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Merge pull request #31157 from Ladsgroup/main
2 parents 5af314d + b2b8d7c commit 0b6700b

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numpy/_core/einsumfunc.py

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@@ -968,7 +968,7 @@ def _parse_eq_to_pure_multiplication(a_term, shape_a, b_term, shape_b, out):
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@functools.lru_cache(2**12)
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def _parse_eq_to_batch_matmul(eq, shape_a, shape_b):
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"""Cached parsing of a two term einsum equation into the necessary
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sequence of arguments for contracttion via batched matrix multiplication.
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sequence of arguments for contraction via batched matrix multiplication.
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The steps we need to specify are:
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1. Remove repeated and trivial indices from the left and right terms,

numpy/_core/tests/test_simd.py

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@@ -1227,8 +1227,8 @@ def trunc_div(a, d):
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continue
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dividend = self.load(self._data(dividend))
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data_divc = [trunc_div(a, divisor) for a in dividend]
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divisor_parms = self.divisor(divisor)
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divc = self.divc(dividend, divisor_parms)
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divisor_params = self.divisor(divisor)
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divc = self.divc(dividend, divisor_params)
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assert divc == data_divc
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def test_arithmetic_reduce_sum(self):

numpy/lib/_npyio_impl.py

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@@ -841,7 +841,7 @@ def _preprocess_comments(iterable, comments, encoding):
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Generator that consumes a line iterated iterable and strips out the
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multiple (or multi-character) comments from lines.
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This is a pre-processing step to achieve feature parity with loadtxt
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(we assume that this feature is a nieche feature).
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(we assume that this feature is a niche feature).
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"""
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for line in iterable:
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if isinstance(line, bytes):

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