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PR #4906 changed the typing.Generic class hierarchy, leaving an outdated comment in the library reference. User-defined Generic ABCs now must get a abc.ABCMeta metaclass from something other than typing.Generic inheritance.
1375 lines
42 KiB
ReStructuredText
1375 lines
42 KiB
ReStructuredText
:mod:`typing` --- Support for type hints
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========================================
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.. module:: typing
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:synopsis: Support for type hints (see :pep:`484`).
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.. versionadded:: 3.5
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**Source code:** :source:`Lib/typing.py`
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.. note::
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The Python runtime does not enforce function and variable type annotations.
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They can be used by third party tools such as type checkers, IDEs, linters,
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etc.
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--------------
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This module provides runtime support for type hints as specified by
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:pep:`484`, :pep:`526`, :pep:`544`, :pep:`586`, :pep:`589`, and :pep:`591`.
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The most fundamental support consists of the types :data:`Any`, :data:`Union`,
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:data:`Tuple`, :data:`Callable`, :class:`TypeVar`, and
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:class:`Generic`. For full specification please see :pep:`484`. For
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a simplified introduction to type hints see :pep:`483`.
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The function below takes and returns a string and is annotated as follows::
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def greeting(name: str) -> str:
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return 'Hello ' + name
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In the function ``greeting``, the argument ``name`` is expected to be of type
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:class:`str` and the return type :class:`str`. Subtypes are accepted as
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arguments.
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Type aliases
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------------
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A type alias is defined by assigning the type to the alias. In this example,
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``Vector`` and ``List[float]`` will be treated as interchangeable synonyms::
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from typing import List
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Vector = List[float]
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def scale(scalar: float, vector: Vector) -> Vector:
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return [scalar * num for num in vector]
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# typechecks; a list of floats qualifies as a Vector.
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new_vector = scale(2.0, [1.0, -4.2, 5.4])
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Type aliases are useful for simplifying complex type signatures. For example::
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from typing import Dict, Tuple, Sequence
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ConnectionOptions = Dict[str, str]
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Address = Tuple[str, int]
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Server = Tuple[Address, ConnectionOptions]
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def broadcast_message(message: str, servers: Sequence[Server]) -> None:
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...
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# The static type checker will treat the previous type signature as
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# being exactly equivalent to this one.
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def broadcast_message(
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message: str,
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servers: Sequence[Tuple[Tuple[str, int], Dict[str, str]]]) -> None:
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...
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Note that ``None`` as a type hint is a special case and is replaced by
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``type(None)``.
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.. _distinct:
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NewType
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-------
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Use the :func:`NewType` helper function to create distinct types::
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from typing import NewType
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UserId = NewType('UserId', int)
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some_id = UserId(524313)
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The static type checker will treat the new type as if it were a subclass
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of the original type. This is useful in helping catch logical errors::
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def get_user_name(user_id: UserId) -> str:
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...
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# typechecks
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user_a = get_user_name(UserId(42351))
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# does not typecheck; an int is not a UserId
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user_b = get_user_name(-1)
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You may still perform all ``int`` operations on a variable of type ``UserId``,
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but the result will always be of type ``int``. This lets you pass in a
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``UserId`` wherever an ``int`` might be expected, but will prevent you from
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accidentally creating a ``UserId`` in an invalid way::
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# 'output' is of type 'int', not 'UserId'
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output = UserId(23413) + UserId(54341)
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Note that these checks are enforced only by the static type checker. At runtime,
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the statement ``Derived = NewType('Derived', Base)`` will make ``Derived`` a
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function that immediately returns whatever parameter you pass it. That means
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the expression ``Derived(some_value)`` does not create a new class or introduce
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any overhead beyond that of a regular function call.
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More precisely, the expression ``some_value is Derived(some_value)`` is always
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true at runtime.
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This also means that it is not possible to create a subtype of ``Derived``
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since it is an identity function at runtime, not an actual type::
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from typing import NewType
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UserId = NewType('UserId', int)
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# Fails at runtime and does not typecheck
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class AdminUserId(UserId): pass
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However, it is possible to create a :func:`NewType` based on a 'derived' ``NewType``::
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from typing import NewType
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UserId = NewType('UserId', int)
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ProUserId = NewType('ProUserId', UserId)
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and typechecking for ``ProUserId`` will work as expected.
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See :pep:`484` for more details.
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.. note::
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Recall that the use of a type alias declares two types to be *equivalent* to
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one another. Doing ``Alias = Original`` will make the static type checker
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treat ``Alias`` as being *exactly equivalent* to ``Original`` in all cases.
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This is useful when you want to simplify complex type signatures.
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In contrast, ``NewType`` declares one type to be a *subtype* of another.
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Doing ``Derived = NewType('Derived', Original)`` will make the static type
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checker treat ``Derived`` as a *subclass* of ``Original``, which means a
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value of type ``Original`` cannot be used in places where a value of type
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``Derived`` is expected. This is useful when you want to prevent logic
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errors with minimal runtime cost.
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.. versionadded:: 3.5.2
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Callable
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--------
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Frameworks expecting callback functions of specific signatures might be
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type hinted using ``Callable[[Arg1Type, Arg2Type], ReturnType]``.
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For example::
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from typing import Callable
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def feeder(get_next_item: Callable[[], str]) -> None:
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# Body
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def async_query(on_success: Callable[[int], None],
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on_error: Callable[[int, Exception], None]) -> None:
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# Body
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It is possible to declare the return type of a callable without specifying
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the call signature by substituting a literal ellipsis
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for the list of arguments in the type hint: ``Callable[..., ReturnType]``.
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.. _generics:
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Generics
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--------
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Since type information about objects kept in containers cannot be statically
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inferred in a generic way, abstract base classes have been extended to support
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subscription to denote expected types for container elements.
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::
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from typing import Mapping, Sequence
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def notify_by_email(employees: Sequence[Employee],
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overrides: Mapping[str, str]) -> None: ...
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Generics can be parameterized by using a new factory available in typing
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called :class:`TypeVar`.
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::
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from typing import Sequence, TypeVar
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T = TypeVar('T') # Declare type variable
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def first(l: Sequence[T]) -> T: # Generic function
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return l[0]
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User-defined generic types
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--------------------------
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A user-defined class can be defined as a generic class.
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::
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from typing import TypeVar, Generic
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from logging import Logger
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T = TypeVar('T')
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class LoggedVar(Generic[T]):
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def __init__(self, value: T, name: str, logger: Logger) -> None:
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self.name = name
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self.logger = logger
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self.value = value
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def set(self, new: T) -> None:
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self.log('Set ' + repr(self.value))
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self.value = new
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def get(self) -> T:
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self.log('Get ' + repr(self.value))
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return self.value
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def log(self, message: str) -> None:
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self.logger.info('%s: %s', self.name, message)
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``Generic[T]`` as a base class defines that the class ``LoggedVar`` takes a
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single type parameter ``T`` . This also makes ``T`` valid as a type within the
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class body.
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The :class:`Generic` base class uses a metaclass that defines
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:meth:`__getitem__` so that ``LoggedVar[t]`` is valid as a type::
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from typing import Iterable
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def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
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for var in vars:
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var.set(0)
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A generic type can have any number of type variables, and type variables may
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be constrained::
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from typing import TypeVar, Generic
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...
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T = TypeVar('T')
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S = TypeVar('S', int, str)
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class StrangePair(Generic[T, S]):
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...
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Each type variable argument to :class:`Generic` must be distinct.
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This is thus invalid::
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from typing import TypeVar, Generic
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...
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T = TypeVar('T')
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class Pair(Generic[T, T]): # INVALID
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...
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You can use multiple inheritance with :class:`Generic`::
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from typing import TypeVar, Generic, Sized
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T = TypeVar('T')
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class LinkedList(Sized, Generic[T]):
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...
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When inheriting from generic classes, some type variables could be fixed::
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from typing import TypeVar, Mapping
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T = TypeVar('T')
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class MyDict(Mapping[str, T]):
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...
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In this case ``MyDict`` has a single parameter, ``T``.
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Using a generic class without specifying type parameters assumes
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:data:`Any` for each position. In the following example, ``MyIterable`` is
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not generic but implicitly inherits from ``Iterable[Any]``::
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from typing import Iterable
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class MyIterable(Iterable): # Same as Iterable[Any]
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User defined generic type aliases are also supported. Examples::
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from typing import TypeVar, Iterable, Tuple, Union
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S = TypeVar('S')
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Response = Union[Iterable[S], int]
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# Return type here is same as Union[Iterable[str], int]
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def response(query: str) -> Response[str]:
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...
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T = TypeVar('T', int, float, complex)
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Vec = Iterable[Tuple[T, T]]
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def inproduct(v: Vec[T]) -> T: # Same as Iterable[Tuple[T, T]]
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return sum(x*y for x, y in v)
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.. versionchanged:: 3.7
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:class:`Generic` no longer has a custom metaclass.
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A user-defined generic class can have ABCs as base classes without a metaclass
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conflict. Generic metaclasses are not supported. The outcome of parameterizing
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generics is cached, and most types in the typing module are hashable and
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comparable for equality.
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The :data:`Any` type
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--------------------
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A special kind of type is :data:`Any`. A static type checker will treat
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every type as being compatible with :data:`Any` and :data:`Any` as being
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compatible with every type.
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This means that it is possible to perform any operation or method call on a
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value of type on :data:`Any` and assign it to any variable::
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from typing import Any
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a = None # type: Any
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a = [] # OK
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a = 2 # OK
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s = '' # type: str
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s = a # OK
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def foo(item: Any) -> int:
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# Typechecks; 'item' could be any type,
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# and that type might have a 'bar' method
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item.bar()
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...
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Notice that no typechecking is performed when assigning a value of type
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:data:`Any` to a more precise type. For example, the static type checker did
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not report an error when assigning ``a`` to ``s`` even though ``s`` was
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declared to be of type :class:`str` and receives an :class:`int` value at
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runtime!
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Furthermore, all functions without a return type or parameter types will
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implicitly default to using :data:`Any`::
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def legacy_parser(text):
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...
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return data
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# A static type checker will treat the above
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# as having the same signature as:
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def legacy_parser(text: Any) -> Any:
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...
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return data
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This behavior allows :data:`Any` to be used as an *escape hatch* when you
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need to mix dynamically and statically typed code.
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Contrast the behavior of :data:`Any` with the behavior of :class:`object`.
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Similar to :data:`Any`, every type is a subtype of :class:`object`. However,
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unlike :data:`Any`, the reverse is not true: :class:`object` is *not* a
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subtype of every other type.
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That means when the type of a value is :class:`object`, a type checker will
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reject almost all operations on it, and assigning it to a variable (or using
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it as a return value) of a more specialized type is a type error. For example::
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def hash_a(item: object) -> int:
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# Fails; an object does not have a 'magic' method.
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item.magic()
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...
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def hash_b(item: Any) -> int:
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# Typechecks
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item.magic()
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...
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# Typechecks, since ints and strs are subclasses of object
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hash_a(42)
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hash_a("foo")
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# Typechecks, since Any is compatible with all types
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hash_b(42)
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hash_b("foo")
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Use :class:`object` to indicate that a value could be any type in a typesafe
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manner. Use :data:`Any` to indicate that a value is dynamically typed.
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Nominal vs structural subtyping
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-------------------------------
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Initially :pep:`484` defined Python static type system as using
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*nominal subtyping*. This means that a class ``A`` is allowed where
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a class ``B`` is expected if and only if ``A`` is a subclass of ``B``.
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This requirement previously also applied to abstract base classes, such as
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:class:`Iterable`. The problem with this approach is that a class had
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to be explicitly marked to support them, which is unpythonic and unlike
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what one would normally do in idiomatic dynamically typed Python code.
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For example, this conforms to the :pep:`484`::
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from typing import Sized, Iterable, Iterator
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class Bucket(Sized, Iterable[int]):
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...
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def __len__(self) -> int: ...
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def __iter__(self) -> Iterator[int]: ...
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:pep:`544` allows to solve this problem by allowing users to write
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the above code without explicit base classes in the class definition,
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allowing ``Bucket`` to be implicitly considered a subtype of both ``Sized``
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and ``Iterable[int]`` by static type checkers. This is known as
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*structural subtyping* (or static duck-typing)::
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from typing import Iterator, Iterable
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class Bucket: # Note: no base classes
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...
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def __len__(self) -> int: ...
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def __iter__(self) -> Iterator[int]: ...
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def collect(items: Iterable[int]) -> int: ...
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result = collect(Bucket()) # Passes type check
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Moreover, by subclassing a special class :class:`Protocol`, a user
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can define new custom protocols to fully enjoy structural subtyping
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(see examples below).
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Classes, functions, and decorators
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----------------------------------
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The module defines the following classes, functions and decorators:
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.. class:: TypeVar
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Type variable.
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Usage::
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T = TypeVar('T') # Can be anything
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A = TypeVar('A', str, bytes) # Must be str or bytes
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Type variables exist primarily for the benefit of static type
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checkers. They serve as the parameters for generic types as well
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as for generic function definitions. See class Generic for more
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information on generic types. Generic functions work as follows::
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def repeat(x: T, n: int) -> Sequence[T]:
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"""Return a list containing n references to x."""
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return [x]*n
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def longest(x: A, y: A) -> A:
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"""Return the longest of two strings."""
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return x if len(x) >= len(y) else y
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The latter example's signature is essentially the overloading
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of ``(str, str) -> str`` and ``(bytes, bytes) -> bytes``. Also note
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that if the arguments are instances of some subclass of :class:`str`,
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the return type is still plain :class:`str`.
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At runtime, ``isinstance(x, T)`` will raise :exc:`TypeError`. In general,
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:func:`isinstance` and :func:`issubclass` should not be used with types.
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Type variables may be marked covariant or contravariant by passing
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``covariant=True`` or ``contravariant=True``. See :pep:`484` for more
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details. By default type variables are invariant. Alternatively,
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a type variable may specify an upper bound using ``bound=<type>``.
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This means that an actual type substituted (explicitly or implicitly)
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for the type variable must be a subclass of the boundary type,
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see :pep:`484`.
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.. class:: Generic
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Abstract base class for generic types.
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A generic type is typically declared by inheriting from an
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instantiation of this class with one or more type variables.
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For example, a generic mapping type might be defined as::
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class Mapping(Generic[KT, VT]):
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def __getitem__(self, key: KT) -> VT:
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...
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# Etc.
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This class can then be used as follows::
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X = TypeVar('X')
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Y = TypeVar('Y')
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def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
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try:
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return mapping[key]
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except KeyError:
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return default
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.. class:: Protocol(Generic)
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Base class for protocol classes. Protocol classes are defined like this::
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class Proto(Protocol):
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def meth(self) -> int:
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...
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Such classes are primarily used with static type checkers that recognize
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structural subtyping (static duck-typing), for example::
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class C:
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def meth(self) -> int:
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return 0
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def func(x: Proto) -> int:
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return x.meth()
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func(C()) # Passes static type check
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See :pep:`544` for details. Protocol classes decorated with
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:func:`runtime_checkable` (described later) act as simple-minded runtime
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protocols that check only the presence of given attributes, ignoring their
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type signatures.
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Protocol classes can be generic, for example::
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class GenProto(Protocol[T]):
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def meth(self) -> T:
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...
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.. versionadded:: 3.8
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.. class:: Type(Generic[CT_co])
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A variable annotated with ``C`` may accept a value of type ``C``. In
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contrast, a variable annotated with ``Type[C]`` may accept values that are
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classes themselves -- specifically, it will accept the *class object* of
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``C``. For example::
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a = 3 # Has type 'int'
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b = int # Has type 'Type[int]'
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c = type(a) # Also has type 'Type[int]'
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Note that ``Type[C]`` is covariant::
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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 :class:`Type` are classes, :data:`Any`,
|
|
:ref:`type variables <generics>`, and unions of any of these types.
|
|
For example::
|
|
|
|
def new_non_team_user(user_class: Type[Union[BaseUser, ProUser]]): ...
|
|
|
|
``Type[Any]`` is equivalent to ``Type`` which in turn is equivalent
|
|
to ``type``, which is the root of Python's metaclass hierarchy.
|
|
|
|
.. versionadded:: 3.5.2
|
|
|
|
.. class:: Iterable(Generic[T_co])
|
|
|
|
A generic version of :class:`collections.abc.Iterable`.
|
|
|
|
.. class:: Iterator(Iterable[T_co])
|
|
|
|
A generic version of :class:`collections.abc.Iterator`.
|
|
|
|
.. class:: Reversible(Iterable[T_co])
|
|
|
|
A generic version of :class:`collections.abc.Reversible`.
|
|
|
|
.. class:: SupportsInt
|
|
|
|
An ABC with one abstract method ``__int__``.
|
|
|
|
.. class:: SupportsFloat
|
|
|
|
An ABC with one abstract method ``__float__``.
|
|
|
|
.. class:: SupportsComplex
|
|
|
|
An ABC with one abstract method ``__complex__``.
|
|
|
|
.. class:: SupportsBytes
|
|
|
|
An ABC with one abstract method ``__bytes__``.
|
|
|
|
.. class:: SupportsIndex
|
|
|
|
An ABC with one abstract method ``__index__``.
|
|
|
|
.. versionadded:: 3.8
|
|
|
|
.. class:: SupportsAbs
|
|
|
|
An ABC with one abstract method ``__abs__`` that is covariant
|
|
in its return type.
|
|
|
|
.. class:: SupportsRound
|
|
|
|
An ABC with one abstract method ``__round__``
|
|
that is covariant in its return type.
|
|
|
|
.. class:: Container(Generic[T_co])
|
|
|
|
A generic version of :class:`collections.abc.Container`.
|
|
|
|
.. class:: Hashable
|
|
|
|
An alias to :class:`collections.abc.Hashable`
|
|
|
|
.. class:: Sized
|
|
|
|
An alias to :class:`collections.abc.Sized`
|
|
|
|
.. class:: Collection(Sized, Iterable[T_co], Container[T_co])
|
|
|
|
A generic version of :class:`collections.abc.Collection`
|
|
|
|
.. versionadded:: 3.6.0
|
|
|
|
.. class:: AbstractSet(Sized, Collection[T_co])
|
|
|
|
A generic version of :class:`collections.abc.Set`.
|
|
|
|
.. class:: MutableSet(AbstractSet[T])
|
|
|
|
A generic version of :class:`collections.abc.MutableSet`.
|
|
|
|
.. class:: Mapping(Sized, Collection[KT], Generic[VT_co])
|
|
|
|
A generic version of :class:`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]
|
|
|
|
.. class:: MutableMapping(Mapping[KT, VT])
|
|
|
|
A generic version of :class:`collections.abc.MutableMapping`.
|
|
|
|
.. class:: Sequence(Reversible[T_co], Collection[T_co])
|
|
|
|
A generic version of :class:`collections.abc.Sequence`.
|
|
|
|
.. class:: MutableSequence(Sequence[T])
|
|
|
|
A generic version of :class:`collections.abc.MutableSequence`.
|
|
|
|
.. class:: ByteString(Sequence[int])
|
|
|
|
A generic version of :class:`collections.abc.ByteString`.
|
|
|
|
This type represents the types :class:`bytes`, :class:`bytearray`,
|
|
and :class:`memoryview`.
|
|
|
|
As a shorthand for this type, :class:`bytes` can be used to
|
|
annotate arguments of any of the types mentioned above.
|
|
|
|
.. class:: Deque(deque, MutableSequence[T])
|
|
|
|
A generic version of :class:`collections.deque`.
|
|
|
|
.. versionadded:: 3.5.4
|
|
.. versionadded:: 3.6.1
|
|
|
|
.. class:: List(list, MutableSequence[T])
|
|
|
|
Generic version of :class:`list`.
|
|
Useful for annotating return types. To annotate arguments it is preferred
|
|
to use an abstract collection type such as :class:`Sequence` or
|
|
:class:`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]
|
|
|
|
.. class:: Set(set, MutableSet[T])
|
|
|
|
A generic version of :class:`builtins.set <set>`.
|
|
Useful for annotating return types. To annotate arguments it is preferred
|
|
to use an abstract collection type such as :class:`AbstractSet`.
|
|
|
|
.. class:: FrozenSet(frozenset, AbstractSet[T_co])
|
|
|
|
A generic version of :class:`builtins.frozenset <frozenset>`.
|
|
|
|
.. class:: MappingView(Sized, Iterable[T_co])
|
|
|
|
A generic version of :class:`collections.abc.MappingView`.
|
|
|
|
.. class:: KeysView(MappingView[KT_co], AbstractSet[KT_co])
|
|
|
|
A generic version of :class:`collections.abc.KeysView`.
|
|
|
|
.. class:: ItemsView(MappingView, Generic[KT_co, VT_co])
|
|
|
|
A generic version of :class:`collections.abc.ItemsView`.
|
|
|
|
.. class:: ValuesView(MappingView[VT_co])
|
|
|
|
A generic version of :class:`collections.abc.ValuesView`.
|
|
|
|
.. class:: Awaitable(Generic[T_co])
|
|
|
|
A generic version of :class:`collections.abc.Awaitable`.
|
|
|
|
.. versionadded:: 3.5.2
|
|
|
|
.. class:: Coroutine(Awaitable[V_co], Generic[T_co T_contra, V_co])
|
|
|
|
A generic version of :class:`collections.abc.Coroutine`.
|
|
The variance and order of type variables
|
|
correspond to those of :class:`Generator`, for example::
|
|
|
|
from typing import List, Coroutine
|
|
c = None # type: Coroutine[List[str], str, int]
|
|
...
|
|
x = c.send('hi') # type: List[str]
|
|
async def bar() -> None:
|
|
x = await c # type: int
|
|
|
|
.. versionadded:: 3.5.3
|
|
|
|
.. class:: AsyncIterable(Generic[T_co])
|
|
|
|
A generic version of :class:`collections.abc.AsyncIterable`.
|
|
|
|
.. versionadded:: 3.5.2
|
|
|
|
.. class:: AsyncIterator(AsyncIterable[T_co])
|
|
|
|
A generic version of :class:`collections.abc.AsyncIterator`.
|
|
|
|
.. versionadded:: 3.5.2
|
|
|
|
.. class:: ContextManager(Generic[T_co])
|
|
|
|
A generic version of :class:`contextlib.AbstractContextManager`.
|
|
|
|
.. versionadded:: 3.5.4
|
|
.. versionadded:: 3.6.0
|
|
|
|
.. class:: AsyncContextManager(Generic[T_co])
|
|
|
|
A generic version of :class:`contextlib.AbstractAsyncContextManager`.
|
|
|
|
.. versionadded:: 3.5.4
|
|
.. versionadded:: 3.6.2
|
|
|
|
.. class:: Dict(dict, MutableMapping[KT, VT])
|
|
|
|
A generic version of :class:`dict`.
|
|
Useful for annotating return types. To annotate arguments it is preferred
|
|
to use an abstract collection type such as :class:`Mapping`.
|
|
|
|
This type can be used as follows::
|
|
|
|
def count_words(text: str) -> Dict[str, int]:
|
|
...
|
|
|
|
.. class:: DefaultDict(collections.defaultdict, MutableMapping[KT, VT])
|
|
|
|
A generic version of :class:`collections.defaultdict`.
|
|
|
|
.. versionadded:: 3.5.2
|
|
|
|
.. class:: OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])
|
|
|
|
A generic version of :class:`collections.OrderedDict`.
|
|
|
|
.. versionadded:: 3.7.2
|
|
|
|
.. class:: Counter(collections.Counter, Dict[T, int])
|
|
|
|
A generic version of :class:`collections.Counter`.
|
|
|
|
.. versionadded:: 3.5.4
|
|
.. versionadded:: 3.6.1
|
|
|
|
.. class:: ChainMap(collections.ChainMap, MutableMapping[KT, VT])
|
|
|
|
A generic version of :class:`collections.ChainMap`.
|
|
|
|
.. versionadded:: 3.5.4
|
|
.. versionadded:: 3.6.1
|
|
|
|
.. class:: 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 :class:`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
|
|
|
|
.. class:: 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 :class:`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)
|
|
|
|
.. versionadded:: 3.6.1
|
|
|
|
.. class:: 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'
|
|
|
|
.. versionadded:: 3.5.2
|
|
|
|
.. class:: IO
|
|
TextIO
|
|
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
|
|
:func:`open`.
|
|
|
|
.. class:: Pattern
|
|
Match
|
|
|
|
These type aliases
|
|
correspond to the return types from :func:`re.compile` and
|
|
:func:`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]``.
|
|
|
|
.. class:: NamedTuple
|
|
|
|
Typed version of :func:`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)])
|
|
|
|
.. versionchanged:: 3.6
|
|
Added support for :pep:`526` variable annotation syntax.
|
|
|
|
.. versionchanged:: 3.6.1
|
|
Added support for default values, methods, and docstrings.
|
|
|
|
.. versionchanged:: 3.8
|
|
Deprecated the ``_field_types`` attribute in favor of the more
|
|
standard ``__annotations__`` attribute which has the same information.
|
|
|
|
.. versionchanged:: 3.8
|
|
The ``_field_types`` and ``__annotations__`` attributes are
|
|
now regular dictionaries instead of instances of ``OrderedDict``.
|
|
|
|
.. class:: TypedDict(dict)
|
|
|
|
A simple typed namespace. At runtime it is equivalent to
|
|
a plain :class:`dict`.
|
|
|
|
``TypedDict`` creates 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')
|
|
|
|
The type info for introspection can be accessed via ``Point2D.__annotations__``
|
|
and ``Point2D.__total__``. 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})
|
|
|
|
See :pep:`589` for more examples and detailed rules of using ``TypedDict``
|
|
with type checkers.
|
|
|
|
.. versionadded:: 3.8
|
|
|
|
.. class:: 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.
|
|
|
|
.. function:: NewType(typ)
|
|
|
|
A helper function to indicate a distinct types to a typechecker,
|
|
see :ref:`distinct`. At runtime it returns a function that returns
|
|
its argument. Usage::
|
|
|
|
UserId = NewType('UserId', int)
|
|
first_user = UserId(1)
|
|
|
|
.. versionadded:: 3.5.2
|
|
|
|
.. function:: 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).
|
|
|
|
.. function:: get_type_hints(obj[, globals[, locals]])
|
|
|
|
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.
|
|
|
|
.. function:: get_origin(typ)
|
|
.. function:: get_args(typ)
|
|
|
|
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
|
|
:mod:`collections` class, it gets normalized to the original class.
|
|
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)
|
|
|
|
.. versionadded:: 3.8
|
|
|
|
.. decorator:: 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
|
|
:exc:`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.
|
|
|
|
.. decorator:: 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.
|
|
|
|
.. versionadded:: 3.8
|
|
|
|
.. decorator:: no_type_check
|
|
|
|
Decorator to indicate that annotations are not type hints.
|
|
|
|
This works as class or function :term:`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.
|
|
|
|
.. decorator:: no_type_check_decorator
|
|
|
|
Decorator to give another decorator the :func:`no_type_check` effect.
|
|
|
|
This wraps the decorator with something that wraps the decorated
|
|
function in :func:`no_type_check`.
|
|
|
|
.. decorator:: 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.
|
|
|
|
.. decorator:: runtime_checkable
|
|
|
|
Mark a protocol class as a runtime protocol.
|
|
|
|
Such a protocol can be used with :func:`isinstance` and :func:`issubclass`.
|
|
This raises :exc:`TypeError` when applied to a non-protocol class. This
|
|
allows a simple-minded structural check, very similar to "one trick ponies"
|
|
in :mod:`collections.abc` such as :class:`Iterable`. For example::
|
|
|
|
@runtime_checkable
|
|
class Closable(Protocol):
|
|
def close(self): ...
|
|
|
|
assert isinstance(open('/some/file'), Closable)
|
|
|
|
**Warning:** this will check only the presence of the required methods,
|
|
not their type signatures!
|
|
|
|
.. versionadded:: 3.8
|
|
|
|
.. data:: Any
|
|
|
|
Special type indicating an unconstrained type.
|
|
|
|
* Every type is compatible with :data:`Any`.
|
|
* :data:`Any` is compatible with every type.
|
|
|
|
.. data:: NoReturn
|
|
|
|
Special type indicating that a function never returns.
|
|
For example::
|
|
|
|
from typing import NoReturn
|
|
|
|
def stop() -> NoReturn:
|
|
raise RuntimeError('no way')
|
|
|
|
.. versionadded:: 3.5.4
|
|
.. versionadded:: 3.6.2
|
|
|
|
.. data:: 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]
|
|
|
|
* 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]``.
|
|
|
|
.. versionchanged:: 3.7
|
|
Don't remove explicit subclasses from unions at runtime.
|
|
|
|
.. data:: Optional
|
|
|
|
Optional type.
|
|
|
|
``Optional[X]`` is equivalent to ``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:
|
|
...
|
|
|
|
.. data:: 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 :data:`Tuple`
|
|
is equivalent to ``Tuple[Any, ...]``, and in turn to :class:`tuple`.
|
|
|
|
.. data:: 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 :data:`Callable` is equivalent to
|
|
``Callable[..., Any]``, and in turn to
|
|
:class:`collections.abc.Callable`.
|
|
|
|
.. data:: 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.
|
|
|
|
.. versionadded:: 3.8
|
|
|
|
.. data:: 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
|
|
|
|
:data:`ClassVar` accepts only types and cannot be further subscribed.
|
|
|
|
:data:`ClassVar` is not a class itself, and should not
|
|
be used with :func:`isinstance` or :func:`issubclass`.
|
|
:data:`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
|
|
|
|
.. versionadded:: 3.5.3
|
|
|
|
.. data:: 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.
|
|
|
|
.. versionadded:: 3.8
|
|
|
|
.. data:: AnyStr
|
|
|
|
``AnyStr`` is a 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
|
|
|
|
.. data:: 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()
|
|
|
|
Note that 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.
|
|
|
|
.. versionadded:: 3.5.2
|