TYP: unique_{all,inverse} improved shape-typing - #32727
Merged
Merged
Conversation
Member
|
Thanks Joren. |
Riaz1729
pushed a commit
to Riaz1729/numpy
that referenced
this pull request
Sep 23, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
The result type's
inverse_indicesarray ofnp.unique_allandnp.unique_inversesis now shape-typed. I had to revert to using legacyTypeVar's because it turns out that Pyright has a bug in it's "variance" (which is actually called "polarity" in the literature) inference algorithm, causing Pyright to incorrectly infer the generic scalar-type and shape-type parameters as invariant rather than covariant (NamedTupleis immutable,dtypeis covariant in scalar-type, andndarrayis covariant in shape-type, so it should be covariant here as well).The corresponding type-tests for these functions, as well as those for
np.unique_counts, are now also more representative of real-world usage (instead of asserting against a nominal private-API named tuple type with type-check-only generic params).Pair programmed with AI.