# `typing` — Suporte para dicas de tipo

Adicionado na versão 3.5.

Código-fonte: Lib/typing.py

Nota

O tempo de execução do Python não força anotações de tipos de variáveis e funções. Elas podem ser usadas por ferramentas de terceiros como verificadores de tipo, IDEs, linters, etc.

Este módulo fornece suporte em tempo de execução para dicas de tipo.

Considere a função abaixo:

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

A função `surface_area_of_cube` recebe um argumento que se espera ser uma instância de `float`, conforme indicado pela dica de tipo `edge_length: float`. Espera-se que a função retorne uma instância de `str`, conforme indicado pela dica `-> str`.

Embora as dicas de tipo possam ser classes simples como `float` ou `str`, elas também podem ser mais complexas. O módulo `typing` fornece um vocabulário de dicas de tipo mais avançadas.

Novos recursos são frequentemente adicionados ao módulo `typing`. O pacote typing_extensions fornece backports desses novos recursos para versões mais antigas do Python.

Ver também

“Guia rápido sobre Dicas de Tipo”

  Uma visão geral das dicas de tipo (hospedado por mypy docs, em inglês).

“Referência sobre Sistema de Tipo” seção de the mypy docs

  O sistema de tipagem do Python é padronizado pelas PEPs, portanto esta referência deve se aplicar a maioria do verificadores de tipo do Python. (Alguns trechos podem se referir especificamente ao mypy. Documento em inglês).

“Tipagem Estática com Python”

  Documentação independente de verificador de tipo escrita pela comunidade, detalhando os recursos do sistema de tipo, ferramentas úteis de tipagem e melhores práticas.

## Especificação para o sistema de tipos do Python

A especificação canônica e atualizada do sistema de tipos Python pode ser encontrada em “Specification for the Python type system”.

## Apelidos de tipo

Um apelido de tipo é definido utilizando a instrução `type`, que por sua vez cria uma instância da classe `TypeAliasType`. Neste exemplo, `Vector` e `list[float]` serão tratados de maneira equivalente pelos verificadores de tipo estático:

    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])

Apelidos de tipo são úteis para simplificar assinaturas de tipo complexas. Por exemplo:

    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:
        ...

A instrução `type` é nova no Python 3.12. Para compatibilidade retroativa, apelidos de tipo também podem ser criados através da simples atribuição:

    Vector = list[float]

Ou marcado com `TypeAlias` para tornar explícito que se trata de um apelido de tipo e não uma atribuição de variável comum:

    from typing import TypeAlias

    Vector: TypeAlias = list[float]

## NewType

Utilize o auxiliar `NewType` para criar tipos únicos:

    from typing import NewType

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

O verificador de tipo estático tratará o novo tipo como se fosse uma subclasse do tipo original. Isso é útil para ajudar a encontrar erros de lógica:

    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)

Você ainda pode executar todas as operações `int` em uma variável do tipo `UserId`, mas o resultado sempre será do tipo `int`. Isso permite que você passe um `UserId` em qualquer ocasião que `int` possa ser esperado, mas previne que você acidentalmente crie um `UserId` de uma forma inválida:

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

Note que essas verificações são aplicadas apenas pelo verificador de tipo estático. Em tempo de execução, a instrução `Derived = NewType('Derived', Base)` irá tornar `Derived` um chamável que retornará imediatamente qualquer parâmetro que você passar. Isso significa que a expressão `Derived(some_value)` não cria uma nova classe ou introduz sobrecarga além de uma chamada regular de função.instrução

Mais precisamente, a expressão `some_value is Derived(some_value)` é sempre verdadeira em tempo de execução.

É inválido criar um subtipo de `Derived`:

    from typing import NewType

    UserId = NewType('UserId', int)

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

No entanto, é possível criar um `NewType` baseado em um ‘derivado’ `NewType`:

    from typing import NewType

    UserId = NewType('UserId', int)

    ProUserId = NewType('ProUserId', UserId)

e a verificação de tipo para `ProUserId` funcionará como esperado.

Veja PEP 484 para mais detalhes.

Nota

Lembre-se que o uso de um apelido de tipo declara que dois tipos serão equivalentes entre si. Efetuar `type Alias = Original` fará o verificador de tipo estático tratar `Alias` como sendo exatamente equivalente a `Original` em todos os casos. Isso é útil quando você deseja simplificar assinaturas de tipo complexas.

Em contraste, `NewType` declara que um tipo será subtipo de outro. Efetuando `Derived = NewType('Derived', Original)` irá fazer o verificador de tipo estático tratar `Derived` como uma subclasse de `Original`, o que significa que um valor do tipo `Original` não pode ser utilizado onde um valor do tipo `Derived` é esperado. Isso é útil quando você deseja evitar erros de lógica com custo mínimo de tempo de execução.

Adicionado na versão 3.5.2.

Alterado na versão 3.10: `NewType` é agora uma classe ao invés de uma função. Como consequência, existem alguns custos em tempo de execução ao chamar `NewType` ao invés de uma função comum.

Alterado na versão 3.11: O desempenho de chamar `NewType` retornou ao mesmo nível da versão Python 3.9.

## Anotações de objetos chamáveis

Funções – ou outros objetos chamáveis – podem ser anotados utilizando-se `collections.abc.Callable` ou `typing.Callable`. `Callable[[int], str]`. Significa uma função que recebe um único parâmetro do tipo `int`. e retorna um `str`.

Por exemplo:

    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

A sintaxe da subscrição deve sempre ser usada com exatamente dois valores: uma lista de argumentos e o tipo de retorno. A lista de argumentos deve ser uma lista de tipos, um `ParamSpec`, `Concatenate`, ou reticências. O tipo de retorno deve ser um único tipo.

Se uma reticências literal `...` é passada no lugar de uma lista de argumentos, indica que um chamável com umas lista de qualquer parâmetro arbitrário seria aceita.

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

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

`Callable` não pode representar assinaturas complexas, como funções que aceitam um número variado de argumentos, funções sobrecarregadas, or funções que recebem apenas parâmetros somente-nomeados. No entanto, essas assinaturas podem ser expressas ao se definir uma `Protocol` com um método `__call__()`:

    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

Chamáveis que recebem outros chamáveis como argumentos podem indicar que seus tipos de parâmetro são dependentes uns dos outros usando `ParamSpec`. Além disso, se esse chamável adiciona ou retira argumentos de outros chamáveis, o operador `Concatenate` pode ser usado. Eles assumem a forma de `Callable[ParamSpecVariable, ReturnType]` e `Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]`, respectivamente.

Alterado na versão 3.10: `Callable` agora oferece suporte a `ParamSpec` e `Concatenate`. Veja PEP 612 para mais detalhes.

Ver também

A documentação para `ParamSpec` e `Concatenate` contém exemplos de uso em `Callable`.

## Genéricos

Como a informação de tipo sobre objetos mantidos em contêineres não pode ser inferida estaticamente de uma maneira genérica, muitas classes de contêiner na biblioteca padrão suportam subscrição para denotar tipos esperados de elementos de contêiner.

    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: ...

Funções e classes genéricas podem ser parametrizadas utilizando-se sintaxe do parâmetro de tipo:

    from collections.abc import Sequence

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

Ou utilizando a fábrica `TypeVar` diretamente:

    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]

Alterado na versão 3.12: O suporte sintático para genéricos é novo no Python 3.12.

## Anotando tuplas

Para a maior parte dos tipos containers em Python, o sistema de tipagem presume que todos os elementos do contêiner são do mesmo tipo. Por exemplo:

    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` aceita apenas um tipo de argumento, e assim o verificador de tipos irá emitir um erro na atribuição `y` acima. Da mesma forma, `Mapping` aceita apenas dois tipos de argumento: O primeiro indica o tipo das chaves, e o segundo indica o tipo dos valores.

Ao contrário da maioria dos outros contêineres Python, é comum no código Python idiomático que as tuplas tenham elementos que não sejam todos do mesmo tipo. Por esse motivo, as tuplas têm um caso especial no sistema de tipagem do Python. `tuple` aceita qualquer número do tipo argumento:

    # 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)

Para indicar um tupla que pode ser de qualquer comprimento, e no qual todos os elementos são do mesmo tipo `T`, use `tuple[T, ...]`. Para denotar um tupla vazia, use `tuple[()]`. Usando apenas `tuple` como anotação, é equivalente a usar `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 = ()

## O tipo de objetos de classe

Uma variável anotada com `C` pode aceitar um valor do tipo `C`. Por outro lado, uma variável anotada com `type[C]` (ou `typing.Type[C]`) pode aceitar valores que são classes – especificamente, ela aceitará o objeto classe de `C`. Por exemplo:

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

Observe que `type[C]` é covariante:

    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]``

Os únicos parâmetros válidos para `type` são classes, `Any`, type variables e uniões de qualquer um desses tipos. Por exemplo:

    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]` é equivalente a `type`, que é a raiz da hierarquia de metaclasses do Python.

## Tipos genéricos definidos pelo usuário

Uma classe definida pelo usuário pode ser definica como uma classe genérica.

    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)

Esta sintaxe indica que o classe `LoggedVar` é parametrizada em torno de uma única type variable `T`. Isso também torna `T` válido como um tipo dentro do corpo da classe.

Classes genéricas implicitamente herdar de `Generic`. Para compatibilidade com Python 3.11 e versões inferiores, também é possível herdar explicitamente de `Generic` para indicar uma classe genérica:

    from typing import TypeVar, Generic

    T = TypeVar('T')

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

Classes genéricas têm métodos `__class_getitem__()`, o que significa que podem ser parametrizadas em tempo de execução (por exemplo, `LoggedVar[int]` abaixo):

    from collections.abc import Iterable

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

Um tipo genérico pode ter qualquer número de tipos de variáveis. Todas as variedades de `TypeVar` são permitidas como parâmetros para um tipo genérico:

    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]):
        ...

Cada tipo dos argumentos para `Generic` devem ser distintos. Assim, os seguintes exemplos são inválidos:

    from typing import TypeVar, Generic
    ...

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

    T = TypeVar('T')

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

Classes genéricas podem também herdar de outras classes:

    from collections.abc import Sized

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

Ao herdar das classes genérico, algun tipos podem ser fixos:

    from collections.abc import Mapping

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

Neste caso `MyDict` possui um único parâmetro, `T`.

O uso de uma classe genérica sem especificar tipos pressupõe `Any` para cada posição. No exemplo a seguir, `MyIterable` não é genérico, mas herda implicitamente de `Iterable[Any]`:

    from collections.abc import Iterable

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

Também há suporte para tipos genéricos definidos pelo usuário. Exemplos:

    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)

Para compatibilidade retroativa, os apelidos para tipos genéricos também podem ser criados por meio de um simples atribuição:

    from collections.abc import Iterable
    from typing import TypeVar

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

Alterado na versão 3.7: `Generic` não possui mais uma metaclasse personalizada.

Alterado na versão 3.12: Suporte sintático para apelidos de tipo e genéricos é novo na versão 3.12. Anteriormente, as classes genéricas precisavam explicitamente herdar de `Generic` ou conter um tipo de variável em uma de suas bases.

Genéricos definidos pelo usuário para expressões de parâmetros também oferecem suporte por meio de variáveis de especificação de parâmetros no formato `[**P]`. O comportamento é consistente com as variáveis de tipo descritas acima, pois as variáveis de especificação de parâmetro são tratadas pelo módulo typing como uma variável de tipo especializada. A única exceção a isso é que uma lista de tipos pode ser usada para substituir um `ParamSpec`:

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

Classes genéricas sobre um `ParamSpec` também podem ser criadas usando herança explícita de `Generic`. Neste caso, `**` não é usado:

    from typing import ParamSpec, Generic

    P = ParamSpec('P')

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

Outra diferença entre `TypeVar` e `ParamSpec` é que um genérico com apenas uma variável de especificação de parâmetro aceitará listas de parâmetros nos formatos `X[[Type1, Type2, ...]]` e também `X[Type1, Type2, ...]` por razões estéticas. Internamente, o último é convertido no primeiro, portanto são equivalentes:

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

Observe que genéricos com `ParamSpec` podem não ter `__parameters__` corretos após a substituição em alguns casos porque eles são destinados principalmente à verificação de tipo estático.

Alterado na versão 3.10: `Generic` agora pode ser parametrizado através de expressões de parâmetros. Veja `ParamSpec` e PEP 612 para mais detalhes.

Uma classe genérica definida pelo usuário pode ter ABCs como classes base sem conflito de metaclasse. Não há suporte a metaclasses genéricas. O resultado da parametrização de genéricos é armazenado em cache, e a maioria dos tipos no módulo typing são hasheáveis e comparáveis em termos de igualdade.

## O tipo `Any`

Um tipo especial de tipo é `Any`. Um verificador de tipo estático tratará cada tipo como sendo compatível com `Any` e `Any` como sendo compatível com todos os tipos.

Isso significa que é possível realizar qualquer operação ou chamada de método sobre um valor do tipo `Any` e atribuí-lo a qualquer variável:

    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()
        ...

Observe que nenhuma verificação de tipo é realizada ao atribuir um valor do tipo `Any` a um tipo mais preciso. Por exemplo, o verificador de tipo estático não relatou um erro ao atribuir `a` a `s` mesmo que `s` tenha sido declarado como sendo do tipo `str` e receba um valor `int` em tempo de execução!

Além disso, todas as funções sem um tipo de retorno ou tipos de parâmetro terão como padrão implicitamente o uso de `Any`:

    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

Este comportamento permite que `Any` seja usado como uma saída de emergência quando você precisar misturar código tipado dinamicamente e estaticamente.

Compare o comportamento de `Any` com o comportamento de `object`. Semelhante a `Any`, todo tipo é um subtipo de `object`. No entanto, ao contrário de `Any`, o inverso não é verdadeiro: `object` não é um subtipo de qualquer outro tipo.

Isso significa que quando o tipo de um valor é `object`, um verificador de tipo rejeitará quase todas as operações nele, e atribuí-lo a uma variável (ou usá-la como valor de retorno) de um tipo mais especializado é um tipo erro. Por exemplo:

    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")

Use `object` para indicar que um valor pode ser de qualquer tipo de maneira segura. Use `Any` para indicar que um valor é tipado dinamicamente.

## Subtipagem nominal vs estrutural

Inicialmente a PEP 484 definiu o sistema de tipos estáticos do Python como usando subtipagem nominal. Isto significa que uma classe `A` é permitida onde uma classe `B` é esperada se e somente se `A` for uma subclasse de `B`.

Este requisito anteriormente também se aplicava a classes base abstratas, como `Iterable`. O problema com essa abordagem é que uma classe teve que ser marcada explicitamente para suportá-los, o que não é pythônico e diferente do que normalmente seria feito em código Python de tipo dinamicamente idiomático. Por exemplo, isso está em conformidade com PEP 484:

    from collections.abc import Sized, Iterable, Iterator

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

PEP 544 permite resolver este problema permitindo que os usuários escrevam o código acima sem classes base explícitas na definição de classe, permitindo que `Bucket` seja implicitamente considerado um subtipo de `Sized` e `Iterable[int]` por verificador de tipo estático. Isso é conhecido como subtipagem estrutural (ou tipagem pato estática):

    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

Além disso, ao criar uma subclasse de uma classe especial `Protocol`, um usuário pode definir novos protocolos personalizados para aproveitar ao máximo a subtipagem estrutural (veja exemplos abaixo).

## Conteúdo do módulo

O módulo `typing` define as seguintes classes, funções e decoracores.

### Tipos primitivos especiais

#### Tipos especiais

Eles podem ser usados como tipos em anotações. Eles não oferecem suporte a subscrição usando `[]`.

typing.Any

  Tipo especial que indica um tipo irrestrito.

  - Todos os tipos são compatíveis com `Any`.

  - `Any` é compatível com todos os tipos.

  Alterado na versão 3.11: `Any` agora pode ser usado como classe base. Isso pode ser útil para evitar erros do verificador de tipo com classes que podem digitar em qualquer lugar ou são altamente dinâmicas.

typing.AnyStr

  Uma variável de tipo restrito.

  Definição:

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

  `AnyStr` deve ser usado para funções que podem aceitar argumentos `str` ou `bytes` mas não podem permitir que os dois se misturem.

  Por exemplo:

      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 que, apesar do nome, `AnyStr` não tem nada a ver com o tipo `Any`, nem significa “qualquer string”. Em particular, `AnyStr` e `str | bytes` são diferentes entre si e têm casos de uso diferentes:

      # 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!"

  Descontinuado desde a versão 3.13, será removido na versão 3.18: Descontinuado em favor da nova sintaxe de parâmetro de tipo. Use `class A[T: (str, bytes)]: ...` em vez de importar `AnyStr`. Veja a PEP 695 para mais detalhes.

  No Python 3.16, `AnyStr` será removido de `typing.__all__`, e avisos de descontinuação serão emitidos em tempo de execução quando ele for acessado ou importado de `typing`. `AnyStr` será removido do `typing` no Python 3.18.

typing.LiteralString

  Tipo especial que inclui apenas strings literais.

  Qualquer literal de string é compatível com `LiteralString`, assim como outro `LiteralString`. Entretanto, um objeto digitado apenas `str` não é. Uma string criada pela composição de objetos do tipo `LiteralString` também é aceitável como uma `LiteralString`.

  Exemplo:

      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` é útil para APIs sensíveis onde strings arbitrárias geradas pelo usuário podem gerar problemas. Por exemplo, os dois casos acima que geram erros no verificador de tipo podem ser vulneráveis a um ataque de injeção de SQL.

  Veja PEP 675 para mais detalhes.

  Adicionado na versão 3.11.

typing.Never
typing.NoReturn

  `Never` e `NoReturn` representam o tipo inferior, um tipo que não possui membros.

  Eles podem ser usados ​​para indicar que uma função nunca retorna, como `sys.exit()`:

      from typing import Never  # or NoReturn

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

  Ou para definir uma função que nunca deve ser chamada, pois não existem argumentos válidos, como `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` e `NoReturn` têm o mesmo significado no sistema de tipos e os verificadores de tipo estático tratam ambos de forma equivalente.

  Adicionado na versão 3.6.2: Adicionado `NoReturn`.

  Adicionado na versão 3.11: Adicionado `Never`.

typing.Self

  Tipo especial para representar a classe atual inclusa.

  Por exemplo:

      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"

  Esta anotação é semanticamente equivalente à seguinte, embora de forma mais sucinta:

      from typing import TypeVar

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

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

  Em geral, se algo retorna `self`, como nos exemplos acima, você deve usar `Self` como anotação de retorno. Se `Foo.return_self` foi anotado como retornando `"Foo"`, então o verificador de tipo inferiria o objeto retornado de `SubclassOfFoo.return_self` como sendo do tipo `Foo` em vez de `SubclassOfFoo`.

  Outros casos de uso comuns incluem:

  - `classmethod`s que são usados como construtores alternativos e retornam instâncias do parâmetro `cls`.

  - Anotando um método `__enter__()` que retorna 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()

  Veja PEP 673 para mais detalhes.

  Adicionado na versão 3.11.

typing.TypeAlias

  Anotações especiais para declarar explicitamente um apelido de tipo.

  Por exemplo:

      from typing import TypeAlias

      Factors: TypeAlias = list[int]

  `TypeAlias` é particularmente útil em versões mais antigas do Python para anotar apelidos que fazem uso de referências futuras, pois pode ser difícil para os verificadores de tipo distingui-los das atribuições normais de variáveis:

      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: ...

  Veja PEP 613 para mais detalhes.

  Adicionado na versão 3.10.

  Obsoleto desde a versão 3.12: `TypeAlias` foi descontinuado em favor da instrução `type`, que cria instâncias de `TypeAliasType` e que oferece suporte a nativamente referências futuras. Observe que embora `TypeAlias` e `TypeAliasType` sirvam propósitos semelhantes e tenham nomes semelhantes, eles são distintos e o último não é o tipo do primeiro. A remoção de `TypeAlias` não está planejada atualmente, mas os usuários são encorajados a migrar para instruções `type`.

#### Formas especiais

Eles podem ser usados ​​como tipos em anotações. Todos eles oferecem suporte a subscrição usando `[]`, mas cada um tem uma sintaxe única.

typing.Union

  Tipo de união; `Union[X, Y]` é equivalente a `X | Y` e significa X ou Y.

  Para definir uma união, use, por exemplo. `Union[int, str]` ou a abreviatura `int | str`. Usar essa abreviação é recomendado. Detalhes:

  - Os argumentos devem ser tipos e deve haver pelo menos um.

  - As uniões de uniões são achatadas, por exemplo:

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

  - As uniões de um único argumento desaparecem, por exemplo:

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

  - Redundant arguments are skipped, e.g.:

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

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

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

  - You cannot subclass or instantiate a `Union`.

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

  Alterado na versão 3.7: Don’t remove explicit subclasses from unions at runtime.

  Alterado na versão 3.10: 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]`).

  Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the `Optional` qualifier on its type annotation just because it is optional. For example:

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

  On the other hand, if an explicit value of `None` is allowed, the use of `Optional` is appropriate, whether the argument is optional or not. For example:

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

  Alterado na versão 3.10: 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])

  Adicionado na versão 3.10.

  Ver também

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

  - `ParamSpec`

  - Anotações de objetos chamáveis

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.

  Por exemplo:

      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[...]` cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to `Literal[...]`, but type checkers may impose restrictions. See PEP 586 for more details about literal types.

  Adicionado na versão 3.8.

  Alterado na versão 3.9.1: `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

  Special type construct to mark class variables.

  As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:

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

  `ClassVar` accepts only types and cannot be further subscribed.

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

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

  Adicionado na versão 3.5.3.

  Alterado na versão 3.13: `ClassVar` can now be nested in `Final` and vice versa.

typing.Final

  Special typing construct to indicate final names to type checkers.

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

  Por exemplo:

      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

  There is no runtime checking of these properties. See PEP 591 for more details.

  Adicionado na versão 3.8.

  Alterado na versão 3.13: `Final` can now be nested in `ClassVar` and vice versa.

typing.Required

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

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

  Adicionado na versão 3.11.

typing.NotRequired

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

  See `TypedDict` and PEP 655 for more details.

  Adicionado na versão 3.11.

typing.ReadOnly

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

  Por exemplo:

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

      def mutate_movie(m: Movie) -> None:
         m["year"] = 1992  # allowed
         m["title"] = "The Matrix"  # typechecker error

  There is no runtime checking for this property.

  See `TypedDict` and PEP 705 for more details.

  Adicionado na versão 3.13.

typing.Annotated

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

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

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

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

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

  Annotated[<type>, <metadata>]

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

      @dataclass
      class ValueRange:
          lo: int
          hi: int

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

  Detalhes da sintaxe:

  - The first argument to `Annotated` must be a valid type

  - Multiple metadata elements can be supplied (`Annotated` supports variadic arguments):

        @dataclass
        class ctype:
            kind: str

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

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

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

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

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

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

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

  - Elementos duplicados de metadata não são removidos:

        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

    Isso deve ser equivalente a

        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')

  Ver também

  PEP 593 - Flexible function and variable annotations

    The PEP introducing `Annotated` to the standard library.

  Adicionado na versão 3.9.

typing.TypeIs

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

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

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

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

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

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

  1. O valor de retorno é um booleano.

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

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

  Por exemplo:

      from typing import assert_type, final, TypeIs

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

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

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

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

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

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

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

  Adicionado na versão 3.13.

typing.TypeGuard

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

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

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

  1. O valor de retorno é um booleano.

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

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

  Por exemplo:

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

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

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

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

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

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

  Adicionado na versão 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.

  Adicionado na versão 3.11.

#### Criando tipos genéricos e apelidos de tipo

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

  Classe base abstrata para tipos genéricos

  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.

  Esta classe pode ser utilizada como segue:

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

  Aqui os colchetes depois no nome da função indica uma função genérica.

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

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

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

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

  Tipo variável.

  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 bound and constrained type variables:

      class StrSequence[S: str]:  # S is a TypeVar bound to 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 bound, constrained, or neither, but cannot be both bound 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.

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

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

      class StringSubclass(str):
          pass

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

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

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

      # 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.

    Adicionado na versão 3.12.

  __bound__

    The bound of the type variable, if any.

    Alterado na versão 3.12: 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 Avaliação preguiçosa).

  __constraints__

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

    Alterado na versão 3.12: 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 Avaliação preguiçosa).

  __default__

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

    Adicionado na versão 3.13.

  has_default()

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

    Adicionado na versão 3.13.

  Alterado na versão 3.12: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The `infer_variance` parameter was added.

  Alterado na versão 3.13: Support for default values was added.

class typing.TypeVarTuple(name, *, default=typing.NoDefault)

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

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

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

  Or by explicitly invoking the `TypeVarTuple` constructor:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  See PEP 646 for more details on type variable tuples.

  __name__

    The name of the type variable tuple.

  __default__

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

    Adicionado na versão 3.13.

  has_default()

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

    Adicionado na versão 3.13.

  Adicionado na versão 3.11.

  Alterado na versão 3.12: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

  Alterado na versão 3.13: Support for default values was added.

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

  Parameter specification variable. A specialized version of type variables.

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

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

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

      P = ParamSpec('P')

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

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

      from collections.abc import Callable
      import logging

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

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

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

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

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

  args
  kwargs

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

  __name__

    The name of the parameter specification.

  __default__

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

    Adicionado na versão 3.13.

  has_default()

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

    Adicionado na versão 3.13.

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

  Adicionado na versão 3.10.

  Alterado na versão 3.12: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

  Alterado na versão 3.13: Support for default values was added.

  Nota

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

  Ver também

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

  - `Concatenate`

  - Anotações de objetos chamáveis

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

  Adicionado na versão 3.10.

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

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

  Exemplo:

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

  Adicionado na versão 3.12.

  __name__

    The name of the type alias:

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

  __module__

    O módulo na qual o apelido de tipo foi definido:

        >>> 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

#### Outras diretivas especiais

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

  Typed version of `collections.namedtuple()`.

  Uso:

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

  Isso equivale a:

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

  To give a field a default value, you can assign to it in the class body:

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

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

  Fields with a default value must come after any fields without a default.

  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` subclasses can also have docstrings and methods:

      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]

  Backward-compatible usage:

      # For creating a generic NamedTuple on Python 3.11 or lower
      class Group(NamedTuple, Generic[T]):
          key: T
          group: list[T]

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

  Alterado na versão 3.6: Added support for PEP 526 variable annotation syntax.

  Alterado na versão 3.6.1: Added support for default values, methods, and docstrings.

  Alterado na versão 3.8: The `_field_types` and `__annotations__` attributes are now regular dictionaries instead of instances of `OrderedDict`.

  Alterado na versão 3.9: Removed the `_field_types` attribute in favor of the more standard `__annotations__` attribute which has the same information.

  Alterado na versão 3.11: Added support for generic namedtuples.

  Descontinuado desde a versão 3.13, será removido na versão 3.15: The undocumented keyword argument syntax for creating NamedTuple classes (`NT = NamedTuple("NT", x=int)`) is deprecated, and will be disallowed in 3.15. Use the class-based syntax or the functional syntax instead.

  Descontinuado desde a versão 3.13, será removido na versão 3.15: When using the functional syntax to create a NamedTuple class, failing to pass a value to the ‘fields’ parameter (`NT = NamedTuple("NT")`) is deprecated. Passing `None` to the ‘fields’ parameter (`NT = NamedTuple("NT", None)`) is also deprecated. Both will be disallowed in Python 3.15. To create a NamedTuple class with 0 fields, use `class NT(NamedTuple): pass` or `NT = NamedTuple("NT", [])`.

class typing.NewType(name, tp)

  Helper class to create low-overhead distinct types.

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

  Uso:

      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__

    O nome do novo tipo.

  __supertype__

    O tipo na qual o novo tipo é baseado.

  Adicionado na versão 3.5.2.

  Alterado na versão 3.10: `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:
              ...

  Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

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

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

      func(C())  # Passes static type check

  See PEP 544 for more details. Protocol classes decorated with `runtime_checkable()` (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.

  Protocol classes can be generic, for example:

      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:
              ...

  Adicionado na versão 3.8.

@typing.runtime_checkable

  Mark a protocol class as a runtime protocol.

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

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

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

      @runtime_checkable
      class Named(Protocol):
          name: str

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

  Nota

  `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`.

  Nota

  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.

  Adicionado na versão 3.8.

  Alterado na versão 3.12: 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.

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

class typing.TypedDict(dict)

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

  `TypedDict` declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:

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

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

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

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

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

  This functional syntax allows defining keys which are not valid identifiers, for example because they are keywords or contain hyphens:

      # 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

  Um `TypedDict` pode ser genérico:

      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 Boas Práticas de Anotação 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__

    Adicionado na versão 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

    Adicionado na versão 3.9.

    Nota

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

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

  __readonly_keys__

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

    Adicionado na versão 3.13.

  __mutable_keys__

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

    Adicionado na versão 3.13.

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

  Adicionado na versão 3.8.

  Alterado na versão 3.11: Added support for marking individual keys as `Required` or `NotRequired`. See PEP 655.

  Alterado na versão 3.11: Adicionado suporte para `TypedDict`s genéricos.

  Alterado na versão 3.13: Removed support for the keyword-argument method of creating `TypedDict`s.

  Alterado na versão 3.13: Support for the `ReadOnly` qualifier was added.

  Descontinuado desde a versão 3.13, será removido na versão 3.15: When using the functional syntax to create a TypedDict class, failing to pass a value to the ‘fields’ parameter (`TD = TypedDict("TD")`) is deprecated. Passing `None` to the ‘fields’ parameter (`TD = TypedDict("TD", None)`) is also deprecated. Both will be disallowed in Python 3.15. To create a TypedDict class with 0 fields, use `class TD(TypedDict): pass` or `TD = TypedDict("TD", {})`.

### Protocolos

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

class typing.SupportsAbs

  An ABC with one abstract method `__abs__` that is covariant in its return type.

class typing.SupportsBytes

  An ABC with one abstract method `__bytes__`.

class typing.SupportsComplex

  An ABC with one abstract method `__complex__`.

class typing.SupportsFloat

  An ABC with one abstract method `__float__`.

class typing.SupportsIndex

  An ABC with one abstract method `__index__`.

  Adicionado na versão 3.8.

class typing.SupportsInt

  An ABC with one abstract method `__int__`.

class typing.SupportsRound

  An ABC with one abstract method `__round__` that is covariant in its return type.

### ABCs para trabalhar com 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()`.

### Funções e decoradores

typing.cast(typ, val)

  Define um valor para um tipo.

  This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).

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)

  Adicionado na versão 3.11.

typing.assert_never(arg, /)

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

  Exemplo:

      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.

  Ver também

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

  Adicionado na versão 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.

  Adicionado na versão 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:

  Parâmetros:

    - 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`.

      Adicionado na versão 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
  Nome do parâmetro | Descrição
  --- | ---
  `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.

  Veja PEP 681 para mais detalhes.

  Adicionado na versão 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.

  Alterado na versão 3.11: 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.

  Adicionado na versão 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.

  Adicionado na versão 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.

  Por exemplo:

      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
          ...

  There is no runtime checking of these properties. See PEP 591 for more details.

  Adicionado na versão 3.8.

  Alterado na versão 3.11: 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

  Decorator to indicate that annotations are not type hints.

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

  `@no_type_check` mutates the decorated object in place.

@typing.no_type_check_decorator

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

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

  Descontinuado desde a versão 3.13, será removido na versão 3.15: No type checker ever added support for `@no_type_check_decorator`. It is therefore deprecated, and will be removed in Python 3.15.

@typing.override

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

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

  Por exemplo:

      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.

  Consulte PEP 698 para obter mais detalhes.

  Adicionado na versão 3.12.

@typing.type_check_only

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

  This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:

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

      def fetch_response() -> Response: ...

  Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.

### Introspection helpers

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

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

  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.

  Nota

  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`.

  Alterado na versão 3.9: Added `include_extras` parameter as part of PEP 593. See the documentation on `Annotated` for more information.

  Alterado na versão 3.11: 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.

  Exemplos:

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

  Adicionado na versão 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.

  Exemplos:

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

  Adicionado na versão 3.8.

typing.get_protocol_members(tp)

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

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

  Raise `TypeError` for arguments that are not Protocols.

  Adicionado na versão 3.13.

typing.is_protocol(tp)

  Determine if a type is a `Protocol`.

  Por exemplo:

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

      is_protocol(P)    # => True
      is_protocol(int)  # => False

  Adicionado na versão 3.13.

typing.is_typeddict(tp)

  Check if a type is a `TypedDict`.

  Por exemplo:

      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)

  Adicionado na versão 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.

  Nota

  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]`.

  Adicionado na versão 3.7.4.

typing.NoDefault

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

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

  Adicionado na versão 3.13.

### Constante

typing.TYPE_CHECKING

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

  Uso:

      if TYPE_CHECKING:
          import expensive_mod

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

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

  Nota

  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).

  Adicionado na versão 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`.

  This type can be used as follows:

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

  Obsoleto desde a versão 3.9: `builtins.dict` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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`.

  This type may be used as follows:

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

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

  Obsoleto desde a versão 3.9: `builtins.list` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `builtins.set`.

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

  Obsoleto desde a versão 3.9: `builtins.set` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `builtins.frozenset`.

  Obsoleto desde a versão 3.9: `builtins.frozenset` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

typing.Tuple

  Deprecated alias for `tuple`.

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

  Obsoleto desde a versão 3.9: `builtins.tuple` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.Type(Generic[CT_co])

  Deprecated alias to `type`.

  See O tipo de objetos de classe for details on using `type` or `typing.Type` in type annotations.

  Adicionado na versão 3.5.2.

  Obsoleto desde a versão 3.9: `builtins.type` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

#### Aliases to types in `collections`

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

  Deprecated alias to `collections.defaultdict`.

  Adicionado na versão 3.5.2.

  Obsoleto desde a versão 3.9: `collections.defaultdict` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.OrderedDict`.

  Adicionado na versão 3.7.2.

  Obsoleto desde a versão 3.9: `collections.OrderedDict` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.ChainMap`.

  Adicionado na versão 3.6.1.

  Obsoleto desde a versão 3.9: `collections.ChainMap` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.Counter`.

  Adicionado na versão 3.6.1.

  Obsoleto desde a versão 3.9: `collections.Counter` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.deque`.

  Adicionado na versão 3.6.1.

  Obsoleto desde a versão 3.9: `collections.deque` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

#### Aliases to other concrete types

class typing.Pattern
class typing.Match

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

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

  Obsoleto desde a versão 3.9: Classes `Pattern` and `Match` from `re` now support `[]`. See PEP 585 and Tipo Generic Alias.

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`.

  Use `Text` to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:

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

  Adicionado na versão 3.5.2.

  Obsoleto desde a versão 3.11: 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`.

  Obsoleto desde a versão 3.9: `collections.abc.Set` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.ByteString(Sequence[int])

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

  Descontinuado desde a versão 3.9, será removido na versão 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`.

  Adicionado na versão 3.6.

  Obsoleto desde a versão 3.9: `collections.abc.Collection` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.Container(Generic[T_co])

  Deprecated alias to `collections.abc.Container`.

  Obsoleto desde a versão 3.9: `collections.abc.Container` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.abc.ItemsView`.

  Obsoleto desde a versão 3.9: `collections.abc.ItemsView` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.abc.KeysView`.

  Obsoleto desde a versão 3.9: `collections.abc.KeysView` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.abc.Mapping`.

  This type can be used as follows:

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

  Obsoleto desde a versão 3.9: `collections.abc.Mapping` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.MappingView(Sized)

  Deprecated alias to `collections.abc.MappingView`.

  Obsoleto desde a versão 3.9: `collections.abc.MappingView` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.abc.MutableMapping`.

  Obsoleto desde a versão 3.9: `collections.abc.MutableMapping` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.MutableSequence(Sequence[T])

  Deprecated alias to `collections.abc.MutableSequence`.

  Obsoleto desde a versão 3.9: `collections.abc.MutableSequence` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.MutableSet(AbstractSet[T])

  Deprecated alias to `collections.abc.MutableSet`.

  Obsoleto desde a versão 3.9: `collections.abc.MutableSet` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.abc.Sequence`.

  Obsoleto desde a versão 3.9: `collections.abc.Sequence` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.abc.ValuesView`.

  Obsoleto desde a versão 3.9: `collections.abc.ValuesView` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

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

  Deprecated alias to `collections.abc.Coroutine`.

  The variance and order of type variables correspond to those of `Generator`, for example:

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

  Adicionado na versão 3.5.3.

  Obsoleto desde a versão 3.9: `collections.abc.Coroutine` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

  Deprecated alias to `collections.abc.AsyncGenerator`.

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

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

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

  The `SendType` defaults to `None`:

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

  It is also possible to set this type explicitly:

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

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

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

  Adicionado na versão 3.6.1.

  Obsoleto desde a versão 3.9: `collections.abc.AsyncGenerator` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

  Alterado na versão 3.13: The `SendType` parameter now has a default.

class typing.AsyncIterable(Generic[T_co])

  Deprecated alias to `collections.abc.AsyncIterable`.

  Adicionado na versão 3.5.2.

  Obsoleto desde a versão 3.9: `collections.abc.AsyncIterable` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.AsyncIterator(AsyncIterable[T_co])

  Deprecated alias to `collections.abc.AsyncIterator`.

  Adicionado na versão 3.5.2.

  Obsoleto desde a versão 3.9: `collections.abc.AsyncIterator` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.Awaitable(Generic[T_co])

  Deprecated alias to `collections.abc.Awaitable`.

  Adicionado na versão 3.5.2.

  Obsoleto desde a versão 3.9: `collections.abc.Awaitable` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

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

class typing.Iterable(Generic[T_co])

  Deprecated alias to `collections.abc.Iterable`.

  Obsoleto desde a versão 3.9: `collections.abc.Iterable` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.Iterator(Iterable[T_co])

  Deprecated alias to `collections.abc.Iterator`.

  Obsoleto desde a versão 3.9: `collections.abc.Iterator` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

typing.Callable

  Deprecated alias to `collections.abc.Callable`.

  See Anotações de objetos chamáveis for details on how to use `collections.abc.Callable` and `typing.Callable` in type annotations.

  Obsoleto desde a versão 3.9: `collections.abc.Callable` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

  Alterado na versão 3.10: `Callable` agora oferece suporte a `ParamSpec` e `Concatenate`. Veja PEP 612 para mais detalhes.

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

  Deprecated alias to `collections.abc.Generator`.

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

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

  Note that unlike many other generics in the typing module, the `SendType` of `Generator` behaves contravariantly, not covariantly or invariantly.

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

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

  It is also possible to set these types explicitly:

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

  Alternatively, annotate your generator as having a return type of either `Iterable[YieldType]` or `Iterator[YieldType]`:

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

  Obsoleto desde a versão 3.9: `collections.abc.Generator` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

  Alterado na versão 3.13: Default values for the send and return types were added.

class typing.Hashable

  Deprecated alias to `collections.abc.Hashable`.

  Obsoleto desde a versão 3.12: Use `collections.abc.Hashable` directly instead.

class typing.Reversible(Iterable[T_co])

  Deprecated alias to `collections.abc.Reversible`.

  Obsoleto desde a versão 3.9: `collections.abc.Reversible` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

class typing.Sized

  Deprecated alias to `collections.abc.Sized`.

  Obsoleto desde a versão 3.12: Use `collections.abc.Sized` directly instead.

#### Aliases to `contextlib` ABCs

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

  Deprecated alias to `contextlib.AbstractContextManager`.

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

  Adicionado na versão 3.5.4.

  Obsoleto desde a versão 3.9: `contextlib.AbstractContextManager` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

  Alterado na versão 3.13: Added the optional second type parameter, `ExitT_co`.

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

  Deprecated alias to `contextlib.AbstractAsyncContextManager`.

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

  Adicionado na versão 3.6.2.

  Obsoleto desde a versão 3.9: `contextlib.AbstractAsyncContextManager` now supports subscripting (`[]`). See PEP 585 and Tipo Generic Alias.

  Alterado na versão 3.13: Added the optional second type parameter, `AExitT_co`.

## Cronograma de Descontinuação dos Principais Recursos

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 | Descontinuado em | Projected removal | PEP/issue
--- | --- | --- | ---
`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
`@typing.no_type_check_decorator` | 3.13 | 3.15 | gh-106309
`typing.AnyStr` | 3.13 | 3.18 | gh-105578