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[BE] Update flake8-comprehensions and adapt to rule C418 (#99178)
Applies rule C418 and fixes all instances of it. Also updates flake8-comprehension Pull Request resolved: https://github.com/pytorch/pytorch/pull/99178 Approved by: https://github.com/ezyang
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2
.flake8
2
.flake8
@ -14,7 +14,7 @@ ignore =
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# these ignores are from flake8-bugbear; please fix!
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B007,B008,B017,B019,B020,B023,B024,B026,B027,B028,B903,B904,B905,B906,B907
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# these ignores are from flake8-comprehensions; please fix!
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C407
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C407,C419,
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# these ignores are from flake8-logging-format; please fix!
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G100,G101,G200,G201,G202
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# these ignores are from flake8-simplify. please fix or ignore with commented reason
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@ -35,7 +35,7 @@ init_command = [
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'--dry-run={{DRYRUN}}',
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'flake8==6.0.0',
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'flake8-bugbear==23.3.23',
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'flake8-comprehensions==3.11.1',
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'flake8-comprehensions==3.12.0',
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'flake8-executable==2.1.3',
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'flake8-logging-format==0.9.0',
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'flake8-pyi==23.3.1',
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@ -263,7 +263,7 @@ class TestList(JitTestCase):
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def test_dict_keyword_with_mapping(self):
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def fn():
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return dict({"foo" : 1, "bar" : 2, "baz" : 3})
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return {"foo" : 1, "bar" : 2, "baz" : 3}
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self.checkScript(fn, ())
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@ -275,7 +275,7 @@ class TestList(JitTestCase):
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def test_dict_keyword_with_dict_comprehension(self):
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def fn():
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return dict({i: chr(i + 65) for i in range(4)})
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return {i: chr(i + 65) for i in range(4)}
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self.checkScript(fn, ())
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@ -287,7 +287,7 @@ class TestList(JitTestCase):
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def test_dict_keyword_with_empty_dict_comprehension(self):
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def fn():
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return dict({})
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return {}
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self.checkScript(fn, ())
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@ -2185,7 +2185,7 @@ class TestTyping(TestCase):
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self.assertFalse(issubinstance(d, t[int])) # type: ignore[index]
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# dict
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d = dict({'1': 1, '2': 2.})
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d = {'1': 1, '2': 2.}
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self.assertTrue(issubinstance(d, Dict))
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self.assertTrue(issubinstance(d, Dict[str, T_co]))
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self.assertFalse(issubinstance(d, Dict[str, int]))
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@ -5,14 +5,14 @@ __all__ = [
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def get_static_sparse_quantized_mapping():
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import torch.ao.nn.sparse
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_static_sparse_quantized_mapping = dict({
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_static_sparse_quantized_mapping = {
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torch.nn.Linear: torch.ao.nn.sparse.quantized.Linear,
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})
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}
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return _static_sparse_quantized_mapping
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def get_dynamic_sparse_quantized_mapping():
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import torch.ao.nn.sparse
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_dynamic_sparse_quantized_mapping = dict({
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_dynamic_sparse_quantized_mapping = {
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torch.nn.Linear: torch.ao.nn.sparse.quantized.dynamic.Linear,
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})
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}
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return _dynamic_sparse_quantized_mapping
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