Protocols and structural subtyping#

Mypy supports two ways of deciding whether two classes are compatible as types: nominal subtyping and structural subtyping. Nominal subtyping is strictly based on the class hierarchy. If class D inherits class C, it’s also a subtype of C, and instances of D can be used when C instances are expected. This form of subtyping is used by default in mypy, since it’s easy to understand and produces clear and concise error messages, and since it matches how the native isinstance check works – based on class hierarchy. Structural subtyping can also be useful. Class D is a structural subtype of class C 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. Mypy provides support for structural subtyping via protocol classes described below. See PEP 544 for the detailed specification of protocols and structural subtyping in Python.

Predefined protocols#

The typing module defines various protocol classes that correspond to common Python protocols, such as Iterable[T]. If a class defines a suitable __iter__ method, mypy understands that it implements the iterable protocol and is compatible with Iterable[T]. For example, IntList below is iterable, over int values:

from typing import Iterator, Iterable, Optional

class IntList:
    def __init__(self, value: int, next: Optional['IntList']) -> 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

Predefined protocol reference lists all protocols defined in typing and the signatures of the corresponding methods you need to define to implement each protocol (the signatures can be left out, as always, but mypy won’t type check unannotated methods).

Simple user-defined protocols#

You can define your own protocol class by inheriting the special Protocol class:

from typing import Iterable
from typing_extensions import Protocol

class SupportsClose(Protocol):
    def close(self) -> None:
       ...  # Empty method body (explicit '...')

class Resource:  # No SupportsClose base class!
    # ... some methods ...

    def close(self) -> None:
       self.resource.release()

def close_all(items: Iterable[SupportsClose]) -> None:
    for item in items:
        item.close()

close_all([Resource(), open('some/file')])  # Okay!

Resource is a subtype of the SupportsClose protocol since it defines a compatible close method. Regular file objects returned by open() are similarly compatible with the protocol, as they support close().

Note

The Protocol base class is provided in the typing_extensions package for Python 3.4-3.7. Starting with Python 3.8, Protocol is included in the typing module.

Defining subprotocols and subclassing protocols#

You can also define subprotocols. Existing protocols can be extended and merged using multiple inheritance. Example:

# ... 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:

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:

class SomeProto(Protocol):
    attr: int  # Note, no right hand side
    def method(self) -> str: ...  # Literal ... here
class ExplicitSubclass(SomeProto):
    pass
ExplicitSubclass()  # error: Cannot instantiate abstract class 'ExplicitSubclass'
                    # with abstract attributes 'attr' and 'method'

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:

from typing import TypeVar, Optional
from typing_extensions import Protocol

class TreeLike(Protocol):
    value: int

    @property
    def left(self) -> Optional['TreeLike']: ...

    @property
    def right(self) -> Optional['TreeLike']: ...

class SimpleTree:
    def __init__(self, value: int) -> None:
        self.value = value
        self.left: Optional['SimpleTree'] = None
        self.right: Optional['SimpleTree'] = None

root: TreeLike = SimpleTree(0)  # OK

Using isinstance() with protocols#

You can use a protocol class with isinstance() if you decorate it with the @runtime_checkable class decorator. The decorator adds support for basic runtime structural checks:

from typing_extensions 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):
   use(mug.handles)  # Works statically and at runtime

isinstance() also works with the predefined protocols in typing such as Iterable.

Note

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 are defined.

Callback protocols#

Protocols can be used to define flexible callback types that are hard (or even impossible) to express using the Callable[...] syntax, such as variadic, overloaded, and complex generic callbacks. They are defined with a special __call__ member:

from typing import Optional, Iterable
from typing_extensions import Protocol

class Combiner(Protocol):
    def __call__(self, *vals: bytes, maxlen: Optional[int] = None) -> list[bytes]: ...

def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes:
    for item in data:
        ...

def good_cb(*vals: bytes, maxlen: Optional[int] = None) -> list[bytes]:
    ...
def bad_cb(*vals: bytes, maxitems: Optional[int]) -> 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 Callable types can be used interchangeably. Argument names in __call__ methods must be identical, unless a double underscore prefix is used. For example:

from typing import Callable, TypeVar
from typing_extensions import Protocol

T = TypeVar('T')

class Copy(Protocol):
    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 protocol reference#

Iteration protocols#

The iteration protocols are useful in many contexts. For example, they allow iteration of objects in for loops.

Iterable[T]#

The example above has a simple implementation of an __iter__ method.

def __iter__(self) -> Iterator[T]

Iterator[T]#

def __next__(self) -> T
def __iter__(self) -> Iterator[T]

Collection protocols#

Many of these are implemented by built-in container types such as list and dict, and these are also useful for user-defined collection objects.

Sized#

This is a type for objects that support len(x).

def __len__(self) -> int

Container[T]#

This is a type for objects that support the in operator.

def __contains__(self, x: object) -> bool

Collection[T]#

def __len__(self) -> int
def __iter__(self) -> Iterator[T]
def __contains__(self, x: object) -> bool

One-off protocols#

These protocols are typically only useful with a single standard library function or class.

Reversible[T]#

This is a type for objects that support reversed(x).

def __reversed__(self) -> Iterator[T]

SupportsAbs[T]#

This is a type for objects that support abs(x). T is the type of value returned by abs(x).

def __abs__(self) -> T

SupportsBytes#

This is a type for objects that support bytes(x).

def __bytes__(self) -> bytes

SupportsComplex#

This is a type for objects that support complex(x). Note that no arithmetic operations are supported.

def __complex__(self) -> complex

SupportsFloat#

This is a type for objects that support float(x). Note that no arithmetic operations are supported.

def __float__(self) -> float

SupportsInt#

This is a type for objects that support int(x). Note that no arithmetic operations are supported.

def __int__(self) -> int

SupportsRound[T]#

This is a type for objects that support round(x).

def __round__(self) -> T

Async protocols#

These protocols can be useful in async code. See Typing async/await for more information.

Awaitable[T]#

def __await__(self) -> Generator[Any, None, T]

AsyncIterable[T]#

def __aiter__(self) -> AsyncIterator[T]

AsyncIterator[T]#

def __anext__(self) -> Awaitable[T]
def __aiter__(self) -> AsyncIterator[T]

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.

ContextManager[T]#

def __enter__(self) -> T
def __exit__(self,
             exc_type: Optional[Type[BaseException]],
             exc_value: Optional[BaseException],
             traceback: Optional[TracebackType]) -> Optional[bool]

AsyncContextManager[T]#

def __aenter__(self) -> Awaitable[T]
def __aexit__(self,
              exc_type: Optional[Type[BaseException]],
              exc_value: Optional[BaseException],
              traceback: Optional[TracebackType]) -> Awaitable[Optional[bool]]