Do we have a b.p.o. issue about this? If no, then I would recommend to open
one, so that we will not loose track of this.
--
Ivan
On Sat, 22 Sep 2018 at 16:32, David Hagen <da...@drhagen.com> wrote:
> 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.
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