"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.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, *, 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.

   __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.

   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.

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.

   __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

class typing.ParamSpecArgs
class 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=())

   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'

   __module__

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

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

   __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".

   バージョン 3.13 で非推奨、バージョン 3.15 で削除予定: The
   undocumented keyword argument syntax for creating NamedTuple
   classes ("NT = NamedTuple("NT", x=int)") is deprecated, and will be
   disallowed in 3.15. Use the class-based syntax or the functional
   syntax instead.

   バージョン 3.13 で非推奨、バージョン 3.15 で削除予定: When using
   the functional syntax to create a NamedTuple class, failing to pass
   a value to the 'fields' parameter ("NT = NamedTuple("NT")") is
   deprecated. Passing "None" to the 'fields' parameter ("NT =
   NamedTuple("NT", None)") is also deprecated. Both will be
   disallowed in Python 3.15. To create a NamedTuple class with 0
   fields, use "class NT(NamedTuple): pass" or "NT = NamedTuple("NT",
   [])".

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.

@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)

   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.

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

   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.

   Support for "ReadOnly" is reflected in the following attributes:

   __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.

   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.

   バージョン 3.13 で非推奨、バージョン 3.15 で削除予定: When using
   the functional syntax to create a TypedDict class, failing to pass
   a value to the 'fields' parameter ("TD = TypedDict("TD")") is
   deprecated. Passing "None" to the 'fields' parameter ("TD =
   TypedDict("TD", None)") is also deprecated. Both will be disallowed
   in Python 3.15. To create a TypedDict class with 0 fields, use
   "class TD(TypedDict): pass" or "TD = TypedDict("TD", {})".


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.no_type_check_decorator

   Decorator to give another decorator the "no_type_check()" effect.

   This wraps the decorator with something that wraps the decorated
   function in "no_type_check()".

   バージョン 3.13 で非推奨、バージョン 3.15 で削除予定: No type
   checker ever added support for "@no_type_check_decorator". It is
   therefore deprecated, and will be removed in Python 3.15.

@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.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.


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.match()".

   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  | 3.9                       | 未定（非推奨のエイリアス  | **PEP 585**               |
| アス                      |                           | を参照）                  |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.ByteString"       | 3.9                       | 3.17                      | gh-91896                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Text"             | 3.11                      | 未定                      | gh-92332                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Hashable"、       | 3.12                      | 未定                      | gh-94309                  |
| "typing.Sized"            |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.TypeAlias"        | 3.12                      | 未定                      | **PEP 695**               |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "@typing.no_type_check_d  | 3.13                      | 3.15                      | gh-106309                 |
| ecorator"                 |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.AnyStr"           | 3.13                      | 3.18                      | gh-105578                 |
+---------------------------+---------------------------+---------------------------+---------------------------+
