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

tests/models/layoutlmv2/test_image_processing_layoutlmv2.py

@@ -64,7 +64,6 @@ class LayoutLMv2ImageProcessingTester(unittest.TestCase):
 @require_torch
 @require_pytesseract
 class LayoutLMv2ImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
-
     image_processing_class = LayoutLMv2ImageProcessor if is_pytesseract_available() else None
 
     def setUp(self):

tests/models/layoutlmv2/test_modeling_layoutlmv2.py

@@ -258,7 +258,6 @@ class LayoutLMv2ModelTester:
 @require_torch
 @require_detectron2
 class LayoutLMv2ModelTest(ModelTesterMixin, unittest.TestCase):
-
     test_pruning = False
     test_torchscript = True
     test_mismatched_shapes = False

tests/models/layoutlmv2/test_tokenization_layoutlmv2.py

@@ -291,7 +291,6 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         tokenizers: List[LayoutLMv2Tokenizer] = self.get_tokenizers(do_lower_case=False)
         for tokenizer in tokenizers:
             with self.subTest(f"{tokenizer.__class__.__name__}"):
-
                 special_token = "[SPECIAL_TOKEN]"
                 special_token_box = [1000, 1000, 1000, 1000]
 
@@ -526,7 +525,6 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         tokenizers = self.get_tokenizers(do_lower_case=False)
         for tokenizer in tokenizers:
             with self.subTest(f"{tokenizer.__class__.__name__}"):
-
                 # test 1: single sequence
                 words, boxes = self.get_words_and_boxes()
 
@@ -1195,7 +1193,6 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         tokenizers = self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(f"{tokenizer.__class__.__name__}"):
-
                 # test 1: single sequence
                 words, boxes = self.get_words_and_boxes()
 
@@ -1283,7 +1280,6 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         tokenizers = self.get_tokenizers(do_lower_case=False)
         for tokenizer in tokenizers:
             with self.subTest(f"{tokenizer.__class__.__name__}"):
-
                 if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
                     return
 
@@ -1551,7 +1547,6 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     def test_special_tokens_initialization(self):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
-
                 added_tokens = [AddedToken("<special>", lstrip=True)]
 
                 tokenizer_r = self.rust_tokenizer_class.from_pretrained(
@@ -1787,7 +1782,6 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
             tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
 
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"):
-
                 if is_torch_available():
                     returned_tensor = "pt"
                 elif is_tf_available():
@@ -2398,7 +2392,6 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
     @slow
     def test_layoutlmv2_integration_test(self):
-
         tokenizer_p = LayoutLMv2Tokenizer.from_pretrained("microsoft/layoutlmv2-base-uncased")
         tokenizer_r = LayoutLMv2TokenizerFast.from_pretrained("microsoft/layoutlmv2-base-uncased")