"typing" --- Support for type hints
***********************************

バージョン 3.5 で追加.

**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。

======================================================================

This module provides runtime support for type hints. For the original
specification of the typing system, see **PEP 484**. For a simplified
introduction to type hints, see **PEP 483**.

The function below takes and returns a string and is annotated as
follows:

   def greeting(name: str) -> str:
       return 'Hello ' + name

In the function "greeting", the argument "name" is expected to be of
type "str" and the return type "str". Subtypes are accepted as
arguments.

New features are frequently added to the "typing" module. The
typing_extensions package provides backports of these new features to
older versions of Python.

For a summary of deprecated features and a deprecation timeline,
please see Deprecation Timeline of Major Features.

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)

  mypy ドキュメンテーション の "Type System Reference" セクション
     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。


Relevant PEPs
=============

Since the initial introduction of type hints in **PEP 484** and **PEP
483**, a number of PEPs have modified and enhanced Python's framework
for type annotations:

* **PEP 526**: Syntax for Variable Annotations
     *Introducing* syntax for annotating variables outside of function
     definitions, and "ClassVar"

* **PEP 544**: Protocols: Structural subtyping (static duck typing)
     *Introducing* "Protocol" and the "@runtime_checkable" decorator

* **PEP 585**: Type Hinting Generics In Standard Collections
     *Introducing* "types.GenericAlias" and the ability to use
     standard library classes as generic types

* **PEP 586**: Literal Types
     *Introducing* "Literal"

* **PEP 589**: TypedDict: Type Hints for Dictionaries with a Fixed Set
  of Keys
     *Introducing* "TypedDict"

* **PEP 591**: Adding a final qualifier to typing
     *Introducing* "Final" and the "@final" decorator

* **PEP 593**: Flexible function and variable annotations
     *Introducing* "Annotated"

* **PEP 604**: Allow writing union types as "X | Y"
     *Introducing* "types.UnionType" and the ability to use the
     binary-or operator "|" to signify a union of types

* **PEP 612**: Parameter Specification Variables
     *Introducing* "ParamSpec" and "Concatenate"

* **PEP 613**: Explicit Type Aliases
     *Introducing* "TypeAlias"

* **PEP 646**: Variadic Generics
     *Introducing* "TypeVarTuple"

* **PEP 647**: User-Defined Type Guards
     *Introducing* "TypeGuard"

* **PEP 655**: Marking individual TypedDict items as required or
  potentially missing
     *Introducing* "Required" and "NotRequired"

* **PEP 673**: Self type
     *Introducing* "Self"

* **PEP 675**: Arbitrary Literal String Type
     *Introducing* "LiteralString"

* **PEP 681**: Data Class Transforms
     *Introducing* the "@dataclass_transform" decorator


型エイリアス
============

A type alias is defined by assigning the type to the alias. In this
example, "Vector" and "list[float]" will be treated as interchangeable
synonyms:

   Vector = list[float]

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

   # passes type checking; a list of floats qualifies as a Vector.
   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

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

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

   # The static type checker will treat the previous type signature as
   # being exactly equivalent to this one.
   def broadcast_message(
           message: str,
           servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
       ...

Type aliases may be marked with "TypeAlias" to make it explicit that
the statement is a type alias declaration, not a normal variable
assignment:

   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:
       ...

   # passes type checking
   user_a = get_user_name(UserId(42351))

   # fails type checking; an int is not a UserId
   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' is of type 'int', not 'UserId'
   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)

   # Fails at runtime and does not pass type checking
   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:

  Recall that the use of a type alias declares two types to be
  *equivalent* to one another. Doing "Alias = Original" will make the
  static type checker treat "Alias" as being *exactly equivalent* to
  "Original" in all cases. This is useful when you want to simplify
  complex type signatures.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)" とすると静的型検査器は "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。これは論理的な誤り
  を最小の実行時のコストで防ぎたい時に有用です。

バージョン 3.5.2 で追加.

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
========================================

Functions -- or other *callable* objects -- can be annotated using
"collections.abc.Callable" or "typing.Callable". "Callable[[int],
str]" signifies a function that takes a single parameter of type "int"
and returns a "str".

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

添字表記は常に2つの値とともに使われなければなりません。実引数のリスト
と返り値の型です。 実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  # Also 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)   # Error! Argument 2 has incompatible type because of
                            # different name and kind in the callback

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] indicates that all elements in the sequence
   # must be instances of "Employee".
   # Mapping[str, str] indicates that all keys and all values in the mapping
   # must be strings.
   def notify_by_email(employees: Sequence[Employee],
                       overrides: Mapping[str, str]) -> None: ...

Generics can be parameterized by using a factory available in typing
called "TypeVar".

   from collections.abc import Sequence
   from typing import TypeVar

   T = TypeVar('T')                  # Declare type variable "T"

   def first(l: Sequence[T]) -> T:   # Function is generic over the TypeVar "T"
       return l[0]


タプルのアノテーション
======================

For most containers in Python, the typing system assumes that all
elements in the container will be of the same type. For example:

   from collections.abc import Mapping

   # Type checker will infer that all elements in ``x`` are meant to be ints
   x: list[int] = []

   # Type checker error: ``list`` only accepts a single type argument:
   y: list[int, str] = [1, 'foo']

   # Type checker will infer that all keys in ``z`` are meant to be strings,
   # and that all values in ``z`` are meant to be either strings or ints
   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`` is assigned to a tuple of length 1 where the sole element is an int
   x: tuple[int] = (5,)

   # OK: ``y`` is assigned to a tuple of length 2;
   # element 1 is an int, element 2 is a str
   y: tuple[int, str] = (5, "foo")

   # Error: the type annotation indicates a tuple of length 1,
   # but ``z`` has been assigned to a tuple of length 3
   z: tuple[int] = (1, 2, 3)

*任意の* 長さで、全ての要素が同じ型 "T" であるタプルを示すには
"tuple[T, ...]" を使います。空のタプルを示すには "tuple[()]" を使いま
す。単に "tuple" とアノテーションすることは、"tuple[Any, ...]" と等価
です。

   x: tuple[int, ...] = (1, 2)
   # These reassignments are OK: ``tuple[int, ...]`` indicates x can be of any length
   x = (1, 2, 3)
   x = ()
   # This reassignment is an error: all elements in ``x`` must be ints
   x = ("foo", "bar")

   # ``y`` can only ever be assigned to an empty tuple
   y: tuple[()] = ()

   z: tuple = ("foo", "bar")
   # These reassignments are OK: plain ``tuple`` is equivalent to ``tuple[Any, ...]``
   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 "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.


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 typing import TypeVar, Generic
   from logging import Logger

   T = TypeVar('T')

   class LoggedVar(Generic[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)

"Generic[T]" as a base class defines that the class "LoggedVar" takes
a single type parameter "T" . This also makes "T" valid as a type
within the class body.

The "Generic" base class defines "__class_getitem__()" so that
"LoggedVar[T]" is valid as a type:

   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

   T = TypeVar('T', contravariant=True)
   B = TypeVar('B', bound=Sequence[bytes], covariant=True)
   S = TypeVar('S', int, str)

   class WeirdTrio(Generic[T, B, S]):
       ...

"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
   ...

   T = TypeVar('T')

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

You can use multiple inheritance with "Generic":

   from collections.abc import Sized
   from typing import TypeVar, Generic

   T = TypeVar('T')

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

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

   from collections.abc import Mapping
   from typing import TypeVar

   T = TypeVar('T')

   class MyDict(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
   from typing import TypeVar
   S = TypeVar('S')
   Response = Iterable[S] | int

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

   T = TypeVar('T', int, float, complex)
   Vec = Iterable[tuple[T, T]]

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

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。

User-defined generics for parameter expressions are also supported via
parameter specification variables in the form "Generic[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":

   >>> from typing import Generic, ParamSpec, TypeVar

   >>> T = TypeVar('T')
   >>> P = ParamSpec('P')

   >>> class Z(Generic[T, P]): ...
   ...
   >>> Z[int, [dict, float]]
   __main__.Z[int, (<class 'dict'>, <class 'float'>)]

Furthermore, 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(Generic[P]): ...
   ...
   >>> X[int, str]
   __main__.X[(<class 'int'>, <class 'str'>)]
   >>> X[[int, str]]
   __main__.X[(<class 'int'>, <class '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
========

"Any" は特別な種類の型です。静的型検査器はすべての型を "Any" と互換と
して扱い、 "Any" をすべての型と互換として扱います。

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 compatible with 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** によって上にあるようなクラス定義で基底クラスを明示しないコ
ードをユーザーが書け、静的型チェッカーで "Bucket" が "Sized" と
"Iterable[int]" 両方のサブタイプだと暗黙的に見なせるようになり、この問
題が解決しました。 これは *structural subtyping (構造的部分型)* (ある
いは、静的ダックタイピング) として知られています:

   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。

   * 任意の型は "Any" と互換です。

   * "Any" は任意の型と互換です。

   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!"

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。

   バージョン 3.11 で追加.

typing.Never

   The bottom type, a type that has no members.

   This can be used to define a function that should never be called,
   or a function that never returns:

      from typing import Never

      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

   バージョン 3.11 で追加: On older Python versions, "NoReturn" may be
   used to express the same concept. "Never" was added to make the
   intended meaning more explicit.

typing.NoReturn

   Special type indicating that a function never returns.

   例えば:

      from typing import NoReturn

      def stop() -> NoReturn:
          raise RuntimeError('no way')

   "NoReturn" can also be used as a bottom type, a type that has no
   values. Starting in Python 3.11, the "Never" type should be used
   for this concept instead. Type checkers should treat the two
   equivalently.

   バージョン 3.6.2 で追加.

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。

   バージョン 3.11 で追加.

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 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.
      # 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。

   バージョン 3.10 で追加.


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。

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]

   * 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。

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 currently only valid when used as the first
   argument to a Callable. 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, ParamSpec, TypeVar

      P = ParamSpec('P')
      R = TypeVar('R')

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

      def with_lock(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])

   バージョン 3.10 で追加.

   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
          ...

      Mode: TypeAlias = 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。

   バージョン 3.8 で追加.

   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" はクラスそのものではなく、"isinstance()" や
   "issubclass()" で使うべきではありません。 "ClassVar" は Python の実
   行時の挙動を変えませんが、サードパーティの型検査器で使えます。 例え
   ば、型チェッカーは次のコードをエラーとするかもしれません:

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

   バージョン 3.5.3 で追加.

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。

   バージョン 3.8 で追加.

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.

   バージョン 3.11 で追加.

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。

   バージョン 3.11 で追加.

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)]

   Details of the syntax:

   * The first argument to "Annotated" must be a valid type

   * Multiple metadata elements can be supplied ("Annotated" supports
     variadic arguments):

        @dataclass
        class ctype:
            kind: str

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

     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.

   * "Annotated" must be subscripted with at least two arguments (
     "Annotated[int]" is not valid)

   * The order of the metadata elements is preserved and matters for
     equality checks:

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

   * 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")
        ]

   * 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

        T = TypeVar("T")
        Vec: TypeAlias = Annotated[list[tuple[T, T]], MaxLen(10)]

        assert Vec[int] == Annotated[list[tuple[int, int]], MaxLen(10)]

   * "Annotated" cannot be used with an unpacked "TypeVarTuple":

        Variadic: TypeAlias = Annotated[*Ts, Ann1]  # NOT valid

     This would be equivalent to:

        Annotated[T1, T2, T3, ..., Ann1]

     where "T1", "T2", etc. are "TypeVars". This would be invalid:
     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')

   参考:

     **PEP 593** - Flexible function and variable annotations
        The PEP introducing "Annotated" to the standard library.

   バージョン 3.9 で追加.

typing.TypeGuard

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

   "TypeGuard" can be used to annotate the return type of a user-
   defined type guard function.  "TypeGuard" only accepts a single
   type argument. At runtime, functions marked this way should return
   a boolean.

   "TypeGuard" 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 guard":

      def is_str(val: str | float):
          # "isinstance" type guard
          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 guard.  Such a function should use
   "TypeGuard[...]" as its return type to alert static type checkers
   to this intention.

   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".

   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!")

   If "is_str_list" is a class or instance method, then the type in
   "TypeGuard" maps to the type of the second parameter after "cls" or
   "self".

   In short, the form "def foo(arg: TypeA) -> TypeGuard[TypeB]: ...",
   means that if "foo(arg)" returns "True", then "arg" narrows from
   "TypeA" to "TypeB".

   注釈:

     "TypeB" need not be a narrower form of "TypeA" -- it can even be
     a wider form. 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. The
     responsibility of writing type-safe type guards is left to the
     user.

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

   バージョン 3.10 で追加.

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

   バージョン 3.11 で追加.


Building generic types
~~~~~~~~~~~~~~~~~~~~~~

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

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 inheriting from an
   instantiation of this class with one or more type variables. For
   example, a generic mapping type might be defined as:

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

   このクラスは次のように使用することが出来ます:

      X = TypeVar('X')
      Y = TypeVar('Y')

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

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

   型変数です。

   使い方:

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


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


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

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

   Type variables may be marked covariant or contravariant by passing
   "covariant=True" or "contravariant=True".  See **PEP 484** for more
   details.  By default, type variables are invariant.

   Bound type variables and constrained type variables have different
   semantics in several important ways. Using a *bound* 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

   Type variables can be bound to concrete types, abstract types (ABCs
   or protocols), and even unions of types:

      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 marked as covariant.

   __contravariant__

      Whether the type var has been marked as contravariant.

   __bound__

      The bound of the type variable, if any.

   __constraints__

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

class typing.TypeVarTuple(name)

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

   使い方:

      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:

      Shape = TypeVarTuple("Shape")
      class Array(Generic[*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:

      DType = TypeVar('DType')
      Shape = TypeVarTuple('Shape')

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

      class Array2(Generic[*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(Generic[*Shape, *Shape]):  # Not valid
          pass

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

      def call_soon(
               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.

   バージョン 3.11 で追加.

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

   Parameter specification variable.  A specialized version of type
   variables.

   使い方:

      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
      from typing import TypeVar, ParamSpec
      import logging

      T = TypeVar('T')
      P = ParamSpec('P')

      def add_logging(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 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.

   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.

   バージョン 3.10 で追加.

   注釈:

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

   参考:

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

     * "Concatenate"

     * 呼び出し可能オブジェクトのアノテーション

typing.ParamSpecArgs

typing.ParamSpecKwargs

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

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

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

   バージョン 3.10 で追加.


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。

   最終的に出来上がるクラスには、フィールド名をフィールド型へ対応付け
   る辞書を提供する "__annotations__" 属性が追加されています。 (フィー
   ルド名は "_fields" 属性に、デフォルト値は "_field_defaults" 属性に
   格納されていて、両方とも "namedtuple()" API の一部分です。)

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

   後方互換な使用法:

      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 3.11 で変更: Added support for generic namedtuples.

class typing.NewType(name, tp)

   Helper class to create low-overhead distinct types.

   A "NewType" is considered a distinct type by a typechecker. 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 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.

   バージョン 3.5.2 で追加.

   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.

   プロトコルクラスはジェネリックにもできます。例えば:

      T = TypeVar("T")

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

   バージョン 3.8 で追加.

@typing.runtime_checkable

   Mark a protocol class as a runtime protocol.

   Such a protocol can be used with "isinstance()" and "issubclass()".
   This raises "TypeError" when applied to a non-protocol class.  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)

   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.

   バージョン 3.8 で追加.

class typing.TypedDict(dict)

   Special construct to add type hints to a dictionary. At runtime it
   is a plain "dict".

   "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')

   To allow using this feature with older versions of Python that do
   not support **PEP 526**, "TypedDict" supports two additional
   equivalent syntactic forms:

   * Using a literal "dict" as the second argument:

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

   * Using keyword arguments:

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

   バージョン 3.11 で非推奨、バージョン 3.13 で削除予定: The keyword-
   argument syntax is deprecated in 3.11 and will be removed in 3.13.
   It may also be unsupported by static type checkers.

   The functional syntax should also be used when any of the keys are
   not valid identifiers, for example because they are keywords or
   contain hyphens. Example:

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

      # OK, functional syntax
      Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})

   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:

      T = TypeVar("T")

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

   A "TypedDict" can be introspected via annotations dicts (see
   Annotations Best Practices for more information on annotations best
   practices), "__total__", "__required_keys__", and
   "__optional_keys__".

   __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__

      バージョン 3.9 で追加.

   __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

      バージョン 3.9 で追加.

      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.

   See **PEP 589** for more examples and detailed rules of using
   "TypedDict".

   バージョン 3.8 で追加.

   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.


プロトコル
----------

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

class typing.SupportsAbs

   返り値の型と共変な抽象メソッド "__abs__" を備えた ABC です。

class typing.SupportsBytes

   抽象メソッド "__bytes__" を備えた ABC です。

class typing.SupportsComplex

   抽象メソッド "__complex__" を備えた ABC です。

class typing.SupportsFloat

   抽象メソッド "__float__" を備えた ABC です。

class typing.SupportsIndex

   抽象メソッド "__index__" を備えた ABC です。

   バージョン 3.8 で追加.

class typing.SupportsInt

   抽象メソッド "__int__" を備えた ABC です。

class typing.SupportsRound

   返り値の型と共変な抽象メソッド "__round__" を備えた ABC です。


ABCs for working with IO
------------------------

class typing.IO
class typing.TextIO
class typing.BinaryIO

   Generic type "IO[AnyStr]" and its subclasses "TextIO(IO[str])" and
   "BinaryIO(IO[bytes])" represent the types of I/O streams such as
   returned by "open()".


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)

   バージョン 3.11 で追加.

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.

   バージョン 3.11 で追加.

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.

   バージョン 3.11 で追加.

@typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_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:

      T = TypeVar("T")

      @dataclass_transform()
      def create_model(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".

      * **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。

   バージョン 3.11 で追加.

@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.

   バージョン 3.11 で追加.

typing.clear_overloads()

   Clear all registered overloads in the internal registry.

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

   バージョン 3.11 で追加.

@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。

   バージョン 3.8 で追加.

   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

   別のデコレータに "no_type_check()" の効果を与えるデコレータです。

   これは何かの関数をラップするデコレータを "no_type_check()" でラップ
   します。

@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)

   関数、メソッド、モジュールまたはクラスのオブジェクトの型ヒントを含
   む辞書を返します。

   This is often the same as "obj.__annotations__". In addition,
   forward references encoded as string literals are handled by
   evaluating them in "globals" and "locals" namespaces. For a class
   "C", return a dictionary constructed by merging all the
   "__annotations__" along "C.__mro__" in reverse order.

   The function recursively replaces all "Annotated[T, ...]" with "T",
   unless "include_extras" is set to "True" (see "Annotated" for more
   information). For example:

      class Student(NamedTuple):
          name: Annotated[str, 'some marker']

      assert get_type_hints(Student) == {'name': str}
      assert get_type_hints(Student, include_extras=False) == {'name': str}
      assert get_type_hints(Student, include_extras=True) == {
          'name': Annotated[str, 'some marker']
      }

   注釈:

     "get_type_hints()" does not work with imported type aliases that
     include forward references. Enabling postponed evaluation of
     annotations (**PEP 563**) may remove the need for most forward
     references.

   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.

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
      P = ParamSpec('P')
      assert get_origin(P.args) is P
      assert get_origin(P.kwargs) is P

   バージョン 3.8 で追加.

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)

   バージョン 3.8 で追加.

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)

   バージョン 3.10 で追加.

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]".

   バージョン 3.7.4 で追加.


定数
----

typing.TYPE_CHECKING

   A special constant that is assumed to be "True" by 3rd party static
   type checkers. It is "False" at runtime.

   使い方:

      if TYPE_CHECKING:
          import expensive_mod

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

   The first type annotation must be enclosed in quotes, making it a
   "forward reference", to hide the "expensive_mod" reference from the
   interpreter runtime.  Type annotations for local variables are not
   evaluated, so the second annotation does not need to be enclosed in
   quotes.

   注釈:

     "from __future__ import annotations" が使われた場合、アノーテーシ
     ョンは関数定義時に評価されません。代わりにアノーテーションは
     "__annotations__" 属性に文字列として保存されます。これによりアノ
     ーテーションをシングルクォートで囲む必要がなくなります (**PEP
     563** を参照してください)。

   バージョン 3.5.2 で追加.


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".

   This type can be used as follows:

      def count_words(text: str) -> Dict[str, int]:
          ...

   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".

   This type may be used as follows:

      T = TypeVar('T', int, float)

      def vec2(x: T, y: T) -> List[T]:
          return [x, y]

      def keep_positives(vector: Sequence[T]) -> List[T]:
          return [item for item in vector if item > 0]

   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 "AbstractSet" 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.

   バージョン 3.5.2 で追加.

   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。

   バージョン 3.5.2 で追加.

   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。

   バージョン 3.7.2 で追加.

   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。

   バージョン 3.6.1 で追加.

   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。

   バージョン 3.6.1 で追加.

   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。

   バージョン 3.6.1 で追加.

   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 3.8 で非推奨、バージョン 3.13 で削除予定: The
   "typing.re" namespace is deprecated and will be removed. These
   types should be directly imported from "typing" instead.

   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'

   バージョン 3.5.2 で追加.

   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])

   This type represents the types "bytes", "bytearray", and
   "memoryview" of byte sequences.

   バージョン 3.9 で非推奨、バージョン 3.14 で削除予定: Prefer
   "typing_extensions.Buffer", or a union like "bytes | bytearray |
   memoryview".

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

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

   バージョン 3.6 で追加.

   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。

   This type can be used as follows:

      def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
          return word_list[word]

   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。

   The variance and order of type variables 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

   バージョン 3.5.3 で追加.

   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。

   An async generator can be annotated by the generic type
   "AsyncGenerator[YieldType, SendType]". For example:

      async def echo_round() -> AsyncGenerator[int, float]:
          sent = yield 0
          while sent >= 0.0:
              rounded = await round(sent)
              sent = yield rounded

   Unlike normal generators, async generators cannot return a value,
   so there is no "ReturnType" type parameter. As with "Generator",
   the "SendType" behaves contravariantly.

   If your generator will only yield values, set the "SendType" to
   "None":

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

   Alternatively, annotate your generator as having a return type of
   either "AsyncIterable[YieldType]" or "AsyncIterator[YieldType]":

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

   バージョン 3.6.1 で追加.

   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。

class typing.AsyncIterable(Generic[T_co])

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

   バージョン 3.5.2 で追加.

   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。

   バージョン 3.5.2 で追加.

   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。

   バージョン 3.5.2 で追加.

   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。

   A generator can be annotated by 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 generics in the typing module, the
   "SendType" of "Generator" behaves contravariantly, not covariantly
   or invariantly.

   If your generator will only yield values, set the "SendType" and
   "ReturnType" to "None":

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

   Alternatively, annotate your generator 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

   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。

class typing.Hashable

   Alias to "collections.abc.Hashable".

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

   Alias to "collections.abc.Sized".


Aliases to "contextlib" ABCs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.ContextManager(Generic[T_co])

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

   バージョン 3.5.4 で追加.

   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。

class typing.AsyncContextManager(Generic[T_co])

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

   バージョン 3.6.2 で追加.

   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。


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                 |
|===========================|===========================|===========================|===========================|
| "typing.io" and           | 3.8                       | 3.13                      | bpo-38291                 |
| "typing.re" submodules    |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| 標準コレクションのエイリ  | 3.9                       | Undecided (see 非推奨のエ | **PEP 585**               |
| アス                      |                           | イリアス for more         |                           |
|                           |                           | information)              |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.ByteString"       | 3.9                       | 3.14                      | gh-91896                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Text"             | 3.11                      | 未定                      | gh-92332                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
