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/pytorch/audio-classification/run_audio_classification.py

@@ -23,10 +23,10 @@ from random import randint
 from typing import Optional
 
 import datasets
+import evaluate
 import numpy as np
 from datasets import DatasetDict, load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     AutoConfig,

examples/pytorch/image-classification/run_image_classification.py

@@ -19,6 +19,7 @@ import sys
 from dataclasses import dataclass, field
 from typing import Optional
 
+import evaluate
 import numpy as np
 import torch
 from datasets import load_dataset
@@ -33,7 +34,6 @@ from torchvision.transforms import (
     ToTensor,
 )
 
-import evaluate
 import transformers
 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -21,8 +21,13 @@ import os
 from pathlib import Path
 
 import datasets
+import evaluate
 import torch
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
 from datasets import load_dataset
+from huggingface_hub import Repository, create_repo
 from torch.utils.data import DataLoader
 from torchvision.transforms import (
     CenterCrop,
@@ -35,12 +40,7 @@ from torchvision.transforms import (
 )
 from tqdm.auto import tqdm
 
-import evaluate
 import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from huggingface_hub import Repository, create_repo
 from transformers import AutoConfig, AutoImageProcessor, AutoModelForImageClassification, SchedulerType, get_scheduler
 from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version

examples/pytorch/language-modeling/run_clm.py

@@ -30,10 +30,10 @@ from itertools import chain
 from typing import Optional
 
 import datasets
+import evaluate
 import torch
 from datasets import load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     CONFIG_MAPPING,

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -33,15 +33,15 @@ from pathlib import Path
 
 import datasets
 import torch
+from accelerate import Accelerator, DistributedType
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
 from datasets import load_dataset
+from huggingface_hub import Repository, create_repo
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers
-from accelerate import Accelerator, DistributedType
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,

examples/pytorch/language-modeling/run_mlm.py

@@ -30,9 +30,9 @@ from itertools import chain
 from typing import Optional
 
 import datasets
+import evaluate
 from datasets import load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     CONFIG_MAPPING,

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -33,15 +33,15 @@ from pathlib import Path
 
 import datasets
 import torch
+from accelerate import Accelerator, DistributedType
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
 from datasets import load_dataset
+from huggingface_hub import Repository, create_repo
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers
-from accelerate import Accelerator, DistributedType
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -30,17 +30,17 @@ from pathlib import Path
 from typing import Optional, Union
 
 import datasets
-import torch
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
 import evaluate
-import transformers
+import torch
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
+from datasets import load_dataset
 from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,

examples/pytorch/question-answering/run_qa.py

@@ -25,11 +25,12 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
-from datasets import load_dataset
-
 import evaluate
-import transformers
+from datasets import load_dataset
 from trainer_qa import QuestionAnsweringTrainer
+from utils_qa import postprocess_qa_predictions
+
+import transformers
 from transformers import (
     AutoConfig,
     AutoModelForQuestionAnswering,
@@ -45,7 +46,6 @@ from transformers import (
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
-from utils_qa import postprocess_qa_predictions
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -25,11 +25,12 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
-from datasets import load_dataset
-
 import evaluate
-import transformers
+from datasets import load_dataset
 from trainer_qa import QuestionAnsweringTrainer
+from utils_qa import postprocess_qa_predictions_with_beam_search
+
+import transformers
 from transformers import (
     DataCollatorWithPadding,
     EvalPrediction,
@@ -44,7 +45,6 @@ from transformers import (
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
-from utils_qa import postprocess_qa_predictions_with_beam_search
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -27,18 +27,19 @@ import random
 from pathlib import Path
 
 import datasets
+import evaluate
 import numpy as np
 import torch
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
-import evaluate
-import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
+from datasets import load_dataset
 from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+from utils_qa import postprocess_qa_predictions_with_beam_search
+
+import transformers
 from transformers import (
     AdamW,
     DataCollatorWithPadding,
@@ -52,7 +53,6 @@ from transformers import (
 )
 from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
-from utils_qa import postprocess_qa_predictions_with_beam_search
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -27,18 +27,19 @@ import random
 from pathlib import Path
 
 import datasets
+import evaluate
 import numpy as np
 import torch
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
-import evaluate
-import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
+from datasets import load_dataset
 from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+from utils_qa import postprocess_qa_predictions
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -53,7 +54,6 @@ from transformers import (
 )
 from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
-from utils_qa import postprocess_qa_predictions
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -25,11 +25,11 @@ from dataclasses import dataclass, field
 from typing import List, Optional, Tuple
 
 import datasets
-from datasets import load_dataset
-
 import evaluate
-import transformers
+from datasets import load_dataset
 from trainer_seq2seq_qa import QuestionAnsweringSeq2SeqTrainer
+
+import transformers
 from transformers import (
     AutoConfig,
     AutoModelForSeq2SeqLM,

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py

@@ -21,17 +21,17 @@ import sys
 from dataclasses import dataclass, field
 from typing import Optional
 
+import evaluate
 import numpy as np
 import torch
 from datasets import load_dataset
+from huggingface_hub import hf_hub_download
 from PIL import Image
 from torch import nn
 from torchvision import transforms
 from torchvision.transforms import functional
 
-import evaluate
 import transformers
-from huggingface_hub import hf_hub_download
 from transformers import (
     AutoConfig,
     AutoImageProcessor,

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -22,21 +22,21 @@ import random
 from pathlib import Path
 
 import datasets
+import evaluate
 import numpy as np
 import torch
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
 from datasets import load_dataset
+from huggingface_hub import Repository, create_repo, hf_hub_download
 from PIL import Image
 from torch.utils.data import DataLoader
 from torchvision import transforms
 from torchvision.transforms import functional
 from tqdm.auto import tqdm
 
-import evaluate
 import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from huggingface_hub import Repository, create_repo, hf_hub_download
 from transformers import (
     AutoConfig,
     AutoImageProcessor,

examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py

@@ -24,14 +24,14 @@ from typing import Dict, List, Optional, Union
 
 import datasets
 import torch
+from accelerate import Accelerator
+from accelerate.logging import get_logger
 from datasets import DatasetDict, concatenate_datasets, load_dataset
+from huggingface_hub import Repository, create_repo
 from torch.utils.data.dataloader import DataLoader
 from tqdm.auto import tqdm
 
 import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from huggingface_hub import Repository, create_repo
 from transformers import (
     AdamW,
     SchedulerType,
@@ -641,7 +641,6 @@ def main():
 
             # update step
             if (step + 1) % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
-
                 # compute grad norm for monitoring
                 scale = (
                     accelerator.scaler._scale.item()

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -26,11 +26,11 @@ from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Union
 
 import datasets
+import evaluate
 import numpy as np
 import torch
 from datasets import DatasetDict, load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     AutoConfig,
@@ -708,7 +708,6 @@ def main():
 
     # Training
     if training_args.do_train:
-
         # use last checkpoint if exist
         if last_checkpoint is not None:
             checkpoint = last_checkpoint

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -26,10 +26,10 @@ from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional, Union
 
 import datasets
+import evaluate
 import torch
 from datasets import DatasetDict, load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     AutoConfig,

examples/pytorch/summarization/run_summarization.py

@@ -25,13 +25,13 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
+import evaluate
 import nltk  # Here to have a nice missing dependency error message early on
 import numpy as np
 from datasets import load_dataset
-
-import evaluate
-import transformers
 from filelock import FileLock
+
+import transformers
 from transformers import (
     AutoConfig,
     AutoModelForSeq2SeqLM,

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -27,20 +27,20 @@ import random
 from pathlib import Path
 
 import datasets
+import evaluate
 import nltk
 import numpy as np
 import torch
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
-import evaluate
-import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
+from datasets import load_dataset
 from filelock import FileLock
 from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,

examples/pytorch/test_accelerate_examples.py

@@ -24,8 +24,8 @@ import tempfile
 from unittest import mock
 
 import torch
-
 from accelerate.utils import write_basic_config
+
 from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
 from transformers.utils import is_apex_available
 

examples/pytorch/text-classification/run_glue.py

@@ -24,10 +24,10 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
+import evaluate
 import numpy as np
 from datasets import load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     AutoConfig,

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -22,17 +22,17 @@ import random
 from pathlib import Path
 
 import datasets
-import torch
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
 import evaluate
-import transformers
+import torch
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
+from datasets import load_dataset
 from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+
+import transformers
 from transformers import (
     AutoConfig,
     AutoModelForSequenceClassification,

examples/pytorch/text-classification/run_xnli.py

@@ -25,10 +25,10 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
+import evaluate
 import numpy as np
 from datasets import load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     AutoConfig,

examples/pytorch/token-classification/run_ner.py

@@ -26,10 +26,10 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
+import evaluate
 import numpy as np
 from datasets import ClassLabel, load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     AutoConfig,

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -27,17 +27,17 @@ import random
 from pathlib import Path
 
 import datasets
-import torch
-from datasets import ClassLabel, load_dataset
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
 import evaluate
-import transformers
+import torch
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
+from datasets import ClassLabel, load_dataset
 from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,

examples/pytorch/translation/run_translation.py

@@ -25,10 +25,10 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
+import evaluate
 import numpy as np
 from datasets import load_dataset
 
-import evaluate
 import transformers
 from transformers import (
     AutoConfig,

examples/pytorch/translation/run_translation_no_trainer.py

@@ -27,18 +27,18 @@ import random
 from pathlib import Path
 
 import datasets
+import evaluate
 import numpy as np
 import torch
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
-import evaluate
-import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
+from datasets import load_dataset
 from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -69,7 +69,6 @@ MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
 
 # Parsing input arguments
 def parse_args():
-
     parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
     parser.add_argument(
         "--dataset_name",
@@ -751,5 +750,4 @@ def main():
 
 
 if __name__ == "__main__":
-
     main()