Hi all,

After collecting suggestions in the previous discussion on python-dev https://mail.python.org/pipermail/python-dev/2017-March/thread.html#147629 and playing with implementation, here is an updated version of PEP 544.


A link for those who don't like reading long e-mails: https://www.python.org/dev/peps/pep-0544/


PEP: 544
Title: Protocols
Version: $Revision$
Last-Modified: $Date$
Author: Ivan Levkivskyi <levkivskyi@gmail.com>, Jukka Lehtosalo <jukka.lehtosalo@iki.fi>, Łukasz Langa <lukasz@langa.pl>
Discussions-To: Python-Dev <python-dev@python.org>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 05-Mar-2017
Python-Version: 3.7


Type hints introduced in PEP 484 can be used to specify type metadata
for static type checkers and other third party tools. However, PEP 484
only specifies the semantics of *nominal* subtyping. In this PEP we specify
static and runtime semantics of protocol classes that will provide a support
for *structural* subtyping (static duck typing).

.. _rationale:

Rationale and Goals

Currently, PEP 484 and the ``typing`` module [typing]_ define abstract
base classes for several common Python protocols such as ``Iterable`` and
``Sized``. The problem with them is that a class has to be explicitly marked
to support them, which is unpythonic and unlike what one would
normally do in idiomatic dynamically typed Python code. For example,
this conforms to PEP 484::

  from typing import Sized, Iterable, Iterator

  class Bucket(Sized, Iterable[int]):
      def __len__(self) -> int: ...
      def __iter__(self) -> Iterator[int]: ...

The same problem appears with user-defined ABCs: they must be explicitly
subclassed or registered. This is particularly difficult to do with library
types as the type objects may be hidden deep in the implementation
of the library. Also, extensive use of ABCs might impose additional
runtime costs.

The intention of this PEP is to solve all these problems
by allowing users to write the above code without explicit base classes in
the class definition, allowing ``Bucket`` to be implicitly considered
a subtype of both ``Sized`` and ``Iterable[int]`` by static type checkers
using structural [wiki-structural]_ subtyping::

  from typing import Iterator, Iterable

  class Bucket:
      def __len__(self) -> int: ...
      def __iter__(self) -> Iterator[int]: ...

  def collect(items: Iterable[int]) -> int: ...
  result: int = collect(Bucket())  # Passes type check

Note that ABCs in ``typing`` module already provide structural behavior
at runtime, ``isinstance(Bucket(), Iterable)`` returns ``True``.
The main goal of this proposal is to support such behavior statically.
The same functionality will be provided for user-defined protocols, as
specified below. The above code with a protocol class matches common Python
conventions much better. It is also automatically extensible and works
with additional, unrelated classes that happen to implement
the required protocol.

Nominal vs structural subtyping

Structural subtyping is natural for Python programmers since it matches
the runtime semantics of duck typing: an object that has certain properties
is treated independently of its actual runtime class.
However, as discussed in PEP 483, both nominal and structural
subtyping have their strengths and weaknesses. Therefore, in this PEP we
*do not propose* to replace the nominal subtyping described by PEP 484 with
structural subtyping completely. Instead, protocol classes as specified in
this PEP complement normal classes, and users are free to choose
where to apply a particular solution. See section on `rejected`_ ideas at the
end of this PEP for additional motivation.


At runtime, protocol classes will be simple ABCs. There is no intent to
provide sophisticated runtime instance and class checks against protocol
classes. This would be difficult and error-prone and will contradict the logic
of PEP 484. As well, following PEP 484 and PEP 526 we state that protocols are
**completely optional**:

* No runtime semantics will be imposed for variables or parameters annotated
  with a protocol class.
* Any checks will be performed only by third-party type checkers and
  other tools.
* Programmers are free to not use them even if they use type annotations.
* There is no intent to make protocols non-optional in the future.

Existing Approaches to Structural Subtyping

Before describing the actual specification, we review and comment on existing
approaches related to structural subtyping in Python and other languages:

* ``zope.interface`` [zope-interfaces]_ was one of the first widely used
  approaches to structural subtyping in Python. It is implemented by providing
  special classes to distinguish interface classes from normal classes,
  to mark interface attributes, and to explicitly declare implementation.
  For example::

    from zope.interface import Interface, Attribute, implementer

    class IEmployee(Interface):

        name = Attribute("Name of employee")

        def do(work):
            """Do some work"""

    class Employee:

        name = 'Anonymous'

        def do(self, work):
            return work.start()

  Zope interfaces support various contracts and constraints for interface
  classes. For example::

    from zope.interface import invariant

    def required_contact(obj):
        if not (obj.email or obj.phone):
            raise Exception("At least one contact info is required")

    class IPerson(Interface):

        name = Attribute("Name")
        email = Attribute("Email Address")
        phone = Attribute("Phone Number")


  Even more detailed invariants are supported. However, Zope interfaces rely
  entirely on runtime validation. Such focus on runtime properties goes
  beyond the scope of the current proposal, and static support for invariants
  might be difficult to implement. However, the idea of marking an interface
  class with a special base class is reasonable and easy to implement both
  statically and at runtime.

* Python abstract base classes [abstract-classes]_ are the standard
  library tool to provide some functionality similar to structural subtyping.
  The drawback of this approach is the necessity to either subclass
  the abstract class or register an implementation explicitly::

    from abc import ABC

    class MyTuple(ABC):


    assert issubclass(tuple, MyTuple)
    assert isinstance((), MyTuple)

  As mentioned in the `rationale`_, we want to avoid such necessity, especially
  in static context. However, in a runtime context, ABCs are good candidates for
  protocol classes and they are already used extensively in
  the ``typing`` module.

* Abstract classes defined in ``collections.abc`` module [collections-abc]_
  are slightly more advanced since they implement a custom
  ``__subclasshook__()`` method that allows runtime structural checks without
  explicit registration::

    from collections.abc import Iterable

    class MyIterable:
        def __iter__(self):
            return []

    assert isinstance(MyIterable(), Iterable)

  Such behavior seems to be a perfect fit for both runtime and static behavior
  of protocols. As discussed in `rationale`_, we propose to add static support
  for such behavior. In addition, to allow users to achieve such runtime
  behavior for *user-defined* protocols a special ``@runtime`` decorator will
  be provided, see detailed `discussion`_ below.

* TypeScript [typescript]_ provides support for user-defined classes and
  interfaces. Explicit implementation declaration is not required and
  structural subtyping is verified statically. For example::

    interface LabeledItem {
        label: string;
        size?: int;

    function printLabel(obj: LabeledItem) {

    let myObj = {size: 10, label: "Size 10 Object"};

  Note that optional interface members are supported. Also, TypeScript
  prohibits redundant members in implementations. While the idea of
  optional members looks interesting, it would complicate this proposal and
  it is not clear how useful it will be. Therefore it is proposed to postpone
  this; see `rejected`_ ideas. In general, the idea of static protocol
  checking without runtime implications looks reasonable, and basically
  this proposal follows the same line.

* Go [golang]_ uses a more radical approach and makes interfaces the primary
  way to provide type information. Also, assignments are used to explicitly
  ensure implementation::

    type SomeInterface interface {
        SomeMethod() ([]byte, error)

    if _, ok := someval.(SomeInterface); ok {
        fmt.Printf("value implements some interface")

  Both these ideas are questionable in the context of this proposal. See
  the section on `rejected`_ ideas.

.. _specification:



We propose to use the term *protocols* 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

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 propose to 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 other class in order to be considered a structural subtype are called
protocol members.

.. _definition:

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:

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

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

  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

      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.

  from typing import Protocol, List

  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

  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. ``typing.Sequence`` is a good
example of a protocol with useful default methods. 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):
        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 comlex_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]

        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 form 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 Sized, Protocol

  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 typing import Sized

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

  class SizedAndClosable(Sized, SupportsClose, Protocol):

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, see `rejected`_
ideas for rationale. 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

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]):
      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
(see `rejected`_ ideas for details). 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'

Using Protocols

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 ``Union``, ``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.

Unions and intersections of protocols

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

  from typing import Union, Optional, Protocol

  class Exitable(Protocol):
      def exit(self) -> int:
  class Quittable(Protocol):
      def quit(self) -> Optional[int]:

  def finish(task: Union[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.

  from typing import Sequence, Hashable

  class HashableFloats(Sequence[float], Hashable, Protocol):

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

If this will prove to be a widely used scenario, then a special
intersection type construct could be added in future as specified by PEP 483,
see `rejected`_ ideas for more details.

``Type[]`` with 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 type. For example::

  class Proto(Protocol):
      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.

``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, 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

  from typing import TypeVar, Reversible, Iterable, Sized

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

.. _discussion:

``@runtime`` 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`` 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, Protocol

  class Closable(Protocol):
      def close(self):

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

Static type checkers will understand ``isinstance(x, Proto)`` and
``issubclass(C, Proto)`` for protocols defined with this decorator (as they
already do for ``Iterable`` etc.). Static type checkers will narrow types
after such checks by the type erased ``Proto`` (i.e. with all variables
having type ``Any`` and all methods having type ``Callable[..., Any]``).
Note that ``isinstance(x, Proto[int])`` etc. will always fail in agreement
with PEP 484. Examples::

  from typing import Iterable, Iterator, Sequence

  def process(items: Iterable[int]) -> None:
      if isinstance(items, Iterator):
          # 'items' has type 'Iterator[int]' here
      elif isinstance(items, Sequence[int]):
          # Error! Can't use 'isinstance()' with subscripted protocols

Note that instance checks are not 100% reliable statically, this is why
this behavior is opt-in, see section on `rejected`_ ideas for examples.

Using Protocols in Python 2.7 - 3.5

Variable annotation syntax was added in Python 3.6, so that the syntax
for defining protocol variables proposed in `specification`_ section can't
be used if support for earlier versions is needed. To define these
in a manner compatible with older versions of Python one can use properties.
Properties can be settable and/or abstract if needed::

  class Foo(Protocol):
      def c(self) -> int:
          return 42         # Default value can be provided for property...

      def d(self) -> int:   # ... or it can be abstract
          return 0

Also function type comments can be used as per PEP 484 (for example
to provide compatibility with Python 2). The ``typing`` module changes
proposed in this PEP will also be backported to earlier versions via the
backport currently available on PyPI.

Runtime Implementation of Protocol Classes

Implementation details

The runtime implementation could be done in pure Python without any
effects on the core interpreter and standard library except in the
``typing`` module, and a minor update to ``collections.abc``:

* Define class ``typing.Protocol`` similar to ``typing.Generic``.
* Implement metaclass functionality to detect whether a class is
  a protocol or not. Add a class attribute ``_is_protocol = True``
  if that is the case. Verify that a protocol class only has protocol
  base classes in the MRO (except for object).
* Implement ``@runtime`` that allows ``__subclasshook__()`` performing
  structural instance and subclass checks as in ``collections.abc`` classes.
* All structural subtyping checks will be performed by static type checkers,
  such as ``mypy`` [mypy]_. No additional support for protocol validation will
  be provided at runtime.
* Classes ``Mapping``, ``MutableMapping``, ``Sequence``, and
  ``MutableSequence`` in ``collections.abc`` module will support structural
  instance and subclass checks (like e.g. ``collections.abc.Iterable``).

Changes in the typing module

The following classes in ``typing`` module will be protocols:

* ``Callable``
* ``Awaitable``
* ``Iterable``, ``Iterator``
* ``AsyncIterable``, ``AsyncIterator``
* ``Hashable``
* ``Sized``
* ``Container``
* ``Collection``
* ``Reversible``
* ``Sequence``, ``MutableSequence``
* ``Mapping``, ``MutableMapping``
* ``ContextManager``, ``AsyncContextManager``
* ``SupportsAbs`` (and other ``Supports*`` classes)

Most of these classes are small and conceptually simple. It is easy to see
what are the methods these protocols implement, and immediately recognize
the corresponding runtime protocol counterpart.
Practically, few changes will be needed in ``typing`` since some of these
classes already behave the necessary way at runtime. Most of these will need
to be updated only in the corresponding ``typeshed`` stubs [typeshed]_.

All other concrete generic classes such as ``List``, ``Set``, ``IO``,
``Deque``, etc are sufficiently complex that it makes sense to keep
them non-protocols (i.e. require code to be explicit about them). Also, it is
too easy to leave some methods unimplemented by accident, and explicitly
marking the subclass relationship allows type checkers to pinpoint the missing


The existing class introspection machinery (``dir``, ``__annotations__`` etc)
can be used with protocols. In addition, all introspection tools implemented
in the ``typing`` module will support protocols. Since all attributes need
to be defined in the class body based on this proposal, protocol classes will
have even better perspective for introspection than regular classes where
attributes can be defined implicitly -- protocol attributes can't be
initialized in ways that are not visible to introspection
(using ``setattr()``, assignment via ``self``, etc.). Still, some things like
types of attributes will not be visible at runtime in Python 3.5 and earlier,
but this looks like a reasonable limitation.

There will be only limited support of ``isinstance()`` and ``issubclass()``
as discussed above (these will *always* fail with ``TypeError`` for
subscripted generic protocols, since a reliable answer could not be given
at runtime in this case). But together with other introspection tools this
give a reasonable perspective for runtime type checking tools.

.. _rejected:

Rejected/Postponed Ideas

The ideas in this section were previously discussed in [several]_
[discussions]_ [elsewhere]_.

Make every class a protocol by default

Some languages such as Go make structural subtyping the only or the primary
form of subtyping. We could achieve a similar result by making all classes
protocols by default (or even always). However we believe that it is better
to require classes to be explicitly marked as protocols, for the following

* Protocols don't have some properties of regular classes. In particular,
  ``isinstance()``, as defined for normal classes, is based on the nominal
  hierarchy. In order to make everything a protocol by default, and have
  ``isinstance()`` work would require changing its semantics,
  which won't happen.
* Protocol classes should generally not have many method implementations,
  as they describe an interface, not an implementation.
  Most classes have many method implementations, making them bad protocol
* Experience suggests that many classes are not practical as protocols anyway,
  mainly because their interfaces are too large, complex or
  implementation-oriented (for example, they may include de facto
  private attributes and methods without a ``__`` prefix).
* Most actually useful protocols in existing Python code seem to be implicit.
  The ABCs in ``typing`` and ``collections.abc`` are rather an exception, but
  even they are recent additions to Python and most programmers
  do not use them yet.
* Many built-in functions only accept concrete instances of ``int``
  (and subclass instances), and similarly for other built-in classes. Making
  ``int`` a structural type wouldn't be safe without major changes to the
  Python runtime, which won't happen.

Protocols subclassing normal classes

The main rationale to prohibit this is to preserve transitivity of subtyping,
consider this example::

  from typing import Protocol

  class Base:
      attr: str

  class Proto(Base, Protocol):
      def meth(self) -> int:

  class C:
      attr: str
      def meth(self) -> int:
          return 0

Now, ``C`` is a subtype of ``Proto``, and ``Proto`` is a subtype of ``Base``.
But ``C`` cannot be a subtype of ``Base`` (since the latter is not
a protocol). This situation would be really weird. In addition, there is
an ambiguity about whether attributes of ``Base`` should become protocol
members of ``Proto``.

Support optional protocol members

We can come up with examples where it would be handy to be able to say
that a method or data attribute does not need to be present in a class
implementing a protocol, but if it is present, it must conform to a specific
signature or type. One could use a ``hasattr()`` check to determine whether
they can use the attribute on a particular instance.

Languages such as TypeScript have similar features and
apparently they are pretty commonly used. The current realistic potential
use cases for protocols in Python don't require these. In the interest
of simplicity, we propose to not support optional methods or attributes.
We can always revisit this later if there is an actual need.

Allow only protocol methods and force use of getters and setters

One could argue that protocols typically only define methods, but not
variables. However, using getters and setters in cases where only a
simple variable is needed would be quite unpythonic. Moreover, the widespread
use of properties (that often act as type validators) in large code bases
is partially due to previous absence of static type checkers for Python,
the problem that PEP 484 and this PEP are aiming to solve. For example::

  # without static types

  class MyClass:
      def my_attr(self):
          return self._my_attr
      def my_attr(self, value):
          if not isinstance(value, int):
              raise ValidationError("An integer expected for my_attr")
          self._my_attr = value

  # with static types

  class MyClass:
      my_attr: int

Support non-protocol members

There was an idea to make some methods "non-protocol" (i.e. not necessary
to implement, and inherited in explicit subclassing), but it was rejected,
since this complicates things. For example, consider this situation::

  class Proto(Protocol):
      def first(self) -> int:
          raise NotImplementedError
      def second(self) -> int:
          return self.first() + 1

  def fun(arg: Proto) -> None:

The question is should this be an error? We think most people would expect
this to be valid. Therefore, to be on the safe side, we need to require both
methods to be implemented in implicit subclasses. In addition, if one looks
at definitions in ``collections.abc``, there are very few methods that could
be considered "non-protocol". Therefore, it was decided to not introduce
"non-protocol" methods.

There is only one downside to this: it will require some boilerplate for
implicit subtypes of ``Mapping`` and few other "large" protocols. But, this
applies to few "built-in" protocols (like ``Mapping`` and ``Sequence``) and
people are already subclassing them. Also, such style is discouraged for
user-defined protocols. It is recommended to create compact protocols and
combine them.

Make protocols interoperable with other approaches

The protocols as described here are basically a minimal extension to
the existing concept of ABCs. We argue that this is the way they should
be understood, instead of as something that *replaces* Zope interfaces,
for example. Attempting such interoperabilities will significantly
complicate both the concept and the implementation.

On the other hand, Zope interfaces are conceptually a superset of protocols
defined here, but using an incompatible syntax to define them,
because before PEP 526 there was no straightforward way to annotate attributes.
In the 3.6+ world, ``zope.interface`` might potentially adopt the ``Protocol``
syntax. In this case, type checkers could be taught to recognize interfaces
as protocols and make simple structural checks with respect to them.

Use assignments to check explicitly that a class implements a protocol

In the Go language the explicit checks for implementation are performed
via dummy assignments [golang]_. Such a way is also possible with the
current proposal. Example::

  class A:
      def __len__(self) -> float:
          return ...

  _: Sized = A()  # Error: A.__len__ doesn't conform to 'Sized'
                  # (Incompatible return type 'float')

This approach moves the check away from
the class definition and it almost requires a comment as otherwise
the code probably would not make any sense to an average reader
-- it looks like dead code. Besides, in the simplest form it requires one
to construct an instance of ``A``, which could be problematic if this requires
accessing or allocating some resources such as files or sockets.
We could work around the latter by using a cast, for example, but then
the code would be ugly. Therefore we discourage the use of this pattern.

Support ``isinstance()`` checks by default

The problem with this is instance checks could be unreliable, except for
situations where there is a common signature convention such as ``Iterable``.
For example::

  class P(Protocol):
      def common_method_name(self, x: int) -> int: ...

  class X:
      <a bunch of methods>
      def common_method_name(self) -> None: ... # Note different signature

  def do_stuff(o: Union[P, X]) -> int:
      if isinstance(o, P):
          return o.common_method_name(1)  # oops, what if it's an X instance?

Another potentially problematic case is assignment of attributes
*after* instantiation::

  class P(Protocol):
      x: int

  class C:
      def initialize(self) -> None:
          self.x = 0

  c = C()
  isinstance(c1, P)  # False
  isinstance(c, P)  # True

  def f(x: Union[P, int]) -> None:
      if isinstance(x, P):
          # static type of x is P here
          # type of x is "int" here?
          print(x + 1)

  f(C())   # oops

We argue that requiring an explicit class decorator would be better, since
one can then attach warnings about problems like this in the documentation.
The user would be able to evaluate whether the benefits outweigh
the potential for confusion for each protocol and explicitly opt in -- but
the default behavior would be safer. Finally, it will be easy to make this
behavior default if necessary, while it might be problematic to make it opt-in
after being default.

Provide a special intersection type construct

There was an idea to allow ``Proto = All[Proto1, Proto2, ...]`` as a shorthand

  class Proto(Proto1, Proto2, ..., Protocol):

However, it is not yet clear how popular/useful it will be and implementing
this in type checkers for non-protocol classes could be difficult. Finally, it
will be very easy to add this later if needed.

Prohibit explicit subclassing of protocols by non-protocols

This was rejected for the following reasons:

* Backward compatibility: People are already using ABCs, including generic
  ABCs from ``typing`` module. If we prohibit explicit subclassing of these
  ABCs, then quite a lot of code will break.

* Convenience: There are existing protocol-like ABCs (that will be turned
  into protocols) that have many useful "mix-in" (non-abstract) methods.
  For example in the case of ``Sequence`` one only needs to implement
  ``__getitem__`` and ``__len__`` in an explicit subclass, and one gets
  ``__iter__``, ``__contains__``, ``__reversed__``, ``index``, and ``count``
  for free.

* Explicit subclassing makes it explicit that a class implements a particular
  protocol, making subtyping relationships easier to see.

* Type checkers can warn about missing protocol members or members with
  incompatible types more easily, without having to use hacks like dummy
  assignments discussed above in this section.

* Explicit subclassing makes it possible to force a class to be considered
  a subtype of a protocol (by using ``# type: ignore`` together with an
  explicit base class) when it is not strictly compatible, such as when
  it has an unsafe override.

Covariant subtyping of mutable attributes

Rejected because covariant subtyping of mutable attributes is not safe.
Consider this example::

  class P(Protocol):
      x: float

  def f(arg: P) -> None:
      arg.x = 0.42

  class C:
      x: int

  c = C()
  f(c)  # Would typecheck if covariant subtyping
        # of mutable attributes were allowed
  c.x >> 1  # But this fails at runtime

It was initially proposed to allow this for practical reasons, but it was
subsequently rejected, since this may mask some hard to spot bugs.

Overriding inferred variance of protocol classes

It was proposed to allow declaring protocols as invariant if they are actually
covariant or contravariant (as it is possible for nominal classes, see PEP 484).
However, it was decided not to do this because of several downsides:

* Declared protocol invariance breaks transitivity of sub-typing. Consider
  this situation::

    T = TypeVar('T')

    class P(Protocol[T]):  # Declared as invariant
        def meth(self) -> T:
    class C:
        def meth(self) -> float:
    class D(C):
        def meth(self) -> int:

  Now we have that ``D`` is a subtype of ``C``, and ``C`` is a subtype of
  ``P[float]``. But ``D`` is *not* a subtype of ``P[float]`` since ``D``
  implements ``P[int]``, and ``P`` is invariant. There is a possibility
  to "cure" this by looking for protocol implementations in MROs but this
  will be too complex in a general case, and this "cure" requires abandoning
  simple idea of purely structural subtyping for protocols.

* Subtyping checks will always require type inference for protocols. In the
  above example a user may complain: "Why did you infer ``P[int]`` for
  my ``D``? It implements ``P[float]``!". Normally, inference can be overruled
  by an explicit annotation, but here this will require explicit subclassing,
  defeating the purpose of using protocols.

* Allowing overriding variance will make impossible more detailed error
  messages in type checkers citing particular conflicts in member
  type signatures.

* Finally, explicit is better than implicit in this case. Requiring user to
  declare correct variance will simplify understanding the code and will avoid
  unexpected errors at the point of use.

Support adapters and adaptation

Adaptation was proposed by PEP 246 (rejected) and is supported by
``zope.interface``, see https://docs.zope.org/zope.interface/adapter.html.
Adapters is quite an advanced concept, and PEP 484 supports unions and
generic aliases that can be used instead of adapters. This can be illustrated
with an example of ``Iterable`` protocol, there is another way of supporting
iteration by providing ``__getitem__`` and ``__len__``. If a function
supports both this way and the now standard ``__iter__`` method, then it could
be annotated by a union type::

  class OldIterable(Sized, Protocol[T]):
      def __getitem__(self, item: int) -> T: ...

  CompatIterable = Union[Iterable[T], OldIterable[T]]

  class A:
      def __iter__(self) -> Iterator[str]: ...
  class B:
      def __len__(self) -> int: ...
      def __getitem__(self, item: int) -> str: ...

  def iterate(it: CompatIterable[str]) -> None:

  iterate(A())  # OK
  iterate(B())  # OK

Since there is a reasonable alternative for such cases with existing tooling,
it is therefore proposed not to include adaptation in this PEP.

Backwards Compatibility

This PEP is almost fully backwards compatible. Few collection classes such as
``Sequence`` and ``Mapping`` will be turned into runtime protocols, therefore
results of ``isinstance()`` checks are going to change in some edge cases.
For example, a class that implements the ``Sequence`` protocol but does not
explicitly inherit from ``Sequence`` currently returns ``False`` in
corresponding instance and class checks. With this PEP implemented, such
checks will return ``True``.


A working implementation of this PEP for ``mypy`` type checker is found on
GitHub repo at https://github.com/ilevkivskyi/mypy/tree/protocols,
corresponding ``typeshed`` stubs for more flavor are found at
https://github.com/ilevkivskyi/typeshed/tree/protocols. Installation steps::

  git clone --recurse-submodules https://github.com/ilevkivskyi/mypy/
  cd mypy && git checkout protocols && cd typeshed
  git remote add proto https://github.com/ilevkivskyi/typeshed
  git fetch proto && git checkout proto/protocols
  cd .. && git add typeshed && sudo python3 -m pip install -U .

The runtime implementation of protocols in ``typing`` module is
found at https://github.com/ilevkivskyi/typehinting/tree/protocols.
The version of ``collections.abc`` with structural behavior for mappings and
sequences is found at https://github.com/ilevkivskyi/cpython/tree/protocols.


.. [typing]

.. [wiki-structural]

.. [zope-interfaces]

.. [abstract-classes]

.. [collections-abc]

.. [typescript]

.. [golang]

.. [data-model]

.. [typeshed]

.. [mypy]

.. [several]

.. [discussions]

.. [elsewhere]


This document has been placed in the public domain.