Скачать или смотреть Resolving NotImplementedError with @ singledispatch and @ typechecked in PyTest

Learn how to successfully handle exceptions in PyTest when using `@ singledispatch` and `@ typechecked`, ensuring your tests pass smoothly with the correct configuration.
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Understanding the Issue: Testing NotImplementedError with PyTest

When working with Python's @ singledispatch and @ typechecked decorators, you may encounter challenges in testing functions that are expected to raise a NotImplementedError. This issue often arises when your test cases do not match the expected signatures of your functions, resulting in different exceptions such as TypeCheckError.

In this guide, we will explore a specific scenario where you are trying to validate that a NotImplementedError raises for certain invalid types in your tests, and how to effectively adjust your code to ensure that all tests pass as expected.

The Setup

In your implementation, you are using @ singledispatch to create a function named score, which raises a NotImplementedError if the provided arguments do not conform to the expected types: Tuple[int, int] or Tuple[List[int], List[int]].

Example Code

Here is the simplified implementation of your scoring function along with the test case:

[[See Video to Reveal this Text or Code Snippet]]

The Problem with Your Tests

Upon running your tests, you noticed some of them failed with TypeCheckError instead of NotImplementedError. Here’s a closer look at the issues:

Type Mismatches: When you pass parameters of the wrong type (e.g., a str where an int is expected), typechecked raises a TypeCheckError before your code reaches the logic that would raise NotImplementedError.

Analysis of Your Test Cases

Here’s a breakdown of the individual test cases:

Case: ('0', 0)

Expected Result: Raises NotImplementedError

Case: (0, '0')

Outcome: Fails with a TypeCheckError because an int and str do not match the expected types.

Case: ('0', '0')

Expected Result: Raises NotImplementedError

Case: (['0'], [0])

Outcome: Fails with a TypeCheckError for passing a list of strings instead of integers.

Case: ([0], ['0'])

Outcome: Similar to the previous, where type does not match.

Case: (['0'], ['0'])

Outcome: Fails with a TypeCheckError for the same reasons.

Case: (None, None)

Expected Result: Raises NotImplementedError

Fixing the Issue

To ensure your test cases work correctly, you need to modify the way you parametrize your tests by including the expected error type for each case:

[[See Video to Reveal this Text or Code Snippet]]

Key Changes Explained

Include Error in Parametrization: By specifying the expected error for each test case, your tests can appropriately expect either a NotImplementedError or a TypeCheckError based on the input types.

Using Modern Type Annotations: The future-proofing of your code with more modern type hinting ensures better readability and code correctness.

Avoid Redundant Assertions: When you are using pytest.raises, the test automatically fails if an unexpected error is raised, removing the need for an additional assertion inside the context.

Conclusion

By adjusting your test cases and clearly defining what each one should raise, you can successfully run your tests and ensure you’re correctly validating the functionality of your @ singledispatch and @ typechecked decorated functions. This approach helps in maintaining cleaner and more effective tests while allowing you to catch errors instantly.

Final Thoughts

The challenge of managing multiple decorators and their implications on error handling can be tricky, but with proper structuring of your tests, you can minimize confusion and ensure that your code behaves as expected.