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Protocols

(Originally specified in PEP 544.)

Terminology

The term protocols is used for types supporting structural subtyping. The reason is that the term iterator protocol, for example, is widely understood in the community, and coming up with a new term for this concept in a statically typed context would just create confusion.

This has the drawback that the term protocol becomes overloaded with two subtly different meanings: the first is the traditional, well-known but slightly fuzzy concept of protocols such as iterator; the second is the more explicitly defined concept of protocols in statically typed code. The distinction is not important most of the time, and in other cases we can just add a qualifier such as protocol classes when referring to the static type concept.

If a class includes a protocol in its MRO, the class is called an explicit subclass of the protocol. If a class is a structural subtype of a protocol, it is said to implement the protocol and to be compatible with a protocol. If a class is compatible with a protocol but the protocol is not included in the MRO, the class is an implicit subtype of the protocol. (Note that one can explicitly subclass a protocol and still not implement it if a protocol attribute is set to None in the subclass, see Python data model for details.)

The attributes (variables and methods) of a protocol that are mandatory for another class in order to be considered a structural subtype are called protocol members.

Defining a protocol

Protocols are defined by including a special new class typing.Protocol (an instance of abc.ABCMeta) in the base classes list, typically at the end of the list. Here is a simple example:

from typing import Protocol

class SupportsClose(Protocol):
    def close(self) -> None:
        ...

Now if one defines a class Resource with a close() method that has a compatible signature, it would implicitly be a subtype of SupportsClose, since the structural subtyping is used for protocol types:

class Resource:
    ...
    def close(self) -> None:
        self.file.close()
        self.lock.release()

Apart from a few restrictions explicitly mentioned below, protocol types can be used in every context where normal types can:

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

f = open('foo.txt')
r = Resource()
close_all([f, r])  # OK!
close_all([1])     # Error: 'int' has no 'close' method

Note that both the user-defined class Resource and the built-in IO type (the return type of open()) are considered subtypes of SupportsClose, because they provide a close() method with a compatible type signature.

Protocol members

All methods defined in the protocol class body are protocol members, both normal and decorated with @abstractmethod. If any parameters of a protocol method are not annotated, then their types are assumed to be Any (see PEP 484). Bodies of protocol methods are type checked. An abstract method that should not be called via super() ought to raise NotImplementedError. Example:

from typing import Protocol
from abc import abstractmethod

class Example(Protocol):
    def first(self) -> int:     # This is a protocol member
        return 42

    @abstractmethod
    def second(self) -> int:    # Method without a default implementation
        raise NotImplementedError

Static methods, class methods, and properties are equally allowed in protocols.

To define a protocol variable, one can use PEP 526 variable annotations in the class body. Additional attributes only defined in the body of a method by assignment via self are not allowed. The rationale for this is that the protocol class implementation is often not shared by subtypes, so the interface should not depend on the default implementation. Examples:

from typing import Protocol

class Template(Protocol):
    name: str        # This is a protocol member
    value: int = 0   # This one too (with default)

    def method(self) -> None:
        self.temp: list[int] = [] # Error in type checker

class Concrete:
    def __init__(self, name: str, value: int) -> None:
        self.name = name
        self.value = value

    def method(self) -> None:
        return

var: Template = Concrete('value', 42)  # OK

To distinguish between protocol class variables and protocol instance variables, the special ClassVar annotation should be used as specified by PEP 526. By default, protocol variables as defined above are considered readable and writable. To define a read-only protocol variable, one can use an (abstract) property.

Explicitly declaring implementation

To explicitly declare that a certain class implements a given protocol, it can be used as a regular base class. In this case a class could use default implementations of protocol members. Static analysis tools are expected to automatically detect that a class implements a given protocol. So while it's possible to subclass a protocol explicitly, it's not necessary to do so for the sake of type-checking.

The default implementations cannot be used if the subtype relationship is implicit and only via structural subtyping -- the semantics of inheritance is not changed. Examples:

class PColor(Protocol):
    @abstractmethod
    def draw(self) -> str:
        ...
    def complex_method(self) -> int:
        # some complex code here

class NiceColor(PColor):
    def draw(self) -> str:
        return "deep blue"

class BadColor(PColor):
    def draw(self) -> str:
        return super().draw()  # Error, no default implementation

class ImplicitColor:   # Note no 'PColor' base here
    def draw(self) -> str:
        return "probably gray"
    def complex_method(self) -> int:
        # class needs to implement this

nice: NiceColor
another: ImplicitColor

def represent(c: PColor) -> None:
    print(c.draw(), c.complex_method())

represent(nice) # OK
represent(another) # Also OK

Note that there is little difference between explicit and implicit subtypes; the main benefit of explicit subclassing is to get some protocol methods "for free". In addition, type checkers can statically verify that the class actually implements the protocol correctly:

class RGB(Protocol):
    rgb: tuple[int, int, int]

    @abstractmethod
    def intensity(self) -> int:
        return 0

class Point(RGB):
    def __init__(self, red: int, green: int, blue: str) -> None:
        self.rgb = red, green, blue  # Error, 'blue' must be 'int'

    # Type checker might warn that 'intensity' is not defined

A class can explicitly inherit from multiple protocols and also from normal classes. In this case methods are resolved using normal MRO and a type checker verifies that all subtyping are correct. The semantics of @abstractmethod is not changed; all of them must be implemented by an explicit subclass before it can be instantiated.

Merging and extending protocols

The general philosophy is that protocols are mostly like regular ABCs, but a static type checker will handle them specially. Subclassing a protocol class would not turn the subclass into a protocol unless it also has typing.Protocol as an explicit base class. Without this base, the class is "downgraded" to a regular ABC that cannot be used with structural subtyping. The rationale for this rule is that we don't want to accidentally have some class act as a protocol just because one of its base classes happens to be one. We still slightly prefer nominal subtyping over structural subtyping in the static typing world.

A subprotocol can be defined by having both one or more protocols as immediate base classes and also having typing.Protocol as an immediate base class:

from typing import Protocol
from collections.abc import Sized

class SizedAndClosable(Sized, Protocol):
    def close(self) -> None:
        ...

Now the protocol SizedAndClosable is a protocol with two methods, __len__ and close. If one omits Protocol in the base class list, this would be a regular (non-protocol) class that must implement Sized. Alternatively, one can implement SizedAndClosable protocol by merging the SupportsClose protocol from the example in the definition section with typing.Sized:

from collections.abc import Sized

class SupportsClose(Protocol):
    def close(self) -> None:
        ...

class SizedAndClosable(Sized, SupportsClose, Protocol):
    pass

The two definitions of SizedAndClosable are equivalent. Subclass relationships between protocols are not meaningful when considering subtyping, since structural compatibility is the criterion, not the MRO.

If Protocol is included in the base class list, all the other base classes must be protocols. A protocol can't extend a regular class. Note that rules around explicit subclassing are different from regular ABCs, where abstractness is simply defined by having at least one abstract method being unimplemented. Protocol classes must be marked explicitly.

Generic protocols

Generic protocols are important. For example, SupportsAbs, Iterable and Iterator are generic protocols. They are defined similar to normal non-protocol generic types:

class Iterable(Protocol[T]):
    @abstractmethod
    def __iter__(self) -> Iterator[T]:
        ...

Protocol[T, S, ...] is allowed as a shorthand for Protocol, Generic[T, S, ...].

User-defined generic protocols support explicitly declared variance. Type checkers will warn if the inferred variance is different from the declared variance. Examples:

T = TypeVar('T')
T_co = TypeVar('T_co', covariant=True)
T_contra = TypeVar('T_contra', contravariant=True)

class Box(Protocol[T_co]):
    def content(self) -> T_co:
        ...

box: Box[float]
second_box: Box[int]
box = second_box  # This is OK due to the covariance of 'Box'.

class Sender(Protocol[T_contra]):
    def send(self, data: T_contra) -> int:
        ...

sender: Sender[float]
new_sender: Sender[int]
new_sender = sender  # OK, 'Sender' is contravariant.

class Proto(Protocol[T]):
    attr: T  # this class is invariant, since it has a mutable attribute

var: Proto[float]
another_var: Proto[int]
var = another_var  # Error! 'Proto[float]' is incompatible with 'Proto[int]'.

Note that unlike nominal classes, de facto covariant protocols cannot be declared as invariant, since this can break transitivity of subtyping. For example:

T = TypeVar('T')

class AnotherBox(Protocol[T]):  # Error, this protocol is covariant in T,
    def content(self) -> T:     # not invariant.
        ...

Recursive protocols

Recursive protocols are also supported. Forward references to the protocol class names can be given as strings as specified by PEP 484. Recursive protocols are useful for representing self-referential data structures like trees in an abstract fashion:

class Traversable(Protocol):
    def leaves(self) -> Iterable['Traversable']:
        ...

Note that for recursive protocols, a class is considered a subtype of the protocol in situations where the decision depends on itself. Continuing the previous example:

class SimpleTree:
    def leaves(self) -> list['SimpleTree']:
        ...

root: Traversable = SimpleTree()  # OK

class Tree(Generic[T]):
    def leaves(self) -> list['Tree[T]']:
        ...

def walk(graph: Traversable) -> None:
    ...
tree: Tree[float] = Tree()
walk(tree)  # OK, 'Tree[float]' is a subtype of 'Traversable'

Self-types in protocols

The self-types in protocols follow the :pep:`corresponding specification <484#annotating-instance-and-class-methods>` of PEP 484. For example:

C = TypeVar('C', bound='Copyable')
class Copyable(Protocol):
    def copy(self: C) -> C:

class One:
    def copy(self) -> 'One':
        ...

T = TypeVar('T', bound='Other')
class Other:
    def copy(self: T) -> T:
        ...

c: Copyable
c = One()  # OK
c = Other()  # Also OK

Subtyping relationships with other types

Protocols cannot be instantiated, so there are no values whose runtime type is a protocol. For variables and parameters with protocol types, subtyping relationships are subject to the following rules:

  • A protocol is never a subtype of a concrete type.
  • A concrete type X is a subtype of protocol P if and only if X implements all protocol members of P with compatible types. In other words, subtyping with respect to a protocol is always structural.
  • A protocol P1 is a subtype of another protocol P2 if P1 defines all protocol members of P2 with compatible types.

Generic protocol types follow the same rules of variance as non-protocol types. Protocol types can be used in all contexts where any other types can be used, such as in unions, ClassVar, type variables bounds, etc. Generic protocols follow the rules for generic abstract classes, except for using structural compatibility instead of compatibility defined by inheritance relationships.

Static type checkers will recognize protocol implementations, even if the corresponding protocols are not imported:

# file lib.py
from collections.abc import Sized

T = TypeVar('T', contravariant=True)
class ListLike(Sized, Protocol[T]):
    def append(self, x: T) -> None:
        pass

def populate(lst: ListLike[int]) -> None:
    ...

# file main.py
from lib import populate  # Note that ListLike is NOT imported

class MockStack:
    def __len__(self) -> int:
        return 42
    def append(self, x: int) -> None:
        print(x)

populate([1, 2, 3])    # Passes type check
populate(MockStack())  # Also OK

Unions and intersections of protocols

Unions of protocol classes behaves the same way as for non-protocol classes. For example:

from typing importt Protocol

class Exitable(Protocol):
    def exit(self) -> int:
        ...
class Quittable(Protocol):
    def quit(self) -> int | None:
        ...

def finish(task: Exitable | Quittable) -> int:
    ...
class DefaultJob:
    ...
    def quit(self) -> int:
        return 0
finish(DefaultJob()) # OK

One can use multiple inheritance to define an intersection of protocols. Example:

from collections.abc import Iterable, Hashable

class HashableFloats(Iterable[float], Hashable, Protocol):
    pass

def cached_func(args: HashableFloats) -> float:
    ...
cached_func((1, 2, 3)) # OK, tuple is both hashable and iterable

Type[] and class objects vs protocols

Variables and parameters annotated with Type[Proto] accept only concrete (non-protocol) subtypes of Proto. The main reason for this is to allow instantiation of parameters with such types. For example:

class Proto(Protocol):
    @abstractmethod
    def meth(self) -> int:
        ...
class Concrete:
    def meth(self) -> int:
        return 42

def fun(cls: Type[Proto]) -> int:
    return cls().meth() # OK
fun(Proto)              # Error
fun(Concrete)           # OK

The same rule applies to variables:

var: Type[Proto]
var = Proto    # Error
var = Concrete # OK
var().meth()   # OK

Assigning an ABC or a protocol class to a variable is allowed if it is not explicitly typed, and such assignment creates a type alias. For normal (non-abstract) classes, the behavior of Type[] is not changed.

A class object is considered an implementation of a protocol if accessing all members on it results in types compatible with the protocol members. For example:

from typing import Any, Protocol

class ProtoA(Protocol):
    def meth(self, x: int) -> int: ...
class ProtoB(Protocol):
    def meth(self, obj: Any, x: int) -> int: ...

class C:
    def meth(self, x: int) -> int: ...

a: ProtoA = C  # Type check error, signatures don't match!
b: ProtoB = C  # OK

NewType() and type aliases

Protocols are essentially anonymous. To emphasize this point, static type checkers might refuse protocol classes inside NewType() to avoid an illusion that a distinct type is provided:

from typing import NewType, Protocol
from collections.abc import Iterator

class Id(Protocol):
    code: int
    secrets: Iterator[bytes]

UserId = NewType('UserId', Id)  # Error, can't provide distinct type

In contrast, type aliases are fully supported, including generic type aliases:

from typing import TypeVar
from collections.abc import Reversible, Iterable, Sized

T = TypeVar('T')
class SizedIterable(Iterable[T], Sized, Protocol):
    pass
CompatReversible = Reversible[T] | SizedIterable[T]

Modules as implementations of protocols

A module object is accepted where a protocol is expected if the public interface of the given module is compatible with the expected protocol. For example:

# file default_config.py
timeout = 100
one_flag = True
other_flag = False

# file main.py
import default_config
from typing import Protocol

class Options(Protocol):
    timeout: int
    one_flag: bool
    other_flag: bool

def setup(options: Options) -> None:
    ...

setup(default_config)  # OK

To determine compatibility of module level functions, the self argument of the corresponding protocol methods is dropped. For example:

# callbacks.py
def on_error(x: int) -> None:
    ...
def on_success() -> None:
    ...

# main.py
import callbacks
from typing import Protocol

class Reporter(Protocol):
    def on_error(self, x: int) -> None:
        ...
    def on_success(self) -> None:
        ...

rp: Reporter = callbacks  # Passes type check

@runtime_checkable decorator and narrowing types by isinstance()

The default semantics is that isinstance() and issubclass() fail for protocol types. This is in the spirit of duck typing -- protocols basically would be used to model duck typing statically, not explicitly at runtime.

However, it should be possible for protocol types to implement custom instance and class checks when this makes sense, similar to how Iterable and other ABCs in collections.abc and typing already do it, but this is limited to non-generic and unsubscripted generic protocols (Iterable is statically equivalent to Iterable[Any]). The typing module will define a special @runtime_checkable class decorator that provides the same semantics for class and instance checks as for collections.abc classes, essentially making them "runtime protocols":

from typing import runtime_checkable, Protocol

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

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

Note that instance checks are not 100% reliable statically, which is why this behavior is opt-in. The most type checkers can do is to treat isinstance(obj, Iterator) roughly as a simpler way to write hasattr(x, '__iter__') and hasattr(x, '__next__'). To minimize the risks for this feature, the following rules are applied.

Definitions:

  • Data and non-data protocols: A protocol is called a non-data protocol if it only contains methods as members (for example Sized, Iterator, etc). A protocol that contains at least one non-method member (like x: int) is called a data protocol.
  • Unsafe overlap: A type X is called unsafely overlapping with a protocol P, if X is not a subtype of P, but it is a subtype of the type erased version of P where all members have type Any. In addition, if at least one element of a union unsafely overlaps with a protocol P, then the whole union is unsafely overlapping with P.

Specification:

  • A protocol can be used as a second argument in isinstance() and issubclass() only if it is explicitly opt-in by @runtime_checkable decorator. This requirement exists because protocol checks are not type safe in case of dynamically set attributes, and because type checkers can only prove that an isinstance() check is safe only for a given class, not for all its subclasses.
  • isinstance() can be used with both data and non-data protocols, while issubclass() can be used only with non-data protocols. This restriction exists because some data attributes can be set on an instance in constructor and this information is not always available on the class object.
  • Type checkers should reject an isinstance() or issubclass() call, if there is an unsafe overlap between the type of the first argument and the protocol.
  • Type checkers should be able to select a correct element from a union after a safe isinstance() or issubclass() call. For narrowing from non-union types, type checkers can use their best judgement (this is intentionally unspecified, since a precise specification would require intersection types).