[Python-Dev] Postponed annotations break inspection of dataclasses

[Eric V. Smith]

Yes, it’s https://bugs.python.org/issue34776

--
Eric

> On Sep 27, 2018, at 12:05 PM, Ivan Levkivskyi <levkivskyi at gmail.com> wrote:
> 
> 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 <david at 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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