"typing" — Prise en charge des annotations de type
**************************************************

Nouveau dans la version 3.5.

**Code source :** Lib/typing.py

Note:

  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. For the original
specification of the typing system, see **PEP 484**. For a simplified
introduction to type hints, see **PEP 483**.

La fonction ci-dessous prend et renvoie une chaîne de caractères, et
est annotée comme suit :

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

La fonction "greeting" s'attend à ce que l'argument "name" soit de
type "str" et le type de retour "str". Les sous-types sont acceptés
comme arguments.

Le module "typing" est fréquemment enrichi de nouvelles
fonctionnalités. Le package typing_extensions fournit des rétro-
portages de ces fonctionnalités vers les anciennes versions de Python.

Pour un résumé des fonctionnalités obsolètes et leur planification
d'obsolescence, consultez les Etapes d'Obsolescence des
Fonctionnalités Majeures.

Voir aussi:

  "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.


PEPs pertinentes
================

Since the initial introduction of type hints in **PEP 484** and **PEP
483**, a number of PEPs have modified and enhanced Python's framework
for type annotations:

* **PEP 526** : Syntaxe pour les Annotations de Variables
     *Introduction* d'une syntaxe permettant d'annoter les variables
     autrement qu'au sein de la définition d'une fonction et de
     "ClassVar"

* **PEP 544**: Protocoles : Sous-typage Structurel (*duck-typing*
  statique)
     *Ajout* de "Protocol" et du décorateur "@runtime_checkable"

* **PEP 585**: Annotations de Type Générique dans les Collections
  Natives
     *Ajout* de "types.GenericAlias" et de la possibilité d'utiliser
     les classes de bibliothèques natives comme les types génériques

* **PEP 586**: Types Littéraux
     *Ajout* de "Literal"

* **PEP 589**: TypedDict: Annotations de Type pour les Dictionnaires
  ayant un Ensemble Fixe de Clés
     *Ajout* de "TypedDict"

* **PEP 591**: Ajout d'un qualificatif final au typage
     *Ajout* de "Final" et du décorateur "@final"

* **PEP 593**: fonction Flexible et annotations de variables
     *Ajout* de "Annotated"

* **PEP 604**: Permettre l'écriture de types union tels que "X | Y"
     *Ajout* de "types.UnionType" et la possibilité d'utiliser
     l'opérateur binaire "|" (*ou*) pour signifier union of types

* **PEP 612**: Variables de Spécification de Paramètre
     *Ajout* de "ParamSpec" et de "Concatenate"

* **PEP 613**: Explicit Type Aliases
     *Ajout* de "TypeAlias"

* **PEP 646**: Génériques Variadiques
     *Ajout* de "TypeVarTuple"

* **PEP 647**: Gardes de Types Définies par l'Utilisateur
     *Ajout* de "TypeGuard"

* **PEP 655**: Marquer les items individuels TypedDict comme
  nécessaires ou potentiellement manquants
     *Ajout* de "Required" et de "NotRequired"

* **PEP 673**: Type self
     *Ajout* de "Self"

* **PEP 675**: Type String Littéral Arbitraire
     *Ajout* de "LiteralString"

* **PEP 681**: Transformateurs de Classes de Données
     *Ajout* du décorateur "@dataclass_transform"


Alias de type
=============

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

   Vector = list[float]

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

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

Les alias de type sont utiles pour simplifier les signatures
complexes. Par exemple :

   from collections.abc import Sequence

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

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

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

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

   from typing import TypeAlias

   Vector: TypeAlias = list[float]


*NewType*
=========

Utilisez la classe "NewType" pour créer des types distincts :

   from typing import NewType

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

Le vérificateur de types statiques traite le nouveau type comme s'il
s'agissait d'une sous-classe du type original. C'est utile pour aider
à détecter les erreurs logiques :

   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)

Vous pouvez toujours effectuer toutes les opérations applicables à un
entier (type "int") sur une variable de type "UserId", mais le
résultat sera toujours de type "int". Ceci vous permet de passer un
"UserId" partout où un "int" est attendu, mais vous empêche de créer
accidentellement un "UserId" d'une manière invalide :

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

Notez que ces contrôles ne sont exécutés que par le vérificateur de
types statique. À l'exécution, l'instruction "Derived =
NewType('Derived', Base)" fait de "Derived" une fonction qui renvoie
immédiatement le paramètre que vous lui passez. Cela signifie que
l'expression "Derived(some_value)" ne crée pas une nouvelle classe et
n'introduit pas de surcharge au-delà de celle d'un appel de fonction
normal.

Plus précisément, l'expression "some_value is Derived(some_value)" est
toujours vraie au moment de l'exécution.

La création d'un sous-type de "Derived" est invalide :

   from typing import NewType

   UserId = NewType('UserId', int)

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

Il est néanmoins possible de créer un "NewType" basé sur un "NewType"
« dérivé » :

   from typing import NewType

   UserId = NewType('UserId', int)

   ProUserId = NewType('ProUserId', UserId)

et la vérification de type pour "ProUserId" fonctionne comme prévu.

Voir la **PEP 484** pour plus de détails.

Note:

  Rappelons que l'utilisation d'un alias de type déclare que deux
  types sont *équivalents* l'un à l'autre. Écrire "Alias = Original"
  fait que le vérificateur de types statiques traite "Alias" comme
  étant *exactement équivalent* à "Original" dans tous les cas. C'est
  utile lorsque vous voulez simplifier des signatures complexes.En
  revanche, "NewType" déclare qu'un type est un *sous-type* d'un
  autre. Écrire "Derived = NewType('Derived', Original)" fait que le
  vérificateur de type statique traite "Derived" comme une *sous-
  classe* de "Original", ce qui signifie qu'une valeur de type
  "Original" ne peut être utilisée dans les endroits où une valeur de
  type "Derived" est prévue. C'est utile lorsque vous voulez éviter
  les erreurs logiques avec un coût d'exécution minimal.

Nouveau dans la version 3.5.2.

Modifié dans la version 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.

Modifié dans la version 3.11: The performance of calling "NewType" has
been restored to its level in Python 3.9.


Annotating callable objects
===========================

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

For example:

   from collections.abc import Callable, Awaitable

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

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

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

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

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

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

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

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

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

   from collections.abc import Iterable
   from typing import Protocol

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

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

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

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

Les appelables qui prennent en argument d'autres appelables peuvent
indiquer que leurs types de paramètres dépendent les uns des autres en
utilisant "ParamSpec". De plus, si un appelable ajoute ou supprime des
arguments d'autres appelables, l'opérateur "Concatenate" peut être
utilisé.  Ils prennent la forme "Callable[ParamSpecVariable,
ReturnType]" et "Callable[Concatenate[Arg1Type, Arg2Type, ...,
ParamSpecVariable], ReturnType]" respectivement.

Modifié dans la version 3.10: "Callable" prend désormais en charge
"ParamSpec" et "Concatenate". Voir **PEP 612** pour plus de détails.

Voir aussi:

  La documentation pour "ParamSpec" et "Concatenate" fournit des
  exemples d'utilisation dans "Callable".


Génériques
==========

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: ...

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

   from collections.abc import Sequence
   from typing import TypeVar

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

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


Annotating tuples
=================

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

   from collections.abc import Mapping

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

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

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

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

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

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

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

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

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

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

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

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


The type of class objects
=========================

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

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

Note that "type[C]" is covariant:

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

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

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

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

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

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

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


Types génériques définis par l'utilisateur
==========================================

Une classe définie par l'utilisateur peut être définie comme une
classe générique.

   from typing import TypeVar, Generic
   from logging import Logger

   T = TypeVar('T')

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

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

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

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

"Generic[T]" en tant que classe mère définit que la classe "LoggedVar"
prend un paramètre de type unique "T". Ceci rend également "T" valide
en tant que type dans le corps de la classe.

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

   from collections.abc import Iterable

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

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

   from typing import TypeVar, Generic, Sequence

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

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

Chaque argument de variable de type "Generic" doit être distinct. Ceci
n'est donc pas valable :

   from typing import TypeVar, Generic
   ...

   T = TypeVar('T')

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

Vous pouvez utiliser l'héritage multiple avec "Generic" :

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

   T = TypeVar('T')

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

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

   from collections.abc import Mapping
   from typing import TypeVar

   T = TypeVar('T')

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

Dans ce cas, "MyDict" a un seul paramètre, "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
   from typing import TypeVar
   S = TypeVar('S')
   Response = Iterable[S] | int

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

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

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

Modifié dans la version 3.7: "Generic" n'a plus de métaclasse
personnalisée.

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

   >>> from typing import Generic, ParamSpec, TypeVar

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

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

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

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

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

Modifié dans la version 3.10: "Generic" can now be parameterized over
parameter expressions. See "ParamSpec" and **PEP 612** for more
details.

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


Le type "Any"
=============

Un type particulier est "Any". Un vérificateur de types statiques
traite chaque type comme étant compatible avec "Any" et "Any" comme
étant compatible avec chaque type.

This means that it is possible to perform any operation or method call
on a value of type "Any" and assign it to any 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()
       ...

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

De plus, toutes les fonctions sans type de retour ni type de paramètre
sont considérées comme utilisant "Any" implicitement par défaut :

   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

Ce comportement permet à "Any" d'être utilisé comme succédané lorsque
vous avez besoin de mélanger du code typé dynamiquement et
statiquement.

Comparons le comportement de "Any" avec celui de "object". De la même
manière que pour "Any", chaque type est un sous-type de "object".
Cependant, contrairement à "Any", l'inverse n'est pas vrai : "object"
n'est *pas* un sous-type de chaque autre type.

Cela signifie que lorsque le type d'une valeur est "object", un
vérificateur de types rejette presque toutes les opérations sur celle-
ci, et l'affecter à une variable (ou l'utiliser comme une valeur de
retour) d'un type plus spécialisé est une erreur de typage. Par
exemple :

   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")

Utilisez "object" pour indiquer qu'une valeur peut être de n'importe
quel type de manière sûre. Utiliser "Any" pour indiquer qu'une valeur
est typée dynamiquement.


Sous-typage nominal et sous-typage structurel
=============================================

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

This requirement previously also applied to abstract base classes,
such as "Iterable". The problem with this approach is that a class had
to be explicitly marked to support them, which is unpythonic and
unlike what one would normally do in idiomatic dynamically typed
Python code. For example, this conforms to **PEP 484**:

   from collections.abc import Sized, Iterable, Iterator

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

La **PEP 544** permet de résoudre ce problème en permettant aux
utilisateurs d'écrire le code ci-dessus sans classes mères explicites
dans la définition de classe, permettant à "Bucket" d'être
implicitement considéré comme un sous-type de "Sized" et
"Iterable[int]" par des vérificateurs de type statique. C'est ce qu'on
appelle le *sous-typage structurel* (ou typage canard) :

   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

De plus, en sous-classant une classe spéciale "Protocol", un
utilisateur peut définir de nouveaux protocoles personnalisés pour
profiter pleinement du sous-typage structurel (voir exemples ci-
dessous).


Module contents
===============

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


Special typing primitives
-------------------------


Special types
~~~~~~~~~~~~~

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

typing.Any

   Type spécial indiquant un type non contraint.

   * Chaque type est compatible avec "Any".

   * "Any" est compatible avec tous les types.

   Modifié dans la version 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.

   Par exemple :

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

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

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

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

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

typing.LiteralString

   Special type that includes only literal strings.

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

   Example:

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

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

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

   See **PEP 675** for more details.

   Nouveau dans la version 3.11.

typing.Never

   The bottom type, a type that has no members.

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

      from typing import Never

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

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

   Nouveau dans la version 3.11: On older Python versions, "NoReturn"
   may be used to express the same concept. "Never" was added to make
   the intended meaning more explicit.

typing.NoReturn

   Special type indicating that a function never returns.

   Par exemple :

      from typing import NoReturn

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

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

   Nouveau dans la version 3.6.2.

typing.Self

   Special type to represent the current enclosed class.

   Par exemple :

      from typing import Self, reveal_type

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

      class SubclassOfFoo(Foo): pass

      reveal_type(Foo().return_self())  # Revealed type is "Foo"
      reveal_type(SubclassOfFoo().return_self())  # Revealed type is "SubclassOfFoo"

   This annotation is semantically equivalent to the following, albeit
   in a more succinct fashion:

      from typing import TypeVar

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

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

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

   Other common use cases include:

   * "classmethod"s that are used as alternative constructors and
     return instances of the "cls" parameter.

   * Annotating an "__enter__()" method which returns self.

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

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

   See **PEP 673** for more details.

   Nouveau dans la version 3.11.

typing.TypeAlias

   Special annotation for explicitly declaring a type alias.

   Par exemple :

      from typing import TypeAlias

      Factors: TypeAlias = list[int]

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

      from typing import Generic, TypeAlias, TypeVar

      T = TypeVar("T")

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

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

   See **PEP 613** for more details.

   Nouveau dans la version 3.10.


Special forms
~~~~~~~~~~~~~

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

typing.Union

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

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

   * Les arguments doivent être des types et il doit y en avoir au
     moins un.

   * Les unions d'unions sont aplanies, par exemple :

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

   * Les unions d'un seul argument disparaissent, par exemple :

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

   * Les arguments redondants sont ignorés, par exemple :

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

   * Lors de la comparaison d'unions, l'ordre des arguments est
     ignoré, par exemple :

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

   * You cannot subclass or instantiate a "Union".

   * Vous ne pouvez pas écrire "Union[X][Y]".

   Modifié dans la version 3.7: Ne supprime pas les sous-classes
   explicites des unions à l'exécution.

   Modifié dans la version 3.10: Unions can now be written as "X | Y".
   See union type expressions.

typing.Optional

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

   Notez que ce n'est pas le même concept qu'un argument optionnel,
   qui est un argument qui possède une valeur par défaut. Un argument
   optionnel (qui a une valeur par défaut) ne nécessite pas, à ce
   titre, le qualificatif "Optional" sur son annotation de type. Par
   exemple :

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

   Par contre, si une valeur explicite de "None" est permise,
   l'utilisation de "Optional" est appropriée, que l'argument soit
   facultatif ou non. Par exemple :

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

   Modifié dans la version 3.10: Optional can now be written as "X |
   None". See union type expressions.

typing.Concatenate

   Special form for annotating higher-order functions.

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

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

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

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

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

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

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

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

   Nouveau dans la version 3.10.

   Voir aussi:

     * **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.

   Par exemple :

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

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

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

   "Literal[...]" ne peut être sous-classé. Lors de l'exécution, une
   valeur arbitraire est autorisée comme argument de type pour
   "Literal[...]", mais les vérificateurs de type peuvent imposer des
   restrictions. Voir la **PEP 586** pour plus de détails sur les
   types littéraux.

   Nouveau dans la version 3.8.

   Modifié dans la version 3.9.1: "Literal" now de-duplicates
   parameters.  Equality comparisons of "Literal" objects are no
   longer order dependent. "Literal" objects will now raise a
   "TypeError" exception during equality comparisons if one of their
   parameters are not *hashable*.

typing.ClassVar

   Construction de type particulière pour indiquer les variables de
   classe.

   Telle qu'introduite dans la **PEP 526**, une annotation de variable
   enveloppée dans ClassVar indique qu'un attribut donné est destiné à
   être utilisé comme une variable de classe et ne doit pas être
   défini sur des instances de cette classe. Utilisation :

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

   "ClassVar" n'accepte que les types et ne peut plus être dérivé.

   "ClassVar" n'est pas une classe en soi, et ne devrait pas être
   utilisée avec "isinstance()" ou "issubclass()". "ClassVar" ne
   modifie pas le comportement d'exécution Python, mais il peut être
   utilisé par des vérificateurs tiers. Par exemple, un vérificateur
   de types peut marquer le code suivant comme une erreur :

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

   Nouveau dans la version 3.5.3.

typing.Final

   Special typing construct to indicate final names to type checkers.

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

   Par exemple :

      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

   Ces propriétés ne sont pas vérifiées à l'exécution. Voir la **PEP
   591** pour plus de détails.

   Nouveau dans la version 3.8.

typing.Required

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

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

   Nouveau dans la version 3.11.

typing.NotRequired

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

   See "TypedDict" and **PEP 655** for more details.

   Nouveau dans la version 3.11.

typing.Annotated

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

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

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

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

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

   Annotated[<type>, <metadata>]

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

      @dataclass
      class ValueRange:
          lo: int
          hi: int

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

   Details of the syntax:

   * The first argument to "Annotated" must be a valid type

   * Multiple metadata elements can be supplied ("Annotated" supports
     variadic arguments):

        @dataclass
        class ctype:
            kind: str

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

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

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

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

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

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

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

   * 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

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

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

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

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

     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')

   Voir aussi:

     **PEP 593** - Flexible function and variable annotations
        The PEP introducing "Annotated" to the standard library.

   Nouveau dans la version 3.9.

typing.TypeGuard

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

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

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

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

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

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

   1. The return value is a boolean.

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

   Par exemple :

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

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

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

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

   Note:

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

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

   Nouveau dans la 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

   Nouveau dans la version 3.11.


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

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

class typing.Generic

   Classe de base abstraite pour les types génériques.

   Un type générique est généralement déclaré en héritant d'une
   instanciation de cette classe avec une ou plusieurs variables de
   type. Par exemple, un type de correspondance générique peut être
   défini comme suit :

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

   Cette classe peut alors être utilisée comme suit :

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

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

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

   Variables de type.

   Utilisation :

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

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

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


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


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

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

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

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

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

      class StringSubclass(str):
          pass

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

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

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

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

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

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

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

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

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

   __name__

      The name of the type variable.

   __covariant__

      Whether the type var has been marked as covariant.

   __contravariant__

      Whether the type var has been marked as contravariant.

   __bound__

      The bound of the type variable, if any.

   __constraints__

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

class typing.TypeVarTuple(name)

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

   Utilisation :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

   __name__

      The name of the type variable tuple.

   Nouveau dans la version 3.11.

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

   Parameter specification variable.  A specialized version of type
   variables.

   Utilisation :

      P = ParamSpec('P')

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

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

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

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

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

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

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

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

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

   args

   kwargs

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

   __name__

      The name of the parameter specification.

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

   Nouveau dans la version 3.10.

   Note:

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

   Voir aussi:

     * **PEP 612** -- Parameter Specification Variables (the PEP which
       introduced "ParamSpec" and "Concatenate")

     * "Concatenate"

     * Annotating callable objects

typing.ParamSpecArgs

typing.ParamSpecKwargs

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

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

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

   Nouveau dans la version 3.10.


Other special directives
~~~~~~~~~~~~~~~~~~~~~~~~

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

   Version typée de "collections.namedtuple()".

   Utilisation :

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

   Ce qui est équivalent à :

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

   Pour assigner une valeur par défaut à un champ, vous pouvez lui
   donner dans le corps de classe :

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

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

   Les champs avec une valeur par défaut doivent venir après tous les
   champs sans valeur par défaut.

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

   Les sous-classes de "NamedTuple" peuvent aussi avoir des
   *docstrings* et des méthodes :

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

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

   "NamedTuple" subclasses can be generic:

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

   Utilisation rétrocompatible :

      Employee = NamedTuple('Employee', [('name', str), ('id', int)])

   Modifié dans la version 3.6: Ajout de la gestion de la syntaxe
   d'annotation variable de la **PEP 526**.

   Modifié dans la version 3.6.1: Ajout de la prise en charge des
   valeurs par défaut, des méthodes et des chaînes de caractères
   *docstrings*.

   Modifié dans la version 3.8: Les attributs "_field_types" et
   "__annotations__" sont maintenant des dictionnaires standards au
   lieu d'instances de "OrderedDict".

   Modifié dans la version 3.9: rend l'attribut "_field_types"
   obsolète en faveur de l'attribut plus standard "__annotations__"
   qui a la même information.

   Modifié dans la version 3.11: Added support for generic
   namedtuples.

class typing.NewType(name, tp)

   Helper class to create low-overhead distinct types.

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

   Utilisation :

      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.

   Nouveau dans la version 3.5.2.

   Modifié dans la version 3.10: "NewType" is now a class rather than
   a function.

class typing.Protocol(Generic)

   Base class for protocol classes.

   Protocol classes are defined like this:

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

   Ces classes sont principalement utilisées avec les vérificateurs
   statiques de type qui reconnaissent les sous-types structurels
   (typage canard statique), par exemple :

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

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

      func(C())  # Passes static type check

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

   Les classes de protocole peuvent être génériques, par exemple :

      T = TypeVar("T")

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

   Nouveau dans la version 3.8.

@typing.runtime_checkable

   Marquez une classe de protocole comme protocole d'exécution.

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

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

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

      @runtime_checkable
      class Named(Protocol):
          name: str

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

   Note:

     "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".

   Note:

     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.

   Nouveau dans la version 3.8.

class typing.TypedDict(dict)

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

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

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

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

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

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

   * Using a literal "dict" as the second argument:

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

   * Using keyword arguments:

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

   Obsolète depuis la version 3.11, sera supprimé dans la version
   3.13: The keyword-argument syntax is deprecated in 3.11 and will be
   removed in 3.13. It may also be unsupported by static type
   checkers.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      class Point3D(Point2D):
          z: int

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

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

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

      class X(TypedDict):
          x: int

      class Y(TypedDict):
          y: int

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

      class XY(X, Y): pass  # OK

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

   A "TypedDict" can be generic:

      T = TypeVar("T")

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

   A "TypedDict" can be introspected via annotations dicts (see Bonnes
   pratiques concernant les annotations 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__

      Nouveau dans la version 3.9.

   __optional_keys__

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

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

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

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

      Nouveau dans la version 3.9.

      Note:

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

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

   Nouveau dans la version 3.8.

   Modifié dans la version 3.11: Added support for marking individual
   keys as "Required" or "NotRequired". See **PEP 655**.

   Modifié dans la version 3.11: Added support for generic
   "TypedDict"s.


Protocoles
----------

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

class typing.SupportsAbs

   Une ABC avec une méthode abstraite "__abs__" qui est covariante
   dans son type de retour.

class typing.SupportsBytes

   Une ABC avec une méthode abstraite "__bytes__".

class typing.SupportsComplex

   Une ABC avec une méthode abstraite "__complex__".

class typing.SupportsFloat

   Une ABC avec une méthode abstraite "__float__".

class typing.SupportsIndex

   Une ABC avec une méthode abstraite "__index__".

   Nouveau dans la version 3.8.

class typing.SupportsInt

   Une ABC avec une méthode abstraite "__int__".

class typing.SupportsRound

   Une ABC avec une méthode abstraite "__round__" qui est covariante
   dans son type de retour.


ABCs for working with IO
------------------------

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

   Generic type "IO[AnyStr]" and its subclasses "TextIO(IO[str])" and
   "BinaryIO(IO[bytes])" represent the types of I/O streams such as
   returned by "open()".


Functions and decorators
------------------------

typing.cast(typ, val)

   Convertit une valeur en un type.

   Ceci renvoie la valeur inchangée. Pour le vérificateur de types,
   cela signifie que la valeur de retour a le type désigné mais, à
   l'exécution, intentionnellement, rien n'est vérifié (afin que cela
   soit aussi rapide que possible).

typing.assert_type(val, typ, /)

   Vérifie que *val* est bien du type *typ*.

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

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

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

   Cette fonction permet de s'assurer de la compréhension du
   vérificateur de type d'un script par rapport aux intentions du
   développeur :

      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)

   Nouveau dans la version 3.11.

typing.assert_never(arg, /)

   Demande une confirmation de la part du vérificateur statique de
   type qu'une ligne de code est inaccessible.

   Example:

      def int_or_str(arg: int | str) -> None:
          match arg:
              case int():
                  print("It's an int")
              case str():
                  print("It's a str")
              case _ as unreachable:
                  assert_never(unreachable)

   Here, the annotations allow the type checker to infer that the last
   case can never execute, because "arg" is either an "int" or a
   "str", and both options are covered by earlier cases.

   If a type checker finds that a call to "assert_never()" is
   reachable, it will emit an error. For example, if the type
   annotation for "arg" was instead "int | str | float", the type
   checker would emit an error pointing out that "unreachable" is of
   type "float". For a call to "assert_never" to pass type checking,
   the inferred type of the argument passed in must be the bottom
   type, "Never", and nothing else.

   Une erreur est levé si la fonction est appelé lors de l'exécution.

   Voir aussi:

     Unreachable Code and Exhaustiveness Checking pour plus détails
     sur la vérification exhaustive statique de type.

   Nouveau dans la 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"

   Cela est utile afin de comprendre comment le vérificateur de types
   va traiter un bout de code précis.

   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.

   Nouveau dans la version 3.11.

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

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

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

   Example usage with a decorator function:

      T = TypeVar("T")

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

      @create_model
      class CustomerModel:
          id: int
          name: str

   Avec une classe de base :

      @dataclass_transform()
      class ModelBase: ...

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

   Avec une métaclasse :

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

      class ModelBase(metaclass=ModelMeta): ...

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

   Les classes "CustomerModel" définis ci-dessus sont traitées par les
   vérificateurs de type de la même que les classes créées avec
   "@dataclasses.dataclass". Par exemple, les vérificateurs de type
   déduisent que ces classes possèdent une méthode "__init__"
   acceptant "id" et "name" comme arguments.

   Les arguments booléens suivants sont acceptés,les vérificateurs de
   type supposent qu'ils ont le même effet qu'ils auraient eu sur le
   décorateur "@dataclasses.dataclass" : "init", "eq", "order",
   "unsafe_hash", "frozen", "match_args", "kw_only", et "slots". Il
   est possible d'évaluer statiquement les valeurs de ces arguments
   ("True" or "False").

   Les arguments du décorateur "dataclass_transform" permettent de
   personnaliser le comportement par défaut de la classe, métaclasse
   ou fonction décorée :

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

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

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

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

      * ****kwargs** (*Any*) -- D'autres arguments sont acceptés afin
        d'autoriser de futurs possibles extensions.

   Type checkers recognize the following optional parameters on field
   specifiers:


   **Recognised parameters for field specifiers**
   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

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

   Lors de l'exécution, les arguments de ce décorateur sont
   enregistrés au sein de l'attribut "__dataclass_transform__" de
   l'objet décoré. Il n'y pas d'autre effet à l'exécution.

   See **PEP 681** for more details.

   Nouveau dans la version 3.11.

@typing.overload

   Decorator for creating overloaded functions and methods.

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

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

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

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

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

   Modifié dans la version 3.11: Les fonctions surchargées peuvent
   maintenant être inspectées durant l'exécution via
   "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()" peut être utilisé afin d'inspecter une fonction
   surchargée durant l'exécution.

   Nouveau dans la 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.

   Nouveau dans la 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.

   Par exemple :

      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
          ...

   Ces propriétés ne sont pas vérifiées à l'exécution. Voir la **PEP
   591** pour plus de détails.

   Nouveau dans la version 3.8.

   Modifié dans la version 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

   Décorateur pour indiquer que les annotations ne sont pas des
   indications de type.

   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

   Décorateur pour donner à un autre décorateur l'effet
   "no_type_check()".

   Ceci enveloppe le décorateur avec quelque chose qui enveloppe la
   fonction décorée dans "no_type_check()".

@typing.type_check_only

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

   Ce décorateur n'est pas disponible à l'exécution. Il est
   principalement destiné à marquer les classes qui sont définies dans
   des fichiers séparés d'annotations de type (*type stub file*, en
   anglais) si une implémentation renvoie une instance d'une classe
   privée :

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

      def fetch_response() -> Response: ...

   Notez qu'il n'est pas recommandé de renvoyer les instances des
   classes privées. Il est généralement préférable de rendre ces
   classes publiques.


Utilitaires d'introspection
---------------------------

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

   Renvoie un dictionnaire contenant des annotations de type pour une
   fonction, une méthode, un module ou un objet de classe.

   C'est souvent équivalent à "obj.__annotations__". De plus, les
   références postérieures encodées sous forme de chaîne de caractères
   sont évaluées dans les espaces de nommage "globals" et "locals".
   Pour une classe "C", elle renvoie un dictionnaire construit en
   fusionnant toutes les "__annotations__" en parcourant "C.__mro__"
   en ordre inverse.

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

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

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

   Note:

     "get_type_hints()" ne fonctionne pas avec les alias de type
     importés contenant des références postérieures. L'activation
     d'évaluation différée des annotations (**PEP 563**) permet de
     supprimer le besoin de références postérieures supplémentaires.

   Modifié dans la version 3.9: Added "include_extras" parameter as
   part of **PEP 593**. See the documentation on "Annotated" for more
   information.

   Modifié dans la version 3.11: Avant, "Optional[t]" était ajouté
   pour les annotations de fonctions et de méthodes dans le cas où une
   valeur par défaut était égal à "None". Maintenant, les annotations
   sont renvoyées inchangées.

typing.get_origin(tp)

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

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

   Examples:

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

   Nouveau dans la 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)

   Nouveau dans la version 3.8.

typing.is_typeddict(tp)

   Vérifier si un type est un "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)

   Nouveau dans la 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.

   Note:

     Les types **PEP 585** tels que "list["SomeClass"]" ne seront pas
     implicitement transformés en "list[ForwardRef("SomeClass")]" et
     ne seront donc pas automatiquement résolus en "list[SomeClass]".

   Nouveau dans la version 3.7.4.


Constante
---------

typing.TYPE_CHECKING

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

   Utilisation :

      if TYPE_CHECKING:
          import expensive_mod

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

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

   Note:

     Si "from __future__ import annotations" est utilisé, les
     annotations ne sont pas évaluées au moment de la définition de
     fonction. Elles sont alors stockées comme des chaînes de
     caractères dans "__annotations__", ce qui rend inutile
     l'utilisation de guillemets autour de l'annotation (Voir **PEP
     563**).

   Nouveau dans la 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".

   Ce type peut être utilisé comme suit :

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

   Obsolète depuis la version 3.9: "builtins.dict" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.

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".

   Ce type peut être utilisé comme suit :

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

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

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

   Obsolète depuis la version 3.9: "builtins.list" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.

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

   Deprecated alias to "builtins.set".

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

   Obsolète depuis la version 3.9: "builtins.set" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.

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

   Deprecated alias to "builtins.frozenset".

   Obsolète depuis la version 3.9: "builtins.frozenset" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.

typing.Tuple

   Deprecated alias for "tuple".

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

   Obsolète depuis la version 3.9: "builtins.tuple" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.

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.

   Nouveau dans la version 3.5.2.

   Obsolète depuis la version 3.9: "builtins.type" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.


Aliases to types in "collections"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

   Deprecated alias to "collections.defaultdict".

   Nouveau dans la version 3.5.2.

   Obsolète depuis la version 3.9: "collections.defaultdict" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.OrderedDict".

   Nouveau dans la version 3.7.2.

   Obsolète depuis la version 3.9: "collections.OrderedDict" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.ChainMap".

   Nouveau dans la version 3.6.1.

   Obsolète depuis la version 3.9: "collections.ChainMap" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.

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

   Deprecated alias to "collections.Counter".

   Nouveau dans la version 3.6.1.

   Obsolète depuis la version 3.9: "collections.Counter" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.

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

   Deprecated alias to "collections.deque".

   Nouveau dans la version 3.6.1.

   Obsolète depuis la version 3.9: "collections.deque" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.


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]".

   Obsolète depuis la version 3.8, sera supprimé dans la version 3.13:
   The "typing.re" namespace is deprecated and will be removed. These
   types should be directly imported from "typing" instead.

   Obsolète depuis la version 3.9: Classes "Pattern" and "Match" from
   "re" now support "[]". See **PEP 585** and Type Alias générique.

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".

   Utilisez "Text" pour indiquer qu'une valeur doit contenir une
   chaîne Unicode d'une manière compatible avec Python 2 et Python 3 :

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

   Nouveau dans la version 3.5.2.

   Obsolète depuis la version 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".

   Obsolète depuis la version 3.9: "collections.abc.Set" now supports
   subscripting ("[]"). See **PEP 585** and Type Alias générique.

class typing.ByteString(Sequence[int])

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

   Obsolète depuis la version 3.9, sera supprimé dans la version 3.14:
   Prefer "typing_extensions.Buffer", or a union like "bytes |
   bytearray | memoryview".

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

   Deprecated alias to "collections.abc.Collection".

   Nouveau dans la version 3.6.

   Obsolète depuis la version 3.9: "collections.abc.Collection" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.Container(Generic[T_co])

   Deprecated alias to "collections.abc.Container".

   Obsolète depuis la version 3.9: "collections.abc.Container" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.abc.ItemsView".

   Obsolète depuis la version 3.9: "collections.abc.ItemsView" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.abc.KeysView".

   Obsolète depuis la version 3.9: "collections.abc.KeysView" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.abc.Mapping".

   Ce type peut être utilisé comme suit :

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

   Obsolète depuis la version 3.9: "collections.abc.Mapping" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.MappingView(Sized)

   Deprecated alias to "collections.abc.MappingView".

   Obsolète depuis la version 3.9: "collections.abc.MappingView" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.abc.MutableMapping".

   Obsolète depuis la version 3.9: "collections.abc.MutableMapping"
   now supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.MutableSequence(Sequence[T])

   Deprecated alias to "collections.abc.MutableSequence".

   Obsolète depuis la version 3.9: "collections.abc.MutableSequence"
   now supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.MutableSet(AbstractSet[T])

   Deprecated alias to "collections.abc.MutableSet".

   Obsolète depuis la version 3.9: "collections.abc.MutableSet" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.abc.Sequence".

   Obsolète depuis la version 3.9: "collections.abc.Sequence" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.abc.ValuesView".

   Obsolète depuis la version 3.9: "collections.abc.ValuesView" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.


Aliases to asynchronous ABCs in "collections.abc"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

   Deprecated alias to "collections.abc.Coroutine".

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

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

   Nouveau dans la version 3.5.3.

   Obsolète depuis la version 3.9: "collections.abc.Coroutine" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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

   Deprecated alias to "collections.abc.AsyncGenerator".

   Un générateur asynchrone peut être annoté par le type générique
   "AsyncGenerator[YieldType, SendType]". Par exemple :

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

   Contrairement aux générateurs normaux, les générateurs asynchrones
   ne peuvent pas renvoyer une valeur, il n'y a donc pas de paramètre
   de type "ReturnType". Comme avec "Generator", le "SendType" se
   comporte de manière contravariante.

   Si votre générateur ne donne que des valeurs, réglez le paramètre
   "SendType" sur "None" :

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

   Alternativement, annotez votre générateur comme ayant un type de
   retour soit "AsyncIterable[YieldType]" ou
   "AsyncIterator[YieldType]" :

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

   Nouveau dans la version 3.6.1.

   Obsolète depuis la version 3.9: "collections.abc.AsyncGenerator"
   now supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.AsyncIterable(Generic[T_co])

   Deprecated alias to "collections.abc.AsyncIterable".

   Nouveau dans la version 3.5.2.

   Obsolète depuis la version 3.9: "collections.abc.AsyncIterable" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.AsyncIterator(AsyncIterable[T_co])

   Deprecated alias to "collections.abc.AsyncIterator".

   Nouveau dans la version 3.5.2.

   Obsolète depuis la version 3.9: "collections.abc.AsyncIterator" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.Awaitable(Generic[T_co])

   Deprecated alias to "collections.abc.Awaitable".

   Nouveau dans la version 3.5.2.

   Obsolète depuis la version 3.9: "collections.abc.Awaitable" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.


Aliases to other ABCs in "collections.abc"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.Iterable(Generic[T_co])

   Deprecated alias to "collections.abc.Iterable".

   Obsolète depuis la version 3.9: "collections.abc.Iterable" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.Iterator(Iterable[T_co])

   Deprecated alias to "collections.abc.Iterator".

   Obsolète depuis la version 3.9: "collections.abc.Iterator" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

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.

   Obsolète depuis la version 3.9: "collections.abc.Callable" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

   Modifié dans la version 3.10: "Callable" prend désormais en charge
   "ParamSpec" et "Concatenate". Voir **PEP 612** pour plus de
   détails.

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

   Deprecated alias to "collections.abc.Generator".

   Un générateur peut être annoté par le type générique
   "Generator[YieldType, SendType, ReturnType]". Par exemple :

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

   Notez que contrairement à beaucoup d'autres génériques dans le
   module *typing*, le "SendType" de "Generator" se comporte de
   manière contravariante, pas de manière covariante ou invariante.

   Si votre générateur ne donne que des valeurs, réglez les paramètres
   "SendType" et "ReturnType" sur "None" :

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

   Alternativement, annotez votre générateur comme ayant un type de
   retour soit "Iterable[YieldType]" ou "Iterator[YieldType]" :

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

   Obsolète depuis la version 3.9: "collections.abc.Generator" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.Hashable

   Alias to "collections.abc.Hashable".

class typing.Reversible(Iterable[T_co])

   Deprecated alias to "collections.abc.Reversible".

   Obsolète depuis la version 3.9: "collections.abc.Reversible" now
   supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.Sized

   Alias to "collections.abc.Sized".


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

class typing.ContextManager(Generic[T_co])

   Deprecated alias to "contextlib.AbstractContextManager".

   Nouveau dans la version 3.5.4.

   Obsolète depuis la version 3.9: "contextlib.AbstractContextManager"
   now supports subscripting ("[]"). See **PEP 585** and Type Alias
   générique.

class typing.AsyncContextManager(Generic[T_co])

   Deprecated alias to "contextlib.AbstractAsyncContextManager".

   Nouveau dans la version 3.6.2.

   Obsolète depuis la version 3.9:
   "contextlib.AbstractAsyncContextManager" now supports subscripting
   ("[]"). See **PEP 585** and Type Alias générique.


Étapes d'Obsolescence des Fonctionnalités Majeures
==================================================

Certaines fonctionnalités dans "typing" sont obsolètes et peuvent être
supprimées dans une future version de Python. Le tableau suivant
résume les principales dépréciations. Celui-ci peut changer et toutes
les dépréciations ne sont pas listées.

+---------------------------+---------------------------+---------------------------+---------------------------+
| Fonctionnalité            | Obsolète en               | Suppression prévue        | PEP/issue                 |
|===========================|===========================|===========================|===========================|
| sous-modules "typing.io"  | 3.8                       | 3.13                      | bpo-38291                 |
| et "typing.re"            |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| Versions de typage des    | 3.9                       | Undecided (see Deprecated | **PEP 585**               |
| collections standards     |                           | aliases for more          |                           |
|                           |                           | information)              |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.ByteString"       | 3.9                       | 3.14                      | gh-91896                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Text"             | 3.11                      | Non défini                | gh-92332                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
