PEP: 749 Title: Implementing PEP 649 Author: Jelle Zijlstra
<jelle.zijlstra@gmail.com> Discussions-To:
https://discuss.python.org/t/pep-749-implementing-pep-649/54974 Status:
Draft Type: Standards Track Topic: Typing Requires: 649 Created:
28-May-2024 Python-Version: 3.14 Post-History: 04-Jun-2024

Abstract

This PEP supplements PEP 649 by providing various tweaks and additions
to its specification:

-   from __future__ import annotations (PEP 563) will continue to exist
    with its current behavior at least until Python 3.13 reaches its
    end-of-life. Subsequently, it will be deprecated and eventually
    removed.
-   A new standard library module, annotationlib, is added to provide
    tooling for annotations. It will include the get_annotations()
    function, an enum for annotation formats, a ForwardRef class, and a
    helper function for calling __annotate__ functions.
-   Annotations in the REPL are lazily evaluated, just like other
    module-level annotations.
-   We specify the behavior of wrapper objects that provide annotations,
    such as classmethod and code that uses functools.wraps.
-   There will not be a code flag for marking __annotate__ functions
    that can be run in a "fake globals" environment. Instead, we add a
    fourth format, VALUE_WITH_FAKE_GLOBALS, to allow third-party
    implementors of annotate functions to indicate what formats they
    support.
-   Deleting the __annotations__ attribute directly will also clear
    __annotate__.
-   We add functionality to allow evaluating type alias values and type
    parameter bounds and defaults (which were added by PEP 695 and
    PEP 696) using PEP 649-like semantics.
-   The SOURCE format is renamed to STRING to improve clarity and reduce
    the risk of user confusion.

Motivation

PEP 649 provides an excellent framework for creating better semantics
for annotations in Python. It solves a common pain point for users of
annotations, including those using static type hints as well as those
using runtime typing, and it makes the language more elegant and
powerful. The PEP was originally proposed in 2021 for Python 3.10, and
it was accepted in 2023. However, the implementation took longer than
anticipated, and now the PEP is expected to be implemented in Python
3.14.

I have started working on the implementation of the PEP in CPython. I
found that the PEP leaves some areas underspecified, and some of its
decisions in corner cases are questionable. This new PEP proposes
several changes and additions to the specification to address these
issues.

This PEP supplements rather than supersedes PEP 649. The changes
proposed here should make the overall user experience better, but they
do not change the general framework of the earlier PEP.

The future of from __future__ import annotations

PEP 563 previously introduced the future import
from __future__ import annotations, which changes all annotations to
strings. PEP 649 proposes an alternative approach that does not require
this future import, and states:

  If this PEP is accepted, PEP 563 will be deprecated and eventually
  removed.

However, the PEP does not provide a detailed plan for this deprecation.

There is some previous discussion of this topic on Discourse (note that
in the linked post I proposed something different from what is proposed
here).

Specification

We suggest the following deprecation plan:

-   In Python 3.14, from __future__ import annotations will continue to
    work as it did before, converting annotations into strings.
    -   If the future import is active, the __annotate__ function of
        objects with annotations will return the annotations as strings
        when called with the VALUE format, reflecting the behavior of
        __annotations__.
-   Sometime after the last release that did not support PEP 649
    semantics (expected to be 3.13) reaches its end-of-life,
    from __future__ import annotations is deprecated. Compiling any code
    that uses the future import will emit a DeprecationWarning. This
    will happen no sooner than the first release after Python 3.13
    reaches its end-of-life, but the community may decide to wait
    longer.
-   After at least two releases, the future import is removed, and
    annotations are always evaluated as per PEP 649. Code that continues
    to use the future import will raise a SyntaxError, similar to any
    other undefined future import.

Rejected alternatives

Immediately make the future import a no-op: We considered applying PEP
649 semantics to all code in Python 3.14, making the future import a
no-op. However, this would break code that works in 3.13 under the
following set of conditions:

-   __future__ import annotations is active
-   There are annotations that rely on forward references
-   Annotations are eagerly evaluated at import time, for example by a
    metaclass or class or function decorator. For example, this
    currently applies to the released version of
    typing_extensions.TypedDict.

This is expected to be a common pattern, so we cannot afford to break
such code during the upgrade from 3.13 to 3.14.

Such code would still break when the future import is eventually
removed. However, this is many years in the future, giving affected
decorators plenty of time to update their code.

Immediately deprecate the future import: Instead of waiting until Python
3.13 reaches its end-of-life, we could immediately start emitting
warnings when the future import is used. However, many libraries are
already using from __future__ import annotations as an elegant way to
enable unrestricted forward references in their annotations. If we
deprecate the future import immediately, it would be impossible for
these libraries to use unrestricted forward references on all supported
Python versions while avoiding deprecation warnings: unlike other
features deprecated from the standard library, a __future__ import must
be the first statement in a given module, meaning it would be impossible
to only conditionally import __future__.annotations on Python 3.13 and
lower. (The necessary sys.version_info check would count as a statement
preceding the __future__ import.)

Keep the future import around forever: We could also decide to keep the
future import indefinitely. However, this would permanently bifurcate
the behavior of the Python language. This is undesirable; the language
should have only a single set of semantics, not two permanently
different modes.

Make the future import a no-op in the future: Instead of eventually
making from __future__ import annotations a SyntaxError, we could make
it do nothing instead at some point after Python 3.13 reaches its
end-of-life. This still has some of the same issues outlined above
around making it a no-op now, although the ecosystem would have had much
longer to adapt. It is better to have users explicitly remove the future
import from their code in the future once they have confirmed they do
not rely on stringized annotations.

New annotationlib module

PEP 649 proposes to add tooling related to annotations to the inspect
module. However, that module is rather large, has direct or indirect
dependencies on at least 35 other standard library modules, and is so
slow to import that other standard library modules are often discouraged
from importing it. Furthermore, we anticipate adding more tools in
addition to the inspect.get_annotations function and the VALUE,
FORWARDREF, and SOURCE formats.

A new standard library module provides a logical home for this
functionality and also enables us to add more tooling that is useful for
consumers of annotations.

Rationale

PEP 649 indicates that !typing.ForwardRef should be used to implement
the FORWARDREF format in inspect.get_annotations. However, the existing
implementation of !typing.ForwardRef is intertwined with the rest of the
!typing module, and it would not make sense to add !typing-specific
behavior to the generic get_annotations() function. Furthermore,
!typing.ForwardRef is a problematic class: it is public and documented,
but the documentation lists no attributes or methods for it.
Nonetheless, third-party libraries make use of some of its undocumented
attributes. For instance, Pydantic and Typeguard use the _evaluate
method; beartype and pyanalyze use the __forward_arg__ attribute.

We replace the existing but poorly specified !typing.ForwardRef with a
new class, annotationlib.ForwardRef. It is designed to be mostly
compatible with existing uses of the !typing.ForwardRef class, but
without the behaviors specific to the !typing module. For compatibility
with existing users, we keep the private _evaluate method, but mark it
as deprecated. It delegates to a new public function in the !typing
module, typing.evaluate_forward_ref, that is designed to evaluate
forward references in a way that is specific to type hints.

We add a function annotationlib.call_annotate_function as a helper for
calling __annotate__ functions. This is a useful building block when
implementing functionality that needs to partially evaluate annotations
while a class is being constructed. For example, the implementation of
typing.NamedTuple needs to retrieve the annotations from a class
namespace dictionary before the namedtuple class itself can be
constructed, because the annotations determine what fields exist on the
namedtuple.

Specification

A new module, annotationlib, is added to the standard library. Its aim
is to provide tooling for introspecting and wrapping annotations.

The exact contents of the module are not yet specified. We will add
support for PEP 649 semantics to standard library functionality that
uses annotations, such as dataclasses and typing.TypedDict, and use the
experience to inform the design of the new module.

The module will contain the following functionality:

-   get_annotations(): A function that returns the annotations of a
    function, module, or class. This will replace
    inspect.get_annotations. The latter will delegate to the new
    function. It may eventually be deprecated, but to minimize
    disruption, we do not propose an immediate deprecation.
-   get_annotate_function(): A function that returns the __annotate__
    function of an object, if it has one, or None if it does not. This
    is usually equivalent to accessing the .__annotate__ attribute,
    except in the presence of metaclasses (see
    below <pep749-metaclasses>).
-   Format: an enum that contains the possible formats of annotations.
    This will replace the VALUE, FORWARDREF, and SOURCE formats in
    PEP 649. PEP 649 proposed to make these values global members of the
    inspect module; we prefer to place them within an enum. We propose
    to add a fourth format, VALUE_WITH_FAKE_GLOBALS (see below).
-   ForwardRef: a class representing a forward reference; it may be
    returned by get_annotations() when the format is FORWARDREF. The
    existing class typing.ForwardRef will become an alias of this class.
    Its members include:
    -   __forward_arg__: the string argument of the forward reference
    -   evaluate(globals=None, locals=None, type_params=None, owner=None):
        a method that attempts to evaluate the forward reference. The
        ForwardRef object may hold a reference to the globals and other
        namespaces of the object that it originated from. If so, these
        namespaces may be used to evaluate the forward reference. The
        owner argument may be the object that holds the original
        annotation, such as the class or module object; it is used to
        extract the globals and locals namespaces if these are not
        provided.
    -   _evaluate(), with the same interface as the existing
        ForwardRef._evaluate method. It will be undocumented and
        immediately deprecated. It is provided for compatibility with
        existing users of typing.ForwardRef.
-   call_annotate_function(func: Callable, format: Format): a helper for
    calling an __annotate__ function with a given format. If the
    function does not support this format, call_annotate_function() will
    set up a "fake globals" environment, as described in PEP 649, and
    use that environment to return the desired annotations format.
-   call_evaluate_function(func: Callable | None, format: Format):
    similar to call_annotate_function, but does not rely on the function
    returning an annotations dictionary. This is intended to be used for
    evaluating deferred attributes introduced by PEP 695 and PEP 696;
    see below for details. func may be None for convenience; if None is
    passed, the function also returns None.
-   annotations_to_string(annotations: dict[str, object]) -> dict[str, str]:
    a function that converts each value in an annotations dictionary to
    a string representation. This is useful for implementing the SOURCE
    format in cases where the original source is not available, such as
    in the functional syntax for typing.TypedDict.
-   value_to_string(value: object) -> str: a function that converts a
    single value to a string representation. This is used by
    annotations_to_string. It uses repr() for most values, but for types
    it returns the fully qualified name. It is also useful as a helper
    for the repr() of a number of objects in the typing and
    collections.abc modules.

A new function is also added to the !typing module,
typing.evaluate_forward_ref. This function is a wrapper around the
ForwardRef.evaluate method, but it performs additional work that is
specific to type hints. For example, it recurses into complex types and
evaluates additional forward references within these types.

Contrary to PEP 649, the annotation formats (VALUE, FORWARDREF, and
SOURCE) will not be added as global members of the inspect module. The
only recommended way to refer to these constants will be as
annotationlib.Format.VALUE.

Open issues

What should this module be called? Some ideas:

-   annotations: The most obvious name, but it may cause confusion with
    the existing from __future__ import annotations, because users may
    have both import annotations and from __future__ import annotations
    in the same module. The use of a common word as the name will make
    the module harder to search for. There is a PyPI package
    annotations, but it had only a single release in 2015 and looks
    abandoned.
-   annotation (in the singular): Similar, but does not cause confusion
    with the future import. There is an abandoned PyPI package
    annotation, but it apparently never released any artifacts.
-   annotools: Analogous to itertools and functools, but "anno" is a
    less obvious abbreviation than "iter" or "func". As of this writing,
    there is no PyPI package with this name.
-   annotationtools: A more explicit version. There is a PyPI package
    annotationtools, which had a release in 2023.
-   annotation_tools: A variation of the above but without a PyPI
    conflict. However, no other public standard library module has an
    underscore in its name.
-   annotationslib: Analogous to tomllib, pathlib, and importlib. There
    is no PyPI package with this name.
-   annotationlib: Similar to the above, but one character shorter and
    subjectively reads better. Also not taken on PyPI.

Rejected alternatives

Add the functionality to the inspect module: As described above, the
inspect module is already quite large, and its import time is
prohibitive for some use cases.

Add the functionality to the typing module: While annotations are mostly
used for typing, they may also be used for other purposes. We prefer to
keep a clean separation between functionality for introspecting
annotations and functionality that is exclusively meant for type hints.

Add the functionality to the types module: The types module is meant for
functionality related to types, and annotations can exist on functions
and modules, not only on types.

Develop this functionality in a third-party package: The functionality
in this new module will be pure Python code, and it is possible to
implement a third-party package that provides the same functionality by
interacting directly with __annotate__ functions generated by the
interpreter. However, the functionality of the proposed new module will
certainly be useful in the standard library itself (e.g., for
implementing dataclasses and typing.NamedTuple), so it makes sense to
include it in the standard library.

Add this functionality to a private module: It would be possible to
initially develop the module in a private standard library module (e.g.,
_annotations), and publicize it only after we have gained more
experience with the API. However, we already know that we will need
parts of this module for the standard library itself (e.g., for
implementing !dataclasses and !typing.NamedTuple). Even if we make it
private, the module will inevitably get used by third-party users. It is
preferable to start with a clear, documented API from the beginning, to
enable third-party users to support PEP 649 semantics as thoroughly as
the standard library. The module will immediately be used in other parts
of the standard library, ensuring that it covers a reasonable set of use
cases.

Behavior of the REPL

PEP 649 specifies the following behavior of the interactive REPL:

  For the sake of simplicity, in this case we forego delayed evaluation.
  Module-level annotations in the REPL shell will continue to work
  exactly as they do with “stock semantics”, evaluating immediately and
  setting the result directly inside the __annotations__ dict.

There are several problems with this proposed behavior. It makes the
REPL the only context where annotations are still evaluated immediately,
which is confusing for users and complicates the language.

It also makes the implementation of the REPL more complex, as it needs
to ensure that all statements are compiled in "interactive" mode, even
if their output does not need to be displayed. (This matters if there
are multiple statements in a single line evaluated by the REPL.)

Most importantly, this breaks some plausible use cases that
inexperienced users could run into. A user might write the following in
a file:

    a: X | None = None
    class X: ...

Under PEP 649 this would work fine: X is not yet defined when it is used
in the annotation for a, but the annotation is lazily evaluated.
However, if a user were to paste this same code into the REPL and
execute it line by line, it would throw a NameError, because the name X
is not yet defined.

This topic was previously discussed on Discourse.

Specification

We propose to treat the interactive console like any other module-level
code, and make annotations lazily evaluated. This makes the language
more consistent and avoids subtle behavior changes between modules and
the REPL.

Because the REPL is evaluated line by line, we would generate a new
__annotate__ function for every evaluated statement in the global scope
that contains annotations. Whenever a line containing annotations is
evaluated, the previous __annotate__ function is lost:

    >>> x: int
    >>> __annotate__(1)
    {'x': <class 'int'>}
    >>> y: str
    >>> __annotate__(1)
    {'y': <class 'str'>}
    >>> z: doesntexist
    >>> __annotate__(1)
    Traceback (most recent call last):
    File "<python-input-5>", line 1, in <module>
        __annotate__(1)
        ~~~~~~~~~~~~^^^
    File "<python-input-4>", line 1, in __annotate__
        z: doesntexist
           ^^^^^^^^^^^
    NameError: name 'doesntexist' is not defined

There will be no __annotations__ key in the global namespace of the
REPL. In module namespaces, this key is created lazily when the
__annotations__ descriptor of the module object is accessed, but in the
REPL there is no such module object.

Classes and functions defined within the REPL will also work like any
other classes, so evaluation of their annotations will be deferred. It
is possible to access the __annotations__ and __annotate__ attributes or
use the annotationlib module to introspect the annotations.

Wrappers that provide __annotations__

Several objects in the standard library and elsewhere provide
annotations for their wrapped object. PEP 649 does not specify how such
wrappers should behave.

Specification

Wrappers that provide annotations should be designed with the following
goals in mind:

-   Evaluation of __annotations__ should be deferred for as long as
    possible, consistent with the behavior of built-in functions,
    classes, and modules.
-   Backward compatibility with the behavior prior to the implementation
    of PEP 649 should be preserved.
-   The __annotate__ and __annotations__ attributes should both be
    supplied with semantics consistent to those of the wrapped object.

More specifically:

-   functools.update_wrapper (and therefore functools.wraps) will copy
    only the __annotate__ attribute from the wrapped object to the
    wrapper. The __annotations__ descriptor on the wrapper function will
    use the copied __annotate__.
-   The constructors for classmethod and staticmethod currently copy the
    __annotations__ attribute from the wrapped object to the wrapper.
    They will instead have writable attributes for __annotate__ and
    __annotations__. Reading these attributes will retrieve the
    corresponding attribute from the underlying callable and cache it in
    the wrapper's __dict__. Writing to these attributes will directly
    update the __dict__, without affecting the wrapped callable.

Annotations and metaclasses

Testing of the initial implementation of this PEP revealed serious
problems with the interaction between metaclasses and class annotations.

Pre-existing bugs

We found several bugs in the existing behavior of __annotations__ on
classes while investigating the behaviors to be specified in this PEP.
Fixing these bugs on Python 3.13 and earlier is outside the scope of
this PEP, but they are noted here to explain the corner cases that need
to be dealt with.

For context, on Python 3.10 through 3.13 the __annotations__ dictionary
is placed in the class namespace if the class has any annotations. If it
does not, there is no __annotations__ class dictionary key when the
class is created, but accessing cls.__annotations__ invokes a descriptor
defined on type that returns an empty dictionary and stores it in the
class dictionary. Static types <static-types> are an exception: they
never have annotations, and accessing .__annotations__ raises
AttributeError. On Python 3.9 and earlier, the behavior was different;
see gh-88067.

The following code fails identically on Python 3.10 through 3.13:

    class Meta(type): pass

    class X(metaclass=Meta):
        a: str

    class Y(X): pass

    Meta.__annotations__  # important
    assert Y.__annotations__ == {}, Y.__annotations__  # fails: {'a': <class 'str'>}

If the annotations on the metaclass Meta are accessed before the
annotations on Y, then the annotations for the base class X are leaked
to Y. However, if the metaclass's annotations are not accessed (i.e.,
the line Meta.__annotations__ above is removed), then the annotations
for Y are correctly empty.

Similarly, annotations from annotated metaclasses leak to unannotated
classes that are instances of the metaclass:

    class Meta(type):
        a: str

    class X(metaclass=Meta):
        pass

    assert X.__annotations__ == {}, X.__annotations__  # fails: {'a': <class 'str'>}

The reason for these behaviors is that if the metaclass contains an
__annotations__ entry in its class dictionary, this prevents instances
of the metaclass from using the __annotations__ data descriptor on the
base type class. In the first case, accessing Meta.__annotations__ sets
Meta.__dict__["__annotations__"] = {} as a side effect. Then, looking up
the __annotations__ attribute on Y first sees the metaclass attribute,
but skips it because it is a data descriptor. Next, it looks in the
class dictionaries of the classes in its method resolution order (MRO),
finds X.__annotations__, and returns it. In the second example, there
are no annotations anywhere in the MRO, so type.__getattribute__ falls
back to returning the metaclass attribute.

Metaclass behavior with PEP 649

With PEP 649, the behavior of accessing the .__annotations__ attribute
on classes when metaclasses are involved becomes even more erratic,
because now __annotations__ is only lazily added to the class dictionary
even for classes with annotations. The new __annotate__ attribute is
also lazily created on classes without annotations, which causes further
misbehaviors when metaclasses are involved.

The cause of these problems is that we set the __annotate__ and
__annotations__ class dictionary entries only under some circumstances,
and rely on descriptors defined on type to fill them in if they are not
set. When normal attribute lookup is used, this approach breaks down in
the presence of metaclasses, because entries in the metaclass's own
class dictionary can render the descriptors invisible.

While we considered several approaches that would allow
cls.__annotations__ and cls.__annotate__ to work reliably when cls is a
type with a custom metaclass, any such approach would expose significant
complexity to advanced users. Instead, we recommend a simpler approach
that confines the complexity to the annotationlib module: in
annotationlib.get_annotations, we bypass normal attribute lookup by
using the type.__annotations__ descriptor directly.

Specification

Users should always use annotationlib.get_annotations to access the
annotations of a class object, and annotationlib.get_annotate_function
to access the __annotate__ function. These functions will return only
the class's own annotations, even when metaclasses are involved.

The behavior of accessing the __annotations__ and __annotate__
attributes on classes with a metaclass other than builtins.type is
unspecified. The documentation should warn against direct use of these
attributes and recommend using the annotationlib module instead.

Similarly, the presence of __annotations__ and __annotate__ keys in the
class dictionary is an implementation detail and should not be relied
upon.

Rejected alternatives

We considered two broad approaches for dealing with the behavior of the
__annotations__ and __annotate__ entries in classes:

-   Ensure that the entry is always present in the class dictionary,
    even if it is empty or has not yet been evaluated. This means we do
    not have to rely on the descriptors defined on type to fill in the
    field, and therefore the metaclass's attributes will not interfere.
    (Prototype in gh-120719.)
-   Ensure that the entry is never present in the class dictionary, or
    at least never added by logic in the language core. This means that
    the descriptors on type will always be used, without interference
    from the metaclass. (Prototype in gh-120816.)

Alex Waygood suggested an implementation using the first approach. When
a heap type (such as a class created through the class statement) is
created, cls.__dict__["__annotations__"] is set to a special descriptor.
On __get__, the descriptor evaluates the annotations by calling
__annotate__ and returning the result. The annotations dictionary is
cached within the descriptor instance. The descriptor also behaves like
a mapping, so that code that uses cls.__dict__["__annotations__"] will
still usually work: treating the object as a mapping will evaluate the
annotations and behave as if the descriptor itself was the annotations
dictionary. (Code that assumes that cls.__dict__["__annotations__"] is
specifically an instance of dict may break, however.)

This approach is also straightforward to implement for __annotate__:
this attribute is already always set for classes with annotations, and
we can set it explicitly to None for classes without annotations.

While this approach would fix the known edge cases with metaclasses, it
introduces significant complexity to all classes, including a new
built-in type (for the annotations descriptor) with unusual behavior.

The alternative approach would be to never set
__dict__["__annotations__"] and use some other storage to store the
cached annotations. This behavior change would have to apply even to
classes defined under from __future__ import annotations, because
otherwise there could be buggy behavior if a class is defined without
from __future__ import annotations but its metaclass does have the
future enabled. As PEP 649 previously noted, removing __annotations__
from class dictionaries also has backwards compatibility implications:
cls.__dict__.get("__annotations__") is a common idiom to retrieve
annotations.

This approach would also mean that accessing .__annotations__ on an
instance of an annotated class no longer works. While this behavior is
not documented, it is a long-standing feature of Python and is relied
upon by some users.

Adding the VALUE_WITH_FAKE_GLOBALS format

PEP 649 specifies:

  This PEP assumes that third-party libraries may implement their own
  __annotate__ methods, and those functions would almost certainly work
  incorrectly when run in this "fake globals" environment. For that
  reason, this PEP allocates a flag on code objects, one of the unused
  bits in co_flags, to mean "This code object can be run in a 'fake
  globals' environment." This makes the "fake globals" environment
  strictly opt-in, and it's expected that only __annotate__ methods
  generated by the Python compiler will set it.

However, this mechanism couples the implementation with low-level
details of the code object. The code object flags are CPython-specific
and the documentation explicitly warns <inspect-module-co-flags> against
relying on the values.

Larry Hastings suggested an alternative approach that does not rely on
code flags: a fourth format, VALUE_WITH_FAKE_GLOBALS. Compiler-generated
annotate functions would support only the VALUE and
VALUE_WITH_FAKE_GLOBALS formats, both of which are implemented
identically. The standard library would use the VALUE_WITH_FAKE_GLOBALS
format when invoking an annotate function in one of the special "fake
globals" environments.

This approach is useful as a forward-compatible mechanism for adding new
annotation formats in the future. Users who manually write annotate
functions should raise NotImplementedError if the
VALUE_WITH_FAKE_GLOBALS format is requested, so the standard library
will not call the manually written annotate function with "fake
globals", which could have unpredictable results.

Specification

An additional format, FAKE_GLOBALS_VALUE, is added to the Format enum in
the annotationlib module, with value equal to 2. (As a result, the
values of the other formats will shift relative to PEP 649: FORWARDREF
will be 3 and SOURCE will be 4.)

Compiler-generated annotate functions will support this format and
return the same value as they would return for the VALUE format. The
standard library will pass this format to the __annotate__ function when
it is called in a "fake globals" environment, as used to implement the
FORWARDREF and SOURCE formats. All public functions in the annotationlib
module that accept a format argument will raise NotImplementedError if
the format is FAKE_GLOBALS_VALUE.

Third-party code that implements __annotate__ functions should raise
NotImplementedError if the FAKE_GLOBALS_VALUE format is passed and the
function is not prepared to be run in a "fake globals" environment. This
should be mentioned in the data model documentation for __annotate__.

Effect of deleting __annotations__

PEP 649 specifies:

  Setting o.__annotations__ to a legal value automatically sets
  o.__annotate__ to None.

However, the PEP does not say what happens if the __annotations__
attribute is deleted (using del). It seems most consistent that deleting
the attribute will also delete __annotate__.

Specification

Deleting the __annotations__ attribute on functions, modules, and
classes results in setting __annotate__ to None.

Deferred evaluation of PEP 695 and 696 objects

Since PEP 649 was written, Python 3.12 and 3.13 gained support for
several new features that also use deferred evaluation, similar to the
behavior this PEP proposes for annotations:

-   The value of type aliases created through the type statement (PEP
    695)
-   The bound and constraints of typing.TypeVar objects created through
    the syntax for generics (PEP 695)
-   The default value of typing.TypeVar, ParamSpec, and
    typing.TypeVarTuple objects (PEP 696)

Currently, these objects use deferred evaluation, but there is no direct
access to the function object used for deferred evaluation. To enable
the same kind of introspection that is now possible for annotations, we
propose to expose the internal function objects, allowing users to
evaluate them using the FORWARDREF and SOURCE formats.

Specification

We will add the following new attributes:

-   evaluate_value on typing.TypeAliasType
-   evaluate_bound, evaluate_constraints, and evaluate_default on
    typing.TypeVar
-   evaluate_default on typing.ParamSpec
-   evaluate_default on typing.TypeVarTuple

Except for evaluate_value, these attributes may be None if the object
does not have a bound, constraints, or default. Otherwise, the attribute
is a callable, similar to an __annotate__ function, that takes a single
integer argument and returns the evaluated value. Unlike __annotate__
functions, these callables return a single value, not a dictionary of
annotations. These attributes are read-only.

Usually, users would use these attributes in combinations with
annotationlib.call_evaluate_function. For example, to get a TypeVar's
bound in SOURCE format, one could write
annotationlib.call_evaluate_function(T.evaluate_bound, annotationlib.Format.SOURCE).

Behavior of dataclass field types

One consequence of the deferred evaluation of annotations is that
dataclasses can use forward references in their annotations:

    >>> from dataclasses import dataclass
    >>> @dataclass
    ... class D:
    ...     x: undefined
    ...

However, the FORWARDREF format leaks into the field types of the
dataclass:

    >>> fields(D)[0].type
    ForwardRef('undefined')

We considered a change where the .type attribute of a field object would
trigger evaluation of annotations, so that the field type could contain
actual values in the case of forward references that were defined after
the dataclass itself was created, but before the field type is accessed.
However, this would also mean that accessing .type could now run
arbitrary code in the annotation, and potentially throws errors such as
NameError.

Therefore, we consider it more user-friendly to keep the ForwardRef
object in the type, and document that users who want to resolve forward
references can use the ForwardRef.evaluate method.

If use cases come up in the future, we could add additional
functionality, such as a new method that re-evaluates the annotation
from scratch.

Renaming SOURCE to STRING

The SOURCE format is meant for tools that need to show a human-readable
format that is close to the original source code. However, we cannot
retrieve the original source in __annotate__ functions, and in some
cases, we have __annotate__ functions in Python code that do not have
access to the original code. For example, this applies to
dataclasses.make_dataclass and the call-based syntax for
typing.TypedDict.

This makes the name SOURCE a bit of a misnomer. The goal of the format
should indeed be to recreate the source, but the name is likely to
mislead users in practice. A more neutral name would emphasize that the
format returns an annotation dictionary with only strings. We suggest
STRING.

Specification

The SOURCE format is renamed to STRING. To reiterate the changes in this
PEP, the four supported formats are now:

-   VALUE: the default format, which evaluates the annotations and
    returns the resulting values.
-   VALUE_WITH_FAKE_GLOBALS: for internal use; should be handled like
    VALUE by annotate functions that support execution with fake
    globals.
-   FORWARDREF: replaces undefined names with ForwardRef objects.
-   STRING: returns strings, attempts to recreate code close to the
    original source.

Miscellaneous implementation details

PEP 649 goes into considerable detail on some aspects of the
implementation. To avoid confusion, we describe a few aspects where the
current implementation differs from that described in the PEP. However,
these details are not guaranteed to hold in the future, and they may
change without notice in the future, unless they are documented in the
language reference.

Supported operations on ForwardRef objects

The SOURCE format is implemented by the "stringizer" technique, where
the globals dictionary of a function is augmented so that every lookup
results in a special object that can be used to reconstruct the
operations that are performed on the object.

PEP 649 specifies:

  In practice, the "stringizer" functionality will be implemented in the
  ForwardRef object currently defined in the typing module. ForwardRef
  will be extended to implement all stringizer functionality; it will
  also be extended to support evaluating the string it contains, to
  produce the real value (assuming all symbols referenced are defined).

However, this is likely to lead to confusion in practice. An object that
implements stringizer functionality must implement almost all special
methods, including __getattr__ and __eq__, to return a new stringizer.
Such an object is confusing to work with: all operations succeed, but
they are likely to return different objects than the user expects.

The current implementation instead implements only a few useful methods
on the ForwardRef class. During the evaluation of annotations, an
instance of a private stringizer class is used instead of ForwardRef.
After evaluation completes, the implementation of the FORWARDREF format
converts these internal objects into ForwardRef objects.

Signature of __annotate__ functions

PEP 649 specifies the signature of __annotate__ functions as:

  __annotate__(format: int) -> dict

However, using format as a parameter name could lead to collisions if an
annotation uses a symbol named format. The parameter should be
positional-only and have a name that cannot be a legal identifier in
order to avoid this problem.

The current implementation uses the name .format with a leading dot, but
the exact name should be considered an implementation detail and cannot
be relied upon.

The documentation may still use the name format for simplicity.

Backwards Compatibility

PEP 649 provides a thorough discussion of the backwards compatibility
implications on existing code that uses either stock or PEP 563
semantics.

However, there is another set of compatibility problems: new code that
is written assuming PEP 649 semantics, but uses existing tools that
eagerly evaluate annotations. For example, consider a dataclass-like
class decorator @annotator that retrieves the annotated fields in the
class it decorates, either by accessing __annotations__ directly or by
calling inspect.get_annotations.

Once PEP 649 is implemented, code like this will work fine:

    class X:
        y: Y

    class Y: pass

But this will not, unless @annotator is changed to use the new
FORWARDREF format:

    @annotator
    class X:
        y: Y

    class Y: pass

This is not strictly a backwards compatibility issue, since no
previously working code would break; before PEP 649, this code would
have raised NameError at runtime. In a sense, it is no different from
any other new Python feature that needs to be supported by third-party
libraries. Nevertheless, it is a serious issue for libraries that
perform introspection, and it is important that we make it as easy as
possible for libraries to support the new semantics in a
straightforward, user-friendly way.

We will update those parts of the standard library that are affected by
this problem, and we propose to add commonly useful functionality to the
new annotationlib module, so third-party tools can use the same set of
tools.

Security Implications

None.

How to Teach This

The semantics of PEP 649, as modified by this PEP, should largely be
intuitive for users who add annotations to their code. We eliminate the
need for manually adding quotes around annotations that require forward
references, a major source of confusion for users.

For advanced users who need to introspect annotations, the story becomes
more complex. The documentation of the new annotationlib module will
serve as a reference for users who need to interact programmatically
with annotations.

Reference Implementation

The in-progress PR #119891 implements much of this PEP.

Open Issues

We may discover additional areas where PEP 649 needs clarification or
amendment as we make progress on implementing it. Readers are encouraged
to follow the CPython issue tracking the implementation of the PEP and
try out the draft implementation. Any feedback may be incorporated into
future versions of this PEP.

Acknowledgments

First of all, I thank Larry Hastings for writing PEP 649. This PEP
modifies some of his initial decisions, but the overall design is still
his.

I thank Carl Meyer and Alex Waygood for feedback on early drafts of this
PEP. Alex Waygood, Alyssa Coghlan, and David Ellis provided insightful
feedback and suggestions on the interaction between metaclasses and
__annotations__. Larry Hastings also provided useful feedback on this
PEP.

Appendix

Which expressions can be stringified?

PEP 649 acknowledges that the stringifier cannot handle all expressions.
Now that we have a draft implementation, we can be more precise about
the expressions that can and cannot be handled. Below is a list of all
expressions in the Python AST that can and cannot be recovered by the
stringifier. The full list should probably not be added to the
documentation, but creating it is a useful exercise.

First, the stringifier of course cannot recover any information that is
not present in the compiled code, including comments, whitespace,
parenthesization, and operations that get simplified by the AST
optimizer.

Second, the stringifier can intercept almost all operations that involve
names looked up in some scope, but it cannot intercept operations that
operate fully on constants. As a corollary, this also means it is not
safe to request the SOURCE format on untrusted code: Python is powerful
enough that it is possible to achieve arbitrary code execution even with
no access to any globals or builtins. For example:

    >>> def f(x: (1).__class__.__base__.__subclasses__()[-1].__init__.__builtins__["print"]("Hello world")): pass
    ... 
    >>> annotationlib.get_annotations(f, format=annotationlib.Format.SOURCE)
    Hello world
    {'x': 'None'}

(This particular example worked for me on the current implementation of
a draft of this PEP; the exact code may not keep working in the future.)

The following are supported (sometimes with caveats):

-   BinOp
-   UnaryOp
    -   Invert (~), UAdd (+), and USub (-) are supported
    -   Not (not) is not supported
-   Dict (except when using ** unpacking)
-   Set
-   Compare
    -   Eq and NotEq are supported
    -   Lt, LtE, Gt, and GtE are supported, but the operand may be
        flipped
    -   Is, IsNot, In, and NotIn are not supported
-   Call (except when using ** unpacking)
-   Constant (though not the exact representation of the constant; for
    example, escape sequences in strings are lost; hexadecimal numbers
    are converted to decimal)
-   Attribute (assuming the value is not a constant)
-   Subscript (assuming the value is not a constant)
-   Starred (* unpacking)
-   Name
-   List
-   Tuple
-   Slice

The following are unsupported, but throw an informative error when
encountered by the stringifier:

-   FormattedValue (f-strings; error is not detected if conversion
    specifiers like !r are used)
-   JoinedStr (f-strings)

The following are unsupported and result in incorrect output:

-   BoolOp (and and or)
-   IfExp
-   Lambda
-   ListComp
-   SetComp
-   DictComp
-   GeneratorExp

The following are disallowed in annotation scopes and therefore not
relevant:

-   NamedExpr (:=)
-   Await
-   Yield
-   YieldFrom

Copyright

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