[antonwolfy]

dpnp.tensor.repeat, and so dpnp.repeat, accepts a uint64 repeats array, but it checked the array for negative values before casting it to int64. A uint64 value is never negative, so values above the int64 maximum got past the check:

• with more than one element, the value wrapped to a negative number only when copied to int64, and the result was silently wrong
• with a single element, it was turned into a Python int too large for the output shape, which raised an OverflowError

uint64 repeats are now cast to int64 first, so such values become negative and raise the same ValueError as any other negative repeat:

import dpnp as np

x = np.arange(3)
np.repeat(x, np.array([1, 2**64 - 1, 1], dtype=np.uint64))  # was array([2]), now ValueError
np.repeat(x, np.array([2**64 - 1], dtype=np.uint64))        # was OverflowError, now ValueError

test_repeat_arg_validation now covers both cases, and both checks fail without the fix.

• Have you provided a meaningful PR description?
• Have you added a test, reproducer or referred to an issue with a reproducer?
• Have you tested your changes locally for CPU and GPU devices?
• Have you made sure that new changes do not introduce compiler warnings?
• Have you checked performance impact of proposed changes?
• Have you added documentation for your changes, if necessary?
• Have you added your changes to the changelog?

[github-actions]

Array API standard conformance tests for dpnp=0.21.0dev15=np2py314h8d9cdd5_13 ran successfully.
Passed: 1376
Failed: 0
Skipped: 6

[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/pull/3115/index.html

[coveralls]

coverage: 82.617% (+0.002%) from 82.615% — sync-cupy-tests-numpy-2-6 into master

[vlad-perevezentsev]

LGTM
Thank you @antonwolfy