Implemented the following Jax Frontend functions: #5575
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ivy-leaves
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AlphaPro06
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AlphaPro06
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ivy_tests/test_ivy/test_frontends/test_jax/test_jax_numpy_namespace_functions.py
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Hi @AlphaPro06
The PR looks great overall. Could you kindly make the requested changes? Also, the lint tests seem to be failing. Could you kindly fix that as well 😀
AlphaPro06
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Solved merge conflicts, hopefully now the code can merge. @YushaArif99 Please review and merge if my contribution looks good to you!
ivy-leaves
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TensorFlow Frontend
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@YushaArif99 hi I made the changes you've requested, please let me know if there is anything else I would need to change. If there is nothing else for me to change, please approve and merge my pull request as soon as possible! |
| @handle_cmd_line_args | ||
| @given( | ||
| dtype_and_x=helpers.dtype_and_values( | ||
| available_dtypes=helpers.get_dtypes("integer"), num_arrays=1 |
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Could you kindly explain as to why you're using "integers" and "floats" separately for cumsum and cumprod but using "valid" for asarray. Is valid not working with the other 2?
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Yeah "valid" wasn't really working on cumsum and cumprod, it's also why I tried using "integers" and "floats" differently but now I think "valid" is working now for cumsum and prod but I am getting the MultipleFailures exception where Hypothesis found 2 distinct failures I've mentioned in my new comment.
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Hi, @AlphaPro06. Could you kindly review the changes I have requested? Also, kindly resolve the merge conflicts so that the CI tests can run. I have tried running the tests on codespaces and they are failing for all 3 functions. Kindly look into it as well. Thanks!😊
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@YushaArif99 Hi, I hope all is well. I tried to really debug the issues I have facing. So I tried using valid dtypes for both cumsum and cumprod. I really don't understand how else can I fix the Hypothesis errors I am recieving, I apologize but I really feel I don't know what else can I do to fix these errors. Do you have any suggestions that could help? Thank you! Edit: Just to let you know there are 2 distinct failures now as opposed to 3
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Implemented jax numpy frontend functions