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spec/draft/migration_guide.md

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@@ -10,8 +10,8 @@ The first part gives an overview of the {ref}`ecosystem` libraries, that
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are helpful in different contexts when working with the array API standard.
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The second part is dedicated to {ref}`array-producers`. If your library
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mimics, for example, NumPy's or PyTorch's functionality, then you can find in
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here additional instructions and guidance on how to ensure downstream users
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mimics, for example, NumPy's or PyTorch's functionality, you can find
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additional instructions and guidance here on how to ensure downstream users
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can easily pick your solution as an array provider for their system/algorithm.
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The third part delves into details for array API standard compatibility for
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`array-api-strict` is a library that provides a strict and minimal
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implementation of the array API standard. As a consumer, you can use
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`array-api-strict` in parametrising tests over the array namespace
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to ensure your code uses only APIs compliant which are in the standard.
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to ensure your code uses only APIs that are compliant with the standard.
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(array-api-tests)=
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variable to your package import name.
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3. Inside the `array-api-tests` directory run the command for running pytest: `pytest`. There are
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multiple useful options delivered by the test suite. A few worth mentioning:
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- `--max-examples=1000` - maximal number of test cases to generate when using
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- `--max-examples=1000` - maximum number of test cases to generate when using
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hypothesis. This allows you to balance between execution time of the test
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suite and thoroughness of the testing. It's advised to use as many examples
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as the time buget can fit. Each test case is a random combination of
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as the time budget can fit. Each test case is a random combination of
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possible inputs: the more cases, the higher chance of finding an
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unsupported edge case.
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- With the `--xfails-file` option, you can describe which tests are expected

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