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

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

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

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

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 deprecated "typing.Callable".
"Callable[[int], str]" signifies a function that takes a single
parameter of type "int" and returns a "str".

For example:

   from collections.abc import Callable, Awaitable

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

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

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

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

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

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

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

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

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

   from collections.abc import Iterable
   from typing import Protocol

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

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

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

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

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 deprecated
"typing.Type[C]") may accept values that are classes themselves --
specifically, it will accept the *class object* of "C". For example:

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

Note that "type[C]" is covariant:

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

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

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

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

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

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

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


Annotating generators and coroutines
====================================

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

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

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

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

Simple generators that only ever yield values can also be annotated as
having a return type of either "Iterable[YieldType]" or
"Iterator[YieldType]":

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

Async generators are handled in a similar fashion, but don't expect a
"ReturnType" type argument ("AsyncGenerator[YieldType, SendType]").
The "SendType" argument defaults to "None", so the following
definitions are equivalent:

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

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

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

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

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

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


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!"

   Deprecated since version 3.13, will be removed in version 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"] = 1999  # 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')

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

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

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

        >>> get_origin(Password)
        typing.Annotated

   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
      T = TypeVar("T")

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

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

   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.

   Deprecated since version 3.13, will be removed in version 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.

   Deprecated since version 3.13, will be removed in version 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:

   __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.

   Deprecated since version 3.13, will be removed in version 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 has more information
     about exhaustiveness checking with static typing.

   Added in version 3.11.

typing.reveal_type(obj, /)

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

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

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

   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.

   Deprecated since version 3.13, will be removed in version 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__", but this function
   makes the following changes to the annotations dictionary:

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

   * "None" is replaced with "types.NoneType".

   * If "@no_type_check" has been applied to *obj*, an empty
     dictionary is returned.

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

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

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

   Nota:

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

   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
      assert get_origin(Annotated[str, "metadata"]) is Annotated
      P = ParamSpec('P')
      assert get_origin(P.args) is P
      assert get_origin(P.kwargs) is P

   Added in version 3.8.

typing.get_args(tp)

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

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

   Examples:

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

   Added in version 3.8.

typing.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".

   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".

   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 "collections.abc.Set" 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.

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

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

   Deprecated alias to "collections.abc.Collection".

   Added in version 3.6.

   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".

   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".

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

   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".

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

   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".

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

   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, ExitT_co])

   Deprecated alias to "contextlib.AbstractContextManager".

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

   Added in version 3.5.4.

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

   Distinto en la versión 3.13: Added the optional second type
   parameter, "ExitT_co".

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

   Deprecated alias to "contextlib.AbstractAsyncContextManager".

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

   Added in version 3.6.2.

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

   Distinto en la versión 3.13: Added the optional second type
   parameter, "AExitT_co".


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     | 3.9                       | Undecided (see Deprecated | **PEP 585**               |
| colecciones estándares    |                           | aliases for more          |                           |
|                           |                           | information)              |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.ByteString"       | 3.9                       | 3.14                      | gh-91896                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Text"             | 3.11                      | No decidido               | gh-92332                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Hashable" and     | 3.12                      | No decidido               | gh-94309                  |
| "typing.Sized"            |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.TypeAlias"        | 3.12                      | No decidido               | **PEP 695**               |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "@typing.no_type_check_d  | 3.13                      | 3.15                      | gh-106309                 |
| ecorator"                 |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.AnyStr"           | 3.13                      | 3.18                      | gh-105578                 |
+---------------------------+---------------------------+---------------------------+---------------------------+
