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# 26.1. `typing` — Support for type hints

Source code: Lib/typing.py

This module supports type hints as specified by PEP 484. The most fundamental support consists of the type `Any`, `Union`, `Tuple`, `Callable`, `TypeVar`, and `Generic`. For 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 by of type `str` and the return type `str`. Subtypes are accepted as arguments.

## 26.1.1. Type aliases

A type alias is defined by assigning the type to the alias:

    Vector = List[float]

## 26.1.2. Callable

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

For example:

    from typing 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

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

## 26.1.3. 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 typing import Mapping, Sequence

    def notify_by_email(employees: Sequence[Employee],
                        overrides: Mapping[str, str]) -> None: ...

Generics can be parametrized by using a new factory available in typing called TypeVar.

    from typing import Sequence, TypeVar

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

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

## 26.1.4. 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('{}: {}'.format(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 uses a metaclass that defines `__getitem__()` so that `LoggedVar[t]` is valid as a type:

    from typing 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, and type variables may be constrained:

    from typing import TypeVar, Generic
    ...

    T = TypeVar('T')
    S = TypeVar('S', int, str)

    class StrangePair(Generic[T, 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 typing import TypeVar, Generic, Sized

    T = TypeVar('T')

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

Subclassing 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 typing import Iterable

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

Generic metaclasses are not supported.

## 26.1.5. The `Any` type

A special kind of type is `Any`. Every type is a subtype of `Any`. This is also true for the builtin type object. However, to the static type checker these are completely different.

When the type of a value is `object`, the 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. On the other hand, when a value has type `Any`, the type checker will allow all operations on it, and a value of type `Any` can be assigned to a variable (or used as a return value) of a more constrained type.

## 26.1.6. Default argument values

Use a literal ellipsis `...` to declare an argument as having a default value:

    from typing import AnyStr

    def foo(x: AnyStr, y: AnyStr = ...) -> AnyStr: ...

## 26.1.7. Classes, functions, and decorators

The module defines the following classes, functions and decorators:

class `typing.``Any`

  Special type indicating an unconstrained type.

  - Any object is an instance of `Any`.
  - Any class is a subclass of `Any`.
  - As a special case, `Any` and `object` are subclasses of each other.

class `typing.``TypeVar`

  Type variable.

  Usage:

      T = TypeVar('T')  # Can be anything
      A = TypeVar('A', str, bytes)  # Must be 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 class 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 longest(x: A, y: A) -> A:
          """Return the longest of two strings."""
          return x if len(x) >= len(y) else y

  The latter example’s signature is essentially the overloading of `(str, str) -> str` and `(bytes, bytes) -> bytes`. Also note that if the arguments are instances of some subclass of `str`, the return type is still plain `str`.

  At runtime, `isinstance(x, T)` will raise `TypeError`. In general, `isinstance()` and `issublass()` 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.``Union`

  Union type; `Union[X, Y]` means either X or Y.

  To define a union, use e.g. `Union[int, str]`. 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]

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

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

  - If `Any` is present it is the sole survivor, e.g.:

        Union[int, Any] == Any

  - You cannot subclass or instantiate a union.

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

  - You can use `Optional[X]` as a shorthand for `Union[X, None]`.

class `typing.``Optional`

  Optional type.

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

class `typing.``Tuple`

  Tuple type; `Tuple[X, Y]` is the is the type of a tuple of two items with the first item of type X and the second of type Y.

  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, ...]`.

class `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; 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]` could be used to type hint a callable taking any number of arguments and returning `ReturnType`. A plain `Callable` is equivalent to `Callable[..., Any]`.

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.``Iterable`(Generic[T_co])

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

class `typing.``SupportsInt`

class `typing.``SupportsFloat`

class `typing.``SupportsAbs`

class `typing.``SupportsRound`

class `typing.``Reversible`

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

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

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

class `typing.``Mapping`(Sized, Iterable[KT_co], Container[KT_co], Generic[KT_co, VT_co])

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

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

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

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

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

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

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

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

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

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

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

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

class `typing.``io`

  Wrapper namespace for IO generic classes.

class `typing.``re`

  Wrapper namespace for re type classes.

`typing.``NamedTuple`(typename, fields)

  Typed version of namedtuple.

  Usage:

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

  This is equivalent to:

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

  The resulting class has one extra attribute: _field_types, giving a dict mapping field names to types. (The field names are in the _fields attribute, which is part of the namedtuple API.)

`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.``get_type_hints`(obj)

  Return type hints for a function or method object.

  This is often the same as obj.__annotations__, but it handles forward references encoded as string literals, and if necessary adds Optional[t] if a default value equal to None is set.

`@``typing.``no_type_check`(arg)

  Decorator to indicate that annotations are not type hints.

  The argument must be a class or function; if it is 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)

  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.

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