python-peps/pep-0585.rst

PEP: 585
Title: Type Hinting Generics In Standard Collections
Version: $Revision$
Last-Modified: $Date$
Author: Łukasz Langa <lukasz@python.org>
Discussions-To: typing-sig@python.org
Status: Accepted
Type: Standards Track
Content-Type: text/x-rst
Created: 03-Mar-2019
Python-Version: 3.9
Resolution: https://mail.python.org/archives/list/python-dev@python.org/thread/HW2NFOEMCVCTAFLBLC3V7MLM6ZNMKP42/

Abstract
========

Static typing as defined by PEPs 484, 526, 544, 560, and 563 was built
incrementally on top of the existing Python runtime and constrained by
existing syntax and runtime behavior.  This led to the existence of
a duplicated collection hierarchy in the ``typing`` module due to
generics (for example ``typing.List`` and the built-in ``list``).

This PEP proposes to enable support for the generics syntax in all
standard collections currently available in the ``typing`` module.


Rationale and Goals
===================

This change removes the necessity for a parallel type hierarchy in the
``typing`` module, making it easier for users to annotate their programs
and easier for teachers to teach Python.


Terminology
===========

Generic (n.) -- a type that can be parameterized, typically a container.
Also known as a *parametric type* or a *generic type*.  For example:
``dict``.

parameterized generic -- a specific instance of a generic with the
expected types for container elements provided.  Also known as
a *parameterized type*.  For example: ``dict[str, int]``.


Backwards compatibility
=======================

Tooling, including type checkers and linters, will have to be adapted to
recognize standard collections as generics.

On the source level, the newly described functionality requires
Python 3.9.  For use cases restricted to type annotations, Python files
with the "annotations" future-import (available since Python 3.7) can
parameterize standard collections, including builtins.  To reiterate,
that depends on the external tools understanding that this is valid.

Implementation
==============

Starting with Python 3.7, when ``from __future__ import annotations`` is
used, function and variable annotations can parameterize standard
collections directly.  Example::

    from __future__ import annotations

    def find(haystack: dict[str, list[int]]) -> int:
        ...

Usefulness of this syntax before :pep:`585` is limited as external tooling
like Mypy does not recognize standard collections as generic.  Moreover,
certain features of typing like type aliases or casting require putting
types outside of annotations, in runtime context.  While these are
relatively less common than type annotations, it's important to allow
using the same type syntax in all contexts.  This is why starting with
Python 3.9, the following collections become generic using
``__class_getitem__()`` to parameterize contained types:

* ``tuple``  # typing.Tuple
* ``list``  # typing.List
* ``dict``  # typing.Dict
* ``set``  # typing.Set
* ``frozenset``  # typing.FrozenSet
* ``type``  # typing.Type
* ``collections.deque``
* ``collections.defaultdict``
* ``collections.OrderedDict``
* ``collections.Counter``
* ``collections.ChainMap``
* ``collections.abc.Awaitable``
* ``collections.abc.Coroutine``
* ``collections.abc.AsyncIterable``
* ``collections.abc.AsyncIterator``
* ``collections.abc.AsyncGenerator``
* ``collections.abc.Iterable``
* ``collections.abc.Iterator``
* ``collections.abc.Generator``
* ``collections.abc.Reversible``
* ``collections.abc.Container``
* ``collections.abc.Collection``
* ``collections.abc.Callable``
* ``collections.abc.Set``  # typing.AbstractSet
* ``collections.abc.MutableSet``
* ``collections.abc.Mapping``
* ``collections.abc.MutableMapping``
* ``collections.abc.Sequence``
* ``collections.abc.MutableSequence``
* ``collections.abc.ByteString``
* ``collections.abc.MappingView``
* ``collections.abc.KeysView``
* ``collections.abc.ItemsView``
* ``collections.abc.ValuesView``
* ``contextlib.AbstractContextManager``  # typing.ContextManager
* ``contextlib.AbstractAsyncContextManager``  # typing.AsyncContextManager
* ``re.Pattern``  # typing.Pattern, typing.re.Pattern
* ``re.Match``  # typing.Match, typing.re.Match

Importing those from ``typing`` is deprecated.  Due to :pep:`563` and the
intention to minimize the runtime impact of typing, this deprecation
will not generate DeprecationWarnings.  Instead, type checkers may warn
about such deprecated usage when the target version of the checked
program is signalled to be Python 3.9 or newer.  It's recommended to
allow for those warnings to be silenced on a project-wide basis.

The deprecated functionality may eventually be removed from the ``typing``
module. Removal will occur no sooner than Python 3.9's end of life,
scheduled for October 2025.


Parameters to generics are available at runtime
-----------------------------------------------

Preserving the generic type at runtime enables introspection of the type
which can be used for API generation or runtime type checking.  Such
usage is already present in the wild.

Just like with the ``typing`` module today, the parameterized generic
types listed in the previous section all preserve their type parameters
at runtime::

    >>> list[str]
    list[str]
    >>> tuple[int, ...]
    tuple[int, ...]
    >>> ChainMap[str, list[str]]
    collections.ChainMap[str, list[str]]

This is implemented using a thin proxy type that forwards all method
calls and attribute accesses to the bare origin type with the following
exceptions:

* the ``__repr__`` shows the parameterized type;
* the ``__origin__`` attribute points at the non-parameterized
  generic class;
* the ``__args__`` attribute is a tuple (possibly of length
  1) of generic types passed to the original ``__class_getitem__``;
* the ``__parameters__`` attribute is a lazily computed tuple
  (possibly empty) of unique type variables found in ``__args__``;
* the ``__getitem__`` raises an exception to disallow mistakes
  like ``dict[str][str]``.  However it allows e.g. ``dict[str, T][int]``
  and in that case returns ``dict[str, int]``.

This design means that it is possible to create instances of
parameterized collections, like::

    >>> l = list[str]()
    []
    >>> list is list[str]
    False
    >>> list == list[str]
    False
    >>> list[str] == list[str]
    True
    >>> list[str] == list[int]
    False
    >>> isinstance([1, 2, 3], list[str])
    TypeError: isinstance() arg 2 cannot be a parameterized generic
    >>> issubclass(list, list[str])
    TypeError: issubclass() arg 2 cannot be a parameterized generic
    >>> isinstance(list[str], types.GenericAlias)
    True

Objects created with bare types and parameterized types are exactly the
same.  The generic parameters are not preserved in instances created
with parameterized types, in other words generic types erase type
parameters during object creation.

One important consequence of this is that the interpreter does **not**
attempt to type check operations on the collection created with
a parameterized type.  This provides symmetry between::

    l: list[str] = []

and::

    l = list[str]()

For accessing the proxy type from Python code, it will be exported
from the ``types`` module as ``GenericAlias``.

Pickling or (shallow- or deep-) copying a ``GenericAlias`` instance
will preserve the type, origin, attributes and parameters.


Forward compatibility
---------------------

Future standard collections must implement the same behavior.


Reference implementation
========================

A proof-of-concept or prototype `implementation
<https://bugs.python.org/issue39481>`__ exists.


Rejected alternatives
=====================

Do nothing
----------

Keeping the status quo forces Python programmers to perform book-keeping
of imports from the ``typing`` module for standard collections, making
all but the simplest annotations cumbersome to maintain.  The existence
of parallel types is confusing to newcomers (why is there both ``list``
and ``List``?).

The above problems also don't exist in user-built generic classes which
share runtime functionality and the ability to use them as generic type
annotations.  Making standard collections harder to use in type hinting
from user classes hindered typing adoption and usability.

Generics erasure
----------------

It would be easier to implement ``__class_getitem__`` on the listed
standard collections in a way that doesn't preserve the generic type,
in other words::

    >>> list[str]
    <class 'list'>
    >>> tuple[int, ...]
    <class 'tuple'>
    >>> collections.ChainMap[str, list[str]]
    <class 'collections.ChainMap'>

This is problematic as it breaks backwards compatibility: current
equivalents of those types in the ``typing`` module **do** preserve
the generic type::

    >>> from typing import List, Tuple, ChainMap
    >>> List[str]
    typing.List[str]
    >>> Tuple[int, ...]
    typing.Tuple[int, ...]
    >>> ChainMap[str, List[str]]
    typing.ChainMap[str, typing.List[str]]

As mentioned in the "Implementation" section, preserving the generic
type at runtime enables runtime introspection of the type which can be
used for API generation or runtime type checking.  Such usage is already
present in the wild.

Additionally, implementing subscripts as identity functions would make
Python less friendly to beginners.  Say, if a user is mistakenly passing
a list type instead of a list object to a function, and that function is
indexing the received object, the code would no longer raise an error.

Today::

    >>> l = list
    >>> l[-1]
    TypeError: 'type' object is not subscriptable

With ``__class_getitem__`` as an identity function::

    >>> l = list
    >>> l[-1]
    list

The indexing being successful here would likely end up raising an
exception at a distance, confusing the user.

Disallowing instantiation of parameterized types
------------------------------------------------

Given that the proxy type which preserves ``__origin__`` and
``__args__`` is mostly useful for runtime introspection purposes,
we might have disallowed instantiation of parameterized types.

In fact, forbidding instantiation of parameterized types is what the
``typing`` module does today for types which parallel builtin
collections (instantiation of other parameterized types is allowed).

The original reason for this decision was to discourage spurious
parameterization which made object creation up to two orders of magnitude
slower compared to the special syntax available for those builtin
collections.

This rationale is not strong enough to allow the exceptional treatment
of builtins.  All other parameterized types can be instantiated,
including parallels of collections in the standard library.  Moreover,
Python allows for instantiation of lists using ``list()`` and some
builtin collections don't provide special syntax for instantiation.

Making ``isinstance(obj, list[str])`` perform a check ignoring generics
-----------------------------------------------------------------------

An earlier version of this PEP suggested treating parameterized generics
like ``list[str]`` as equivalent to their non-parameterized variants
like ``list`` for purposes of ``isinstance()`` and ``issubclass()``.
This would be symmetrical to how ``list[str]()`` creates a regular list.

This design was rejected because ``isinstance()`` and ``issubclass()``
checks with parameterized generics would read like element-by-element
runtime type checks.  The result of those checks would be surprising,
for example::

    >>> isinstance([1, 2, 3], list[str])
    True

Note the object doesn't match the provided generic type but
``isinstance()`` still returns ``True`` because it only checks whether
the object is a list.

If a library is faced with a parameterized generic and would like to
perform an ``isinstance()`` check using the base type, that type can
be retrieved using the ``__origin__`` attribute on the parameterized
generic.

Making ``isinstance(obj, list[str])`` perform a runtime type check
------------------------------------------------------------------

This functionality requires iterating over the collection which is
a destructive operation in some of them.  This functionality would have
been useful, however implementing the type checker within Python that
would deal with complex types, nested type checking, type variables,
string forward references, and so on is out of scope for this PEP.

Naming the type ``GenericType`` instead of ``GenericAlias``
-----------------------------------------------------------

We considered a different name for this type, but decided
``GenericAlias`` is better -- these aren't real types, they are
aliases for the corresponding container type with some extra metadata
attached.


Note on the initial draft
=========================

An early version of this PEP discussed matters beyond generics in
standard collections.  Those unrelated topics were removed for clarity.


Acknowledgments
===============

Thank you to Guido van Rossum for his work on Python, and the
implementation of this PEP specifically.


Copyright
=========

This document is placed in the public domain or under the
CC0-1.0-Universal license, whichever is more permissive.