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MAINT: result_type cosmetic refactor #142

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Apr 2, 2025
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MAINT: result_type cosmetic refactor #142

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34 changes: 15 additions & 19 deletions array_api_strict/_data_type_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -204,35 +204,31 @@ def result_type(
# required by the spec rather than using np.result_type. NumPy implements
# too many extra type promotions like int64 + uint64 -> float64, and does
# value-based casting on scalar arrays.
A = []
dtypes = []
scalars = []
for a in arrays_and_dtypes:
if isinstance(a, Array):
a = a.dtype
if isinstance(a, DType):
dtypes.append(a)
elif isinstance(a, Array):
dtypes.append(a.dtype)
elif isinstance(a, (bool, int, float, complex)):
scalars.append(a)
elif isinstance(a, np.ndarray) or a not in _all_dtypes:
raise TypeError("result_type() inputs must be array_api arrays or dtypes")
A.append(a)

# remove python scalars
B = [a for a in A if not isinstance(a, (bool, int, float, complex))]
else:
raise TypeError(
"result_type() inputs must be Array API arrays, dtypes, or scalars"
)

if len(B) == 0:
if not dtypes:
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This got me off at a first glance, but yeah it's OK because dtypes contains both arrays and dtypes.
Is fine, even if resembles a bit a snippet I recently saw in somebody's going through the "intro to python" online course: mytuple = [].

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The dtypes list contains exclusively dtypes though.

raise ValueError("at least one array or dtype is required")
elif len(B) == 1:
result = B[0]
else:
t = B[0]
for t2 in B[1:]:
t = _result_type(t, t2)
result = t
result = dtypes[0]
for t2 in dtypes[1:]:
result = _result_type(result, t2)

if len(scalars) == 0:
if not scalars:
return result

if get_array_api_strict_flags()['api_version'] <= '2023.12':
raise TypeError("result_type() inputs must be array_api arrays or dtypes")
raise TypeError("result_type() inputs must be Array API arrays or dtypes")

# promote python scalars given the result_type for all arrays/dtypes
from ._creation_functions import empty
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