modern_types

__modern_types__ aims to provide PEP 585 + PEP 604 backward compatibility for Python <=3.10 deferred type evaluation. Hence, the targeted Python versions are 3.8 and 3.9.

What does it do?

Prevents type errors in evaluating PEP 585 and PEP 604 type annotations for Python 3.8 and 3.9, which might happen in pydantic models for example.

In Python 3.8, the following code

from __future__ import annotations
from collections import defaultdict
from pprint import pprint
from typing import get_type_hints

import __modern_types__

class Foo:
    a: dict[str, int]
    b: list[int]
    c: set[int]
    d: tuple[int, ...] | None
    e: frozenset[int]
    f: defaultdict[str, int]

pprint(get_type_hints(Foo, globals(), locals()))

gives:

{"a": typing.Dict[str, int],
 "b": typing.List[int],
 "c": typing.Set[int],
 "d": typing.Optional[typing.Tuple[int, ...]],
 "e": typing.FrozenSet[int],
 "f": typing.DefaultDict[str, int]}

Use case

Keep your codebase up-to-date by speeding up migration to modern types, even if you support Python versions >=3.8.

Stop using deprecated typing.Dict, typing.List, typing.Set, typing.Tuple, typing.FrozenSet and typing.DefaultDict!

Importing __modern_types__ will make all typing._eval_type-dependent parts of your application, including pydantic models, work with PEP 585 and PEP 604.

How to use?

[!Warning] Remember that the library does not change the built-in scope at runtime!

So dict[str, int] won't render at runtime, but typing.Dict[str, int] will.

__modern_types__ makes it possible to evaluate dict[str, int] only through the typing.get_type_hints function.

You should remember putting from __future__ import annotations at the top of your modules everywhere you want to leverage __modern_types__.

Simply import __modern_types__ in your code, and it will override the default global namespace of typing._eval_type.

And now you can use modern types everywhere in your code and the following replacements will be applied without overwriting your parameters:

[!Note] Some optional replacements will also be performed if possible, according to those listed in the __modern_types__._auto source code.

__modern_types__ additionally makes sure that generic aliases above are instantiable, which isn't the default behavior.

ProTip: How to subclass built-in generic classes in Python 3.8?

Supposing you are subclassing dict, you could write either

from __future__ import annotations

from typing import Dict, Generic

KT = TypeVar("KT")
VT = TypeVar("VT")


class YourDictSubclass(Dict[KT, VT], Generic[KT, VT]):
    pass

or

from __future__ import annotations

from functools import partial
from typing import TypeVar, _GenericAlias  # type: ignore[attr-defined]

KT = TypeVar("KT")
VT = TypeVar("VT")


@partial(_GenericAlias, params=(KT, VT))
class YourDictSubclass(dict):
    pass

so that YourDictSubclass[str, int], for instance, could be used as an evaluable type annotation.

If you want an API that simplifies this, please submit an issue so it has a reason to become a feature.

Can __modern_types__ be used in production?

Yes. It shouldn't break most of the existing codebases, despite the monkeypatching.

The library simply overrides the default global namespace of typing.get_type_hints and tries to re-assign name-to-object references of un-[]-able and un-|-able types in relevant modules to valid generic aliases from the typing module, such as typing.Dict[KT, VT].

If some keys that __modern_types__ would supply to typing.get_type_hints global namespace are present, they are used instead of the __modern_types__ ones.

Installation

If you want to…

…use this tool in your project 💻

You might simply install it with pip:

pip install modern-types

If you use Poetry, then run:

poetry add modern-types

…contribute to modern_types 🚀

[!Note] If you use Windows, it is highly recommended to complete the installation in the way presented below through WSL2.

1. Fork the modern_types repository on GitHub.

2. Install Poetry.
   Poetry is an amazing tool for managing dependencies & virtual environments, building packages and publishing them. You might use pipx to install it globally (recommended):

   pipx install poetry
   

   _{If you encounter any problems, refer to the official documentation for the most up-to-date installation instructions.}

   Be sure to have Python 3.8 installed—if you use pyenv, simply run:

   pyenv install 3.8
   

3. Clone your fork locally and install dependencies.

   git clone https://github.com/your-username/modern_types path/to/modern_types
   cd path/to/modern_types
   poetry env use $(cat .python-version)
   poetry install
   

   Next up, simply activate the virtual environment and install pre-commit hooks:

   poetry shell
   pre-commit install --hook-type pre-commit --hook-type pre-push
   

For more information on how to contribute, check out CONTRIBUTING.md.
Always happy to accept contributions! ❤️

Legal info

© Copyright by Bartosz Sławecki (@bswck).
This software is licensed under the terms of MIT License.