Testing mypy stubs, plugins, and types

By Nikita Sobolev

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Have you ever tried to:

In case you try to do any of these, you will soon find out that you need to test your types. Wait, what? Let me explain this paradox in detail.

The first tests for types in Python

Let’s start with a history lesson. The first time I got interested in mypy, I found their testing technique unique and interesting. That’s how it looks like:

[case testNestedListAssignmentToTuple]
from typing import List
a, b, c = None, None, None # type: (A, B, C) a, b = [a, b]
a, b = [a] # E: Need more than 1 value to unpack (2 expected)
a, b = [a, b, c] # E: Too many values to unpack (2 expected, 3 provided)

This looks familiar:

  • [case] defines a new test, like def test_ does
  • The contents inside are raw python source code lines that are processed with mypy
  • # E: comments are assert statements that tell what mypy output is expected at each line

So, we can write this kind of tests for our libraries as well, right? That was the question when I started writing returns library (which is a typed monad implementation in Python). So, I needed to test what is going on inside and what types are revealed by mypy. Then I tried to reuse this test cases from mypy.

Long story short, it is impossible. This little helper is builtin inside mypy source code and cannot be reused. So, I started to look for other solutions.

Modern approach

I stumbled on pytest-mypy-plugins package. It was originally created to make sure that types for django works fine in TypedDjango project. Check out my previous post about it.

To install pytest-mypy-plugins in your project run:

pip install pytest-mypy-plugins

It works similar to mypy’s own test cases, but with a slightly different design. Let’s create a yaml file and place it as ./typesafety/test_compose.yml:

# ./typesafety/test_compose.yml
- case: compose_two_functions main: | from myapp import first, second reveal_type(second(first(1))) # N: Revealed type is 'builtins.str*' files: - path: myapp.py content: | def first(num: int) -> float: return float(num) def second(num: float) -> str: return str(num)

What do we have here?

  • case definition, this is basically a test’s name
  • main section that contains python source code that is required for the test
  • # N: comment that indicates a note from mypy
  • files section where you can create temporary helper files to be used in this test

Nice! How can we run it? Since pytest-mypy-plugins is a pytest plugin, we only need to run pytest as usual and to specify our mypy configuration file (defaults to mypy.ini):

pytest --mypy-ini-file=setup.cfg

You can have two mypy configurations: one for your project, one for tests. Just saying. Let’s have a look at our setup.cfg contents:

check_untyped_defs = True
ignore_errors = False
ignore_missing_imports = True
strict_optional = True

That’s the invokation result:

» pytest --mypy-ini-file=setup.cfg
================================ test session starts =================================
platform darwin -- Python 3.7.4, pytest-5.1.1, py-1.8.0, pluggy-0.12.0
rootdir: /code/, inifile: setup.cfg
plugins: mypy-plugins-1.0.3
collected 1 item typesafety/test_compose.yml . [100%] ================================= 1 passed in 2.00s ==================================

It works! Let’s complicate our example a little bit.

Checking for errors

We can also use pytest-mypy-plugins to enforce and check constraints on our complex type specs. Let’s imagine you have a type definition with complex generics and you want to make sure that it works correctly.

That’s actually very helpful, because you can check for success cases with raw mypy checks, while you cannot tell mypy to expect an error for a specific expression or call.

Let’s begin with our complex type definition:

# returns/functions.py
from typing import Callable, TypeVar # Aliases:
_FirstType = TypeVar('_FirstType')
_SecondType = TypeVar('_SecondType')
_ThirdType = TypeVar('_ThirdType') def compose( first: Callable[[_FirstType], _SecondType], second: Callable[[_SecondType], _ThirdType],
) -> Callable[[_FirstType], _ThirdType]: """Allows typed function composition.""" return lambda argument: second(first(argument))

This code takes two function and checks that their types match, so they can be composed. Let’s test it:

# ./typesafety/test_compose.yml
- case: compose_two_wrong_functions main: | from returns.functions import compose def first(num: int) -> float: return float(num) def second(num: str) -> str: return str(num) reveal_type(compose(first, second)) out: | main:9: error: Cannot infer type argument 2 of "compose" main:9: note: Revealed type is 'def (Any) -> Any'

In this example I changed how we make a type assertion: out is easier for multi-line output than inline comments.

Now we have two passing tests:

» pytest --mypy-ini-file=setup.cfg
================================ test session starts =================================
platform darwin -- Python 3.7.4, pytest-5.1.1, py-1.8.0, pluggy-0.12.0
rootdir: /code, inifile: setup.cfg
plugins: mypy-plugins-1.0.3
collected 2 items typesafety/test_compose.yml .. [100%] ================================= 2 passed in 2.65s ==================================

Let’s test one more complex case.

We can change mypy configuration on per-test bases. Let’s add some new values to the existing configuration:

- case: compose_optional_functions mypy_config: # appends options for this test no_implicit_optional = True main: | from returns.functions import compose def first(num: int = None) -> float: return float(num) def second(num: float) -> str: return str(num) reveal_type(compose(first, second)) out: | main:3: error: Incompatible default for argument "num" (default has type "None", argument has type "int") main:9: note: Revealed type is 'def (builtins.int*) -> builtins.str*'

We added no_implicit_optional configuration option that requires to add explicit Optional[] type to arguments where we set None as a default value. And our test got it from the mypy_config section that appends options to the base mypy settings from --mypy-ini-file setting.

Custom DSL

pytest-mypy-plugins also allows to create custom yaml-based DSLs to make your testing process easier and test cases shorter.

Imagine, that we want to have reveal_type as a top-level key. It will just reveal a type of a source code line that is passed to it. Like so:

- case: reveal_type_extension_is_loaded main: | def my_function(arg: int) -> float: return float(arg) reveal_type: my_function out: | main:4: note: Revealed type is 'def (arg: builtins.int) -> builtins.float'

Let’s have a look at what it takes to achieve it:

# reveal_type_hook.py
from pytest_mypy.item import YamlTestItem def hook(item: YamlTestItem) -> None: parsed_test_data = item.parsed_test_data main_source = parsed_test_data['main'] obj_to_reveal = parsed_test_data.get('reveal_type') if obj_to_reveal: for file in item.files: if file.path.endswith('main.py'): file.content = f'{main_source}\nreveal_type({obj_to_reveal})'

What do we do here?

  1. We get the source code from the main: key
  2. Then append reveal_type() call from the reveal_type: key

As a result, we have a custom DSL that fulfills our initial idea.


» pytest --mypy-ini-file=setup.cfg --mypy-extension-hook=reveal_type_hook.hook
================================ test session starts =================================
platform darwin -- Python 3.7.4, pytest-5.1.1, py-1.8.0, pluggy-0.12.0
rootdir: /code, inifile: setup.cfg
plugins: mypy-plugins-1.0.3
collected 1 item typesafety/test_hook.yml . [100%] ================================= 1 passed in 0.87s ==================================

We pass a new flag: --mypy-extension-hook which points to our own DSL implementation. And it works perfectly! That’s how one can reuse a large amounts of code in yaml-based tests.


pytest-mypy-plugins is an absolute must for people who work a lot with types or mypy plugins in python. It simplifies the process of refactoring and distributing types.

You can have a look at the real world example usage of these tests in:

Share what your use-cases are! We are still in a pretty early stage of this project and we would like to find out what our users are thinking.