# `typing` — 형 힌트 지원

Added in version 3.5.

소스 코드: Lib/typing.py

참고

The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.

This module provides runtime support for type hints.

Consider the function below:

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

The function `surface_area_of_cube` takes an argument expected to be an instance of `float`, as indicated by the type hint `edge_length: float`. The function is expected to return an instance of `str`, as indicated by the `-> str` hint.

While type hints can be simple classes like `float` or `str`, they can also be more complex. The `typing` module provides a vocabulary of more advanced type hints.

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

더 보기

“Typing cheat sheet”

  A quick overview of type hints (hosted at the mypy docs)

“Type System Reference” section of the mypy docs

  The Python typing system is standardised via PEPs, so this reference should broadly apply to most Python type checkers. (Some parts may still be specific to mypy.)

“Static Typing with Python”

  Type-checker-agnostic documentation written by the community detailing type system features, useful typing related tools and typing best practices.

## Specification for the Python Type System

The canonical, up-to-date specification of the Python type system can be found at “Specification for the Python type system”.

## 형 에일리어스

A type alias is defined using the `type` statement, which creates an instance of `TypeAliasType`. In this example, `Vector` and `list[float]` will be treated equivalently by static type checkers:

    type 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])

형 에일리어스는 복잡한 형 서명을 단순화하는 데 유용합니다. 예를 들면:

    from collections.abc import Sequence

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

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

    # 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:
        ...

The `type` statement is new in Python 3.12. For backwards compatibility, type aliases can also be created through simple assignment:

    Vector = list[float]

Or marked with `TypeAlias` to make it explicit that this is a type alias, not a normal variable assignment:

    from typing import TypeAlias

    Vector: TypeAlias = list[float]

## NewType

Use the `NewType` helper to create distinct types:

    from typing import NewType

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

정적 형 검사기는 새 형을 원래 형의 서브 클래스인 것처럼 다룹니다. 논리 에러를 잡는 데 유용합니다:

    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` 형의 변수에 대해 모든 `int` 연산을 여전히 수행할 수 있지만, 결과는 항상 `int` 형이 됩니다. 이것은 `int`가 기대되는 모든 곳에 `UserId`를 전달할 수 있지만, 잘못된 방식으로 의도하지 않게 `UserId`를 만들지 않도록 합니다:

    # 'output' is of type 'int', not 'UserId'
    output = UserId(23413) + UserId(54341)

Note that these checks are enforced only by the static type checker. At runtime, the statement `Derived = NewType('Derived', Base)` will make `Derived` a callable that immediately returns whatever parameter you pass it. That means the expression `Derived(some_value)` does not create a new class or introduce much overhead beyond that of a regular function call.

더욱 정확하게, 표현식 `some_value is Derived(some_value)`는 실행 시간에 항상 참입니다.

It is invalid to create a subtype of `Derived`:

    from typing import NewType

    UserId = NewType('UserId', int)

    # Fails at runtime and does not pass type checking
    class AdminUserId(UserId): pass

However, it is possible to create a `NewType` based on a ‘derived’ `NewType`:

    from typing import NewType

    UserId = NewType('UserId', int)

    ProUserId = NewType('ProUserId', UserId)

그리고 `ProUserId`에 대한 형 검사는 예상대로 작동합니다.

자세한 내용은 PEP 484를 참조하십시오.

참고

Recall that the use of a type alias declares two types to be equivalent to one another. Doing `type 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.

반면에, `NewType`은 한 형을 다른 형의 서브 형으로 선언합니다. `Derived = NewType('Derived', Original)`은 정적 형 검사기가 `Derived`를 `Original`의 서브 클래스로 취급하게 합니다. 이는 `Original` 형의 값이 `Derived` 형의 값이 예상되는 위치에서 사용될 수 없음을 의미합니다. 실행 시간 비용을 최소화하면서 논리 에러를 방지하려는 경우에 유용합니다.

Added in version 3.5.2.

버전 3.10에서 변경: `NewType` is now a class rather than a function. As a result, there is some additional runtime cost when calling `NewType` over a regular function.

버전 3.11에서 변경: The performance of calling `NewType` has been restored to its level in Python 3.9.

## Annotating callable objects

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

For example:

    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

The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types, a `ParamSpec`, `Concatenate`, or an ellipsis. The return type must be a single type.

If a literal ellipsis `...` is given as the argument list, it indicates that a callable with any arbitrary parameter list would be acceptable:

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

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

`Callable` cannot express complex signatures such as functions that take a variadic number of arguments, overloaded functions, or functions that have keyword-only parameters. However, these signatures can be expressed by defining a `Protocol` class with a `__call__()` method:

    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

Callables which take other callables as arguments may indicate that their parameter types are dependent on each other using `ParamSpec`. Additionally, if that callable adds or removes arguments from other callables, the `Concatenate` operator may be used. They take the form `Callable[ParamSpecVariable, ReturnType]` and `Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]` respectively.

버전 3.10에서 변경: `Callable` now supports `ParamSpec` and `Concatenate`. See PEP 612 for more details.

더 보기

The documentation for `ParamSpec` and `Concatenate` provides examples of usage in `Callable`.

## 제네릭

Since type information about objects kept in containers cannot be statically inferred in a generic way, many container classes in the standard library support subscription to denote the expected types of container elements.

    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: ...

Generic functions and classes can be parameterized by using type parameter syntax:

    from collections.abc import Sequence

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

Or by using the `TypeVar` factory directly:

    from collections.abc import Sequence
    from typing import TypeVar

    U = TypeVar('U')                  # Declare type variable "U"

    def second(l: Sequence[U]) -> U:  # Function is generic over the TypeVar "U"
        return l[1]

버전 3.12에서 변경: Syntactic support for generics is new in Python 3.12.

## Annotating tuples

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` only accepts one type argument, so a type checker would emit an error on the `y` assignment above. Similarly, `Mapping` only accepts two type arguments: the first indicates the type of the keys, and the second indicates the type of the values.

Unlike most other Python containers, however, it is common in idiomatic Python code for tuples to have elements which are not all of the same type. For this reason, tuples are special-cased in Python’s typing system. `tuple` accepts any number of type arguments:

    # 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)

To denote a tuple which could be of any length, and in which all elements are of the same type `T`, use `tuple[T, ...]`. To denote an empty tuple, use `tuple[()]`. Using plain `tuple` as an annotation is equivalent to using `tuple[Any, ...]`:

    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 = ()

## The type of class objects

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

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

Note that `type[C]` is covariant:

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

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

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

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

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

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

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

## Annotating generators and coroutines

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

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

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

제너레이터가 값을 일드(yield)하기만 하면, `SendType`과 `ReturnType`을 `None`으로 설정하십시오:

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

또는, `Iterable[YieldType]`이나 `Iterator[YieldType]` 중 하나의 반환형을 갖는 것으로 제너레이터를 어노테이트 하십시오:

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

Async generators are handled in a similar fashion, but don’t expect a `ReturnType` type argument (`AsyncGenerator[YieldType, SendType]`):

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

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

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

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

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

## 사용자 정의 제네릭 형

사용자 정의 클래스는 제네릭 클래스로 정의 할 수 있습니다.

    from logging import Logger

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

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

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

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

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

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

    from typing import TypeVar, Generic

    T = TypeVar('T')

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

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

    from collections.abc import Iterable

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

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

    from typing import TypeVar, Generic, Sequence

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

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

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

`Generic`에 대한 각 형 변수 인자는 달라야 합니다. 그래서 이것은 잘못되었습니다:

    from typing import TypeVar, Generic
    ...

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

    T = TypeVar('T')

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

Generic classes can also inherit from other classes:

    from collections.abc import Sized

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

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

    from collections.abc import Mapping

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

이 경우 `MyDict`는 단일 매개 변수 `T`를 갖습니다.

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

    from collections.abc import Iterable

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

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

    from collections.abc import Iterable

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

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

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

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

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

    from collections.abc import Iterable
    from typing import TypeVar

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

버전 3.7에서 변경: `Generic`에는 더는 사용자 정의 메타 클래스가 없습니다.

버전 3.12에서 변경: Syntactic support for generics and type aliases is new in version 3.12. Previously, generic classes had to explicitly inherit from `Generic` or contain a type variable in one of their bases.

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

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

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

    from typing import ParamSpec, Generic

    P = ParamSpec('P')

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

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

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

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

버전 3.10에서 변경: `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` 형

특수한 종류의 형은 `Any`입니다. 정적 형 검사기는 모든 형을 `Any`와 호환되는 것으로, `Any`를 모든 형과 호환되는 것으로 취급합니다.

이것은 `Any` 형의 값에 대해 어떤 연산이나 메서드 호출을 수행하고, 그것을 임의의 변수에 대입할 수 있다는 것을 의미합니다:

    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()
        ...

Notice that no type checking is performed when assigning a value of type `Any` to a more precise type. For example, the static type checker did not report an error when assigning `a` to `s` even though `s` was declared to be of type `str` and receives an `int` value at runtime!

또한, 반환형이나 매개 변수 형이 없는 모든 함수는 묵시적으로 `Any` 기본값을 사용합니다:

    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

이 동작은 여러분이 동적으로 형이 지정되는 코드와 정적으로 형이 지정되는 코드를 혼합해야 할 때 `Any`를 탈출구로 사용할 수 있도록 합니다.

`Any`의 동작과 `object`의 동작을 대조하십시오. `Any`와 유사하게, 모든 형은 `object`의 서브 형입니다. 그러나, `Any`와는 달리, 그 반대는 사실이 아닙니다: `object`는 다른 모든 형의 서브 형이 아닙니다.

이것은 값의 형이 `object`일 때, 형 검사기가 그것에 대한 거의 모든 연산을 거부하고, 그것을 더 특수한 형의 변수에 대입(또는 그것을 반환 값으로 사용)하는 것이 형 에러임을 의미합니다. 예를 들면:

    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")

값이 형 안전한 방식으로 모든 형이 될 수 있음을 표시하려면 `object`를 사용하십시오. 값이 동적으로 형이 지정됨을 표시하려면 `Any`를 사용하십시오.

## 명목적 대 구조적 서브 타이핑

Initially PEP 484 defined the Python static type system as using nominal subtyping. This means that a class `A` is allowed where a class `B` is expected if and only if `A` is a subclass of `B`.

이 요구 사항은 이전에 `Iterable`과 같은 추상 베이스 클래스에도 적용되었습니다. 이 접근 방식의 문제점은 이것을 지원하려면 클래스를 명시적으로 표시해야만 한다는 점입니다. 이는 파이썬답지 않고 관용적인 동적으로 형이 지정된 파이썬 코드에서 일반적으로 수행하는 것과는 다릅니다. 예를 들어, 이것은 PEP 484를 만족합니다:

    from collections.abc import Sized, Iterable, Iterator

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

PEP 544는 사용자가 클래스 정의에서 명시적인 베이스 클래스 없이 위의 코드를 작성할 수 있게 함으로써 이 문제를 풀도록 합니다. 정적 형 검사기가 `Bucket`을 `Sized`와 `Iterable[int]`의 서브 형으로 묵시적으로 취급하도록 합니다. 이것은 구조적 서브 타이핑(structural subtyping)(또는 정적 덕 타이핑)으로 알려져 있습니다:

    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

또한, 특별한 클래스 `Protocol`을 서브 클래싱 함으로써, 사용자는 새로운 사용자 정의 프로토콜을 정의하여 구조적 서브 타이핑을 완전히 누릴 수 있습니다 (아래 예를 참조하십시오).

## 모듈 내용

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

### 특수 타이핑 프리미티브

#### 특수형

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

typing.Any

  제한되지 않는 형을 나타내는 특수형.

  - 모든 형은 `Any`와 호환됩니다.

  - `Any`는 모든 형과 호환됩니다.

  버전 3.11에서 변경: `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.

  예를 들면:

      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`.

  Example:

      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.

  See PEP 675 for more details.

  Added in version 3.11.

typing.Never
typing.NoReturn

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

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

      from typing import Never  # or NoReturn

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

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

      from typing import Never  # or NoReturn

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

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

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

  Added in version 3.6.2: Added `NoReturn`.

  Added in version 3.11: Added `Never`.

typing.Self

  Special type to represent the current enclosed class.

  예를 들면:

      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()

  See PEP 673 for more details.

  Added in version 3.11.

typing.TypeAlias

  Special annotation for explicitly declaring a type alias.

  예를 들면:

      from typing import TypeAlias

      Factors: TypeAlias = list[int]

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

      from typing import Generic, TypeAlias, TypeVar

      T = TypeVar("T")

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

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

  See PEP 613 for more details.

  Added in version 3.10.

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

#### 특수 형태

These can be used as types in annotations. They all support subscription using `[]`, but each has a unique syntax.

typing.Union

  Union type; `Union[X, Y]` is equivalent to `X | Y` and means either X or Y.

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

  - 인자는 형이어야 하며 적어도 하나 있어야 합니다.

  - 공용체의 공용체는 펼쳐집니다, 예를 들어:

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

  - 단일 인자의 공용체는 사라집니다. 예를 들어:

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

  - 중복 인자는 건너뜁니다. 예를 들어:

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

  - 공용체를 비교할 때, 인자 순서가 무시됩니다, 예를 들어:

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

  - You cannot subclass or instantiate a `Union`.

  - `Union[X][Y]`라고 쓸 수 없습니다.

  버전 3.7에서 변경: 실행 시간에 공용체의 명시적 서브 클래스를 제거하지 않습니다.

  버전 3.10에서 변경: Unions can now be written as `X | Y`. See union type expressions.

typing.Optional

  `Optional[X]` is equivalent to `X | None` (or `Union[X, None]`).

  이는 기본값을 갖는 선택적 인자와 같은 개념이 아님에 유의하십시오. 단지 선택적이기 때문에 기본값을 갖는 선택적 인자가 형 어노테이션에 `Optional` 한정자가 필요하지는 않습니다. 예를 들면:

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

  한편, 명시적인 `None` 값이 허용되면, 인자가 선택적인지와 관계없이 `Optional`을 사용하는 것이 적합합니다. 예를 들면:

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

  버전 3.10에서 변경: Optional can now be written as `X | None`. See union type expressions.

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

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

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

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

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

  Added in version 3.10.

  더 보기

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

  - `ParamSpec`

  - Annotating callable objects

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.

  예를 들면:

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

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

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

  `Literal[...]`은 서브 클래싱 될 수 없습니다. 실행 시간에는, 임의의 값이 `Literal[...]`에 대한 형 인자로 허용되지만, 형 검사기는 제한을 부과할 수 있습니다. 리터럴 형에 대한 자세한 내용은 PEP 586을 참조하십시오.

  Added in version 3.8.

  버전 3.9.1에서 변경: `Literal` now de-duplicates parameters. Equality comparisons of `Literal` objects are no longer order dependent. `Literal` objects will now raise a `TypeError` exception during equality comparisons if one of their parameters are not hashable.

typing.ClassVar

  클래스 변수를 표시하기 위한 특수 형 구조물.

  PEP 526에서 소개된 것처럼, ClassVar로 감싼 변수 어노테이션은 주어진 어트리뷰트가 클래스 변수로 사용되도록 의도되었으며 해당 클래스의 인스턴스에 설정되어서는 안 됨을 나타냅니다. 용법:

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

  `ClassVar`는 형만 받아들이며 더는 서브 스크립트 할 수 없습니다.

  `ClassVar`는 클래스 자체가 아니므로, `isinstance()`나 `issubclass()`와 함께 사용하면 안 됩니다. `ClassVar`는 파이썬 실행 시간 동작을 변경하지 않지만, 제삼자 형 검사기에서 사용할 수 있습니다. 예를 들어, 형 검사기는 다음 코드를 에러로 표시 할 수 있습니다:

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

  Added in version 3.5.3.

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.

  예를 들면:

      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

  이러한 속성에 대한 실행 시간 검사는 없습니다. 자세한 내용은 PEP 591을 참조하십시오.

  Added in version 3.8.

typing.Required

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

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

  Added in version 3.11.

typing.NotRequired

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

  See `TypedDict` and PEP 655 for more details.

  Added in version 3.11.

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:

  - `Annotated`의 첫 번째 인자는 유효한 형이어야 합니다

  - 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

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

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

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

        type Variadic[*Ts] = Annotated[*Ts, Ann1]  # 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')

  - At runtime, if you want to retrieve the original type wrapped by `Annotated`, use the `__origin__` attribute:

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

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

        >>> get_origin(Password)
        <class 'typing.Annotated'>

  더 보기

  PEP 593 - Flexible function and variable annotations

    The PEP introducing `Annotated` to the standard library.

  Added in version 3.9.

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`.

  예를 들면:

      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`.

  참고

  `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.

  Added in version 3.10.

typing.Unpack

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

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

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

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

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

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

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

      from typing import TypedDict, Unpack

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

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

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

  Added in version 3.11.

#### Building generic types and type aliases

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

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

class typing.Generic

  제네릭 형을 위한 추상 베이스 클래스.

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

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

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

  이 클래스는 다음과 같이 사용할 수 있습니다:

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

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

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

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

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

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

  형 변수.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      class StringSubclass(str):
          pass

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

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

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

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

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

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

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

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

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

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

  __name__

    The name of the type variable.

  __covariant__

    Whether the type var has been explicitly marked as covariant.

  __contravariant__

    Whether the type var has been explicitly marked as contravariant.

  __infer_variance__

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

    Added in version 3.12.

  __bound__

    The upper bound of the type variable, if any.

    버전 3.12에서 변경: For type variables created through type parameter syntax, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

  __constraints__

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

    버전 3.12에서 변경: For type variables created through type parameter syntax, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

  버전 3.12에서 변경: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The `infer_variance` parameter was added.

class typing.TypeVarTuple(name)

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

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

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

  Or by explicitly invoking the `TypeVarTuple` constructor:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  See PEP 646 for more details on type variable tuples.

  __name__

    The name of the type variable tuple.

  Added in version 3.11.

  버전 3.12에서 변경: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

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

  Parameter specification variable. A specialized version of type variables.

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

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

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

      P = ParamSpec('P')

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

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

      from collections.abc import Callable
      import logging

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

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

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

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

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

  args
  kwargs

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

  __name__

    The name of the parameter specification.

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

  Added in version 3.10.

  버전 3.12에서 변경: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

  참고

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

  더 보기

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

  - `Concatenate`

  - Annotating callable objects

typing.ParamSpecArgs
typing.ParamSpecKwargs

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

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

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

  Added in version 3.10.

class typing.TypeAliasType(name, value, *, type_params=())

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

  Example:

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

  Added in version 3.12.

  __name__

    The name of the type alias:

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

  __module__

    The module in which the type alias was defined:

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

  __type_params__

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

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

  __value__

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

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

#### 기타 특수 지시자

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

  형 지정된(typed) `collections.namedtuple()` 버전.

  용법:

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

  이것은 다음과 동등합니다:

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

  필드에 기본값을 부여하려면, 클래스 바디에서 그 값을 대입할 수 있습니다:

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

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

  기본값이 있는 필드는 기본값이 없는 모든 필드 뒤에 와야 합니다.

  The resulting class has an extra attribute `__annotations__` giving a dict that maps the field names to the field types. (The field names are in the `_fields` attribute and the default values are in the `_field_defaults` attribute, both of which are part of the `namedtuple()` API.)

  `NamedTuple` 서브 클래스는 독스트링과 메서드도 가질 수 있습니다:

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

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

  `NamedTuple` subclasses can be generic:

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

  이전 버전과 호환되는 사용법:

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

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

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

  버전 3.6에서 변경: PEP 526 변수 어노테이션 문법 지원을 추가했습니다.

  버전 3.6.1에서 변경: 기본값, 메서드 및 독스트링에 대한 지원을 추가했습니다.

  버전 3.8에서 변경: `_field_types`와 `__annotations__` 어트리뷰트는 이제 `OrderedDict` 인스턴스가 아닌 일반 딕셔너리입니다.

  버전 3.9에서 변경: `_field_types` 어트리뷰트를 제거하고, 같은 정보를 가지는 더 표준적인 `__annotations__` 어트리뷰트로 대체했습니다.

  버전 3.11에서 변경: 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.

  용법:

      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.

  Added in version 3.5.2.

  버전 3.10에서 변경: `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:
              ...

  이러한 클래스는 주로 구조적 서브 타이핑(정적 덕 타이핑)을 인식하는 정적 형 검사기와 함께 사용됩니다, 예를 들어:

      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.

  프로토콜 클래스는 제네릭일 수 있습니다, 예를 들어:

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

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

      T = TypeVar("T")

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

  Added in version 3.8.

@typing.runtime_checkable

  프로토콜 클래스를 실행 시간 프로토콜로 표시합니다.

  이러한 프로토콜은 `isinstance()`와 `issubclass()`와 함께 사용할 수 있습니다. 이것은 비 프로토콜 클래스에 적용될 때 `TypeError`를 발생시킵니다. 이것은 `collections.abc`에 있는 `Iterable`처럼 “한 가지만 잘하는” 것과 매우 유사한 단순한 구조적 검사를 허용합니다. 예를 들면:

      @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)

  참고

  `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`.

  참고

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

  Added in version 3.8.

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

  버전 3.12에서 변경: The members of a runtime-checkable protocol are now considered “frozen” at runtime as soon as the class has been created. Monkey-patching attributes onto a runtime-checkable protocol will still work, but will have no impact on `isinstance()` checks comparing objects to the protocol. See “What’s new in Python 3.12” for more details.

class typing.TypedDict(dict)

  딕셔너리에 형 힌트를 추가하는 특수 구조. 실행 시간에 일반 `dict`입니다.

  `TypedDict`는 모든 인스턴스가 각 키가 일관된 형의 값에 연관되는, 특정한 키 집합을 갖도록 기대되는 딕셔너리 형을 선언합니다. 이 기대는 실행 시간에는 검사되지 않고, 형 검사기에서만 강제됩니다. 사용법:

      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)

    Deprecated since version 3.11, will be removed in version 3.13: 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:

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

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

      T = TypeVar("T")

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

  A `TypedDict` can be introspected via 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__

    Added in version 3.9.

  __optional_keys__

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

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

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

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

    Added in version 3.9.

    참고

    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.

  추가 예제와 `TypedDict`를 사용하는 자세한 규칙은 PEP 589를 참조하십시오.

  Added in version 3.8.

  버전 3.11에서 변경: Added support for marking individual keys as `Required` or `NotRequired`. See PEP 655.

  버전 3.11에서 변경: Added support for generic `TypedDict`s.

### 프로토콜

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

class typing.SupportsAbs

  반환형이 공변적(covariant)인 하나의 추상 메서드 `__abs__`를 가진 ABC.

class typing.SupportsBytes

  하나의 추상 메서드 `__bytes__`를 가진 ABC.

class typing.SupportsComplex

  하나의 추상 메서드 `__complex__`를 가진 ABC.

class typing.SupportsFloat

  하나의 추상 메서드 `__float__`를 가진 ABC.

class typing.SupportsIndex

  하나의 추상 메서드 `__index__`를 가진 ABC.

  Added in version 3.8.

class typing.SupportsInt

  하나의 추상 메서드 `__int__`를 가진 ABC.

class typing.SupportsRound

  반환형이 공변적(covariant)인 하나의 추상 메서드 `__round__`를 가진 ABC.

### 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()`.

### 함수와 데코레이터

typing.cast(typ, val)

  값을 형으로 변환합니다.

  값을 변경하지 않고 반환합니다. 형 검사기에서는 반환 값이 지정된 형임을 나타내지만, 실행 시간에는 의도적으로 아무것도 확인하지 않습니다 (우리는 이것이 가능한 한 빠르기를 원합니다).

typing.assert_type(val, typ, /)

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

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

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

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

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

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

  Added in version 3.11.

typing.assert_never(arg, /)

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

  Example:

      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.

  더 보기

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

  Added in version 3.11.

typing.reveal_type(obj, /)

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

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

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

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

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

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

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

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

  Added in version 3.11.

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

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

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

  Example usage with a decorator function:

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

      @create_model
      class CustomerModel:
          id: int
          name: str

  On a base class:

      @dataclass_transform()
      class ModelBase: ...

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

  On a metaclass:

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

      class ModelBase(metaclass=ModelMeta): ...

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

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

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

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

  매개변수:

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

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

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

    - frozen_default (bool) –

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

      Added in version 3.12.

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

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

  Type checkers recognize the following optional parameters on field specifiers:

  Recognised parameters for field specifiers
  Parameter name | Description
  --- | ---
  `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.

  See PEP 681 for more details.

  Added in version 3.11.

@typing.overload

  Decorator for creating overloaded functions and methods.

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

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

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

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

  See PEP 484 for more details and comparison with other typing semantics.

  버전 3.11에서 변경: Overloaded functions can now be introspected at runtime using `get_overloads()`.

typing.get_overloads(func)

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

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

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

  Added in version 3.11.

typing.clear_overloads()

  Clear all registered overloads in the internal registry.

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

  Added in version 3.11.

@typing.final

  Decorator to indicate final methods and final classes.

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

  예를 들면:

      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
          ...

  이러한 속성에 대한 실행 시간 검사는 없습니다. 자세한 내용은 PEP 591을 참조하십시오.

  Added in version 3.8.

  버전 3.11에서 변경: 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

  어노테이션이 형 힌트가 아님을 나타내는 데코레이터.

  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

  다른 데코레이터에 `no_type_check()` 효과를 주는 데코레이터.

  이것은 데코레이트 된 함수를 `no_type_check()`로 감싸는 무언가로 데코레이터를 감쌉니다.

@typing.override

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

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

  For example:

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

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

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

  There is no runtime checking of this property.

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

  See PEP 698 for more details.

  Added in version 3.12.

@typing.type_check_only

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

  이 데코레이터 자체는 실행 시간에 사용할 수 없습니다. 주로, 구현이 비공개 클래스의 인스턴스를 반환할 때, 형 스텁 파일에 정의된 클래스를 표시하기 위한 용도입니다:

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

      def fetch_response() -> Response: ...

  비공개 클래스의 인스턴스를 반환하는 것은 좋지 않음에 유의하십시오. 일반적으로 그러한 클래스를 공개로 만드는 것이 바람직합니다.

### 인트로스펙션 도우미

typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False)

  함수, 메서드, 모듈 또는 클래스 객체에 대한 형 힌트가 포함된 딕셔너리를 반환합니다.

  This is often the same as `obj.__annotations__`, but this function makes the following changes to the annotations dictionary:

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

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

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

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

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

  See also `inspect.get_annotations()`, a lower-level function that returns annotations more directly.

  참고

  If any forward references in the annotations of obj are not resolvable or are not valid Python code, this function will raise an exception such as `NameError`. For example, this can happen with imported type aliases that include forward references, or with names imported under `if TYPE_CHECKING`.

  버전 3.9에서 변경: Added `include_extras` parameter as part of PEP 593. See the documentation on `Annotated` for more information.

  버전 3.11에서 변경: 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.

  Examples:

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

  Added in version 3.8.

typing.get_args(tp)

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

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

  Examples:

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

  Added in version 3.8.

typing.is_typeddict(tp)

  Check if a type is a `TypedDict`.

  For example:

      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)

  Added in version 3.10.

class typing.ForwardRef

  Class used for internal typing representation of string forward references.

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

  참고

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

  Added in version 3.7.4.

### 상수

typing.TYPE_CHECKING

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

  용법:

      if TYPE_CHECKING:
          import expensive_mod

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

  첫 번째 어노테이션은 따옴표로 묶여야 합니다, “전방 참조”로 만들어서 인터프리터 실행 시간에 `expensive_mod` 참조를 숨깁니다. 지역 변수에 대한 형 어노테이션은 평가되지 않기 때문에, 두 번째 어노테이션을 따옴표로 묶을 필요는 없습니다.

  참고

  If `from __future__ import annotations` is used, annotations are not evaluated at function definition time. Instead, they are stored as strings in `__annotations__`. This makes it unnecessary to use quotes around the annotation (see PEP 563).

  Added in version 3.5.2.

### Deprecated aliases

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])

  Deprecated alias to `dict`.

  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`.

  버전 3.9부터 폐지됨: `builtins.dict` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `list`.

  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`.

  버전 3.9부터 폐지됨: `builtins.list` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `builtins.set`.

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

  버전 3.9부터 폐지됨: `builtins.set` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `builtins.frozenset`.

  버전 3.9부터 폐지됨: `builtins.frozenset` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

typing.Tuple

  Deprecated alias for `tuple`.

  `tuple` and `Tuple` are special-cased in the type system; see Annotating tuples for more details.

  버전 3.9부터 폐지됨: `builtins.tuple` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.Type(Generic[CT_co])

  Deprecated alias to `type`.

  See The type of class objects for details on using `type` or `typing.Type` in type annotations.

  Added in version 3.5.2.

  버전 3.9부터 폐지됨: `builtins.type` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

#### Aliases to types in `collections`

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

  Deprecated alias to `collections.defaultdict`.

  Added in version 3.5.2.

  버전 3.9부터 폐지됨: `collections.defaultdict` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.OrderedDict`.

  Added in version 3.7.2.

  버전 3.9부터 폐지됨: `collections.OrderedDict` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.ChainMap`.

  Added in version 3.6.1.

  버전 3.9부터 폐지됨: `collections.ChainMap` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.Counter`.

  Added in version 3.6.1.

  버전 3.9부터 폐지됨: `collections.Counter` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.deque`.

  Added in version 3.6.1.

  버전 3.9부터 폐지됨: `collections.deque` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

#### Aliases to other concrete types

> Deprecated since version 3.8, will be removed in version 3.13: The `typing.io` namespace is deprecated and will be removed. These types should be directly imported from `typing` instead.

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]`.

  Deprecated since version 3.8, will be removed in version 3.13: The `typing.re` namespace is deprecated and will be removed. These types should be directly imported from `typing` instead.

  버전 3.9부터 폐지됨: `re`의 클래스 `Pattern`과 `Match`는 이제 `[]`를 지원합니다. PEP 585와 제네릭 에일리어스 형을 참조하십시오.

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`를 사용하여 값이 파이썬 2와 파이썬 3 모두와 호환되는 방식으로 유니코드 문자열을 포함해야 함을 나타내십시오:

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

  Added in version 3.5.2.

  버전 3.11부터 폐지됨: 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])

  Deprecated alias to `collections.abc.Set`.

  버전 3.9부터 폐지됨: `collections.abc.Set` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.ByteString(Sequence[int])

  이 형은 `bytes`, `bytearray` 및 바이트 시퀀스의 `memoryview` 형을 나타냅니다.

  Deprecated since version 3.9, will be removed in version 3.14: Prefer `collections.abc.Buffer`, or a union like `bytes | bytearray | memoryview`.

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

  Deprecated alias to `collections.abc.Collection`.

  Added in version 3.6.

  버전 3.9부터 폐지됨: `collections.abc.Collection` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.Container(Generic[T_co])

  Deprecated alias to `collections.abc.Container`.

  버전 3.9부터 폐지됨: `collections.abc.Container` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.abc.ItemsView`.

  버전 3.9부터 폐지됨: `collections.abc.ItemsView` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.abc.KeysView`.

  버전 3.9부터 폐지됨: `collections.abc.KeysView` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.abc.Mapping`.

  버전 3.9부터 폐지됨: `collections.abc.Mapping` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.MappingView(Sized)

  Deprecated alias to `collections.abc.MappingView`.

  버전 3.9부터 폐지됨: `collections.abc.MappingView` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.abc.MutableMapping`.

  버전 3.9부터 폐지됨: `collections.abc.MutableMapping` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.MutableSequence(Sequence[T])

  Deprecated alias to `collections.abc.MutableSequence`.

  버전 3.9부터 폐지됨: `collections.abc.MutableSequence` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.MutableSet(AbstractSet[T])

  Deprecated alias to `collections.abc.MutableSet`.

  버전 3.9부터 폐지됨: `collections.abc.MutableSet` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.abc.Sequence`.

  버전 3.9부터 폐지됨: `collections.abc.Sequence` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.abc.ValuesView`.

  버전 3.9부터 폐지됨: `collections.abc.ValuesView` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

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

  Deprecated alias to `collections.abc.Coroutine`.

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

  Added in version 3.5.3.

  버전 3.9부터 폐지됨: `collections.abc.Coroutine` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

  Deprecated alias to `collections.abc.AsyncGenerator`.

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

  Added in version 3.6.1.

  버전 3.9부터 폐지됨: `collections.abc.AsyncGenerator` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.AsyncIterable(Generic[T_co])

  Deprecated alias to `collections.abc.AsyncIterable`.

  Added in version 3.5.2.

  버전 3.9부터 폐지됨: `collections.abc.AsyncIterable` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.AsyncIterator(AsyncIterable[T_co])

  Deprecated alias to `collections.abc.AsyncIterator`.

  Added in version 3.5.2.

  버전 3.9부터 폐지됨: `collections.abc.AsyncIterator` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.Awaitable(Generic[T_co])

  Deprecated alias to `collections.abc.Awaitable`.

  Added in version 3.5.2.

  버전 3.9부터 폐지됨: `collections.abc.Awaitable` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

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

class typing.Iterable(Generic[T_co])

  Deprecated alias to `collections.abc.Iterable`.

  버전 3.9부터 폐지됨: `collections.abc.Iterable` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.Iterator(Iterable[T_co])

  Deprecated alias to `collections.abc.Iterator`.

  버전 3.9부터 폐지됨: `collections.abc.Iterator` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

typing.Callable

  Deprecated alias to `collections.abc.Callable`.

  See Annotating callable objects for details on how to use `collections.abc.Callable` and `typing.Callable` in type annotations.

  버전 3.9부터 폐지됨: `collections.abc.Callable` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

  버전 3.10에서 변경: `Callable` now supports `ParamSpec` and `Concatenate`. See PEP 612 for more details.

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

  Deprecated alias to `collections.abc.Generator`.

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

  버전 3.9부터 폐지됨: `collections.abc.Generator` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.Hashable

  Deprecated alias to `collections.abc.Hashable`.

  버전 3.12부터 폐지됨: Use `collections.abc.Hashable` directly instead.

class typing.Reversible(Iterable[T_co])

  Deprecated alias to `collections.abc.Reversible`.

  버전 3.9부터 폐지됨: `collections.abc.Reversible` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.Sized

  Deprecated alias to `collections.abc.Sized`.

  버전 3.12부터 폐지됨: Use `collections.abc.Sized` directly instead.

#### Aliases to `contextlib` ABCs

class typing.ContextManager(Generic[T_co])

  Deprecated alias to `contextlib.AbstractContextManager`.

  Added in version 3.5.4.

  버전 3.9부터 폐지됨: `contextlib.AbstractContextManager` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

class typing.AsyncContextManager(Generic[T_co])

  Deprecated alias to `contextlib.AbstractAsyncContextManager`.

  Added in version 3.6.2.

  버전 3.9부터 폐지됨: `contextlib.AbstractAsyncContextManager` now supports subscripting (`[]`). See PEP 585 and 제네릭 에일리어스 형.

## Deprecation Timeline of Major Features

Certain features in `typing` are deprecated and may be removed in a future version of Python. The following table summarizes major deprecations for your convenience. This is subject to change, and not all deprecations are listed.

Feature | Deprecated in | Projected removal | PEP/issue
--- | --- | --- | ---
`typing.io` and `typing.re` submodules | 3.8 | 3.13 | bpo-38291
`typing` versions of standard collections | 3.9 | Undecided (see Deprecated aliases for more information) | PEP 585
`typing.ByteString` | 3.9 | 3.14 | gh-91896
`typing.Text` | 3.11 | Undecided | gh-92332
`typing.Hashable` and `typing.Sized` | 3.12 | Undecided | gh-94309
`typing.TypeAlias` | 3.12 | Undecided | PEP 695