TYP: np.asarray improved shape-typing - #32263
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charris merged 1 commit intoAug 12, 2026
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Diff from mypy_primer, showing the effect of this PR on type check results on a corpus of open source code: jax (https://github.com/google/jax)
+ jax/_src/array.py:414: note: def [ShapeT: tuple[int, ...], DTypeT: dtype[Any]] asarray(a: _SupportsArray[ndarray[ShapeT, DTypeT]], dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[ShapeT, DTypeT]
+ jax/_src/array.py:414: note: def asarray(a: bool, dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[()], dtype[numpy.bool[builtins.bool]]]
+ jax/_src/array.py:414: note: def asarray(a: int, dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[()], dtype[signedinteger[_32Bit | _64Bit] | Any]]
+ jax/_src/array.py:414: note: def asarray(a: float, dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[()], dtype[float64 | Any]]
+ jax/_src/array.py:414: note: def asarray(a: complex, dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[()], dtype[complex128 | Any]]
+ jax/_src/array.py:414: note: def [ScalarT: generic[Any]] asarray(a: complex | str | generic[Any], dtype: type[ScalarT] | dtype[ScalarT] | _HasDType[dtype[ScalarT]] | _HasNumPyDType[dtype[ScalarT]], order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[()], dtype[ScalarT]]
+ jax/_src/array.py:414: note: def asarray(a: complex | str | generic[Any], dtype: type | str | dtype[Any] | _HasDType[dtype[Any]] | _HasNumPyDType[dtype[Any]] | tuple[Any, Any] | list[Any] | _DTypeDict, order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[()], dtype[Any]]
+ jax/_src/array.py:414: note: def asarray(a: Sequence[Sequence[list[float]]], dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int, int, int], dtype[float64]]
+ jax/_src/array.py:414: note: def asarray(a: Sequence[Sequence[Sequence[bool]]], dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int, int, int], dtype[numpy.bool[builtins.bool]]]
+ jax/_src/array.py:414: note: def asarray(a: Sequence[Sequence[list[int]]], dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int, int, int], dtype[signedinteger[_32Bit | _64Bit]]]
+ jax/_src/array.py:414: note: def asarray(a: Sequence[Sequence[list[complex]]], dtype: None = ..., order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int, int, int], dtype[complex128]]
+ jax/_src/array.py:414: note: def [ScalarT: generic[Any]] asarray(a: Sequence[Sequence[Sequence[complex | str | bytes | generic[Any]]]], dtype: type[ScalarT] | dtype[ScalarT] | _HasDType[dtype[ScalarT]] | _HasNumPyDType[dtype[ScalarT]], order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int, int, int], dtype[ScalarT]]
+ jax/_src/array.py:414: note: def asarray(a: Sequence[Sequence[Sequence[complex | str | bytes | generic[Any]]]], dtype: type | str | dtype[Any] | _HasDType[dtype[Any]] | _HasNumPyDType[dtype[Any]] | tuple[Any, Any] | list[Any] | _DTypeDict, order: Literal['K', 'A', 'C', 'F'] | None = ..., *, device: Literal['cpu'] | None = ..., copy: bool | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int, int, int], dtype[Any]]
optuna (https://github.com/optuna/optuna)
./optuna/study/_multi_objective.py:182: error: INTERNAL ERROR -- Please try using mypy master on GitHub:
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The mypy_primer diff for jax are just |
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Thanks Joren. |
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This ports the
np.arrayshape-typing improvements from #32246 tonp.asarray.Mostly mechanical, so I used AI to copy over the overloads, and I then cleaned up some of the mistakes it made myself :p