Copilot commented on code in PR #50203:
URL: https://github.com/apache/arrow/pull/50203#discussion_r3468082600
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python/pyarrow/array.pxi:
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@@ -266,6 +266,24 @@ def array(object obj, type=None, mask=None, size=None,
from_pandas=None,
if type is not None and type.id == _Type_EXTENSION:
extension_type = type
type = type.storage_type
+ # GH-49644: when building a fixed_shape_tensor from a sequence of
arrays,
+ # the converter only sees the flat storage type, so validate the
+ # tensor-specific constraints here where the type is still known.
+ if (isinstance(extension_type, FixedShapeTensorType)
+ and isinstance(obj, (list, tuple))):
Review Comment:
The tensor-specific validation (permutation rejection and multi-dim shape
check) only runs when `obj` is a `list`/`tuple`. Other non-array-like sequences
supported by `ConvertPySequence` (e.g., `collections.deque`, custom `Sequence`
types) will bypass this check and can silently build a permuted
`fixed_shape_tensor` with incorrect layout, or accept multi-dimensional
elements with the wrong shape.
Consider applying the validation to any non-array-like
`collections.abc.Sequence` (excluding `str`/`bytes`) so behavior is consistent
across sequence inputs.
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