# `typing` — Soporte para type hints

Added in version 3.5.

Source code: Lib/typing.py

Nota

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

This module provides runtime support for type hints.

Consider the function below:

    def moon_weight(earth_weight: float) -> str:
        return f'On the moon, you would weigh {earth_weight * 0.166} kilograms.'

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

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

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

Ver también

«Typing cheat sheet»

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

«Type System Reference» section of the mypy docs

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

«Static Typing with Python»

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

## Specification for the Python Type System

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

## Alias de tipo

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

    type Vector = list[float]

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

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

Los alias de tipo son útiles para simplificar firmas de tipo complejas. Por ejemplo:

    from collections.abc import Sequence

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

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

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

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

    Vector = list[float]

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

    from typing import TypeAlias

    Vector: TypeAlias = list[float]

## NewType

Utilícese la clase auxiliar `NewType` para crear tipos distintos:

    from typing import NewType

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

El validador estático de tipos tratará el nuevo tipo como si fuera una subclase del tipo original. Esto es útil para capturar errores lógicos:

    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)

Se pueden realizar todas las operaciones de `int` en una variable de tipo `UserId`, pero el resultado siempre será de tipo `int`. Esto permite pasar un `UserId` allí donde se espere un `int`, pero evitará la creación accidental de un `UserId` de manera incorrecta:

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

Tenga en cuenta que estas validaciones solo las aplica el verificador de tipo estático. En tiempo de ejecución, la declaración `Derived = NewType('Derived', Base)` hará que `Derived` sea una clase que retorna inmediatamente cualquier parámetro que le pase. Eso significa que la expresión `Derived(some_value)` no crea una nueva clase ni introduce mucha sobrecarga más allá de la de una llamada de función regular.

Más concretamente, la expresión `some_value is Derived(some_value)` será siempre verdadera en tiempo de ejecución.

No es válido crear un subtipo de `Derived`:

    from typing import NewType

    UserId = NewType('UserId', int)

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

Sin embargo, es posible crear un `NewType` basado en un `NewType` “derivado”:

    from typing import NewType

    UserId = NewType('UserId', int)

    ProUserId = NewType('ProUserId', UserId)

y la comprobación de tipo para `ProUserId` funcionará como se espera.

Véase PEP 484 para más detalle.

Nota

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

En cambio, `NewType` declara un tipo que es subtipo de otro. Haciendo `Derived = NewType('Derived', Original)` hará que el Validador estático de tipos trate `Derived` como una subclase de `Original`, lo que implica que un valor de tipo `Original` no puede ser usado allí donde se espere un valor de tipo `Derived`. Esto es útil para prevenir errores lógicos con un coste de ejecución mínimo.

Added in version 3.5.2.

Distinto en la versión 3.10: `NewType` is now a class rather than a function. As a result, there is some additional runtime cost when calling `NewType` over a regular function.

Distinto en la versión 3.11: The performance of calling `NewType` has been restored to its level in Python 3.9.

## Annotating callable objects

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

For example:

    from collections.abc import Callable, Awaitable

    def feeder(get_next_item: Callable[[], str]) -> None:
        ...  # Body

    def async_query(on_success: Callable[[int], None],
                    on_error: Callable[[int, Exception], None]) -> None:
        ...  # Body

    async def on_update(value: str) -> None:
        ...  # Body

    callback: Callable[[str], Awaitable[None]] = on_update

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

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

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

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

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

    from collections.abc import Iterable
    from typing import Protocol

    class Combiner(Protocol):
        def __call__(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ...

    def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes:
        for item in data:
            ...

    def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]:
        ...
    def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]:
        ...

    batch_proc([], good_cb)  # OK
    batch_proc([], bad_cb)   # Error! Argument 2 has incompatible type because of
                             # different name and kind in the callback

Los invocables que toman otros invocables como argumentos pueden indicar que sus tipos de parámetros dependen unos de otros utilizando `ParamSpec`. Además, si ese invocable agrega o elimina argumentos de otros invocables, se puede utilizar el operador `Concatenate`. Toman la forma `Callable[ParamSpecVariable, ReturnType]` y `Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]` respectivamente.

Distinto en la versión 3.10: `Callable` ahora es compatible con `ParamSpec` y `Concatenate`. Consulte PEP 612 para obtener más información.

Ver también

La documentación de `ParamSpec` y `Concatenate` proporciona ejemplos de uso en `Callable`.

## Genéricos

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

    from collections.abc import Mapping, Sequence

    class Employee: ...

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

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

    from collections.abc import Sequence

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

Or by using the `TypeVar` factory directly:

    from collections.abc import Sequence
    from typing import TypeVar

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

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

Distinto en la versión 3.12: Syntactic support for generics is new in Python 3.12.

## Annotating tuples

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

    from collections.abc import Mapping

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

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

    # Type checker will infer that all keys in ``z`` are meant to be strings,
    # and that all values in ``z`` are meant to be either strings or ints
    z: Mapping[str, str | int] = {}

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

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

    # OK: ``x`` is assigned to a tuple of length 1 where the sole element is an int
    x: tuple[int] = (5,)

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

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

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

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

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

    z: tuple = ("foo", "bar")
    # These reassignments are OK: plain ``tuple`` is equivalent to ``tuple[Any, ...]``
    z = (1, 2, 3)
    z = ()

## The type of class objects

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

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

Note that `type[C]` is covariant:

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

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

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

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

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

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

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

## Tipos genéricos definidos por el usuario

Una clase definida por el usuario puede ser definida como una clase genérica.

    from logging import Logger

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

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

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

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

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

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

    from typing import TypeVar, Generic

    T = TypeVar('T')

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

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

    from collections.abc import Iterable

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

Un tipo genérico puede tener un numero cualquiera de variables de tipo. Se permiten todas las variaciones de `TypeVar` para ser usadas como parámetros de un tipo genérico:

    from typing import TypeVar, Generic, Sequence

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

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

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

Cada argumento de variable de tipo en una clase `Generic` debe ser distinto. Así, no será válido:

    from typing import TypeVar, Generic
    ...

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

    T = TypeVar('T')

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

Generic classes can also inherit from other classes:

    from collections.abc import Sized

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

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

    from collections.abc import Mapping

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

En este caso `MyDict` tiene un solo parámetro, `T`.

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

    from collections.abc import Iterable

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

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

    from collections.abc import Iterable

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

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

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

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

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

    from collections.abc import Iterable
    from typing import TypeVar

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

Distinto en la versión 3.7: `Generic` ya no posee una metaclase personalizable.

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

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

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

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

    from typing import ParamSpec, Generic

    P = ParamSpec('P')

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

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

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

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

Distinto en la versión 3.10: `Generic` ahora se puede parametrizar sobre expresiones de parámetros. Consulte `ParamSpec` y PEP 612 para obtener más detalles.

A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality.

## El tipo `Any`

Un caso especial de tipo es `Any`. Un Validador estático de tipos tratará cualquier tipo como compatible con `Any`, y `Any` como compatible con todos los tipos.

Esto significa que es posible realizar cualquier operación o llamada a un método en un valor de tipo `Any` y asignarlo a cualquier variable:

    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()
        ...

Nótese que no se realiza comprobación de tipo cuando se asigna un valor de tipo `Any` a un tipo más preciso. Por ejemplo, el Validador estático de tipos no reportó ningún error cuando se asignó `a` a `s`, aún cuando se declaró `s` como de tipo `str` y recibió un valor `int` en tiempo de ejecución!

Además, todas las funciones sin un tipo de retorno o tipos en los parámetros serán asignadas implícitamente a `Any` por defecto:

    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 comportamiento permite que `Any` sea usado como una vía de escape cuando es necesario mezclar código tipado estática y dinámicamente.

Compárese el comportamiento de `Any` con el de `object`. De manera similar a `Any`, todo tipo es un subtipo de `object`. Sin embargo, en oposición a `Any`, lo contrario no es cierto: `object` no es un subtipo de ningún otro tipo.

Esto implica que cuando el tipo de un valor es `object`, un validador de tipos rechazará prácticamente todas las operaciones con él, y al asignarlo a una variable (o usarlo como valor de retorno) de un tipo más preciso será un error de tipo. Por ejemplo:

    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")

Úsese `object` para indicar que un valor puede ser de cualquier tipo de manera segura. Úsese `Any` para indicar que un valor es de tipado dinámico.

## Subtipado nominal vs estructural

Inicialmente, el PEP 484 definió el uso de subtipado nominal para el sistema de tipado estático de Python. Esto implica que una clase `A` será permitida allí donde se espere una clase `B` si y solo si `A` es una subclase de `B`.

Este requisito también se aplicaba anteriormente a clases base abstractas (ABC), tales como `Iterable`. El problema con esta estrategia es que una clase debía de ser marcada explícitamente para proporcionar tal funcionalidad, lo que resulta poco pythónico (idiomático) y poco ajustado a lo que uno normalmente haría en un código Python tipado dinámicamente. Por ejemplo, esto sí se ajusta al PEP 484:

    from collections.abc import Sized, Iterable, Iterator

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

El PEP 544 permite resolver este problema al permitir escribir el código anterior sin una clase base explícita en la definición de la clase, permitiendo que el Validador estático de tipo considere implícitamente que `Bucket` es un subtipo tanto de `Sized` como de `Iterable[int]`. Esto se conoce como tipado estructural (o duck-typing estático):

    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

Asimismo, creando subclases de la clase especial `Protocol`, el usuario puede definir nuevos protocolos personalizados y beneficiarse del tipado estructural (véanse los ejemplos de abajo).

## Contenido del módulo

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

### Primitivos especiales de tipado

#### Tipos especiales

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

typing.Any

  Tipo especial que indica un tipo sin restricciones.

  - Todos los tipos son compatibles con `Any`.

  - `Any` es compatible con todos los tipos.

  Distinto en la versión 3.11: `Any` can now be used as a base class. This can be useful for avoiding type checker errors with classes that can duck type anywhere or are highly dynamic.

typing.AnyStr

  A constrained type variable.

  Definition:

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

  `AnyStr` is meant to be used for functions that may accept `str` or `bytes` arguments but cannot allow the two to mix.

  Por ejemplo:

      def concat(a: AnyStr, b: AnyStr) -> AnyStr:
          return a + b

      concat("foo", "bar")    # OK, output has type 'str'
      concat(b"foo", b"bar")  # OK, output has type 'bytes'
      concat("foo", b"bar")   # Error, cannot mix str and bytes

  Note that, despite its name, `AnyStr` has nothing to do with the `Any` type, nor does it mean «any string». In particular, `AnyStr` and `str | bytes` are different from each other and have different use cases:

      # Invalid use of AnyStr:
      # The type variable is used only once in the function signature,
      # so cannot be "solved" by the type checker
      def greet_bad(cond: bool) -> AnyStr:
          return "hi there!" if cond else b"greetings!"

      # The better way of annotating this function:
      def greet_proper(cond: bool) -> str | bytes:
          return "hi there!" if cond else b"greetings!"

  Obsoleto desde la versión 3.13, se eliminará en la versión 3.18: Deprecated in favor of the new type parameter syntax. Use `class A[T: (str, bytes)]: ...` instead of importing `AnyStr`. See PEP 695 for more details.

  In Python 3.16, `AnyStr` will be removed from `typing.__all__`, and deprecation warnings will be emitted at runtime when it is accessed or imported from `typing`. `AnyStr` will be removed from `typing` in Python 3.18.

typing.LiteralString

  Special type that includes only literal strings.

  Any string literal is compatible with `LiteralString`, as is another `LiteralString`. However, an object typed as just `str` is not. A string created by composing `LiteralString`-typed objects is also acceptable as a `LiteralString`.

  Example:

      def run_query(sql: LiteralString) -> None:
          ...

      def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
          run_query("SELECT * FROM students")  # OK
          run_query(literal_string)  # OK
          run_query("SELECT * FROM " + literal_string)  # OK
          run_query(arbitrary_string)  # type checker error
          run_query(  # type checker error
              f"SELECT * FROM students WHERE name = {arbitrary_string}"
          )

  `LiteralString` is useful for sensitive APIs where arbitrary user-generated strings could generate problems. For example, the two cases above that generate type checker errors could be vulnerable to an SQL injection attack.

  Véase PEP 675 para más detalle.

  Added in version 3.11.

typing.Never
typing.NoReturn

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

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

      from typing import Never  # or NoReturn

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

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

      from typing import Never  # or NoReturn

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

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

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

  Added in version 3.6.2: Added `NoReturn`.

  Added in version 3.11: Added `Never`.

typing.Self

  Special type to represent the current enclosed class.

  Por ejemplo:

      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 anotación es semánticamente equivalente a lo siguiente, aunque de una manera más sucinta:

      from typing import TypeVar

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

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

  In general, if something returns `self`, as in the above examples, you should use `Self` as the return annotation. If `Foo.return_self` was annotated as returning `"Foo"`, then the type checker would infer the object returned from `SubclassOfFoo.return_self` as being of type `Foo` rather than `SubclassOfFoo`.

  Otros casos de uso comunes incluyen:

  - `classmethod` usados como constructores alternativos y retornan instancias del parámetro `cls`.

  - Anotar un método `__enter__()` que retorna self.

  You should not use `Self` as the return annotation if the method is not guaranteed to return an instance of a subclass when the class is subclassed:

      class Eggs:
          # Self would be an incorrect return annotation here,
          # as the object returned is always an instance of Eggs,
          # even in subclasses
          def returns_eggs(self) -> "Eggs":
              return Eggs()

  Véase PEP 673 para más detalle.

  Added in version 3.11.

typing.TypeAlias

  Special annotation for explicitly declaring a type alias.

  Por ejemplo:

      from typing import TypeAlias

      Factors: TypeAlias = list[int]

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

      from typing import Generic, TypeAlias, TypeVar

      T = TypeVar("T")

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

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

  See PEP 613 for more details.

  Added in version 3.10.

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

#### Formas especiales

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

typing.Union

  Tipo de unión; `Union[X, Y]` es equivalente a `X | Y` y significa X o Y.

  Para definir una unión, use p. ej. `Union[int, str]` o la abreviatura `int | str`. Se recomienda el uso de la abreviatura. Detalles:

  - Los argumentos deben ser tipos y haber al menos uno.

  - Las uniones de uniones se simplifican (se aplanan), p. ej.:

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

  - Las uniones con un solo argumento se eliminan, p. ej.:

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

  - Argumentos repetidos se omiten, p. ej.:

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

  - Cuando se comparan uniones, el orden de los argumentos se ignoran, p. ej.:

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

  - No es posible crear una subclase o instanciar un `Union`.

  - No es posible escribir `Union[X][Y]`.

  Distinto en la versión 3.7: No elimina subclases explícitas de una unión en tiempo de ejecución.

  Distinto en la versión 3.10: Las uniones ahora se pueden escribir como `X | Y`. Consulte union type expressions.

typing.Optional

  `Optional[X]` es equivalente a `X | None` (o `Union[X, None]`).

  Nótese que no es lo mismo que un argumento opcional, que es aquel que tiene un valor por defecto. Un argumento opcional con un valor por defecto no necesita el indicador `Optional` en su anotación de tipo simplemente por que sea opcional. Por ejemplo:

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

  Por otro lado, si se permite un valor `None`, es apropiado el uso de `Optional`, independientemente de que sea opcional o no. Por ejemplo:

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

  Distinto en la versión 3.10: Optional ahora se puede escribir como `X | None`. Consulte union type expressions.

typing.Concatenate

  Special form for annotating higher-order functions.

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

  Por ejemplo, para anotar un decorador `with_lock` que proporciona un `threading.Lock` a la función decorada, `Concatenate` puede usarse para indicar que `with_lock` espera un invocable que toma un `Lock` como primer argumento y retorna un invocable con un tipo de firma diferente. En este caso, el `ParamSpec` indica que los tipos de parámetros de los invocables retornados dependen de los tipos de parámetros de los invocables que se pasan en

      from collections.abc import Callable
      from threading import Lock
      from typing import Concatenate

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

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

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

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

  Added in version 3.10.

  Ver también

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

  - `ParamSpec`

  - Annotating callable objects

typing.Literal

  Special typing form to define «literal types».

  `Literal` can be used to indicate to type checkers that the annotated object has a value equivalent to one of the provided literals.

  Por ejemplo:

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

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

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

  `Literal[...]` no puede ser derivado. En tiempo de ejecución, se permite un valor arbitrario como argumento de tipo de `Literal[...]`, pero los validadores de tipos pueden imponer sus restricciones. Véase PEP 585 para más detalles sobre tipos literales.

  Added in version 3.8.

  Distinto en la versión 3.9.1: `Literal` ahora elimina los parámetros duplicados. Las comparaciones de igualdad de los objetos `Literal` ya no dependen del orden. Los objetos `Literal` ahora lanzarán una excepción `TypeError` durante las comparaciones de igualdad si uno de sus parámetros no es hashable.

typing.ClassVar

  Construcción especial para tipado para marcar variables de clase.

  Tal y como introduce PEP 526, una anotación de variable rodeada por ClassVar indica que la intención de un atributo dado es ser usado como variable de clase y que no debería ser modificado en las instancias de esa misma clase. Uso:

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

  `ClassVar` solo acepta tipos y no admite más niveles de subíndices.

  `ClassVar` no es un clase en sí misma, y no debe ser usado con `isinstance()` o `issubclass()`. `ClassVar` no modifica el comportamiento de Python en tiempo de ejecución pero puede ser utilizado por validadores de terceros. Por ejemplo, un validador de tipos puede marcar el siguiente código como erróneo:

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

  Added in version 3.5.3.

  Distinto en la versión 3.13: `ClassVar` can now be nested in `Final` and vice versa.

typing.Final

  Special typing construct to indicate final names to type checkers.

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

  Por ejemplo:

      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

  No hay comprobación en tiempo de ejecución para estas propiedades. Véase PEP 591 para más detalles.

  Added in version 3.8.

  Distinto en la versión 3.13: `Final` can now be nested in `ClassVar` and vice versa.

typing.Required

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

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

  Added in version 3.11.

typing.NotRequired

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

  Véase `TypedDict` y PEP 655 para más detalle.

  Added in version 3.11.

typing.ReadOnly

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

  Por ejemplo:

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

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

  There is no runtime checking for this property.

  See `TypedDict` and PEP 705 for more details.

  Added in version 3.13.

typing.Annotated

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

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

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

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

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

  Annotated[<type>, <metadata>]

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

      @dataclass
      class ValueRange:
          lo: int
          hi: int

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

  Details of the syntax:

  - El primer argumento en `Annotated` debe ser un tipo válido

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

        @dataclass
        class ctype:
            kind: str

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

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

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

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

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

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

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

  - Duplicated metadata elements are not removed:

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

  - `Annotated` can be used with nested and generic aliases:

        @dataclass
        class MaxLen:
            value: int

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

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

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

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

    This would be equivalent to:

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

    where `T1`, `T2`, etc. are `TypeVars`. This would be invalid: only one type should be passed to Annotated.

  - By default, `get_type_hints()` strips the metadata from annotations. Pass `include_extras=True` to have the metadata preserved:

        >>> from typing import Annotated, get_type_hints
        >>> def func(x: Annotated[int, "metadata"]) -> None: pass
        ...
        >>> get_type_hints(func)
        {'x': <class 'int'>, 'return': <class 'NoneType'>}
        >>> get_type_hints(func, include_extras=True)
        {'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}

  - At runtime, the metadata associated with an `Annotated` type can be retrieved via the `__metadata__` attribute:

        >>> from typing import Annotated
        >>> X = Annotated[int, "very", "important", "metadata"]
        >>> X
        typing.Annotated[int, 'very', 'important', 'metadata']
        >>> X.__metadata__
        ('very', 'important', 'metadata')

  Ver también

  PEP 593 - Flexible function and variable annotations

    The PEP introducing `Annotated` to the standard library.

  Added in version 3.9.

typing.TypeIs

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

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

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

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

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

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

  1. El valor de retorno es un booleano.

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

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

  Por ejemplo:

      from typing import assert_type, final, TypeIs

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

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

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

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

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

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

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

  Added in version 3.13.

typing.TypeGuard

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

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

  El uso de `-> TypeGuard` le dice al verificador de tipo estático que para una función determinada:

  1. El valor de retorno es un booleano.

  2. Si el valor de retorno es `True`, el tipo de su argumento es el tipo dentro de `TypeGuard`.

  `TypeGuard` también funciona con variables de tipo. Véase PEP 647 para más detalles.

  Por ejemplo:

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

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

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

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

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

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

  Added in version 3.10.

typing.Unpack

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

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

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

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

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

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

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

      from typing import TypedDict, Unpack

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

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

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

  Added in version 3.11.

#### Building generic types and type aliases

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

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

class typing.Generic

  Clase base abstracta para tipos genéricos.

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

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

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

  Entonces, esta clase se puede usar como sigue:

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

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

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

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

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

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

  Variable de tipo.

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

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

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

      class StrSequence[S: str]:  # S is a TypeVar bound to str
          ...

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

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

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

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

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

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

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

  Nótese que las variables de tipo pueden ser bound (delimitadas), constrained (restringidas), o ninguna, pero no pueden ser al mismo tiempo delimitadas y restringidas.

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

  Las variables de tipo delimitadas y las variables de tipo restringidas tienen semánticas distintas en varios aspectos importantes. Usar una variable de tipo bound (delimitada) significa que la `TypeVar` será resuelta utilizando el tipo más específico posible:

      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

  Las variables de tipo pueden estar delimitadas por tipos concretos, tipos abstractos (ABCs o protocols) e incluso uniones de tipos:

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

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

  Sin embargo, usar una variable de tipo constrained significa que la `TypeVar` sólo podrá ser determinada como exactamente una de las restricciones dadas:

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

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

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

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

  __name__

    The name of the type variable.

  __covariant__

    Whether the type var has been explicitly marked as covariant.

  __contravariant__

    Whether the type var has been explicitly marked as contravariant.

  __infer_variance__

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

    Added in version 3.12.

  __bound__

    The bound of the type variable, if any.

    Distinto en la versión 3.12: For type variables created through type parameter syntax, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see Evaluación perezosa).

  __constraints__

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

    Distinto en la versión 3.12: For type variables created through type parameter syntax, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see Evaluación perezosa).

  __default__

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

    Added in version 3.13.

  has_default()

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

    Added in version 3.13.

  Distinto en la versión 3.12: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The `infer_variance` parameter was added.

  Distinto en la versión 3.13: Support for default values was added.

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

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

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

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

  Or by explicitly invoking the `TypeVarTuple` constructor:

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

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

  Una variable de tipo normal permite parametrizar con un solo tipo. Una tupla de variables de tipo, en contraste, permite la parametrización con un número arbitrario de tipos, al actuar como un número arbitrario de variables de tipo envueltas en una tupla. Por ejemplo:

      # 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=())

  Nótese el uso del operador de desempaquetado `*` en `tuple[T, *Ts]`. Conceptualmente, puede pensarse en `Ts` como una tupla de variables de tipo `(T1, T2, ...)`. `tuple[T, *Ts]` se convertiría en `tuple[T, *(T1, T2, ...)]`, lo que es equivalente a `tuple[T, T1, T2, ...]`. (Nótese que en versiones más antiguas de Python, ésto puede verse escrito usando en cambio `Unpack`, en la forma `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

  Las tuplas de variables de tipo pueden ser utilizadas en los mismos contextos que las variables de tipo normales. Por ejemplo en definiciones de clases, argumentos y tipos de retorno:

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

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

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

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

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

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

  Sin embargo, nótese que en una determinada lista de argumentos de tipo o de parámetros de tipo puede haber como máximo una tupla de variables de tipo:

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

  Finalmente, una tupla de variables de tipo desempaquetada puede ser utilizada como la anotación de tipo de `*args`:

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

  En contraste con las anotaciones no-desempaquetadas de `*args`, por ej. `*args: int`, que especificaría que todos los argumentos son `int` - `*args: *Ts` permite referenciar los tipos de los argumentos individuales en `*args`. Aquí, ésto permite asegurarse de que los tipos de los `*args` que son pasados a `call_soon` calcen con los tipos de los argumentos (posicionales) de `callback`.

  Véase PEP 646 para obtener más detalles sobre las tuplas de variables de tipo.

  __name__

    The name of the type variable tuple.

  __default__

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

    Added in version 3.13.

  has_default()

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

    Added in version 3.13.

  Added in version 3.11.

  Distinto en la versión 3.12: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

  Distinto en la versión 3.13: Support for default values was added.

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

  Parameter specification variable. A specialized version of type variables.

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

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

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

      P = ParamSpec('P')

  Las variables de especificación de parámetros existen principalmente para el beneficio de los verificadores de tipo estático. Se utilizan para reenviar los tipos de parámetros de un invocable a otro invocable, un patrón que se encuentra comúnmente en funciones y decoradores de orden superior. Solo son válidos cuando se utilizan en `Concatenate`, o como primer argumento de `Callable`, o como parámetros para genéricos definidos por el usuario. Consulte `Generic` para obtener más información sobre tipos genéricos.

  Por ejemplo, para agregar un registro básico a una función, se puede crear un decorador `add_logging` para registrar llamadas a funciones. La variable de especificación de parámetros le dice al verificador de tipo que el invocable pasado al decorador y el nuevo invocable retornado por él tienen parámetros de tipo interdependientes:

      from collections.abc import Callable
      import logging

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

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

  Sin `ParamSpec`, la forma más sencilla de anotar esto anteriormente era usar un `TypeVar` con `Callable[..., Any]` enlazado. Sin embargo, esto causa dos problemas:

  1. El verificador de tipo no puede verificar la función `inner` porque `*args` y `**kwargs` deben escribirse `Any`.

  2. Es posible que se requiera `cast()` en el cuerpo del decorador `add_logging` al retornar la función `inner`, o se debe indicar al verificador de tipo estático que ignore el `return inner`.

  args
  kwargs

    Dado que `ParamSpec` captura tanto parámetros posicionales como de palabras clave, `P.args` y `P.kwargs` se pueden utilizar para dividir un `ParamSpec` en sus componentes. `P.args` representa la tupla de parámetros posicionales en una llamada determinada y solo debe usarse para anotar `*args`. `P.kwargs` representa la asignación de parámetros de palabras clave a sus valores en una llamada determinada y solo debe usarse para anotar `**kwargs`. Ambos atributos requieren que el parámetro anotado esté dentro del alcance. En tiempo de ejecución, `P.args` y `P.kwargs` son instancias respectivamente de `ParamSpecArgs` y `ParamSpecKwargs`.

  __name__

    The name of the parameter specification.

  __default__

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

    Added in version 3.13.

  has_default()

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

    Added in version 3.13.

  Las variables de especificación de parámetros creadas con `covariant=True` o `contravariant=True` se pueden utilizar para declarar tipos genéricos covariantes o contravariantes. También se acepta el argumento `bound`, similar a `TypeVar`. Sin embargo, la semántica real de estas palabras clave aún no se ha decidido.

  Added in version 3.10.

  Distinto en la versión 3.12: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

  Distinto en la versión 3.13: Support for default values was added.

  Nota

  Solo las variables de especificación de parámetros definidas en el ámbito global pueden ser serializadas.

  Ver también

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

  - `Concatenate`

  - Annotating callable objects

typing.ParamSpecArgs
typing.ParamSpecKwargs

  Argumentos y atributos de argumentos de palabras clave de un `ParamSpec`. El atributo `P.args` de un `ParamSpec` es una instancia de `ParamSpecArgs` y `P.kwargs` es una instancia de `ParamSpecKwargs`. Están pensados para la introspección en tiempo de ejecución y no tienen un significado especial para los verificadores de tipo estático.

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

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

  Added in version 3.10.

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

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

  Example:

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

  Added in version 3.12.

  __name__

    The name of the type alias:

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

  __module__

    The module in which the type alias was defined:

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

  __type_params__

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

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

  __value__

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

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

#### Otras directivas especiales

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

  Versión para anotación de tipos de `collections.namedtuple()`.

  Uso:

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

  Esto es equivalente a:

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

  Para proporcionar a un campo un valor por defecto se puede asignar en el cuerpo de la clase:

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

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

  Los campos con un valor por defecto deben ir después de los campos sin valor por defecto.

  La clase resultante tiene un atributo extra `__annotations__` que proporciona un diccionario que mapea el nombre de los campos con sus respectivos tipos. (Los nombres de los campos están en el atributo `_fields` y sus valores por defecto en el atributo `_field_defaults`, ambos parte de la API `namedtuple()`.)

  Las subclases de `NamedTuple` también pueden tener docstrings y métodos:

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

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

  Las subclases de `NamedTuple` pueden ser genéricas:

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

  Uso retrocompatible:

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

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

  Distinto en la versión 3.6: Soporte añadido para la sintaxis de anotación de variables propuesto en PEP 526.

  Distinto en la versión 3.6.1: Soporte añadido para valores por defecto, métodos y docstrings.

  Distinto en la versión 3.8: Los atributos `_field_types` y `__annotations__` son simples diccionarios en vez de instancias de `OrderedDict`.

  Distinto en la versión 3.9: Se remueve el atributo `_field_types` en favor del atributo más estándar `__annotations__` que tiene la misma información.

  Distinto en la versión 3.11: Se agrega soporte para namedtuples genéricas.

  Obsoleto desde la versión 3.13, se eliminará en la versión 3.15: The undocumented keyword argument syntax for creating NamedTuple classes (`NT = NamedTuple("NT", x=int)`) is deprecated, and will be disallowed in 3.15. Use the class-based syntax or the functional syntax instead.

  Obsoleto desde la versión 3.13, se eliminará en la versión 3.15: When using the functional syntax to create a NamedTuple class, failing to pass a value to the “fields” parameter (`NT = NamedTuple("NT")`) is deprecated. Passing `None` to the “fields” parameter (`NT = NamedTuple("NT", None)`) is also deprecated. Both will be disallowed in Python 3.15. To create a NamedTuple class with 0 fields, use `class NT(NamedTuple): pass` or `NT = NamedTuple("NT", [])`.

class typing.NewType(name, tp)

  Helper class to create low-overhead distinct types.

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

  Uso:

      UserId = NewType('UserId', int)  # Declare the NewType "UserId"
      first_user = UserId(1)  # "UserId" returns the argument unchanged at runtime

  __module__

    The module in which the new type is defined.

  __name__

    The name of the new type.

  __supertype__

    The type that the new type is based on.

  Added in version 3.5.2.

  Distinto en la versión 3.10: `NewType` es ahora una clase en lugar de una función.

class typing.Protocol(Generic)

  Base class for protocol classes.

  Protocol classes are defined like this:

      class Proto(Protocol):
          def meth(self) -> int:
              ...

  Tales clases son usadas principalmente con validadores estáticos de tipos que detectan subtipado estructural (duck-typing estático), por ejemplo:

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

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

      func(C())  # Passes static type check

  Véase PEP 544 para más detalles. Las clases protocolo decoradas con `runtime_checkable()` (descrito más adelante) se comportan como protocolos simplistas en tiempo de ejecución que solo comprueban la presencia de atributos dados, ignorando su firma de tipo.

  Las clases protocolo pueden ser genéricas, por ejemplo:

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

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

      T = TypeVar("T")

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

  Added in version 3.8.

@typing.runtime_checkable

  Marca una clase protocolo como aplicable en tiempo de ejecución (lo convierte en un runtime protocol).

  Tal protocolo se puede usar con `isinstance()` y `issubclass()`. Esto lanzará una excepción `TypeError` cuando se aplique a una clase que no es un protocolo. Esto permite una comprobación estructural simple, muy semejante a «one trick ponies» en `collections.abc` con `Iterable`. Por ejemplo:

      @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.

  Added in version 3.8.

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

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

class typing.TypedDict(dict)

  Es una construcción especial para añadir indicadores de tipo a un diccionario. En tiempo de ejecución es un `dict` simple.

  `TypedDict` crea un tipo de diccionario que espera que todas sus instancias tenga un cierto conjunto de claves, donde cada clave está asociada con un valor de un tipo determinado. Esta exigencia no se comprueba en tiempo de ejecución y solo es aplicada por validadores de tipo. Uso:

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

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

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

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

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

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

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

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

  De forma predeterminada, todas las llaves deben estar presentes en un `TypedDict`. Es posible marcar llaves individuales como no requeridas utilizando `NotRequired`:

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

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

  Esto significa que en un `TypedDict` que sea una instancia de `Point2D`, será posible omitir la llave `label`.

  Además, es posible marcar todas las llaves como no-requeridas por defecto, al especificar un valor de `False` en el argumento total:

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

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

  Esto significa que un `TypedDict` `Point2D` puede tener cualquiera de las llaves omitidas. Solo se espera que un verificador de tipo admita un `False` literal o `True` como valor del argumento `total`. `True` es el predeterminado y hace que todos los elementos definidos en el cuerpo de la clase sean obligatorios.

  Las llaves individuales de un `TypedDict` `total=False` pueden ser marcadas como requeridas utilizando `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)

  Es posible que un tipo `TypedDict` herede de uno o más tipos `TypedDict` usando la sintaxis de clase. Uso:

      class Point3D(Point2D):
          z: int

  `Point3D` tiene tres elementos: `x`, `y` y `z`. Lo que es equivalente a la siguiente definición:

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

  Un `TypedDict` no puede heredar de una clase que no sea una subclase de `TypedDict`, exceptuando `Generic`. Por ejemplo:

      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

  Un `TypedDict` puede ser genérico:

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

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

      T = TypeVar("T")

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

  A `TypedDict` can be introspected via annotations dicts (see Prácticas recomendadas para las anotaciones for more information on annotations best practices), `__total__`, `__required_keys__`, and `__optional_keys__`.

  __total__

    `Point2D.__total__` gives the value of the `total` argument. Example:

        >>> from typing import TypedDict
        >>> class Point2D(TypedDict): pass
        >>> Point2D.__total__
        True
        >>> class Point2D(TypedDict, total=False): pass
        >>> Point2D.__total__
        False
        >>> class Point3D(Point2D): pass
        >>> Point3D.__total__
        True

    This attribute reflects only the value of the `total` argument to the current `TypedDict` class, not whether the class is semantically total. For example, a `TypedDict` with `__total__` set to True may have keys marked with `NotRequired`, or it may inherit from another `TypedDict` with `total=False`. Therefore, it is generally better to use `__required_keys__` and `__optional_keys__` for introspection.

  __required_keys__

    Added in version 3.9.

  __optional_keys__

    `Point2D.__required_keys__` y `Point2D.__optional_keys__` retornan objetos de la clase `frozenset`, que contienen las llaves requeridas y no requeridas, respectivamente.

    Las llaves marcadas con `Required` siempre aparecerán en `__required_keys__` y las llaves marcadas con `NotRequired` siempre aparecerán en `__optional_keys__`.

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

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

    Added in version 3.9.

    Nota

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

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

      .. attribute:: __readonly_keys__

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

  > Added in version 3.13.

  __mutable_keys__

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

    Added in version 3.13.

  Véase PEP 589 para más ejemplos y reglas detalladas del uso de `TypedDict`.

  Added in version 3.8.

  Distinto en la versión 3.11: Se agrega soporte para marcar llaves individuales como `Required` o `NotRequired`. Véase PEP 655.

  Distinto en la versión 3.11: Se agrega soporte para `TypedDict` genéricos.

  Distinto en la versión 3.13: Removed support for the keyword-argument method of creating `TypedDict`s.

  Distinto en la versión 3.13: Support for the `ReadOnly` qualifier was added.

  Obsoleto desde la versión 3.13, se eliminará en la versión 3.15: When using the functional syntax to create a TypedDict class, failing to pass a value to the “fields” parameter (`TD = TypedDict("TD")`) is deprecated. Passing `None` to the “fields” parameter (`TD = TypedDict("TD", None)`) is also deprecated. Both will be disallowed in Python 3.15. To create a TypedDict class with 0 fields, use `class TD(TypedDict): pass` or `TD = TypedDict("TD", {})`.

### Protocolos

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

class typing.SupportsAbs

  Una ABC con un método abstracto `__abs__` que es covariante en su tipo retornado.

class typing.SupportsBytes

  Una ABC con un método abstracto `__bytes__`.

class typing.SupportsComplex

  Una ABC con un método abstracto `__complex__`.

class typing.SupportsFloat

  Una ABC con un método abstracto `__float__`.

class typing.SupportsIndex

  Una ABC con un método abstracto `__index__`.

  Added in version 3.8.

class typing.SupportsInt

  Una ABC con un método abstracto `__int__`.

class typing.SupportsRound

  Una ABC con un método abstracto `__round__` que es covariantes en su tipo retornado.

### ABCs for working with IO

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

  El tipo genérico `IO[AnyStr]` y sus subclases `TextIO(IO[str])` y `BinaryIO(IO[bytes])` representan los tipos de flujos de E/S como los retornados por `open()`.

### Funciones y decoradores

typing.cast(typ, val)

  Convertir un valor a un tipo.

  Esto retorna el valor sin modificar. Para el validador de tipos esto indica que el valor de retorno tiene el tipo señalado pero, de manera intencionada, no se comprobará en tiempo de ejecución (para maximizar la velocidad).

typing.assert_type(val, typ, /)

  Solicitar a un validador de tipos que confirme que val tiene typ por tipo inferido.

  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

  Esta función es útil para asegurarse de que la comprensión que el validador de tipos tiene sobre un script está alineada con las intenciones de le desarrolladores:

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

  Added in version 3.11.

typing.assert_never(arg, /)

  Solicitar a un validador estático de tipos confirmar que una línea de código no es alcanzable.

  Por ejemplo:

      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.

  En tiempo de ejecución, ésto lanza una excepción cuando es llamado.

  Ver también

  Unreachable Code and Exhaustiveness Checking contiene más información acerca de la verificación de exhaustividad con tipado estático.

  Added in version 3.11.

typing.reveal_type(obj, /)

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

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

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

  Ésto puede ser de utilidad cuando se desea debuguear cómo tu validador de tipos maneja una pieza particular de código.

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

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

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

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

  Added in version 3.11.

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

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

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

  Example usage with a decorator function:

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

      @create_model
      class CustomerModel:
          id: int
          name: str

  En una clase base:

      @dataclass_transform()
      class ModelBase: ...

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

  En una metaclase:

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

      class ModelBase(metaclass=ModelMeta): ...

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

  Las clases `CustomerModel` definidas arribe serán tratadas por los validadores de tipo de forma similar a las clases que sean creadas con `@dataclasses.dataclass`. Por ejemplo, los validadores de tipo asumirán que estas clases tienen métodos `__init__` que aceptan `id` y `name`.

  La clase, metaclase o función decorada puede aceptar los siguientes argumentos booleanos, de los cuales los validadores de tipos asumirán que tienen el mismo efecto que tendrían en el decorador `@dataclasses.dataclass`: `init`, `eq`, `order`, `unsafe_hash`, `frozen`, `match_args`, `kw_only`, y `slots`. Debe ser posible evaluar estáticamente el valor de estos argumentos (`True` o `False`).

  Es posible utilizar los argumentos del decorador `dataclass_transform` para personalizar los comportamientos por defecto de la clase, metaclase o función decorada:

  Parámetros:

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

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

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

    - frozen_default (bool) –

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

      Added in version 3.12.

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

    - **kwargs (Any) – Es posible pasar arbitrariamente otros argumentos nombrados para permitir posibles extensiones futuras.

  Type checkers recognize the following optional parameters on field specifiers:

  Recognised parameters for field specifiers
  Parameter name | Description
  --- | ---
  `init` | Indicates whether the field should be included in the synthesized `__init__` method. If unspecified, `init` defaults to `True`.
  `default` | Provides the default value for the field.
  `default_factory` | Provides a runtime callback that returns the default value for the field. If neither `default` nor `default_factory` are specified, the field is assumed to have no default value and must be provided a value when the class is instantiated.
  `factory` | An alias for the `default_factory` parameter on field specifiers.
  `kw_only` | Indicates whether the field should be marked as keyword-only. If `True`, the field will be keyword-only. If `False`, it will not be keyword-only. If unspecified, the value of the `kw_only` parameter on the object decorated with `dataclass_transform` will be used, or if that is unspecified, the value of `kw_only_default` on `dataclass_transform` will be used.
  `alias` | Provides an alternative name for the field. This alternative name is used in the synthesized `__init__` method.

  En tiempo de ejecución, este decorador registra sus argumentos en el atributo `__dataclass_transform__` del objeto decorado. No tiene otro efecto en tiempo de ejecución.

  Véase PEP 681 para más detalle.

  Added in version 3.11.

@typing.overload

  Decorator for creating overloaded functions and methods.

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

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

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

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

  Véase PEP 484 para más detalle y comparación con otras semánticas de tipado.

  Distinto en la versión 3.11: Ahora es posible introspectar en tiempo de ejecución las funciones sobrecargadas utilizando `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()` puede ser utilizada para introspectar en tiempo de ejecución una función sobrecargada.

  Added in version 3.11.

typing.clear_overloads()

  Clear all registered overloads in the internal registry.

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

  Added in version 3.11.

@typing.final

  Decorator to indicate final methods and final classes.

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

  Por ejemplo:

      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
          ...

  No hay comprobación en tiempo de ejecución para estas propiedades. Véase PEP 591 para más detalles.

  Added in version 3.8.

  Distinto en la versión 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

  Un decorador para indicar que las anotaciones no deben ser comprobadas como indicadores de tipo.

  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

  Un decorador que asigna a otro decorador el efecto de `no_type_check()` (no comprobar tipo).

  Esto hace que el decorador decorado añada el efecto de `no_type_check()` a la función decorada.

  Obsoleto desde la versión 3.13, se eliminará en la versión 3.15: No type checker ever added support for `@no_type_check_decorator`. It is therefore deprecated, and will be removed in Python 3.15.

@typing.override

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

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

  For example:

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

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

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

  There is no runtime checking of this property.

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

  See PEP 698 for more details.

  Added in version 3.12.

@typing.type_check_only

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

  Este decorador no está disponible en tiempo de ejecución. Existe principalmente para marcar clases que se definen en archivos stub para cuando una implementación retorna una instancia de una clase privada:

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

      def fetch_response() -> Response: ...

  Nótese que no se recomienda retornar instancias de clases privadas. Normalmente es preferible convertirlas en clases públicas.

### Ayudas de introspección

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

  Retorna un diccionario que contiene indicaciones de tipo para una función, método, módulo o objeto clase.

  This is often the same as `obj.__annotations__`. In addition, forward references encoded as string literals are handled by evaluating them in `globals`, `locals` and (where applicable) type parameter 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()` no funciona con alias de tipo importados que incluyen referencias hacia adelante. Habilitar la evaluación pospuesta de anotaciones (PEP 563) puede eliminar la necesidad de la mayoría de las referencias futuras.

  Distinto en la versión 3.9: Added `include_extras` parameter as part of PEP 593. See the documentation on `Annotated` for more information.

  Distinto en la versión 3.11: Anteriormente, se agregaba `Optional[t]` en las anotaciones de funciones o métodos si se establecía un valor por defecto igual a `None`. Ahora la anotación es retornada sin cambios.

typing.get_origin(tp)

  Get the unsubscripted version of a type: for a typing object of the form `X[Y, Z, ...]` return `X`.

  If `X` is a typing-module alias for a builtin or `collections` class, it will be normalized to the original class. If `X` is an instance of `ParamSpecArgs` or `ParamSpecKwargs`, return the underlying `ParamSpec`. Return `None` for unsupported objects.

  Examples:

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

  Added in version 3.8.

typing.get_args(tp)

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

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

  Examples:

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

  Added in version 3.8.

typing.get_protocol_members(tp)

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

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

  Raise `TypeError` for arguments that are not Protocols.

  Added in version 3.13.

typing.is_protocol(tp)

  Determine if a type is a `Protocol`.

  Por ejemplo:

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

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

  Added in version 3.13.

typing.is_typeddict(tp)

  Compruebe si un tipo es `TypedDict`.

  For example:

      class Film(TypedDict):
          title: str
          year: int

      assert is_typeddict(Film)
      assert not is_typeddict(list | str)

      # TypedDict is a factory for creating typed dicts,
      # not a typed dict itself
      assert not is_typeddict(TypedDict)

  Added in version 3.10.

class typing.ForwardRef

  Class used for internal typing representation of string forward references.

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

  Nota

  Los tipos genéricos de PEP 585, como `list["SomeClass"]`, no se transformarán implícitamente en `list[ForwardRef("SomeClass")]` y, por lo tanto, no se resolverán automáticamente en `list[SomeClass]`.

  Added in version 3.7.4.

typing.NoDefault

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

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

  Added in version 3.13.

### Constantes

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()

  Nótese que la primera anotación de tipo debe estar rodeada por comillas, convirtiéndola en una «referencia directa», para ocultar al intérprete la referencia `expensive_mod` en tiempo de ejecución. Las anotaciones de tipo para variables locales no se evalúan, así que la segunda anotación no necesita comillas.

  Nota

  Si se utiliza `from __future__ import annotations`, las anotaciones no son evaluadas al momento de la definición de funciones. En cambio, serán almacenadas como cadenas de texto en `__annotations__`. Ésto vuelve innecesario el uso de comillas alrededor de la anotación (véase PEP 563).

  Added in version 3.5.2.

### Deprecated aliases

This module defines several deprecated aliases to pre-existing standard library classes. These were originally included in the typing module in order to support parameterizing these generic classes using `[]`. However, the aliases became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support `[]` (see PEP 585).

The redundant types are deprecated as of Python 3.9. However, while the aliases may be removed at some point, removal of these aliases is not currently planned. As such, no deprecation warnings are currently issued by the interpreter for these aliases.

If at some point it is decided to remove these deprecated aliases, a deprecation warning will be issued by the interpreter for at least two releases prior to removal. The aliases are guaranteed to remain in the typing module without deprecation warnings until at least Python 3.14.

Type checkers are encouraged to flag uses of the deprecated types if the program they are checking targets a minimum Python version of 3.9 or newer.

#### Aliases to built-in types

class typing.Dict(dict, MutableMapping[KT, VT])

  Deprecated alias to `dict`.

  Note that to annotate arguments, it is preferred to use an abstract collection type such as `Mapping` rather than to use `dict` or `typing.Dict`.

  Este tipo se puede usar de la siguiente manera:

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

  Obsoleto desde la versión 3.9: `builtins.dict` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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`.

  Este tipo se puede usar del siguiente modo:

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

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

  Obsoleto desde la versión 3.9: `builtins.list` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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 la versión 3.9: `builtins.set` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `builtins.frozenset`.

  Obsoleto desde la versión 3.9: `builtins.frozenset` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

typing.Tuple

  Deprecated alias for `tuple`.

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

  Obsoleto desde la versión 3.9: `builtins.tuple` ahora soporta el uso de subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.Type(Generic[CT_co])

  Deprecated alias to `type`.

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

  Added in version 3.5.2.

  Obsoleto desde la versión 3.9: `builtins.type` ahora soporta el uso de subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

#### Aliases to types in `collections`

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

  Deprecated alias to `collections.defaultdict`.

  Added in version 3.5.2.

  Obsoleto desde la versión 3.9: `collections.defaultdict` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.OrderedDict`.

  Added in version 3.7.2.

  Obsoleto desde la versión 3.9: `collections.OrderedDict` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.ChainMap`.

  Added in version 3.6.1.

  Obsoleto desde la versión 3.9: `collections.ChainMap` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.Counter`.

  Added in version 3.6.1.

  Obsoleto desde la versión 3.9: `collections.Counter` ahora soporta subíndices (`[]`)`. Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.deque`.

  Added in version 3.6.1.

  Obsoleto desde la versión 3.9: `collections.deque` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

#### Aliases to other concrete types

class typing.Pattern
class typing.Match

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

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

  Obsoleto desde la versión 3.9: Las clases `Pattern` y `Match` de `re` ahora soportan `[]`. Véase PEP 585 y Tipo Alias Genérico.

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`.

  Úsese `Text` para indicar que un valor debe contener una cadena de texto Unicode de manera que sea compatible con Python 2 y Python 3:

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

  Added in version 3.5.2.

  Obsoleto desde la versión 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 la versión 3.9: `collections.abc.Set` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.ByteString(Sequence[int])

  Este tipo representa a los tipos `bytes`, `bytearray`, y `memoryview` de secuencias de bytes.

  Obsoleto desde la versión 3.9, se eliminará en la versión 3.14: Prefer `collections.abc.Buffer`, or a union like `bytes | bytearray | memoryview`.

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

  Deprecated alias to `collections.abc.Collection`.

  Added in version 3.6.

  Obsoleto desde la versión 3.9: `collections.abc.Collection` ahora soporta la sintaxis de subíndice (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.Container(Generic[T_co])

  Deprecated alias to `collections.abc.Container`.

  Obsoleto desde la versión 3.9: `collections.abc.Container` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.abc.ItemsView`.

  Obsoleto desde la versión 3.9: `collections.abc.ItemsView` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.abc.KeysView`.

  Obsoleto desde la versión 3.9: `collections.abc.KeysView` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.abc.Mapping`.

  Este tipo se puede usar de la siguiente manera:

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

  Obsoleto desde la versión 3.9: `collections.abc.Mapping` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.MappingView(Sized)

  Deprecated alias to `collections.abc.MappingView`.

  Obsoleto desde la versión 3.9: `collections.abc.MappingView` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.abc.MutableMapping`.

  Obsoleto desde la versión 3.9: `collections.abc.MutableMapping` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.MutableSequence(Sequence[T])

  Deprecated alias to `collections.abc.MutableSequence`.

  Obsoleto desde la versión 3.9: `collections.abc.MutableSequence` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.MutableSet(AbstractSet[T])

  Deprecated alias to `collections.abc.MutableSet`.

  Obsoleto desde la versión 3.9: `collections.abc.MutableSet` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.abc.Sequence`.

  Obsoleto desde la versión 3.9: `collections.abc.Sequence` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.abc.ValuesView`.

  Obsoleto desde la versión 3.9: `collections.abc.ValuesView` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

#### 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

  Added in version 3.5.3.

  Obsoleto desde la versión 3.9: `collections.abc.Coroutine` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

  Deprecated alias to `collections.abc.AsyncGenerator`.

  Un generador asíncrono se puede anotar con el tipo genérico `AsyncGenerator[YieldType, SendType]`. Por ejemplo:

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

  A diferencia de los generadores normales, los generadores asíncronos no pueden retornar un valor, por lo que no hay un parámetro de tipo `ReturnType`. Igual que `Generator`, `SendType` se comporta como contravariante.

  The `SendType` defaults to `None`:

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

  It is also possible to set this type explicitly:

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

  Opcionalmente, anota el generador con un tipo de retorno `AsyncIterable[YieldType]` o `AsyncIterator[YieldType]`:

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

  Added in version 3.6.1.

  Obsoleto desde la versión 3.9: `collections.abc.AsycGenerator` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

  Distinto en la versión 3.13: The `SendType` parameter now has a default.

class typing.AsyncIterable(Generic[T_co])

  Deprecated alias to `collections.abc.AsyncIterable`.

  Added in version 3.5.2.

  Obsoleto desde la versión 3.9: `collections.abc.AsyncIterable` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.AsyncIterator(AsyncIterable[T_co])

  Deprecated alias to `collections.abc.AsyncIterator`.

  Added in version 3.5.2.

  Obsoleto desde la versión 3.9: `collections.abc.AsyncIterator` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.Awaitable(Generic[T_co])

  Deprecated alias to `collections.abc.Awaitable`.

  Added in version 3.5.2.

  Obsoleto desde la versión 3.9: `collections.abc.Awaitable` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

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

class typing.Iterable(Generic[T_co])

  Deprecated alias to `collections.abc.Iterable`.

  Obsoleto desde la versión 3.9: `collections.abc.Iterable` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.Iterator(Iterable[T_co])

  Deprecated alias to `collections.abc.Iterator`.

  Obsoleto desde la versión 3.9: `collections.abc.Iterator` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

typing.Callable

  Deprecated alias to `collections.abc.Callable`.

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

  Obsoleto desde la versión 3.9: `collections.abc.Callable` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

  Distinto en la versión 3.10: `Callable` ahora es compatible con `ParamSpec` y `Concatenate`. Consulte PEP 612 para obtener más información.

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

  Deprecated alias to `collections.abc.Generator`.

  Un generador puede ser anotado con el tipo genérico `Generator[YieldType, SendType, ReturnType]`. Por ejemplo:

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

  Nótese que en contraste con muchos otros genéricos en el módulo typing, el `SendType` de `Generator` se comporta como contravariante, no covariante ni invariante.

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

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

  It is also possible to set these types explicitly:

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

  Opcionalmente, anota tu generador con un tipo de retorno de `Iterable[YieldType]` o `Iterator[YieldType]`:

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

  Obsoleto desde la versión 3.9: `collections.abc.Generator` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

  Distinto en la versión 3.13: Default values for the send and return types were added.

class typing.Hashable

  Deprecated alias to `collections.abc.Hashable`.

  Obsoleto desde la versión 3.12: Use `collections.abc.Hashable` directly instead.

class typing.Reversible(Iterable[T_co])

  Deprecated alias to `collections.abc.Reversible`.

  Obsoleto desde la versión 3.9: `collections.abc.Reversible` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.Sized

  Deprecated alias to `collections.abc.Sized`.

  Obsoleto desde la versión 3.12: Use `collections.abc.Sized` directly instead.

#### Aliases to `contextlib` ABCs

class typing.ContextManager(Generic[T_co])

  Deprecated alias to `contextlib.AbstractContextManager`.

  Added in version 3.5.4.

  Obsoleto desde la versión 3.9: `contextlib.AbstractContextManager` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

class typing.AsyncContextManager(Generic[T_co])

  Deprecated alias to `contextlib.AbstractAsyncContextManager`.

  Added in version 3.6.2.

  Obsoleto desde la versión 3.9: `contextlib.AbstractAsyncContextManager` ahora soporta subíndices (`[]`). Véase PEP 585 y Tipo Alias Genérico.

## Línea de tiempo de obsolescencia de características principales

Algunas características de `typing` están obsoletas y podrán ser removidas en versiones futuras de Python. Lo que sigue es una tabla que resume las principales obsolescencias para su conveniencia. Ésto está sujeto a cambio y no todas las obsolescencias están representadas.

Característica | En desuso desde | Eliminación proyectada | PEP/issue
--- | --- | --- | ---
Versiones `typing` de colecciones estándares | 3.9 | Undecided (see Deprecated aliases for more information) | PEP 585
`typing.ByteString` | 3.9 | 3.14 | gh-91896
`typing.Text` | 3.11 | No decidido | gh-92332
`typing.Hashable` and `typing.Sized` | 3.12 | No decidido | gh-94309
`typing.TypeAlias` | 3.12 | No decidido | PEP 695
`@typing.no_type_check_decorator` | 3.13 | 3.15 | gh-106309
`typing.AnyStr` | 3.13 | 3.18 | gh-105578