReadOnly` qualifier.

    Added in version 3.13.

  __mutable_keys__

    A `frozenset` containing the names of all mutable keys. Keys are mutable if they do not carry the `ReadOnly` qualifier.

    Added in version 3.13.

  __closed__

    The value of the closed class argument. It can be `True`, `False`, or `None`.

  __extra_items__

    The value of the extra_items class argument. It can be a valid type or `NoExtraItems`.

  See the TypedDict section in the typing documentation for more examples and detailed rules.

  Added in version 3.8.

  バージョン 3.9 で変更: `TypedDict` is now a function rather than a class. It can still be used as a class base, as described above.

  バージョン 3.11 で変更: Added support for marking individual keys as `Required` or `NotRequired`. See PEP 655.

  バージョン 3.11 で変更: Added support for generic `TypedDict`s.

  バージョン 3.13 で変更: Removed support for the keyword-argument method of creating `TypedDict`s.

  バージョン 3.13 で変更: Support for the `ReadOnly` qualifier was added. See PEP 705.

  バージョン 3.15 で変更: Support for the closed and extra_items class arguments was added. See PEP 728.

### プロトコル

The following protocols are provided by the `typing` module. All are decorated with `@runtime_checkable`.

class typing.SupportsAbs

  A protocol with one abstract method `__abs__` that is covariant in its return type.

class typing.SupportsBytes

  A protocol with one abstract method `__bytes__`.

class typing.SupportsComplex

  A protocol with one abstract method `__complex__`.

class typing.SupportsFloat

  A protocol with one abstract method `__float__`.

class typing.SupportsIndex

  A protocol with one abstract method `__index__`.

  Added in version 3.8.

class typing.SupportsInt

  A protocol with one abstract method `__int__`.

class typing.SupportsRound

  A protocol with one abstract method `__round__` that is covariant in its return type.

### ABCs and Protocols for working with I/O

class typing.IO[AnyStr]
class typing.TextIO
class typing.BinaryIO

  Generic class `IO[AnyStr]` and its subclasses `TextIO(IO[str])` and `BinaryIO(IO[bytes])` represent the types of I/O streams such as returned by `open()`. Please note that these classes are not protocols, and their interface is fairly broad.

The protocols `io.Reader` and `io.Writer` offer a simpler alternative for argument types, when only the `read()` or `write()` methods are accessed, respectively:

    def read_and_write(reader: Reader[str], writer: Writer[bytes]):
        data = reader.read()
        writer.write(data.encode())

Also consider using `collections.abc.Iterable` for iterating over the lines of an input stream:

    def read_config(stream: Iterable[str]):
        for line in stream:
            ...

### Functions and decorators

typing.cast(typ, val)

  値をある型にキャストします。

  この関数は値を変更せずに返します。 型検査器に対して、返り値が指定された型を持っていることを通知しますが、実行時には意図的に何も検査しません。 (その理由は、処理をできる限り速くしたかったためです。)

typing.assert_type(val, typ, /)

  Ask a static type checker to confirm that val has an inferred type of typ.

  At runtime this does nothing: it returns the first argument unchanged with no checks or side effects, no matter the actual type of the argument.

  When a static type checker encounters a call to `assert_type()`, it emits an error if the value is not of the specified type:

      def greet(name: str) -> None:
          assert_type(name, str)  # OK, inferred type of `name` is `str`
          assert_type(name, int)  # type checker error

  This function is useful for ensuring the type checker's understanding of a script is in line with the developer's intentions:

      def complex_function(arg: object):
          # Do some complex type-narrowing logic,
          # after which we hope the inferred type will be `int`
          ...
          # Test whether the type checker correctly understands our function
          assert_type(arg, int)

  Added in version 3.11.

typing.assert_never(arg, /)

  Ask a static type checker to confirm that a line of code is unreachable.

  以下はプログラム例です:

      def int_or_str(arg: int | str) -> None:
          match arg:
              case int():
                  print("It's an int")
              case str():
                  print("It's a str")
              case _ as unreachable:
                  assert_never(unreachable)

  Here, the annotations allow the type checker to infer that the last case can never execute, because `arg` is either an `int` or a `str`, and both options are covered by earlier cases.

  If a type checker finds that a call to `assert_never()` is reachable, it will emit an error. For example, if the type annotation for `arg` was instead `int | str | float`, the type checker would emit an error pointing out that `unreachable` is of type `float`. For a call to `assert_never` to pass type checking, the inferred type of the argument passed in must be the bottom type, `Never`, and nothing else.

  At runtime, this throws an exception when called.

  参考

  Unreachable Code and Exhaustiveness Checking has more information about exhaustiveness checking with static typing.

  Added in version 3.11.

typing.reveal_type(obj, /)

  Ask a static type checker to reveal the inferred type of an expression.

  When a static type checker encounters a call to this function, it emits a diagnostic with the inferred type of the argument. For example:

      x: int = 1
      reveal_type(x)  # Revealed type is "builtins.int"

  This can be useful when you want to debug how your type checker handles a particular piece of code.

  At runtime, this function prints the runtime type of its argument to `sys.stderr` and returns the argument unchanged (allowing the call to be used within an expression):

      x = reveal_type(1)  # prints "Runtime type is int"
      print(x)  # prints "1"

  Note that the runtime type may be different from (more or less specific than) the type statically inferred by a type checker.

  Most type checkers support `reveal_type()` anywhere, even if the name is not imported from `typing`. Importing the name from `typing`, however, allows your code to run without runtime errors and communicates intent more clearly.

  Added in version 3.11.

@typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_default=False, frozen_default=False, field_specifiers=(), **kwargs)

  Decorator to mark an object as providing `dataclass`-like behavior.

  `dataclass_transform` may be used to decorate a class, metaclass, or a function that is itself a decorator. The presence of `@dataclass_transform()` tells a static type checker that the decorated object performs runtime "magic" that transforms a class in a similar way to `@dataclasses.dataclass`.

  Example usage with a decorator function:

      @dataclass_transform()
      def create_model[T](cls: type[T]) -> type[T]:
          ...
          return cls

      @create_model
      class CustomerModel:
          id: int
          name: str

  On a base class:

      @dataclass_transform()
      class ModelBase: ...

      class CustomerModel(ModelBase):
          id: int
          name: str

  On a metaclass:

      @dataclass_transform()
      class ModelMeta(type): ...

      class ModelBase(metaclass=ModelMeta): ...

      class CustomerModel(ModelBase):
          id: int
          name: str

  The `CustomerModel` classes defined above will be treated by type checkers similarly to classes created with `@dataclasses.dataclass`. For example, type checkers will assume these classes have `__init__` methods that accept `id` and `name`.

  The decorated class, metaclass, or function may accept the following bool arguments which type checkers will assume have the same effect as they would have on the `@dataclasses.dataclass` decorator: `init`, `eq`, `order`, `unsafe_hash`, `frozen`, `match_args`, `kw_only`, and `slots`. It must be possible for the value of these arguments (`True` or `False`) to be statically evaluated.

  The arguments to the `dataclass_transform` decorator can be used to customize the default behaviors of the decorated class, metaclass, or function:

  パラメータ:

    - eq_default (bool) -- Indicates whether the `eq` parameter is assumed to be `True` or `False` if it is omitted by the caller. Defaults to `True`.

    - order_default (bool) -- Indicates whether the `order` parameter is assumed to be `True` or `False` if it is omitted by the caller. Defaults to `False`.

    - kw_only_default (bool) -- Indicates whether the `kw_only` parameter is assumed to be `True` or `False` if it is omitted by the caller. Defaults to `False`.

    - frozen_default (bool) --

      Indicates whether the `frozen` parameter is assumed to be `True` or `False` if it is omitted by the caller. Defaults to `False`.

      Added in version 3.12.

    - field_specifiers (tuple[Callable[..., Any], ...]) -- Specifies a static list of supported classes or functions that describe fields, similar to `dataclasses.field()`. Defaults to `()`.

    - **kwargs (Any) -- Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.

  Type checkers recognize the following optional parameters on field specifiers:

  Recognised parameters for field specifiers
  Parameter name | 説明
  --- | ---
  `init` | Indicates whether the field should be included in the synthesized `__init__` method. If unspecified, `init` defaults to `True`.
  `default` | Provides the default value for the field.
  `default_factory` | Provides a runtime callback that returns the default value for the field. If neither `default` nor `default_factory` are specified, the field is assumed to have no default value and must be provided a value when the class is instantiated.
  `factory` | An alias for the `default_factory` parameter on field specifiers.
  `kw_only` | Indicates whether the field should be marked as keyword-only. If `True`, the field will be keyword-only. If `False`, it will not be keyword-only. If unspecified, the value of the `kw_only` parameter on the object decorated with `dataclass_transform` will be used, or if that is unspecified, the value of `kw_only_default` on `dataclass_transform` will be used.
  `alias` | Provides an alternative name for the field. This alternative name is used in the synthesized `__init__` method.

  At runtime, this decorator records its arguments in the `__dataclass_transform__` attribute on the decorated object. It has no other runtime effect.

  より詳しくは PEP 681 を参照してください。

  Added in version 3.11.

@typing.overload

  Decorator for creating overloaded functions and methods.

  The `@overload` decorator allows describing functions and methods that support multiple different combinations of argument types. A series of `@overload`-decorated definitions must be followed by exactly one non-`@overload`-decorated definition (for the same function/method).

  `@overload`-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-`@overload`-decorated definition. The non-`@overload`-decorated definition, meanwhile, will be used at runtime but should be ignored by a type checker. At runtime, calling an `@overload`-decorated function directly will raise `NotImplementedError`.

  An example of overload that gives a more precise type than can be expressed using a union or a type variable:

      @overload
      def process(response: None) -> None:
          ...
      @overload
      def process(response: int) -> tuple[int, str]:
          ...
      @overload
      def process(response: bytes) -> str:
          ...
      def process(response):
          ...  # actual implementation goes here

  詳細と他の型付け意味論との比較は PEP 484 を参照してください。

  バージョン 3.11 で変更: Overloaded functions can now be introspected at runtime using `get_overloads()`.

typing.get_overloads(func)

  Return a sequence of `@overload`-decorated definitions for func.

  func is the function object for the implementation of the overloaded function. For example, given the definition of `process` in the documentation for `@overload`, `get_overloads(process)` will return a sequence of three function objects for the three defined overloads. If called on a function with no overloads, `get_overloads()` returns an empty sequence.

  `get_overloads()` can be used for introspecting an overloaded function at runtime.

  Added in version 3.11.

typing.clear_overloads()

  Clear all registered overloads in the internal registry.

  This can be used to reclaim the memory used by the registry.

  Added in version 3.11.

@typing.final

  Decorator to indicate final methods and final classes.

  Decorating a method with `@final` indicates to a type checker that the method cannot be overridden in a subclass. Decorating a class with `@final` indicates that it cannot be subclassed.

  例えば:

      class Base:
          @final
          def done(self) -> None:
              ...
      class Sub(Base):
          def done(self) -> None:  # Error reported by type checker
              ...

      @final
      class Leaf:
          ...
      class Other(Leaf):  # Error reported by type checker
          ...

  この機能は実行時には検査されません。詳細については PEP 591 を参照してください。

  Added in version 3.8.

  バージョン 3.11 で変更: The decorator will now attempt to set a `__final__` attribute to `True` on the decorated object. Thus, a check like `if getattr(obj, "__final__", False)` can be used at runtime to determine whether an object `obj` has been marked as final. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.

@typing.no_type_check

  アノテーションが型ヒントでないことを示すデコレータです。

  This works as a class or function decorator. With a class, it applies recursively to all methods and classes defined in that class (but not to methods defined in its superclasses or subclasses). Type checkers will ignore all annotations in a function or class with this decorator.

  `@no_type_check` mutates the decorated object in place.

@typing.override

  Decorator to indicate that a method in a subclass is intended to override a method or attribute in a superclass.

  Type checkers should emit an error if a method decorated with `@override` does not, in fact, override anything. This helps prevent bugs that may occur when a base class is changed without an equivalent change to a child class.

  例えば:

      class Base:
          def log_status(self) -> None:
              ...

      class Sub(Base):
          @override
          def log_status(self) -> None:  # Okay: overrides Base.log_status
              ...

          @override
          def done(self) -> None:  # Error reported by type checker
              ...

  There is no runtime checking of this property.

  The decorator will attempt to set an `__override__` attribute to `True` on the decorated object. Thus, a check like `if getattr(obj, "__override__", False)` can be used at runtime to determine whether an object `obj` has been marked as an override. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.

  See PEP 698 for more details.

  Added in version 3.12.

@typing.disjoint_base

  Decorator to mark a class as a disjoint base.

  Type checkers do not allow child classes of a disjoint base `C` to inherit from other disjoint bases that are not parent or child classes of `C`.

  例えば:

      @disjoint_base
      class Disjoint1: pass

      @disjoint_base
      class Disjoint2: pass

      class Disjoint3(Disjoint1, Disjoint2): pass  # Type checker error

  Type checkers can use knowledge of disjoint bases to detect unreachable code and determine when two types can overlap.

  The corresponding runtime concept is a solid base (see Multiple inheritance). Classes that are solid bases at runtime can be marked with `@disjoint_base` in stub files. Users may also mark other classes as disjoint bases to indicate to type checkers that multiple inheritance with other disjoint bases should not be allowed.

  Note that the concept of a solid base is a CPython implementation detail, and the exact set of standard library classes that are disjoint bases at runtime may change in future versions of Python.

  Added in version 3.15.

@typing.type_check_only

  Decorator to mark a class or function as unavailable at runtime.

  このデコレータ自身は実行時には使えません。 このデコレータは主に、実装がプライベートクラスのインスタンスを返す場合に、型スタブファイルに定義されているクラスに対して印を付けるためのものです:

      @type_check_only
      class Response:  # private or not available at runtime
          code: int
          def get_header(self, name: str) -> str: ...

      def fetch_response() -> Response: ...

  プライベートクラスのインスタンスを返すのは推奨されません。 そのようなクラスは公開クラスにするのが望ましいです。

### Introspection helpers

typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False, *, format=Format.VALUE)

  Return a dictionary containing type hints for a function, method, module, class object, or other callable object.

  This is often the same as `annotationlib.get_annotations()`, but this function makes the following changes to the annotations dictionary:

  - Forward references encoded as string literals or `ForwardRef` objects are handled by evaluating them in globalns, localns, and (where applicable) obj's type parameter namespace. If globalns or localns is not given, appropriate namespace dictionaries are inferred from obj.

  - `None` is replaced with `types.NoneType`.

  - If `@no_type_check` has been applied to obj, an empty dictionary is returned.

  - If obj is a class `C`, the function returns a dictionary that merges annotations from `C`'s base classes with those on `C` directly. This is done by traversing `C.__mro__` and iteratively combining annotations of each base class. Annotations on classes appearing earlier in the method resolution order always take precedence over annotations on classes appearing later in the method resolution order.

  - The function recursively replaces all occurrences of `Annotated[T, ...]`, `Required[T]`, `NotRequired[T]`, and `ReadOnly[T]` with `T`, unless include_extras is set to `True` (see `Annotated` for more information).

  注意

  This function may execute arbitrary code contained in annotations. See Security implications of introspecting annotations for more information.

  注釈

  If `Format.VALUE` is used and any forward references in the annotations of obj are not resolvable, a `NameError` exception is raised. For example, this can happen with names imported under `if TYPE_CHECKING`. More generally, any kind of exception can be raised if an annotation contains invalid Python code.

  注釈

  Calling `get_type_hints()` on an instance is not supported. To retrieve annotations for an instance, call `get_type_hints()` on the instance's class instead (for example, `get_type_hints(type(obj))`).

  バージョン 3.9 で変更: Added `include_extras` parameter as part of PEP 593. See the documentation on `Annotated` for more information.

  バージョン 3.11 で変更: Previously, `Optional[t]` was added for function and method annotations if a default value equal to `None` was set. Now the annotation is returned unchanged.

  バージョン 3.14 で変更: Added the `format` parameter. See the documentation on `annotationlib.get_annotations()` for more information.

  バージョン 3.14 で変更: Calling `get_type_hints()` on instances is no longer supported. Some instances were accepted in earlier versions as an undocumented implementation detail.

typing.get_origin(tp)

  Get the unsubscripted version of a type: for a typing object of the form `X[Y, Z, ...]` return `X`.

  If `X` is a typing-module alias for a builtin or `collections` class, it will be normalized to the original class. If `X` is an instance of `ParamSpecArgs` or `ParamSpecKwargs`, return the underlying `ParamSpec`. Return `None` for unsupported objects.

  例:

      assert get_origin(str) is None
      assert get_origin(Dict[str, int]) is dict
      assert get_origin(Union[int, str]) is Union
      assert get_origin(Annotated[str, "metadata"]) is Annotated
      P = ParamSpec('P')
      assert get_origin(P.args) is P
      assert get_origin(P.kwargs) is P

  Added in version 3.8.

typing.get_args(tp)

  Get type arguments with all substitutions performed: for a typing object of the form `X[Y, Z, ...]` return `(Y, Z, ...)`.

  If `X` is a union or `Literal` contained in another generic type, the order of `(Y, Z, ...)` may be different from the order of the original arguments `[Y, Z, ...]` due to type caching. Return `()` for unsupported objects.

  例:

      assert get_args(int) == ()
      assert get_args(Dict[int, str]) == (int, str)
      assert get_args(Union[int, str]) == (int, str)

  Added in version 3.8.

typing.get_protocol_members(tp)

  Return the set of members defined in a `Protocol`.

      >>> from typing import Protocol, get_protocol_members
      >>> class P(Protocol):
      ...     def a(self) -> str: ...
      ...     b: int
      >>> get_protocol_members(P) == frozenset({'a', 'b'})
      True

  Raise `TypeError` for arguments that are not Protocols.

  Added in version 3.13.

typing.is_protocol(tp)

  Determine if a type is a `Protocol`.

  例えば:

      class P(Protocol):
          def a(self) -> str: ...
          b: int

      assert is_protocol(P)
      assert not is_protocol(int)

  This function only returns true for `Protocol` classes, not for generic aliases of them:

      class GenericP[T](Protocol):
          def a(self) -> T: ...
          b: int

      assert not is_protocol(GenericP[int])

  Added in version 3.13.

typing.is_typeddict(tp)

  Check if a type is a `TypedDict`.

  例えば:

      class Film(TypedDict):
          title: str
          year: int

      assert is_typeddict(Film)
      assert not is_typeddict(list | str)

      # TypedDict is a factory for creating typed dicts,
      # not a typed dict itself
      assert not is_typeddict(TypedDict)

  This function only returns true for `TypedDict` classes, not for generic aliases of them:

      class GenericFilm[T](TypedDict):
          title: str
          year: T

      assert not is_typeddict(GenericFilm[int])

  Added in version 3.10.

class typing.ForwardRef

  Class used for internal typing representation of string forward references.

  For example, `List["SomeClass"]` is implicitly transformed into `List[ForwardRef("SomeClass")]`. `ForwardRef` should not be instantiated by a user, but may be used by introspection tools.

  注釈

  PEP 585 generic types such as `list["SomeClass"]` will not be implicitly transformed into `list[ForwardRef("SomeClass")]` and thus will not automatically resolve to `list[SomeClass]`.

  Added in version 3.7.4.

  バージョン 3.14 で変更: This is now an alias for `annotationlib.ForwardRef`. Several undocumented behaviors of this class have been changed; for example, after a `ForwardRef` has been evaluated, the evaluated value is no longer cached.

typing.evaluate_forward_ref(forward_ref, *, owner=None, globals=None, locals=None, type_params=None, format=annotationlib.Format.VALUE)

  Evaluate an `annotationlib.ForwardRef` as a type hint.

  This is similar to calling `annotationlib.ForwardRef.evaluate()`, but unlike that method, `evaluate_forward_ref()` also recursively evaluates forward references nested within the type hint.

  See the documentation for `annotationlib.ForwardRef.evaluate()` for the meaning of the owner, globals, locals, type_params, and format parameters.

  注意

  This function may execute arbitrary code contained in annotations. See Security implications of introspecting annotations for more information.

  Added in version 3.14.

typing.NoDefault

  A sentinel object used to indicate that a type parameter has no default value. For example:

      >>> T = TypeVar("T")
      >>> T.__default__ is typing.NoDefault
      True
      >>> S = TypeVar("S", default=None)
      >>> S.__default__ is None
      True

  Added in version 3.13.

typing.NoExtraItems

  A `sentinel` object used to indicate that a `TypedDict` does not have the extra_items class argument.

      >>> from typing import TypedDict, NoExtraItems
      >>> class Point(TypedDict):
      ...     x: int
      ...     y: int
      ...
      >>> Point.__extra_items__ is NoExtraItems
      True

### 定数

typing.TYPE_CHECKING

  A special constant that is assumed to be `True` by static type checkers. It's `False` at runtime.

  A module which is expensive to import, and which only contain types used for typing annotations, can be safely imported inside an `if TYPE_CHECKING:` block. This prevents the module from actually being imported at runtime; annotations aren't eagerly evaluated (see PEP 649) so using undefined symbols in annotations is harmless--as long as you don't later examine them. Your static type analysis tool will set `TYPE_CHECKING` to `True` during static type analysis, which means the module will be imported and the types will be checked properly during such analysis.

  使い方:

      if TYPE_CHECKING:
          import expensive_mod

      def fun(arg: expensive_mod.SomeType) -> None:
          local_var: expensive_mod.AnotherType = other_fun()

  If you occasionally need to examine type annotations at runtime which may contain undefined symbols, use `annotationlib.get_annotations()` with a `format` parameter of `annotationlib.Format.STRING` or `annotationlib.Format.FORWARDREF` to safely retrieve the annotations without raising `NameError`.

  Added in version 3.5.2.

### 非推奨のエイリアス

This module defines several deprecated aliases to pre-existing standard library classes. These were originally included in the `typing` module in order to support parameterizing these generic classes using `[]`. However, the aliases became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support `[]` (see PEP 585).

The redundant types are deprecated as of Python 3.9. However, while the aliases may be removed at some point, removal of these aliases is not currently planned. As such, no deprecation warnings are currently issued by the interpreter for these aliases.

If at some point it is decided to remove these deprecated aliases, a deprecation warning will be issued by the interpreter for at least two releases prior to removal. The aliases are guaranteed to remain in the `typing` module without deprecation warnings until at least Python 3.14.

Type checkers are encouraged to flag uses of the deprecated types if the program they are checking targets a minimum Python version of 3.9 or newer.

#### Aliases to built-in types

class typing.Dict(dict, MutableMapping[KT, VT])

  `dict` の非推奨なエイリアス。

  Note that to annotate arguments, it is preferred to use an abstract collection type such as `Mapping` rather than to use `dict` or `typing.Dict`.

  バージョン 3.9 で非推奨: `builtins.dict` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.List(list, MutableSequence[T])

  `list` の非推奨なエイリアス。

  Note that to annotate arguments, it is preferred to use an abstract collection type such as `Sequence` or `Iterable` rather than to use `list` or `typing.List`.

  バージョン 3.9 で非推奨: `builtins.list` は添字表記 (`[]`) をサポートするようになりました。PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Set(set, MutableSet[T])

  Deprecated alias to `builtins.set`.

  Note that to annotate arguments, it is preferred to use an abstract collection type such as `collections.abc.Set` rather than to use `set` or `typing.Set`.

  バージョン 3.9 で非推奨: `builtins.set` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.FrozenSet(frozenset, AbstractSet[T_co])

  Deprecated alias to `builtins.frozenset`.

  バージョン 3.9 で非推奨: `builtins.frozenset` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

typing.Tuple

  `tuple` の非推奨なエイリアス。

  `tuple` and `Tuple` are special-cased in the type system; see タプルのアノテーション for more details.

  バージョン 3.9 で非推奨: `builtins.tuple` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Type(Generic[CT_co])

  `type` の非推奨なエイリアス。

  See クラスオブジェクトの型 for details on using `type` or `typing.Type` in type annotations.

  Added in version 3.5.2.

  バージョン 3.9 で非推奨: `builtins.type` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

#### Aliases to types in `collections`

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

  `collections.defaultdict` の非推奨なエイリアス。

  Added in version 3.5.2.

  バージョン 3.9 で非推奨: `collections.defaultdict` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])

  `collections.OrderedDict` の非推奨なエイリアス。

  Added in version 3.7.2.

  バージョン 3.9 で非推奨: `collections.OrderedDict` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])

  `collections.ChainMap` の非推奨なエイリアス。

  Added in version 3.6.1.

  バージョン 3.9 で非推奨: `collections.ChainMap` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Counter(collections.Counter, Dict[T, int])

  `collections.Counter` の非推奨なエイリアス。

  Added in version 3.6.1.

  バージョン 3.9 で非推奨: `collections.Counter` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Deque(deque, MutableSequence[T])

  `collections.deque` の非推奨なエイリアス。

  Added in version 3.6.1.

  バージョン 3.9 で非推奨: `collections.deque` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

#### Aliases to other concrete types

class typing.Pattern
class typing.Match

  Deprecated aliases corresponding to the return types from `re.compile()` and `re.search()`.

  These types (and the corresponding functions) are generic over `AnyStr`. `Pattern` can be specialised as `Pattern[str]` or `Pattern[bytes]`; `Match` can be specialised as `Match[str]` or `Match[bytes]`.

  バージョン 3.9 で非推奨: Classes `Pattern` and `Match` from `re` now support `[]`. See PEP 585 and ジェネリックエイリアス型.

class typing.Text

  Deprecated alias for `str`.

  `Text` is provided to supply a forward compatible path for Python 2 code: in Python 2, `Text` is an alias for `unicode`.

  `Text` は Python 2 と Python 3 の両方と互換性のある方法で値が unicode 文字列を含んでいなければならない場合に使用してください。

      def add_unicode_checkmark(text: Text) -> Text:
          return text + u' \u2713'

  Added in version 3.5.2.

  バージョン 3.11 で非推奨: Python 2 is no longer supported, and most type checkers also no longer support type checking Python 2 code. Removal of the alias is not currently planned, but users are encouraged to use `str` instead of `Text`.

#### Aliases to container ABCs in `collections.abc`

class typing.AbstractSet(Collection[T_co])

  `collections.abc.Set` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.Set` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.ByteString(Sequence[int])

  Deprecated alias to `collections.abc.ByteString`.

  Use `isinstance(obj, collections.abc.Buffer)` to test if `obj` implements the buffer protocol at runtime. For use in type annotations, either use `Buffer` or a union that explicitly specifies the types your code supports (e.g., `bytes | bytearray | memoryview`).

  `ByteString` was originally intended to be an abstract class that would serve as a supertype of both `bytes` and `bytearray`. However, since the ABC never had any methods, knowing that an object was an instance of `ByteString` never actually told you anything useful about the object. Other common buffer types such as `memoryview` were also never understood as subtypes of `ByteString` (either at runtime or by static type checkers).

  See PEP 688 for more details.

  バージョン 3.9 で非推奨、バージョン 3.17 で削除予定.

class typing.Collection(Sized, Iterable[T_co], Container[T_co])

  `collections.abc.Collection` の非推奨なエイリアス。

  Added in version 3.6.

  バージョン 3.9 で非推奨: `collections.abc.Collection` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Container(Generic[T_co])

  `collections.abc.Container` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.Container` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.ItemsView(MappingView, AbstractSet[tuple[KT_co, VT_co]])

  `collections.abc.ItemsView` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.ItemsView` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.KeysView(MappingView, AbstractSet[KT_co])

  `collections.abc.KeysView` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.KeysView` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Mapping(Collection[KT], Generic[KT, VT_co])

  `collections.abc.Mapping` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.Mapping` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.MappingView(Sized)

  `collections.abc.MappingView` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.MappingView` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.MutableMapping(Mapping[KT, VT])

  `collections.abc.MutableMapping` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.MutableMapping` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.MutableSequence(Sequence[T])

  `collections.abc.MutableSequence` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.MutableSequence` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.MutableSet(AbstractSet[T])

  `collections.abc.MutableSet` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.MutableSet` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Sequence(Reversible[T_co], Collection[T_co])

  `collections.abc.Sequence` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.Sequence` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.ValuesView(MappingView, Collection[_VT_co])

  `collections.abc.ValuesView` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.ValuesView` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

#### Aliases to asynchronous ABCs in `collections.abc`

class typing.Coroutine(Awaitable[ReturnType], Generic[YieldType, SendType, ReturnType])

  `collections.abc.Coroutine` の非推奨なエイリアス。

  See Annotating generators and coroutines for details on using `collections.abc.Coroutine` and `typing.Coroutine` in type annotations.

  Added in version 3.5.3.

  バージョン 3.9 で非推奨: `collections.abc.Coroutine` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.AsyncGenerator(AsyncIterator[YieldType], Generic[YieldType, SendType])

  `collections.abc.AsyncGenerator` の非推奨なエイリアス。

  See Annotating generators and coroutines for details on using `collections.abc.AsyncGenerator` and `typing.AsyncGenerator` in type annotations.

  Added in version 3.6.1.

  バージョン 3.9 で非推奨: `collections.abc.AsyncGenerator` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

  バージョン 3.13 で変更: The `SendType` parameter now has a default.

class typing.AsyncIterable(Generic[T_co])

  `collections.abc.AsyncIterable` の非推奨なエイリアス。

  Added in version 3.5.2.

  バージョン 3.9 で非推奨: `collections.abc.AsyncIterable` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.AsyncIterator(AsyncIterable[T_co])

  `collections.abc.AsyncIterator` の非推奨なエイリアス。

  Added in version 3.5.2.

  バージョン 3.9 で非推奨: `collections.abc.AsyncIterator` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Awaitable(Generic[T_co])

  `collections.abc.Awaitable` の非推奨なエイリアス。

  Added in version 3.5.2.

  バージョン 3.9 で非推奨: `collections.abc.Awaitable` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

#### Aliases to other ABCs in `collections.abc`

class typing.Iterable(Generic[T_co])

  `collections.abc.Iterable` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.Iterable` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Iterator(Iterable[T_co])

  `collections.abc.Iterator` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.Iterator` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

typing.Callable

  `collections.abc.Callable` の非推奨なエイリアス。

  See 呼び出し可能オブジェクトのアノテーション for details on how to use `collections.abc.Callable` and `typing.Callable` in type annotations.

  バージョン 3.9 で非推奨: `collections.abc.Callable` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

  バージョン 3.10 で変更: `Callable` は `ParamSpec` と `Concatenate` をサポートしました。詳細は PEP 612 を参照してください。

class typing.Generator(Iterator[YieldType], Generic[YieldType, SendType, ReturnType])

  `collections.abc.Generator` の非推奨なエイリアス。

  See Annotating generators and coroutines for details on using `collections.abc.Generator` and `typing.Generator` in type annotations.

  バージョン 3.9 で非推奨: `collections.abc.Generator` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

  バージョン 3.13 で変更: Default values for the send and return types were added.

class typing.Hashable

  `collections.abc.Hashable` の非推奨なエイリアス。

  バージョン 3.12 で非推奨: 代わりに `collections.abc.Hashable` を直接使用してください。

class typing.Reversible(Iterable[T_co])

  `collections.abc.Reversible` の非推奨なエイリアス。

  バージョン 3.9 で非推奨: `collections.abc.Reversible` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

class typing.Sized

  `collections.abc.Sized` の非推奨なエイリアス。

  バージョン 3.12 で非推奨: 代わりに `collections.abc.Sized` を直接使用してください。

#### Aliases to `contextlib` ABCs

class typing.ContextManager(Generic[T_co, ExitT_co])

  `contextlib.AbstractContextManager` の非推奨なエイリアス。

  The first type parameter, `T_co`, represents the type returned by the `__enter__()` method. The optional second type parameter, `ExitT_co`, which defaults to `bool | None`, represents the type returned by the `__exit__()` method.

  Added in version 3.5.4.

  バージョン 3.9 で非推奨: `contextlib.AbstractContextManager` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

  バージョン 3.13 で変更: Added the optional second type parameter, `ExitT_co`.

class typing.AsyncContextManager(Generic[T_co, AExitT_co])

  `contextlib.AbstractAsyncContextManager` の非推奨なエイリアス。

  The first type parameter, `T_co`, represents the type returned by the `__aenter__()` method. The optional second type parameter, `AExitT_co`, which defaults to `bool | None`, represents the type returned by the `__aexit__()` method.

  Added in version 3.6.2.

  バージョン 3.9 で非推奨: `contextlib.AbstractAsyncContextManager` は添字表記 (`[]`) をサポートするようになりました。 PEP 585 と ジェネリックエイリアス型 を参照してください。

  バージョン 3.13 で変更: Added the optional second type parameter, `AExitT_co`.

## メジャーな機能の非推奨時系列

`typing` の機能の中には非推奨のものがあり、Python の将来のバージョンで削除される可能性があります。以下の表は主な非推奨機能をまとめたものです。これは変更される可能性があり、すべての非推奨機能がリストされているわけではありません。

機能 | 非推奨となるバージョン | 削除予定のバージョン | PEP/issue
--- | --- | --- | ---
標準コレクションのエイリアス | 3.9 | 未定（非推奨のエイリアス を参照） | PEP 585
`typing.ByteString` | 3.9 | 3.17 | gh-91896
`typing.Text` | 3.11 | 未定 | gh-92332
`typing.Hashable`、`typing.Sized` | 3.12 | 未定 | gh-94309
`typing.TypeAlias` | 3.12 | 未定 | PEP 695
`typing.AnyStr` | 3.13 | 3.18 | gh-105578