8000 Linting fails with `No overload variant of "__setitem__" of "ndarray" matches argument types` on `numpy · Issue #8966 · dask/distributed · GitHub
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Linting fails with No overload variant of "__setitem__" of "ndarray" matches argument types on `numpy #8966

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hendrikmakait opened this issue Dec 12, 2024 · 2 comments
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Linting fails with No overload variant of "__setitem__" of "ndarray" matches argument types on `numpy #8966

hendrikmakait opened this issue Dec 12, 2024 · 2 comments

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@hendrikmakait
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CI: https://github.com/dask/distributed/actions/runs/12297748764/job/34319586733#step:4:144

Traceback

distributed/shuffle/_rechunk.py:756: error: No overload variant of "__setitem__" of "ndarray" matches argument types "tuple[int, ...]", "TaskRef"  [call-overload]
distributed/shuffle/_rechunk.py:756: note: Possible overload variants:
distributed/shuffle/_rechunk.py:756: note:     def __setitem__(self, str | list[str], _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], /) -> None
distributed/shuffle/_rechunk.py:756: note:     def __setitem__(self, SupportsIndex | slice | EllipsisType | _SupportsArray[dtype[numpy.bool[builtins.bool]] | dtype[integer[Any]]] | _NestedSequence[_SupportsArray[dtype[numpy.bool[builtins.bool]] | dtype[integer[Any]]]] | builtins.bool | int | _NestedSequence[builtins.bool | int] | tuple[SupportsIndex | slice | EllipsisType | _SupportsArray[dtype[numpy.bool[builtins.bool]] | dtype[integer[Any]]] | _NestedSequence[_SupportsArray[dtype[numpy.bool[builtins.bool]] | dtype[integer[Any]]]] | builtins.bool | int | _NestedSequence[builtins.bool | int] | None, ...] | None, _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], /) -> None
Found 1 error in 1 file (checked 339 source files)

Error: Process completed with exit code 1.

This is likely caused by numpy/numpy#27964
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phofl commented Dec 18, 2024

the legacy df removal PR has a fix for that

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phofl commented Dec 20, 2024

fixed in the other pr

@phofl phofl closed this as completed Dec 20, 2024
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