Generic dataclass detection fails for unions

[pculbertson]

Hi Brent,

I have a very low-level bug to flag for you -- when saving/loading nested dataclasses to yaml (using extras.to_yaml(), extras.from_yaml(), if a dataclass has a Union of two custom types, they don't get detected as custom types for the yaml.Loader to construct.

I wrote a MWE to replicate the issue:

import dataclasses
import dcargs

from typing import Union

@dataclasses.dataclass
class TypeA:
    data: int

@dataclasses.dataclass
class TypeB:
    data: int
    
@dataclasses.dataclass
class Wrapper:
    subclass: Union[TypeA, TypeB] = TypeA(1)
    
if __name__ == "__main__":
    wrapper1 = Wrapper() # Create Wrapper object.
    wrapper2 = dcargs.extras.from_yaml(Wrapper, dcargs.extras.to_yaml(wrapper1)) # Errors, no constructor for TypeA

No worries if this is too low-level to deal with right now -- I think we can work around it by just pickling the configs, but wanted to flag something is going awry in the custom type detection.

[pculbertson]

Wow thanks so much for the lightning-quick patch :)

[brentyi]

oops, seems it was too fast and the CI is broken :)

looking into it...

[brentyi]

Actually, everything seems to be in order; just had a typo in a test + an outdated version of typing_extensions locally.

Appreciate the issue report + reproduction instructions! As an FYI I am thinking of deprecating the API though, I can think of a couple other issues and the benefit over just using PyYAML seems fairly incremental. Will file a separate issue.

[pculbertson]

Thanks, appreciate the heads-up! I wanted to file an issue in case the Union typing issue went further than just the YAML utils. Before your patch, we were just pickling the dataclass objects with no problem, we might just stick with that.

[brentyi]

Sounds good!

If you haven't considered it, I'd recommend also looking into serializing with PyYAML + the standard yaml.load(your_yaml, Loader=yaml.Loader) and yaml.dump(your_dataclass_instance) helpers. It should be basically as flexible as pickle, bigger file sizes but still human-readable + editable.