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/rag/test_modeling_rag.py

@@ -48,10 +48,10 @@ TOLERANCE = 1e-3
 
 T5_SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
 if is_torch_available() and is_datasets_available() and is_faiss_available():
+    import faiss
     import torch
     from datasets import Dataset
 
-    import faiss
     from transformers import (
         AutoConfig,
         AutoModel,
@@ -99,7 +99,6 @@ def require_retrieval(test_case):
 @require_retrieval
 @require_sentencepiece
 class RagTestMixin:
-
     all_model_classes = (
         (RagModel, RagTokenForGeneration, RagSequenceForGeneration)
         if is_torch_available() and is_datasets_available() and is_faiss_available()
@@ -493,7 +492,7 @@ class RagTestMixin:
         decoder_attention_mask,
         retriever_n_docs,
         generator_n_docs,
-        **kwargs
+        **kwargs,
     ):
         self.assertIsNotNone(config.question_encoder)
         self.assertIsNotNone(config.generator)

tests/models/rag/test_modeling_tf_rag.py

@@ -16,9 +16,9 @@ from transformers.utils import cached_property, is_datasets_available, is_faiss_
 
 
 if is_tf_available() and is_datasets_available() and is_faiss_available():
+    import faiss
     import tensorflow as tf
     from datasets import Dataset
-    import faiss
 
     from transformers import (
         AutoConfig,
@@ -31,7 +31,6 @@ if is_tf_available() and is_datasets_available() and is_faiss_available():
         TFRagSequenceForGeneration,
         TFRagTokenForGeneration,
     )
-
     from transformers.modeling_tf_outputs import TFBaseModelOutput
 
 from ..bart.test_modeling_tf_bart import TFBartModelTester
@@ -58,7 +57,6 @@ def require_retrieval(test_case):
 @require_retrieval
 @require_sentencepiece
 class TFRagTestMixin:
-
     all_model_classes = (
         (TFRagModel, TFRagTokenForGeneration, TFRagSequenceForGeneration)
         if is_tf_available() and is_datasets_available() and is_faiss_available()
@@ -392,7 +390,7 @@ class TFRagTestMixin:
         decoder_attention_mask,
         retriever_n_docs,
         generator_n_docs,
-        **kwargs
+        **kwargs,
     ):
         self.assertIsNotNone(config.question_encoder)
         self.assertIsNotNone(config.generator)

tests/models/rag/test_retrieval_rag.py

@@ -360,7 +360,6 @@ class RagRetrieverTest(TestCase):
     @require_tokenizers
     @require_sentencepiece
     def test_custom_hf_index_end2end_retriever_call(self):
-
         context_encoder_tokenizer = self.get_dpr_ctx_encoder_tokenizer()
         n_docs = 1
         retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)

tests/models/rag/test_tokenization_rag.py

@@ -110,7 +110,6 @@ class RagTokenizerTest(TestCase):
 
     @require_tokenizers
     def test_save_load_pretrained_with_saved_config(self):
-
         save_dir = os.path.join(self.tmpdirname, "rag_tokenizer")
         rag_config = RagConfig(question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict())
         rag_tokenizer = RagTokenizer(question_encoder=self.get_dpr_tokenizer(), generator=self.get_bart_tokenizer())