"typing" --- Suporte para dicas de tipo
***************************************

Novo 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 oferece suporte para dicas de tipo ao ambiente de
execução. Para a especificação original de tipagem do sistema,
veja:pep:*484*. Para uma introdução simplificada as dicas de tipo,
veja **PEP 483**.

A função abaixo recebe e retorna uma string e é anotada como a seguir:

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

Na função "greeting", é esperado que o argumento "name" seja do tipo
"str" e o retorno do tipo "str". Subtipos são aceitos como argumentos.

Novos recursos são frequentemente adicionados ao módulo "typing". O
pacote typing_extensions provê suporte retroativo a estes novos
recursos em versões anteriores do Python.

Para ter um resumo dos recursos descontinuados e um cronograma de
descontinuação, por favor, veja  Cronograma de Descontinuação dos
Principais Recursos.

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.


PEPs Relevantes
===============

Desde a introdução das dicas de tipo nas  **PEP 484** e **PEP 483**,
várias PEPs tem modificado e aprimorado o framework do Python para
anotações de tipo:

* **PEP 526**: Sintaxe para Anotações de Variável
     "Introduzindo" sintaxe para anotar variáveis fora de definições
     de funções e "ClassVar".

* **PEP 544**: Protocols: Structural subtyping (static duck typing)
     *Introduzindo* "Protocol" e o decorador "@runtime_checkable".

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

* **PEP 586**: Tipos literais
     *Introduzindo* "Literal"

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

* **PEP 591**: Adicionando um qualificador final para escrita
     *Introducing* "Final" and the "@final" decorator

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

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

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

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

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

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

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

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

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

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


Apelidos de tipo
================

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

   Vector = list[float]

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

   # passes type checking; a list of floats qualifies as a Vector.
   new_vector = scale(2.0, [1.0, -4.2, 5.4])

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

   from collections.abc import Sequence

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

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

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

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

   from typing import TypeAlias

   Vector: TypeAlias = list[float]


NewType
=======

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:

  Recall that the use of a type alias declares two types to be
  *equivalent* to one another. Doing "Alias = Original" will make the
  static type checker treat "Alias" as being *exactly equivalent* to
  "Original" in all cases. This is useful when you want to simplify
  complex type signatures.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.

Novo 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: ...

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

   from collections.abc import Sequence
   from typing import TypeVar

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

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


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 typing import TypeVar, Generic
   from logging import Logger

   T = TypeVar('T')

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

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

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

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

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

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

   from collections.abc import Iterable

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

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

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

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

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

   from typing import TypeVar, Generic
   ...

   T = TypeVar('T')

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

You can use multiple inheritance with "Generic":

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

   T = TypeVar('T')

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

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

   from collections.abc import Mapping
   from typing import TypeVar

   T = TypeVar('T')

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

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

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

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

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

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

   >>> from typing import Generic, ParamSpec, TypeVar

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

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

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

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

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!"

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.

   Novo na versão 3.11.

typing.Never

   The bottom type, a type that has no members.

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

      from typing import Never

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

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

   Novo na versão 3.11: On older Python versions, "NoReturn" may be
   used to express the same concept. "Never" was added to make the
   intended meaning more explicit.

typing.NoReturn

   Tipo especial indicando que uma função nunca retorna.

   Por exemplo:

      from typing import NoReturn

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

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

   Novo na versão 3.6.2.

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.

   Você não deveria usar "Self" como a anotação de retorno se não for
   garantido que o método retorne uma instância de uma subclasse
   quando a classe for subclassificada:

      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.

   Novo 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" is particularly useful for annotating aliases that make
   use of forward references, as it can be hard for type checkers to
   distinguish these from normal variable assignments:

      from typing import Generic, TypeAlias, TypeVar

      T = TypeVar("T")

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

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

   Veja **PEP 613** para mais detalhes.

   Novo na versão 3.10.


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

   * Argumento redundantes são pulados, 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]

   * Você não pode estender ou instanciar uma "Union"

   * Você não pode escrever "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

   Forma especial para anotar funções de ordem superior.

   "Concatenate" can be used in conjunction with Callable and
   "ParamSpec" to annotate a higher-order callable which adds,
   removes, or transforms parameters of another callable.  Usage is in
   the form "Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]".
   "Concatenate" is currently only valid when used as the first
   argument to a Callable. The last parameter to "Concatenate" must be
   a "ParamSpec" or ellipsis ("...").

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

      from collections.abc import Callable
      from threading import Lock
      from typing import Concatenate, ParamSpec, TypeVar

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

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

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

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

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

   Novo 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
          ...

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

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

   "Literal[...]" 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.

   Novo 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

   Novo na versão 3.5.3.

typing.Final

   Special typing construct to indicate final names to type checkers.

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

   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.

   Novo na versão 3.8.

typing.Required

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

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

   Novo 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.

   Novo na versão 3.11.

typing.Annotated

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

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

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

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

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

   Annotated[<type>, <metadata>]

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

      @dataclass
      class ValueRange:
          lo: int
          hi: int

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

   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

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

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

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

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

     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.

   Novo na versão 3.9.

typing.TypeGuard

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

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

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

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

   Sometimes it would be convenient to use a user-defined boolean
   function as a type guard.  Such a function should use
   "TypeGuard[...]" as its return type to alert static type checkers
   to this intention.

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

   1. O valor de retorno é um booleano.

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

   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!")

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

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

   Nota:

     "TypeB" need not be a narrower form of "TypeA" -- it can even be
     a wider form. The main reason is to allow for things like
     narrowing "list[object]" to "list[str]" even though the latter is
     not a subtype of the former, since "list" is invariant. The
     responsibility of writing type-safe type guards is left to the
     user.

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

   Novo 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

   Novo na versão 3.11.


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

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

class typing.Generic

   Classe base abstrata para tipos genéricos

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

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

   Esta classe pode ser utilizada como segue:

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

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

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

   Tipo variável.

   Uso:

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

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

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


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


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

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

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

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

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

      class StringSubclass(str):
          pass

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

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

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

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

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

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

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

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

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

   __name__

      The name of the type variable.

   __covariant__

      Whether the type var has been marked as covariant.

   __contravariant__

      Whether the type var has been marked as contravariant.

   __bound__

      The bound of the type variable, if any.

   __constraints__

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

class typing.TypeVarTuple(name)

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

   Uso:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

   See **PEP 646** for more details on type variable tuples.

   __name__

      The name of the type variable tuple.

   Novo na versão 3.11.

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

   Parameter specification variable.  A specialized version of type
   variables.

   Uso:

      P = ParamSpec('P')

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

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

      from collections.abc import Callable
      from typing import TypeVar, ParamSpec
      import logging

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

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

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

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

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

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

   args

   kwargs

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

   __name__

      The name of the parameter specification.

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

   Novo na versão 3.10.

   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

   Novo na versão 3.10.


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

   Backward-compatible usage:

      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.

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.

   Novo 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:

      T = TypeVar("T")

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

   Novo 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.

   Novo na versão 3.8.

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')

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

   * Utilizando um literal "dict" como segundo argumento:

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

   * Using keyword arguments:

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

   Descontinuado desde a versão 3.11, será removido na versão 3.13:
   The keyword-argument syntax is deprecated in 3.11 and will be
   removed in 3.13. It may also be unsupported by static type
   checkers.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      class Point3D(Point2D):
          z: int

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

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

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

      class X(TypedDict):
          x: int

      class Y(TypedDict):
          y: int

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

      class XY(X, Y): pass  # OK

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

   A "TypedDict" can be generic:

      T = TypeVar("T")

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

   A "TypedDict" can be introspected via annotations dicts (see Boas
   práticas para anotações 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__

      Novo 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

      Novo 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.

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

   Novo 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.


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__".

   Novo 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)

   Novo 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.

   Novo 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.

   Novo na versão 3.11.

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

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

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

   Example usage with a decorator function:

      T = TypeVar("T")

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

      @create_model
      class CustomerModel:
          id: int
          name: str

   On a base class:

      @dataclass_transform()
      class ModelBase: ...

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

   On a metaclass:

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

      class ModelBase(metaclass=ModelMeta): ...

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

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

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

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

   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".

      * **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.

   Novo 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.

   Novo 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.

   Novo 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.

   Novo 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()".

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

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

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

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

   Nota:

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

   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

   Novo 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)

   Novo na versão 3.8.

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)

   Novo 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]".

   Novo na versão 3.7.4.


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**).

   Novo 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:

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

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

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

   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.

   Novo 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".

   Novo 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".

   Novo 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".

   Novo 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".

   Novo 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".

   Novo 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]".

   Descontinuado desde a versão 3.8, será removido na versão 3.13: The
   "typing.re" namespace is deprecated and will be removed. These
   types should be directly imported from "typing" instead.

   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'

   Novo 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 "typing_extensions.Buffer", or a union like "bytes |
   bytearray | memoryview".

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

   Deprecated alias to "collections.abc.Collection".

   Novo 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

   Novo 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.

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

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

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

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

   Novo na versão 3.6.1.

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

class typing.AsyncIterable(Generic[T_co])

   Deprecated alias to "collections.abc.AsyncIterable".

   Novo 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".

   Novo 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".

   Novo 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.

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

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

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

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

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

class typing.Hashable

   Alias to "collections.abc.Hashable".

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

   Alias to "collections.abc.Sized".


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

class typing.ContextManager(Generic[T_co])

   Deprecated alias to "contextlib.AbstractContextManager".

   Novo na versão 3.5.4.

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

class typing.AsyncContextManager(Generic[T_co])

   Deprecated alias to "contextlib.AbstractAsyncContextManager".

   Novo na versão 3.6.2.

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


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.io" and           | 3.8                       | 3.13                      | bpo-38291                 |
| "typing.re" submodules    |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing" versions of      | 3.9                       | Undecided (see Deprecated | **PEP 585**               |
| standard collections      |                           | aliases for more          |                           |
|                           |                           | information)              |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.ByteString"       | 3.9                       | 3.14                      | gh-91896                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Text"             | 3.11                      | Undecided                 | gh-92332                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
