.. _protocol-types: Protocols and structural subtyping ================================== The Python type system supports two ways of deciding whether two objects are compatible as types: nominal subtyping and structural subtyping. *Nominal* subtyping is strictly based on the class hierarchy. If class ``Dog`` inherits class ``Animal``, it's a subtype of ``Animal``. Instances of ``Dog`` can be used when ``Animal`` instances are expected. This form of subtyping is what Python's type system predominantly uses: it's easy to understand and produces clear and concise error messages, and matches how the native :py:func:`isinstance ` check works -- based on class hierarchy. *Structural* subtyping is based on the operations that can be performed with an object. Class ``Dog`` is a structural subtype of class ``Animal`` if the former has all attributes and methods of the latter, and with compatible types. Structural subtyping can be seen as a static equivalent of duck typing, which is well known to Python programmers. See :pep:`544` for the detailed specification of protocols and structural subtyping in Python. .. _predefined_protocols: Predefined protocols ******************** The :py:mod:`collections.abc`, :py:mod:`typing` and other stdlib modules define various protocol classes that correspond to common Python protocols, such as :py:class:`Iterable[T] `. If a class defines a suitable :py:meth:`__iter__ ` method, mypy understands that it implements the iterable protocol and is compatible with :py:class:`Iterable[T] `. For example, ``IntList`` below is iterable, over ``int`` values: .. code-block:: python from __future__ import annotations from collections.abc import Iterator, Iterable class IntList: def __init__(self, value: int, next: IntList | None) -> None: self.value = value self.next = next def __iter__(self) -> Iterator[int]: current = self while current: yield current.value current = current.next def print_numbered(items: Iterable[int]) -> None: for n, x in enumerate(items): print(n + 1, x) x = IntList(3, IntList(5, None)) print_numbered(x) # OK print_numbered([4, 5]) # Also OK :ref:`predefined_protocols_reference` lists various protocols defined in :py:mod:`collections.abc` and :py:mod:`typing` and the signatures of the corresponding methods you need to define to implement each protocol. .. note:: ``typing`` also contains deprecated aliases to protocols and ABCs defined in :py:mod:`collections.abc`, such as :py:class:`Iterable[T] `. These are only necessary in Python 3.8 and earlier, since the protocols in ``collections.abc`` didn't yet support subscripting (``[]``) in Python 3.8, but the aliases in ``typing`` have always supported subscripting. In Python 3.9 and later, the aliases in ``typing`` don't provide any extra functionality. Simple user-defined protocols ***************************** You can define your own protocol class by inheriting the special ``Protocol`` class: .. code-block:: python from collections.abc import Iterable from typing import Protocol class SupportsClose(Protocol): # Empty method body (explicit '...') def close(self) -> None: ... class Resource: # No SupportsClose base class! def close(self) -> None: self.resource.release() # ... other methods ... def close_all(items: Iterable[SupportsClose]) -> None: for item in items: item.close() close_all([Resource(), open('some/file')]) # OK ``Resource`` is a subtype of the ``SupportsClose`` protocol since it defines a compatible ``close`` method. Regular file objects returned by :py:func:`open` are similarly compatible with the protocol, as they support ``close()``. Defining subprotocols and subclassing protocols *********************************************** You can also define subprotocols. Existing protocols can be extended and merged using multiple inheritance. Example: .. code-block:: python # ... continuing from the previous example class SupportsRead(Protocol): def read(self, amount: int) -> bytes: ... class TaggedReadableResource(SupportsClose, SupportsRead, Protocol): label: str class AdvancedResource(Resource): def __init__(self, label: str) -> None: self.label = label def read(self, amount: int) -> bytes: # some implementation ... resource: TaggedReadableResource resource = AdvancedResource('handle with care') # OK Note that inheriting from an existing protocol does not automatically turn the subclass into a protocol -- it just creates a regular (non-protocol) class or ABC that implements the given protocol (or protocols). The ``Protocol`` base class must always be explicitly present if you are defining a protocol: .. code-block:: python class NotAProtocol(SupportsClose): # This is NOT a protocol new_attr: int class Concrete: new_attr: int = 0 def close(self) -> None: ... # Error: nominal subtyping used by default x: NotAProtocol = Concrete() # Error! You can also include default implementations of methods in protocols. If you explicitly subclass these protocols you can inherit these default implementations. Explicitly including a protocol as a base class is also a way of documenting that your class implements a particular protocol, and it forces mypy to verify that your class implementation is actually compatible with the protocol. In particular, omitting a value for an attribute or a method body will make it implicitly abstract: .. code-block:: python class SomeProto(Protocol): attr: int # Note, no right hand side def method(self) -> str: ... # Literally just ... here class ExplicitSubclass(SomeProto): pass ExplicitSubclass() # error: Cannot instantiate abstract class 'ExplicitSubclass' # with abstract attributes 'attr' and 'method' Similarly, explicitly assigning to a protocol instance can be a way to ask the type checker to verify that your class implements a protocol: .. code-block:: python _proto: SomeProto = cast(ExplicitSubclass, None) Invariance of protocol attributes ********************************* A common issue with protocols is that protocol attributes are invariant. For example: .. code-block:: python class Box(Protocol): content: object class IntBox: content: int def takes_box(box: Box) -> None: ... takes_box(IntBox()) # error: Argument 1 to "takes_box" has incompatible type "IntBox"; expected "Box" # note: Following member(s) of "IntBox" have conflicts: # note: content: expected "object", got "int" This is because ``Box`` defines ``content`` as a mutable attribute. Here's why this is problematic: .. code-block:: python def takes_box_evil(box: Box) -> None: box.content = "asdf" # This is bad, since box.content is supposed to be an object my_int_box = IntBox() takes_box_evil(my_int_box) my_int_box.content + 1 # Oops, TypeError! This can be fixed by declaring ``content`` to be read-only in the ``Box`` protocol using ``@property``: .. code-block:: python class Box(Protocol): @property def content(self) -> object: ... class IntBox: content: int def takes_box(box: Box) -> None: ... takes_box(IntBox(42)) # OK Recursive protocols ******************* Protocols can be recursive (self-referential) and mutually recursive. This is useful for declaring abstract recursive collections such as trees and linked lists: .. code-block:: python from __future__ import annotations from typing import Protocol class TreeLike(Protocol): value: int @property def left(self) -> TreeLike | None: ... @property def right(self) -> TreeLike | None: ... class SimpleTree: def __init__(self, value: int) -> None: self.value = value self.left: SimpleTree | None = None self.right: SimpleTree | None = None root: TreeLike = SimpleTree(0) # OK Using isinstance() with protocols ********************************* You can use a protocol class with :py:func:`isinstance` if you decorate it with the ``@runtime_checkable`` class decorator. The decorator adds rudimentary support for runtime structural checks: .. code-block:: python from typing import Protocol, runtime_checkable @runtime_checkable class Portable(Protocol): handles: int class Mug: def __init__(self) -> None: self.handles = 1 def use(handles: int) -> None: ... mug = Mug() if isinstance(mug, Portable): # Works at runtime! use(mug.handles) :py:func:`isinstance` also works with the :ref:`predefined protocols ` in :py:mod:`typing` such as :py:class:`~typing.Iterable`. .. warning:: :py:func:`isinstance` with protocols is not completely safe at runtime. For example, signatures of methods are not checked. The runtime implementation only checks that all protocol members exist, not that they have the correct type. :py:func:`issubclass` with protocols will only check for the existence of methods. .. note:: :py:func:`isinstance` with protocols can also be surprisingly slow. In many cases, you're better served by using :py:func:`hasattr` to check for the presence of attributes. .. _callback_protocols: Callback protocols ****************** Protocols can be used to define flexible callback types that are hard (or even impossible) to express using the :py:class:`Callable[...] ` syntax, such as variadic, overloaded, and complex generic callbacks. They are defined with a special :py:meth:`__call__ ` member: .. code-block:: python from collections.abc import Iterable from typing import Optional, Protocol class Combiner(Protocol): def __call__(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ... def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes: for item in data: ... def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]: ... def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]: ... batch_proc([], good_cb) # OK batch_proc([], bad_cb) # Error! Argument 2 has incompatible type because of # different name and kind in the callback Callback protocols and :py:class:`~collections.abc.Callable` types can be used mostly interchangeably. Parameter names in :py:meth:`__call__ ` methods must be identical, unless the parameters are positional-only. Example (using the legacy syntax for generic functions): .. code-block:: python from collections.abc import Callable from typing import Protocol, TypeVar T = TypeVar('T') class Copy(Protocol): # '/' marks the end of positional-only parameters def __call__(self, origin: T, /) -> T: ... copy_a: Callable[[T], T] copy_b: Copy copy_a = copy_b # OK copy_b = copy_a # Also OK .. _predefined_protocols_reference: Predefined protocol reference ***************************** Iteration protocols ................... The iteration protocols are useful in many contexts. For example, they allow iteration of objects in for loops. collections.abc.Iterable[T] --------------------------- The :ref:`example above ` has a simple implementation of an :py:meth:`__iter__ ` method. .. code-block:: python def __iter__(self) -> Iterator[T] See also :py:class:`~collections.abc.Iterable`. collections.abc.Iterator[T] --------------------------- .. code-block:: python def __next__(self) -> T def __iter__(self) -> Iterator[T] See also :py:class:`~collections.abc.Iterator`. Collection protocols .................... Many of these are implemented by built-in container types such as :py:class:`list` and :py:class:`dict`, and these are also useful for user-defined collection objects. collections.abc.Sized --------------------- This is a type for objects that support :py:func:`len(x) `. .. code-block:: python def __len__(self) -> int See also :py:class:`~collections.abc.Sized`. collections.abc.Container[T] ---------------------------- This is a type for objects that support the ``in`` operator. .. code-block:: python def __contains__(self, x: object) -> bool See also :py:class:`~collections.abc.Container`. collections.abc.Collection[T] ----------------------------- .. code-block:: python def __len__(self) -> int def __iter__(self) -> Iterator[T] def __contains__(self, x: object) -> bool See also :py:class:`~collections.abc.Collection`. One-off protocols ................. These protocols are typically only useful with a single standard library function or class. collections.abc.Reversible[T] ----------------------------- This is a type for objects that support :py:func:`reversed(x) `. .. code-block:: python def __reversed__(self) -> Iterator[T] See also :py:class:`~collections.abc.Reversible`. typing.SupportsAbs[T] --------------------- This is a type for objects that support :py:func:`abs(x) `. ``T`` is the type of value returned by :py:func:`abs(x) `. .. code-block:: python def __abs__(self) -> T See also :py:class:`~typing.SupportsAbs`. typing.SupportsBytes -------------------- This is a type for objects that support :py:class:`bytes(x) `. .. code-block:: python def __bytes__(self) -> bytes See also :py:class:`~typing.SupportsBytes`. .. _supports-int-etc: typing.SupportsComplex ---------------------- This is a type for objects that support :py:class:`complex(x) `. Note that no arithmetic operations are supported. .. code-block:: python def __complex__(self) -> complex See also :py:class:`~typing.SupportsComplex`. typing.SupportsFloat -------------------- This is a type for objects that support :py:class:`float(x) `. Note that no arithmetic operations are supported. .. code-block:: python def __float__(self) -> float See also :py:class:`~typing.SupportsFloat`. typing.SupportsInt ------------------ This is a type for objects that support :py:class:`int(x) `. Note that no arithmetic operations are supported. .. code-block:: python def __int__(self) -> int See also :py:class:`~typing.SupportsInt`. typing.SupportsRound[T] ----------------------- This is a type for objects that support :py:func:`round(x) `. .. code-block:: python def __round__(self) -> T See also :py:class:`~typing.SupportsRound`. Async protocols ............... These protocols can be useful in async code. See :ref:`async-and-await` for more information. collections.abc.Awaitable[T] ---------------------------- .. code-block:: python def __await__(self) -> Generator[Any, None, T] See also :py:class:`~collections.abc.Awaitable`. collections.abc.AsyncIterable[T] -------------------------------- .. code-block:: python def __aiter__(self) -> AsyncIterator[T] See also :py:class:`~collections.abc.AsyncIterable`. collections.abc.AsyncIterator[T] -------------------------------- .. code-block:: python def __anext__(self) -> Awaitable[T] def __aiter__(self) -> AsyncIterator[T] See also :py:class:`~collections.abc.AsyncIterator`. Context manager protocols ......................... There are two protocols for context managers -- one for regular context managers and one for async ones. These allow defining objects that can be used in ``with`` and ``async with`` statements. contextlib.AbstractContextManager[T] ------------------------------------ .. code-block:: python def __enter__(self) -> T def __exit__(self, exc_type: type[BaseException] | None, exc_value: BaseException | None, traceback: TracebackType | None) -> bool | None See also :py:class:`~contextlib.AbstractContextManager`. contextlib.AbstractAsyncContextManager[T] ----------------------------------------- .. code-block:: python def __aenter__(self) -> Awaitable[T] def __aexit__(self, exc_type: type[BaseException] | None, exc_value: BaseException | None, traceback: TracebackType | None) -> Awaitable[bool | None] See also :py:class:`~contextlib.AbstractAsyncContextManager`.