# `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:

The full list of PEPs

- 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` et `typing.re` | 3.8 | 3.13 | bpo-38291
Versions de typage des collections standards | 3.9 | Undecided (see Deprecated aliases for more information) | PEP 585
`typing.ByteString` | 3.9 | 3.14 | gh-91896
`typing.Text` | 3.11 | Non défini | gh-92332