python-peps/pep-0593.rst

PEP: 593
Title: Flexible function and variable annotations
Author: Till Varoquaux <till@fb.com>, Konstantin Kashin <kkashin@fb.com>
Sponsor: Ivan Levkivskyi <levkivskyi@gmail.com>
Discussions-To: typing-sig@python.org
Status: Accepted
Type: Standards Track
Content-Type: text/x-rst
Created: 26-Apr-2019
Python-Version: 3.9
Post-History: 20-May-2019

Abstract
--------

This PEP introduces a mechanism to extend the type annotations from PEP
484 with arbitrary metadata.

Motivation
----------

:pep:`484` provides a standard semantic for the annotations introduced in
:pep:`3107`. :pep:`484` is prescriptive but it is the de facto standard
for most of the consumers of annotations; in many statically checked
code bases, where type annotations are widely used, they have
effectively crowded out any other form of annotation. Some of the use
cases for annotations described in :pep:`3107` (database mapping,
foreign languages bridge) are not currently realistic given the
prevalence of type annotations. Furthermore, the standardisation of type
annotations rules out advanced features only supported by specific type
checkers.

Rationale
---------

This PEP adds an ``Annotated`` type to the typing module to decorate
existing types with context-specific metadata. Specifically, a type
``T`` can be annotated with metadata ``x`` via the typehint
``Annotated[T, x]``. This metadata can be used for either static
analysis or at runtime. If a library (or tool) encounters a typehint
``Annotated[T, x]`` and has no special logic for metadata ``x``, it
should ignore it and simply treat the type as ``T``. Unlike the
``no_type_check`` functionality that currently exists in the ``typing``
module which completely disables typechecking annotations on a function
or a class, the ``Annotated`` type allows for both static typechecking
of ``T`` (e.g., via mypy [mypy]_ or Pyre [pyre]_, which can safely ignore ``x``)
together with runtime access to ``x`` within a specific application. The
introduction of this type would address a diverse set of use cases of interest
to the broader Python community.

This was originally brought up as issue 600 [issue-600]_ in the typing github
and then discussed in Python ideas [python-ideas]_.

Motivating examples
-------------------

Combining runtime and static uses of annotations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

There's an emerging trend of libraries leveraging the typing annotations at
runtime (e.g.: dataclasses); having the ability to extend the typing annotations
with external data would be a great boon for those libraries.

Here's an example of how a hypothetical module could leverage annotations to
read c structs::

    UnsignedShort = Annotated[int, struct2.ctype('H')]
    SignedChar = Annotated[int, struct2.ctype('b')]

    class Student(struct2.Packed):
        # mypy typechecks 'name' field as 'str'
        name: Annotated[str, struct2.ctype("<10s")]
        serialnum: UnsignedShort
        school: SignedChar

    # 'unpack' only uses the metadata within the type annotations
    Student.unpack(record)
    # Student(name=b'raymond   ', serialnum=4658, school=264)

Lowering barriers to developing new typing constructs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Typically when adding a new type, a developer need to upstream that type to the
typing module and change mypy, PyCharm [pycharm]_, Pyre, pytype [pytype]_,
etc...
This is particularly important when working on open-source code that
makes use of these types, seeing as the code would not be immediately
transportable to other developers' tools without additional logic. As a result,
there is a high cost to developing and trying out new types in a codebase.
Ideally, authors should be able to introduce new types in a manner that allows
for graceful degradation (e.g.: when clients do not have a custom mypy plugin
[mypy-plugin]_), which would lower the barrier to development and ensure some
degree of backward compatibility.

For example, suppose that an author wanted to add support for tagged unions
[tagged-union]_ to Python. One way to accomplish would be to annotate
``TypedDict`` [typed-dict]_ in Python such that only one field is allowed to be
set::

    Currency = Annotated[
        TypedDict('Currency', {'dollars': float, 'pounds': float}, total=False),
        TaggedUnion,
    ]

This is a somewhat cumbersome syntax but it allows us to iterate on this
proof-of-concept and have people with type checkers (or other tools) that don't
yet support this feature work in a codebase with tagged unions. The author could
easily test this proposal and iron out the kinks before trying to upstream tagged
union to ``typing``, mypy, etc. Moreover, tools that do not have support for
parsing the ``TaggedUnion`` annotation would still be able to treat ``Currency``
as a ``TypedDict``, which is still a close approximation (slightly less strict).

Specification
-------------

Syntax
~~~~~~

``Annotated`` is parameterized with a type and an arbitrary list of
Python values that represent the annotations. Here are the specific
details of the syntax:

* The first argument to ``Annotated`` must be a valid type

* Multiple type annotations are supported (``Annotated`` supports variadic
  arguments)::

    Annotated[int, ValueRange(3, 10), ctype("char")]

* ``Annotated`` must be called with at least two arguments (
  ``Annotated[int]`` is not valid)

* The order of the annotations is preserved and matters for equality
  checks::

    Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
        int, ctype("char"), ValueRange(3, 10)
    ]

* Nested ``Annotated`` types are flattened, with metadata ordered
  starting with the innermost annotation::

    Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
        int, ValueRange(3, 10), ctype("char")
    ]

* Duplicated annotations are not removed::

    Annotated[int, ValueRange(3, 10)] != Annotated[
        int, ValueRange(3, 10), ValueRange(3, 10)
    ]

* ``Annotated`` can be used with nested and generic aliases::

    Typevar T = ...
    Vec = Annotated[List[Tuple[T, T]], MaxLen(10)]
    V = Vec[int]

    V == Annotated[List[Tuple[int, int]], MaxLen(10)]

Consuming annotations
~~~~~~~~~~~~~~~~~~~~~

Ultimately, the responsibility of how to interpret the annotations (if
at all) is the responsibility of the tool or library encountering the
``Annotated`` type. A tool or library encountering an ``Annotated`` type
can scan through the annotations to determine if they are of interest
(e.g., using ``isinstance()``).

**Unknown annotations:** When a tool or a library does not support
annotations or encounters an unknown annotation it should just ignore it
and treat annotated type as the underlying type. For example, when encountering
an annotation that is not an instance of ``struct2.ctype`` to the annotations
for name (e.g., ``Annotated[str, 'foo', struct2.ctype("<10s")]``), the unpack
method should ignore it.

**Namespacing annotations:** Namespaces are not needed for annotations since
the class used by the annotations acts as a namespace.

**Multiple annotations:** It's up to the tool consuming the annotations
to decide whether the client is allowed to have several annotations on
one type and how to merge those annotations.

Since the ``Annotated`` type allows you to put several annotations of
the same (or different) type(s) on any node, the tools or libraries
consuming those annotations are in charge of dealing with potential
duplicates. For example, if you are doing value range analysis you might
allow this::

    T1 = Annotated[int, ValueRange(-10, 5)]
    T2 = Annotated[T1, ValueRange(-20, 3)]

Flattening nested annotations, this translates to::

    T2 = Annotated[int, ValueRange(-10, 5), ValueRange(-20, 3)]

Interaction with ``get_type_hints()``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``typing.get_type_hints()`` will take a new argument ``include_extras`` that
defaults to ``False`` to preserve backward compatibility. When
``include_extras`` is ``False``, the extra annotations will be stripped
out of the returned value. Otherwise, the annotations will be returned
unchanged::

    @struct2.packed
    class Student(NamedTuple):
        name: Annotated[str, struct.ctype("<10s")]

    get_type_hints(Student) == {'name': str}
    get_type_hints(Student, include_extras=False) == {'name': str}
    get_type_hints(Student, include_extras=True) == {
        'name': Annotated[str, struct.ctype("<10s")]
    }

Aliases & Concerns over verbosity
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Writing ``typing.Annotated`` everywhere can be quite verbose;
fortunately, the ability to alias annotations means that in practice we
don't expect clients to have to write lots of boilerplate code::

    T = TypeVar('T')
    Const = Annotated[T, my_annotations.CONST]

    Class C:
        def const_method(self: Const[List[int]]) -> int:
            ...

Rejected ideas
--------------

Some of the proposed ideas were rejected from this PEP because they would
cause ``Annotated`` to not integrate cleanly with the other typing annotations:

* ``Annotated`` cannot infer the decorated type. You could imagine that
  ``Annotated[..., Immutable]`` could be used to mark a value as immutable
  while still inferring its type. Typing does not support using the
  inferred type anywhere else [issue-276]_; it's best to not add this as a
  special case.

* Using ``(Type, Ann1, Ann2, ...)`` instead of
  ``Annotated[Type, Ann1, Ann2, ...]``. This would cause confusion when
  annotations appear in nested positions (``Callable[[A, B], C]`` is too similar
  to ``Callable[[(A, B)], C]``) and would make it impossible for constructors to
  be passthrough (``T(5) == C(5)`` when ``C = Annotation[T, Ann]``).

This feature was left out to keep the design simple:

* ``Annotated`` cannot be called with a single argument. Annotated could support
  returning the underlying value when called with a single argument (e.g.:
  ``Annotated[int] == int``). This complicates the specifications and adds
  little benefit.


References
----------

.. [issue-600]
   https://github.com/python/typing/issues/600

.. [python-ideas]
   https://mail.python.org/pipermail/python-ideas/2019-January/054908.html

.. [struct-doc]
   https://docs.python.org/3/library/struct.html#examples

.. [mypy]
   http://www.mypy-lang.org/

.. [pyre]
   https://pyre-check.org/

.. [pycharm]
   https://www.jetbrains.com/pycharm/

.. [pytype]
   https://github.com/google/pytype

.. [mypy-plugin]
   https://github.com/python/mypy_extensions

.. [tagged-union]
   https://en.wikipedia.org/wiki/Tagged_union

.. [typed-dict]
   https://mypy.readthedocs.io/en/latest/more_types.html#typeddict

.. [issue-276]
   https://github.com/python/typing/issues/276

Copyright
---------

This document has been placed in the public domain.