# `typing` — Support for type hints

New in version 3.5.

Source code: 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. The most fundamental support consists of the types `Any`, `Union`, `Callable`, `TypeVar`, and `Generic`. For a full specification, please see PEP 484. For a simplified introduction to type hints, see PEP 483.

The function below takes and returns a string and is annotated as follows:

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

In the function `greeting`, the argument `name` is expected to be of type `str` and the return type `str`. Subtypes are accepted as arguments.

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

## Relevant PEPs

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. These include:

- PEP 526: Syntax for Variable Annotations

    Introducing syntax for annotating variables outside of function definitions, and `ClassVar`

- PEP 544: Protocols: Structural subtyping (static duck typing)

    Introducing `Protocol` and the `@runtime_checkable` decorator

- PEP 585: Type Hinting Generics In Standard Collections

    Introducing `types.GenericAlias` and the ability to use standard library classes as generic types

- PEP 586: Literal Types

    Introducing `Literal`

- PEP 589: TypedDict: Type Hints for Dictionaries with a Fixed Set of Keys

    Introducing `TypedDict`

- PEP 591: Adding a final qualifier to typing

    Introducing `Final` and the `@final` decorator

- PEP 593: Flexible function and variable annotations

    Introducing `Annotated`

- PEP 604: Allow writing union types as `X | Y`

    Introducing `types.UnionType` and the ability to use the binary-or operator `|` to signify a union of types

- PEP 612: Parameter Specification Variables

    Introducing `ParamSpec` and `Concatenate`

- PEP 613: Explicit Type Aliases

    Introducing `TypeAlias`

- PEP 647: User-Defined Type Guards

    Introducing `TypeGuard`

## Type aliases

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]

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

Type aliases are useful for simplifying complex type signatures. For example:

    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:
        ...

Note that `None` as a type hint is a special case and is replaced by `type(None)`.

## NewType

Use the `NewType` helper to create distinct types:

    from typing import NewType

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

The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors:

    def get_user_name(user_id: UserId) -> str:
        ...

    # typechecks
    user_a = get_user_name(UserId(42351))

    # does not typecheck; an int is not a UserId
    user_b = get_user_name(-1)

You may still perform all `int` operations on a variable of type `UserId`, but the result will always be of type `int`. This lets you pass in a `UserId` wherever an `int` might be expected, but will prevent you from accidentally creating a `UserId` in an invalid way:

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

Note that these checks are enforced only by the static type checker. At runtime, the statement `Derived = NewType('Derived', Base)` will make `Derived` a callable that immediately returns whatever parameter you pass it. That means the expression `Derived(some_value)` does not create a new class or introduce much overhead beyond that of a regular function call.

More precisely, the expression `some_value is Derived(some_value)` is always true at runtime.

It is invalid to create a subtype of `Derived`:

    from typing import NewType

    UserId = NewType('UserId', int)

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

However, it is possible to create a `NewType` based on a ‘derived’ `NewType`:

    from typing import NewType

    UserId = NewType('UserId', int)

    ProUserId = NewType('ProUserId', UserId)

and typechecking for `ProUserId` will work as expected.

See PEP 484 for more details.

Note

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

In contrast, `NewType` declares one type to be a subtype of another. Doing `Derived = NewType('Derived', Original)` will make the static type checker treat `Derived` as a subclass of `Original`, which means a value of type `Original` cannot be used in places where a value of type `Derived` is expected. This is useful when you want to prevent logic errors with minimal runtime cost.

New in version 3.5.2.

Changed in version 3.10: `NewType` is now a class rather than a function. There is some additional runtime cost when calling `NewType` over a regular function. However, this cost will be reduced in 3.11.0.

## Callable

Frameworks expecting callback functions of specific signatures might be type hinted using `Callable[[Arg1Type, Arg2Type], ReturnType]`.

For example:

    from collections.abc import Callable

    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

It is possible to declare the return type of a callable without specifying the call signature by substituting a literal ellipsis for the list of arguments in the type hint: `Callable[..., ReturnType]`.

Callables which take other callables as arguments may indicate that their parameter types are dependent on each other using `ParamSpec`. Additionally, if that callable adds or removes arguments from other callables, the `Concatenate` operator may be used. They take the form `Callable[ParamSpecVariable, ReturnType]` and `Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]` respectively.

Changed in version 3.10: `Callable` now supports `ParamSpec` and `Concatenate`. See PEP 612 for more information.

See also

The documentation for `ParamSpec` and `Concatenate` provides examples of usage in `Callable`.

## Generics

Since type information about objects kept in containers cannot be statically inferred in a generic way, abstract base classes have been extended to support subscription to denote expected types for container elements.

    from collections.abc import Mapping, Sequence

    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

    def first(l: Sequence[T]) -> T:   # Generic function
        return l[0]

## User-defined generic types

A user-defined class can be defined as a generic class.

    from typing import TypeVar, Generic
    from logging import Logger

    T = TypeVar('T')

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

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

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

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

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

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

    from collections.abc import Iterable

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

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]):
        ...

Each type variable argument to `Generic` must be distinct. This is thus invalid:

    from typing import TypeVar, Generic
    ...

    T = TypeVar('T')

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

You can use multiple inheritance with `Generic`:

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

    T = TypeVar('T')

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

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

    from collections.abc import Mapping
    from typing import TypeVar

    T = TypeVar('T')

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

In this case `MyDict` has a single parameter, `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)

Changed in version 3.7: `Generic` no longer has a custom metaclass.

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'>)]

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

Changed in 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.

## The `Any` type

A special kind of type is `Any`. A static type checker will treat every type as being compatible with `Any` and `Any` as being compatible with every 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:
        # Typechecks; 'item' could be any type,
        # and that type might have a 'bar' method
        item.bar()
        ...

Notice that no typechecking 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!

Furthermore, all functions without a return type or parameter types will implicitly default to using `Any`:

    def legacy_parser(text):
        ...
        return data

    # A static type checker will treat the above
    # as having the same signature as:
    def legacy_parser(text: Any) -> Any:
        ...
        return data

This behavior allows `Any` to be used as an escape hatch when you need to mix dynamically and statically typed code.

Contrast the behavior of `Any` with the behavior of `object`. Similar to `Any`, every type is a subtype of `object`. However, unlike `Any`, the reverse is not true: `object` is not a subtype of every other type.

That means when the type of a value is `object`, a type checker will reject almost all operations on it, and assigning it to a variable (or using it as a return value) of a more specialized type is a type error. For example:

    def hash_a(item: object) -> int:
        # Fails; an object does not have a 'magic' method.
        item.magic()
        ...

    def hash_b(item: Any) -> int:
        # Typechecks
        item.magic()
        ...

    # Typechecks, since ints and strs are subclasses of object
    hash_a(42)
    hash_a("foo")

    # Typechecks, since Any is compatible with all types
    hash_b(42)
    hash_b("foo")

Use `object` to indicate that a value could be any type in a typesafe manner. Use `Any` to indicate that a value is dynamically typed.

## Nominal vs structural subtyping

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]: ...

PEP 544 allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing `Bucket` to be implicitly considered a subtype of both `Sized` and `Iterable[int]` by static type checkers. This is known as structural subtyping (or static duck-typing):

    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

Moreover, by subclassing a special class `Protocol`, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).

## Module contents

The module defines the following classes, functions and decorators.

Note

This module defines several types that are subclasses of pre-existing standard library classes which also extend `Generic` to support type variables inside `[]`. These types became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support `[]`.

The redundant types are deprecated as of Python 3.9 but no deprecation warnings will be issued by the interpreter. It is expected that type checkers will flag the deprecated types when the checked program targets Python 3.9 or newer.

The deprecated types will be removed from the `typing` module in the first Python version released 5 years after the release of Python 3.9.0. See details in PEP 585—Type Hinting Generics In Standard Collections.

### Special typing primitives

#### Special types

These can be used as types in annotations and do not support `[]`.

`typing.``Any`

  Special type indicating an unconstrained type.

  - Every type is compatible with `Any`.

  - `Any` is compatible with every type.

`typing.``NoReturn`

  Special type indicating that a function never returns. For example:

      from typing import NoReturn

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

  New in version 3.5.4.

  New in version 3.6.2.

`typing.``TypeAlias`

  Special annotation for explicitly declaring a type alias. For example:

      from typing import TypeAlias

      Factors: TypeAlias = list[int]

  See PEP 613 for more details about explicit type aliases.

  New in version 3.10.

#### Special forms

These can be used as types in annotations using `[]`, each having a unique syntax.

`typing.``Tuple`

  Tuple type; `Tuple[X, Y]` is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written as `Tuple[()]`.

  Example: `Tuple[T1, T2]` is a tuple of two elements corresponding to type variables T1 and T2. `Tuple[int, float, str]` is a tuple of an int, a float and a string.

  To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g. `Tuple[int, ...]`. A plain `Tuple` is equivalent to `Tuple[Any, ...]`, and in turn to `tuple`.

  Deprecated since version 3.9: `builtins.tuple` now supports `[]`. See PEP 585 and Generic Alias Type.

`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:

  - The arguments must be types and there must be at least one.

  - Unions of unions are flattened, e.g.:

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

  - Unions of a single argument vanish, e.g.:

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

  - Redundant arguments are skipped, e.g.:

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

  - When comparing unions, the argument order is ignored, e.g.:

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

  - You cannot subclass or instantiate a `Union`.

  - You cannot write `Union[X][Y]`.

  Changed in version 3.7: Don’t remove explicit subclasses from unions at runtime.

  Changed in version 3.10: Unions can now be written as `X | Y`. See union type expressions.

`typing.``Optional`

  Optional type.

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

  Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the `Optional` qualifier on its type annotation just because it is optional. For example:

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

  On the other hand, if an explicit value of `None` is allowed, the use of `Optional` is appropriate, whether the argument is optional or not. For example:

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

  Changed in version 3.10: Optional can now be written as `X | None`. See union type expressions.

`typing.``Callable`

  Callable type; `Callable[[int], str]` is a function of (int) -> str.

  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 or an ellipsis; the return type must be a single type.

  There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types. `Callable[..., ReturnType]` (literal ellipsis) can be used to type hint a callable taking any number of arguments and returning `ReturnType`. A plain `Callable` is equivalent to `Callable[..., Any]`, and in turn to `collections.abc.Callable`.

  Callables which take other callables as arguments may indicate that their parameter types are dependent on each other using `ParamSpec`. Additionally, if that callable adds or removes arguments from other callables, the `Concatenate` operator may be used. They take the form `Callable[ParamSpecVariable, ReturnType]` and `Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType]` respectively.

  Deprecated since version 3.9: `collections.abc.Callable` now supports `[]`. See PEP 585 and Generic Alias Type.

  Changed in version 3.10: `Callable` now supports `ParamSpec` and `Concatenate`. See PEP 612 for more information.

  See also

  The documentation for `ParamSpec` and `Concatenate` provide examples of usage with `Callable`.

`typing.``Concatenate`

  Used with `Callable` and `ParamSpec` to type 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`.

  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])

New in version 3.10.

See also

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

- `ParamSpec` and `Callable`.

class `typing.``Type`(Generic[CT_co])

  A variable annotated with `C` may accept a value of type `C`. In contrast, a variable annotated with `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 BasicUser(User): ...
      class ProUser(User): ...
      class TeamUser(User): ...

      # Accepts User, BasicUser, ProUser, TeamUser, ...
      def make_new_user(user_class: Type[User]) -> User:
          # ...
          return user_class()

  The fact that `Type[C]` is covariant implies that all subclasses of `C` should implement the same constructor signature and class method signatures as `C`. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484.

  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]): ...

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

  New in version 3.5.2.

  Deprecated since version 3.9: `builtins.type` now supports `[]`. See PEP 585 and Generic Alias Type.

`typing.``Literal`

  A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example:

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

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

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

  `Literal[...]` cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to `Literal[...]`, but type checkers may impose restrictions. See PEP 586 for more details about literal types.

  New in version 3.8.

  Changed in 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`

  Special type construct to mark class variables.

  As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:

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

  `ClassVar` accepts only types and cannot be further subscribed.

  `ClassVar` is not a class itself, and should not be used with `isinstance()` or `issubclass()`. `ClassVar` does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:

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

  New in version 3.5.3.

`typing.``Final`

  A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example:

      MAX_SIZE: Final = 9000
      MAX_SIZE += 1  # Error reported by type checker

      class Connection:
          TIMEOUT: Final[int] = 10

      class FastConnector(Connection):
          TIMEOUT = 1  # Error reported by type checker

  There is no runtime checking of these properties. See PEP 591 for more details.

  New in version 3.8.

`typing.``Annotated`

  A type, introduced in PEP 593 (`Flexible function and variable annotations`), to decorate existing types with context-specific metadata (possibly multiple pieces of it, as `Annotated` is variadic). Specifically, a type `T` can be annotated with metadata `x` via the typehint `Annotated[T, x]`. This metadata can be used for either static analysis or at runtime. If a library (or tool) encounters a typehint `Annotated[T, x]` and has no special logic for metadata `x`, it should ignore it and simply treat the type as `T`. Unlike the `no_type_check` functionality that currently exists in the `typing` module which completely disables typechecking annotations on a function or a class, the `Annotated` type allows for both static typechecking of `T` (which can safely ignore `x`) together with runtime access to `x` within a specific application.

  Ultimately, the responsibility of how to interpret the annotations (if at all) is the responsibility of the tool or library encountering the `Annotated` type. A tool or library encountering an `Annotated` type can scan through the annotations to determine if they are of interest (e.g., using `isinstance()`).

  When a tool or a library does not support annotations or encounters an unknown annotation it should just ignore it and treat annotated type as the underlying type.

  It’s up to the tool consuming the annotations to decide whether the client is allowed to have several annotations on one type and how to merge those annotations.

  Since the `Annotated` type allows you to put several annotations of the same (or different) type(s) on any node, the tools or libraries consuming those annotations are in charge of dealing with potential duplicates. For example, if you are doing value range analysis you might allow this:

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

  Passing `include_extras=True` to `get_type_hints()` lets one access the extra annotations at runtime.

  The details of the syntax:

  - The first argument to `Annotated` must be a valid type

  - Multiple type annotations are supported (`Annotated` supports variadic arguments):

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

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

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

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

  - Nested `Annotated` types are flattened, with metadata ordered starting with the innermost annotation:

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

  - Duplicated annotations are not removed:

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

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

        T = TypeVar('T')
        Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
        V = Vec[int]

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

  New in version 3.9.

`typing.``TypeGuard`

  Special typing form 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`.

  For example:

      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. For more information, see PEP 647 (User-Defined Type Guards).

  New in version 3.10.

#### Building generic types

These are not used in annotations. They are building blocks for creating generic types.

class `typing.``Generic`

  Abstract base class for generic types.

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

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

  This class can then be used as follows:

      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`

  Type variable.

  Usage:

      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 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.

  Constrained type variables and bound type variables have different semantics in several important ways. Using a constrained type variable means that the `TypeVar` can only ever be solved as being exactly one of the constraints given:

      a = concatenate('one', 'two')  # Ok, variable 'a' has type 'str'
      b = concatenate(StringSubclass('one'), StringSubclass('two'))  # Inferred type of variable 'b' 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

  Using a bound type variable, however, means that the `TypeVar` will be solved using the most specific type possible:

      print_capitalized('a string')  # Ok, output has type 'str'

      class StringSubclass(str):
          pass

      print_capitalized(StringSubclass('another string'))  # Ok, output has type 'StringSubclass'
      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

  Bound type variables are particularly useful for annotating `classmethods` that serve as alternative constructors. In the following example (by Raymond Hettinger), the type variable `C` is bound to the `Circle` class through the use of a forward reference. Using this type variable to annotate the `with_circumference` classmethod, rather than hardcoding the return type as `Circle`, means that a type checker can correctly infer the return type even if the method is called on a subclass:

      import math

      C = TypeVar('C', bound='Circle')

      class Circle:
          """An abstract circle"""

          def __init__(self, radius: float) -> None:
              self.radius = radius

          # Use a type variable to show that the return type
          # will always be an instance of whatever ``cls`` is
          @classmethod
          def with_circumference(cls: type[C], circumference: float) -> C:
              """Create a circle with the specified circumference"""
              radius = circumference / (math.pi * 2)
              return cls(radius)

      class Tire(Circle):
          """A specialised circle (made out of rubber)"""

          MATERIAL = 'rubber'

      c = Circle.with_circumference(3)  # Ok, variable 'c' has type 'Circle'
      t = Tire.with_circumference(4)  # Ok, variable 't' has type 'Tire' (not 'Circle')

  At runtime, `isinstance(x, T)` will raise `TypeError`. In general, `isinstance()` and `issubclass()` should not be used with types.

  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.

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

  Parameter specification variable. A specialized version of `type variables`.

  Usage:

      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`.

  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.

  New in version 3.10.

  Note

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

  See also

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

  - `Callable` and `Concatenate`.

`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`:

      P = ParamSpec("P")
      get_origin(P.args)  # returns P
      get_origin(P.kwargs)  # returns P

  New in version 3.10.

`typing.``AnyStr`

  `AnyStr` is a `constrained type variable` defined as `AnyStr = TypeVar('AnyStr', str, bytes)`.

  It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example:

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

      concat(u"foo", u"bar")  # Ok, output has type 'unicode'
      concat(b"foo", b"bar")  # Ok, output has type 'bytes'
      concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes

class `typing.``Protocol`(Generic)

  Base class for protocol classes. Protocol classes are defined like this:

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

  Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

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

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

      func(C())  # Passes static type check

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

  Protocol classes can be generic, for example:

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

  New in version 3.8.

`@``typing.``runtime_checkable`

  Mark a protocol class as a runtime protocol.

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

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

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

  Note

  `runtime_checkable()` will check only the presence of the required methods, not their type signatures. 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`.

  New in version 3.8.

#### Other special directives

These are not used in annotations. They are building blocks for declaring types.

class `typing.``NamedTuple`

  Typed version of `collections.namedtuple()`.

  Usage:

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

  This is equivalent to:

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

  To give a field a default value, you can assign to it in the class body:

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

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

  Fields with a default value must come after any fields without a default.

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

  `NamedTuple` subclasses can also have docstrings and methods:

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

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

  Backward-compatible usage:

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

  Changed in version 3.6: Added support for PEP 526 variable annotation syntax.

  Changed in version 3.6.1: Added support for default values, methods, and docstrings.

  Changed in version 3.8: The `_field_types` and `__annotations__` attributes are now regular dictionaries instead of instances of `OrderedDict`.

  Changed in version 3.9: Removed the `_field_types` attribute in favor of the more standard `__annotations__` attribute which has the same information.

class `typing.``NewType`(name, tp)

  A helper class to indicate a distinct type to a typechecker, see NewType. At runtime it returns an object that returns its argument when called. Usage:

      UserId = NewType('UserId', int)
      first_user = UserId(1)

  New in version 3.5.2.

  Changed in version 3.10: `NewType` is now a class rather than a function.

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:

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

  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 override this by specifying totality. Usage:

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

  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.

  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, notably including `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

      T = TypeVar('T')
      class XT(X, Generic[T]): pass  # raises TypeError

  A `TypedDict` can be introspected via annotations dicts (see Annotations Best Practices 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

  `__required_keys__`
  `__optional_keys__`

    `Point2D.__required_keys__` and `Point2D.__optional_keys__` return `frozenset` objects containing required and non-required keys, respectively. Currently the only way to declare both required and non-required keys in the same `TypedDict` is mixed inheritance, declaring a `TypedDict` with one value for the `total` argument and then inheriting it from another `TypedDict` with a different value for `total`. Usage:

        >>> 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

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

  New in version 3.8.

### Generic concrete collections

#### Corresponding to built-in types

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

  A generic version of `dict`. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as `Mapping`.

  This type can be used as follows:

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

  Deprecated since version 3.9: `builtins.dict` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``List`(list, MutableSequence[T])

  Generic version of `list`. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as `Sequence` or `Iterable`.

  This type may be used as follows:

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

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

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

  Deprecated since version 3.9: `builtins.list` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `builtins.set`. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as `AbstractSet`.

  Deprecated since version 3.9: `builtins.set` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `builtins.frozenset`.

  Deprecated since version 3.9: `builtins.frozenset` now supports `[]`. See PEP 585 and Generic Alias Type.

Note

`Tuple` is a special form.

#### Corresponding to types in `collections`

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

  A generic version of `collections.defaultdict`.

  New in version 3.5.2.

  Deprecated since version 3.9: `collections.defaultdict` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.OrderedDict`.

  New in version 3.7.2.

  Deprecated since version 3.9: `collections.OrderedDict` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.ChainMap`.

  New in version 3.5.4.

  New in version 3.6.1.

  Deprecated since version 3.9: `collections.ChainMap` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.Counter`.

  New in version 3.5.4.

  New in version 3.6.1.

  Deprecated since version 3.9: `collections.Counter` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.deque`.

  New in version 3.5.4.

  New in version 3.6.1.

  Deprecated since version 3.9: `collections.deque` now supports `[]`. See PEP 585 and Generic Alias Type.

#### Other concrete types

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()`.

  Deprecated since version 3.8, will be removed in version 3.12: The `typing.io` namespace is deprecated and will be removed. These types should be directly imported from `typing` instead.

class `typing.``Pattern`
class `typing.``Match`

  These type aliases correspond to the return types from `re.compile()` and `re.match()`. These types (and the corresponding functions) are generic in `AnyStr` and can be made specific by writing `Pattern[str]`, `Pattern[bytes]`, `Match[str]`, or `Match[bytes]`.

  Deprecated since version 3.8, will be removed in version 3.12: The `typing.re` namespace is deprecated and will be removed. These types should be directly imported from `typing` instead.

  Deprecated since version 3.9: Classes `Pattern` and `Match` from `re` now support `[]`. See PEP 585 and Generic Alias Type.

class `typing.``Text`

  `Text` is an alias for `str`. It is provided to supply a forward compatible path for Python 2 code: in Python 2, `Text` is an alias for `unicode`.

  Use `Text` to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:

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

  New in version 3.5.2.

### Abstract Base Classes

#### Corresponding to collections in `collections.abc`

class `typing.``AbstractSet`(Sized, Collection[T_co])

  A generic version of `collections.abc.Set`.

  Deprecated since version 3.9: `collections.abc.Set` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``ByteString`(Sequence[int])

  A generic version of `collections.abc.ByteString`.

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

  As a shorthand for this type, `bytes` can be used to annotate arguments of any of the types mentioned above.

  Deprecated since version 3.9: `collections.abc.ByteString` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.abc.Collection`

  New in version 3.6.0.

  Deprecated since version 3.9: `collections.abc.Collection` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``Container`(Generic[T_co])

  A generic version of `collections.abc.Container`.

  Deprecated since version 3.9: `collections.abc.Container` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``ItemsView`(MappingView, Generic[KT_co, VT_co])

  A generic version of `collections.abc.ItemsView`.

  Deprecated since version 3.9: `collections.abc.ItemsView` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.abc.KeysView`.

  Deprecated since version 3.9: `collections.abc.KeysView` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.abc.Mapping`. This type can be used as follows:

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

  Deprecated since version 3.9: `collections.abc.Mapping` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``MappingView`(Sized, Iterable[T_co])

  A generic version of `collections.abc.MappingView`.

  Deprecated since version 3.9: `collections.abc.MappingView` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.abc.MutableMapping`.

  Deprecated since version 3.9: `collections.abc.MutableMapping` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``MutableSequence`(Sequence[T])

  A generic version of `collections.abc.MutableSequence`.

  Deprecated since version 3.9: `collections.abc.MutableSequence` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``MutableSet`(AbstractSet[T])

  A generic version of `collections.abc.MutableSet`.

  Deprecated since version 3.9: `collections.abc.MutableSet` now supports `[]`. See PEP 585 and Generic Alias Type.

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

  A generic version of `collections.abc.Sequence`.

  Deprecated since version 3.9: `collections.abc.Sequence` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``ValuesView`(MappingView[VT_co])

  A generic version of `collections.abc.ValuesView`.

  Deprecated since version 3.9: `collections.abc.ValuesView` now supports `[]`. See PEP 585 and Generic Alias Type.

#### Corresponding to other types in `collections.abc`

class `typing.``Iterable`(Generic[T_co])

  A generic version of `collections.abc.Iterable`.

  Deprecated since version 3.9: `collections.abc.Iterable` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``Iterator`(Iterable[T_co])

  A generic version of `collections.abc.Iterator`.

  Deprecated since version 3.9: `collections.abc.Iterator` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``Generator`(Iterator[T_co], Generic[T_co, T_contra, V_co])

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

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

  Note that unlike many other generics in the typing module, the `SendType` of `Generator` behaves contravariantly, not covariantly or invariantly.

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

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

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

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

  Deprecated since version 3.9: `collections.abc.Generator` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``Hashable`

  An alias to `collections.abc.Hashable`.

class `typing.``Reversible`(Iterable[T_co])

  A generic version of `collections.abc.Reversible`.

  Deprecated since version 3.9: `collections.abc.Reversible` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``Sized`

  An alias to `collections.abc.Sized`.

#### Asynchronous programming

class `typing.``Coroutine`(Awaitable[V_co], Generic[T_co, T_contra, V_co])

  A generic version of `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

  New in version 3.5.3.

  Deprecated since version 3.9: `collections.abc.Coroutine` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``AsyncGenerator`(AsyncIterator[T_co], Generic[T_co, T_contra])

  An async generator can be annotated by the generic type `AsyncGenerator[YieldType, SendType]`. For example:

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

  Unlike normal generators, async generators cannot return a value, so there is no `ReturnType` type parameter. As with `Generator`, the `SendType` behaves contravariantly.

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

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

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

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

  New in version 3.6.1.

  Deprecated since version 3.9: `collections.abc.AsyncGenerator` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``AsyncIterable`(Generic[T_co])

  A generic version of `collections.abc.AsyncIterable`.

  New in version 3.5.2.

  Deprecated since version 3.9: `collections.abc.AsyncIterable` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``AsyncIterator`(AsyncIterable[T_co])

  A generic version of `collections.abc.AsyncIterator`.

  New in version 3.5.2.

  Deprecated since version 3.9: `collections.abc.AsyncIterator` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``Awaitable`(Generic[T_co])

  A generic version of `collections.abc.Awaitable`.

  New in version 3.5.2.

  Deprecated since version 3.9: `collections.abc.Awaitable` now supports `[]`. See PEP 585 and Generic Alias Type.

#### Context manager types

class `typing.``ContextManager`(Generic[T_co])

  A generic version of `contextlib.AbstractContextManager`.

  New in version 3.5.4.

  New in version 3.6.0.

  Deprecated since version 3.9: `contextlib.AbstractContextManager` now supports `[]`. See PEP 585 and Generic Alias Type.

class `typing.``AsyncContextManager`(Generic[T_co])

  A generic version of `contextlib.AbstractAsyncContextManager`.

  New in version 3.5.4.

  New in version 3.6.2.

  Deprecated since version 3.9: `contextlib.AbstractAsyncContextManager` now supports `[]`. See PEP 585 and Generic Alias Type.

### Protocols

These protocols are decorated with `runtime_checkable()`.

class `typing.``SupportsAbs`

  An ABC with one abstract method `__abs__` that is covariant in its return type.

class `typing.``SupportsBytes`

  An ABC with one abstract method `__bytes__`.

class `typing.``SupportsComplex`

  An ABC with one abstract method `__complex__`.

class `typing.``SupportsFloat`

  An ABC with one abstract method `__float__`.

class `typing.``SupportsIndex`

  An ABC with one abstract method `__index__`.

  New in version 3.8.

class `typing.``SupportsInt`

  An ABC with one abstract method `__int__`.

class `typing.``SupportsRound`

  An ABC with one abstract method `__round__` that is covariant in its return type.

### Functions and decorators

`typing.``cast`(typ, val)

  Cast a value to a type.

  This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).

`@``typing.``overload`

  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). The `@overload`-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-`@overload`-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a `@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>

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

`@``typing.``final`

  A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example:

      class Base:
          @final
          def done(self) -> None:
              ...
      class Sub(Base):
          def done(self) -> None:  # Error reported by type checker
              ...

      @final
      class Leaf:
          ...
      class Other(Leaf):  # Error reported by type checker
          ...

  There is no runtime checking of these properties. See PEP 591 for more details.

  New in version 3.8.

`@``typing.``no_type_check`

  Decorator to indicate that annotations are not type hints.

  This works as class or function decorator. With a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses).

  This mutates the function(s) in place.

`@``typing.``no_type_check_decorator`

  Decorator to give another decorator the `no_type_check()` effect.

  This wraps the decorator with something that wraps the decorated function in `no_type_check()`.

`@``typing.``type_check_only`

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

  This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:

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

      def fetch_response() -> Response: ...

  Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.

### Introspection helpers

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

  Return a dictionary containing type hints for a function, method, module or class object.

  This is often the same as `obj.__annotations__`. In addition, forward references encoded as string literals are handled by evaluating them in `globals` and `locals` namespaces. If necessary, `Optional[t]` is added for function and method annotations if a default value equal to `None` is set. For a class `C`, return a dictionary constructed by merging all the `__annotations__` along `C.__mro__` in reverse order.

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

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

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

  Note

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

  Changed in version 3.9: Added `include_extras` parameter as part of PEP 593.

`typing.``get_args`(tp)

`typing.``get_origin`(tp)

  Provide basic introspection for generic types and special typing forms.

  For a typing object of the form `X[Y, Z, ...]` these functions return `X` and `(Y, Z, ...)`. If `X` is a generic alias for a builtin or `collections` class, it gets normalized to the original class. 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. For unsupported objects return `None` and `()` correspondingly. Examples:

      assert get_origin(Dict[str, int]) is dict
      assert get_args(Dict[int, str]) == (int, str)

      assert get_origin(Union[int, str]) is Union
      assert get_args(Union[int, str]) == (int, str)

  New in version 3.8.

`typing.``is_typeddict`(tp)

  Check if a type is a `TypedDict`.

  For example:

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

      is_typeddict(Film)  # => True
      is_typeddict(list | str)  # => False

  New in version 3.10.

class `typing.``ForwardRef`

  A class used for internal typing representation of string forward references. For example, `List["SomeClass"]` is implicitly transformed into `List[ForwardRef("SomeClass")]`. This class should not be instantiated by a user, but may be used by introspection tools.

  Note

  PEP 585 generic types such as `list["SomeClass"]` will not be implicitly transformed into `list[ForwardRef("SomeClass")]` and thus will not automatically resolve to `list[SomeClass]`.

  New in version 3.7.4.

### Constant

`typing.``TYPE_CHECKING`

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

      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

  If `from __future__ import annotations` is used, annotations are not evaluated at function definition time. Instead, they are stored as strings in `__annotations__`. This makes it unnecessary to use quotes around the annotation (see PEP 563).

  New in version 3.5.2.