Message 326148 - Python tracker (original) (raw)

The new postponed annotations have an unexpected interaction with dataclasses. Namely, you cannot get the type hints of any of the data classes methods.

For example, I have some code that inspects the type parameters of a class's __init__ method. (The real use case is to provide a default serializer for the class, but that is not important here.)

from dataclasses import dataclass
from typing import get_type_hints

class Foo:
    pass

@dataclass
class Bar:
    foo: Foo

print(get_type_hints(Bar.__init__))

In Python 3.6 and 3.7, this does what is expected; it prints {'foo': <class '__main__.Foo'>, 'return': <class 'NoneType'>}.

However, if in Python 3.7, I add from __future__ import annotations, then this fails with an error:

NameError: name 'Foo' is not defined

I know why this is happening. The __init__ method is defined in the dataclasses module which does not have the Foo object in its environment, and the Foo annotation is being passed to dataclass and attached to __init__ as the string "Foo" rather than as the original object Foo, but get_type_hints for the new annotations only does a name lookup in the module where __init__ is defined not where the annotation is defined.

I know that the use of lambdas to implement PEP 563 was rejected for performance reasons. I could be wrong, but I think this was motivated by variable annotations because the lambda would have to be constructed each time the function body ran. I was wondering if I could motivate storing the annotations as lambdas in class bodies and function signatures, in which the environment is already being captured and is code that usually only runs once.

Original mailing list discussion: https://mail.python.org/pipermail/python-dev/2018-September/155289.html