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

examples/legacy/token-classification/run_ner.py

@@ -24,6 +24,7 @@ from typing import Dict, List, Optional, Tuple
 import numpy as np
 from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
 from torch import nn
+from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
 
 import transformers
 from transformers import (
@@ -38,7 +39,6 @@ from transformers import (
     set_seed,
 )
 from transformers.trainer_utils import is_main_process
-from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
 
 
 logger = logging.getLogger(__name__)

examples/legacy/token-classification/run_tf_ner.py

@@ -24,6 +24,7 @@ from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
+from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask
 
 from transformers import (
     AutoConfig,
@@ -35,7 +36,6 @@ from transformers import (
     TFTrainingArguments,
 )
 from transformers.utils import logging as hf_logging
-from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask
 
 
 hf_logging.set_verbosity_info()

examples/legacy/token-classification/tasks.py

@@ -3,7 +3,6 @@ import os
 from typing import List, TextIO, Union
 
 from conllu import parse_incr
-
 from utils_ner import InputExample, Split, TokenClassificationTask
 
 

examples/legacy/token-classification/utils_ner.py

@@ -23,6 +23,7 @@ from enum import Enum
 from typing import List, Optional, Union
 
 from filelock import FileLock
+
 from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
 
 
@@ -240,7 +241,6 @@ if is_torch_available():
             # and the others will use the cache.
             lock_path = cached_features_file + ".lock"
             with FileLock(lock_path):
-
                 if os.path.exists(cached_features_file) and not overwrite_cache:
                     logger.info(f"Loading features from cached file {cached_features_file}")
                     self.features = torch.load(cached_features_file)