Update quality tooling for formatting (#21480)
* Result of black 23.1 * Update target to Python 3.7 * Switch flake8 to ruff * Configure isort * Configure isort * Apply isort with line limit * Put the right black version * adapt black in check copies * Fix copies
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@@ -194,7 +194,6 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizers: List[LayoutXLMTokenizer] = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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special_token = "[SPECIAL_TOKEN]"
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special_token_box = [1000, 1000, 1000, 1000]
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@@ -425,7 +424,6 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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# test 1: single sequence
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words, boxes = self.get_words_and_boxes()
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@@ -1098,7 +1096,6 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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# test 1: single sequence
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words, boxes = self.get_words_and_boxes()
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@@ -1185,7 +1182,6 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
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return
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@@ -1448,7 +1444,6 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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def test_special_tokens_initialization(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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added_tokens = [AddedToken("<special>", lstrip=True)]
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(
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@@ -1684,7 +1679,6 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"):
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if is_torch_available():
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returned_tensor = "pt"
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elif is_tf_available():
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@@ -1853,7 +1847,6 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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@slow
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def test_layoutxlm_integration_test(self):
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tokenizer_p = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")
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tokenizer_r = LayoutXLMTokenizerFast.from_pretrained("microsoft/layoutxlm-base")
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