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

.circleci/config.yml

@@ -134,11 +134,10 @@ jobs:
                 command: pip freeze | tee installed.txt
             - store_artifacts:
                   path: ~/transformers/installed.txt
-            - run: black --check --preview examples tests src utils
-            - run: isort --check-only examples tests src utils
+            - run: black --check examples tests src utils
+            - run: ruff examples tests src utils
             - run: python utils/custom_init_isort.py --check_only
             - run: python utils/sort_auto_mappings.py --check_only
-            - run: flake8 examples tests src utils
             - run: doc-builder style src/transformers docs/source --max_len 119 --check_only --path_to_docs docs/source
             - run: python utils/check_doc_toc.py
 

Makefile

@@ -9,9 +9,8 @@ modified_only_fixup:
 	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
 	@if test -n "$(modified_py_files)"; then \
 		echo "Checking/fixing $(modified_py_files)"; \
-		black --preview $(modified_py_files); \
-		isort $(modified_py_files); \
-		flake8 $(modified_py_files); \
+		black $(modified_py_files); \
+		ruff $(modified_py_files) --fix; \
 	else \
 		echo "No library .py files were modified"; \
 	fi
@@ -48,11 +47,10 @@ repo-consistency:
 # this target runs checks on all files
 
 quality:
-	black --check --preview $(check_dirs)
-	isort --check-only $(check_dirs)
+	black --check $(check_dirs)
 	python utils/custom_init_isort.py --check_only
 	python utils/sort_auto_mappings.py --check_only
-	flake8 $(check_dirs)
+	ruff $(check_dirs)
 	doc-builder style src/transformers docs/source --max_len 119 --check_only --path_to_docs docs/source
 	python utils/check_doc_toc.py
 
@@ -67,8 +65,8 @@ extra_style_checks:
 # this target runs checks on all files and potentially modifies some of them
 
 style:
-	black --preview $(check_dirs)
-	isort $(check_dirs)
+	black $(check_dirs)
+	ruff $(check_dirs) --fix
 	${MAKE} autogenerate_code
 	${MAKE} extra_style_checks
 

docs/source/en/pipeline_webserver.mdx

@@ -96,7 +96,7 @@ while True:
     queues.append(rq)
 strings
 outs = pipe(strings, batch_size=len(strings))
-for (rq, out) in zip(queues, outs):
+for rq, out in zip(queues, outs):
     await rq.put(out)
 ```
 

docs/source/en/tasks/asr.mdx

@@ -166,7 +166,6 @@ Unlike other data collators, this specific data collator needs to apply a differ
 
 >>> @dataclass
 ... class DataCollatorCTCWithPadding:
-
 ...     processor: AutoProcessor
 ...     padding: Union[bool, str] = "longest"
 

docs/source/en/tasks/object_detection.mdx

@@ -213,7 +213,6 @@ The `image_processor` expects the annotations to be in the following format: `{'
 
 ```py
 >>> def formatted_anns(image_id, category, area, bbox):
-
 ...     annotations = []
 ...     for i in range(0, len(category)):
 ...         new_ann = {
@@ -399,6 +398,7 @@ First, prepare the `cppe5["test"]` set: format the annotations and save the data
 ```py
 >>> import json
 
+
 >>> # format annotations the same as for training, no need for data augmentation
 >>> def val_formatted_anns(image_id, objects):
 ...     annotations = []

docs/source/es/tasks/asr.mdx

@@ -159,7 +159,6 @@ A diferencia de otros collators de datos, este tiene que aplicarle un método de
 
 >>> @dataclass
 ... class DataCollatorCTCWithPadding:
-
 ...     processor: AutoProcessor
 ...     padding: Union[bool, str] = "longest"
 

examples/flax/image-captioning/run_image_captioning_flax.py

@@ -29,23 +29,23 @@ from pathlib import Path
 from typing import Callable, Optional
 
 import datasets
-import nltk  # Here to have a nice missing dependency error message early on
-import numpy as np
-from datasets import Dataset, load_dataset
-from PIL import Image
-from tqdm import tqdm
-
 import evaluate
 import jax
 import jax.numpy as jnp
+import nltk  # Here to have a nice missing dependency error message early on
+import numpy as np
 import optax
-import transformers
+from datasets import Dataset, load_dataset
 from filelock import FileLock
 from flax import jax_utils, traverse_util
 from flax.jax_utils import unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
 from huggingface_hub import Repository, create_repo
+from PIL import Image
+from tqdm import tqdm
+
+import transformers
 from transformers import (
     AutoImageProcessor,
     AutoTokenizer,

examples/flax/language-modeling/run_bart_dlm_flax.py

@@ -32,20 +32,20 @@ from itertools import chain
 from pathlib import Path
 from typing import Dict, List, Optional
 
-import nltk
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
 import flax
 import jax
 import jax.numpy as jnp
+import nltk
+import numpy as np
 import optax
+from datasets import load_dataset
 from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,

examples/flax/language-modeling/run_clm_flax.py

@@ -34,19 +34,19 @@ from pathlib import Path
 from typing import Callable, Optional
 
 import datasets
-import numpy as np
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
-import transformers
+from datasets import Dataset, load_dataset
 from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,

examples/flax/language-modeling/run_mlm_flax.py

@@ -34,19 +34,19 @@ from itertools import chain
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
 import flax
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
+from datasets import load_dataset
 from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -33,19 +33,19 @@ from itertools import chain
 from pathlib import Path
 from typing import Dict, List, Optional
 
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
 import flax
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
+from datasets import load_dataset
 from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,

examples/flax/question-answering/run_qa.py

@@ -31,20 +31,21 @@ from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple
 
 import datasets
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
 import evaluate
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
-import transformers
+from datasets import load_dataset
 from flax import struct, traverse_util
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+from utils_qa import postprocess_qa_predictions
+
+import transformers
 from transformers import (
     AutoConfig,
     AutoTokenizer,
@@ -55,7 +56,6 @@ from transformers import (
     is_tensorboard_available,
 )
 from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
-from utils_qa import postprocess_qa_predictions
 
 
 logger = logging.getLogger(__name__)
@@ -301,6 +301,7 @@ class DataTrainingArguments:
 
 # endregion
 
+
 # region Create a train state
 def create_train_state(
     model: FlaxAutoModelForQuestionAnswering,
@@ -387,6 +388,7 @@ def create_learning_rate_fn(
 
 # endregion
 
+
 # region train data iterator
 def train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
     """Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices."""
@@ -405,6 +407,7 @@ def train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
 
 # endregion
 
+
 # region eval data iterator
 def eval_data_collator(dataset: Dataset, batch_size: int):
     """Returns batches of size `batch_size` from `eval dataset`. Sharding handled by `pad_shard_unpad` in the eval loop."""
@@ -934,7 +937,6 @@ def main():
     total_steps = step_per_epoch * num_epochs
     epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
     for epoch in epochs:
-
         train_start = time.time()
         train_metrics = []
 
@@ -975,7 +977,6 @@ def main():
                 and (cur_step % training_args.eval_steps == 0 or cur_step % step_per_epoch == 0)
                 and cur_step > 0
             ):
-
                 eval_metrics = {}
                 all_start_logits = []
                 all_end_logits = []

examples/flax/summarization/run_summarization_flax.py

@@ -31,22 +31,22 @@ from pathlib import Path
 from typing import Callable, Optional
 
 import datasets
-import nltk  # Here to have a nice missing dependency error message early on
-import numpy as np
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
 import evaluate
 import jax
 import jax.numpy as jnp
+import nltk  # Here to have a nice missing dependency error message early on
+import numpy as np
 import optax
-import transformers
+from datasets import Dataset, load_dataset
 from filelock import FileLock
 from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,

examples/flax/text-classification/run_flax_glue.py

@@ -26,20 +26,20 @@ from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple
 
 import datasets
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
 import evaluate
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
-import transformers
+from datasets import load_dataset
 from flax import struct, traverse_util
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+
+import transformers
 from transformers import (
     AutoConfig,
     AutoTokenizer,
@@ -586,7 +586,6 @@ def main():
     total_steps = steps_per_epoch * num_epochs
     epochs = tqdm(range(num_epochs), desc=f"Epoch ... (0/{num_epochs})", position=0)
     for epoch in epochs:
-
         train_start = time.time()
         train_metrics = []
 
@@ -623,7 +622,6 @@ def main():
                 train_metrics = []
 
             if (cur_step % training_args.eval_steps == 0 or cur_step % steps_per_epoch == 0) and cur_step > 0:
-
                 # evaluate
                 eval_loader = glue_eval_data_collator(eval_dataset, eval_batch_size)
                 for batch in tqdm(

examples/flax/token-classification/run_flax_ner.py

@@ -28,20 +28,20 @@ from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple
 
 import datasets
-import numpy as np
-from datasets import ClassLabel, load_dataset
-from tqdm import tqdm
-
 import evaluate
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
-import transformers
+from datasets import ClassLabel, load_dataset
 from flax import struct, traverse_util
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+
+import transformers
 from transformers import (
     AutoConfig,
     AutoTokenizer,
@@ -695,7 +695,6 @@ def main():
     total_steps = step_per_epoch * num_epochs
     epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
     for epoch in epochs:
-
         train_start = time.time()
         train_metrics = []
 
@@ -731,7 +730,6 @@ def main():
                 train_metrics = []
 
             if cur_step % training_args.eval_steps == 0 and cur_step > 0:
-
                 eval_metrics = {}
                 # evaluate
                 for batch in tqdm(

examples/flax/vision/run_image_classification.py

@@ -29,21 +29,22 @@ from enum import Enum
 from pathlib import Path
 from typing import Callable, Optional
 
+import jax
+import jax.numpy as jnp
+import optax
+
 # for dataset and preprocessing
 import torch
 import torchvision
 import torchvision.transforms as transforms
-from tqdm import tqdm
-
-import jax
-import jax.numpy as jnp
-import optax
-import transformers
 from flax import jax_utils
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
 from huggingface_hub import Repository, create_repo
+from tqdm import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,

examples/legacy/multiple_choice/run_multiple_choice.py

@@ -22,6 +22,7 @@ from dataclasses import dataclass, field
 from typing import Dict, Optional
 
 import numpy as np
+from utils_multiple_choice import MultipleChoiceDataset, Split, processors
 
 import transformers
 from transformers import (
@@ -36,7 +37,6 @@ from transformers import (
     set_seed,
 )
 from transformers.trainer_utils import is_main_process
-from utils_multiple_choice import MultipleChoiceDataset, Split, processors
 
 
 logger = logging.getLogger(__name__)

examples/legacy/multiple_choice/utils_multiple_choice.py

@@ -26,8 +26,8 @@ from enum import Enum
 from typing import List, Optional
 
 import tqdm
-
 from filelock import FileLock
+
 from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
 
 
@@ -112,7 +112,6 @@ 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)

examples/legacy/pytorch-lightning/lightning_base.py

@@ -69,7 +69,7 @@ class BaseTransformer(pl.LightningModule):
         config=None,
         tokenizer=None,
         model=None,
-        **config_kwargs
+        **config_kwargs,
     ):
         """Initialize a model, tokenizer and config."""
         super().__init__()
@@ -346,7 +346,7 @@ def generic_train(
     extra_callbacks=[],
     checkpoint_callback=None,
     logging_callback=None,
-    **extra_train_kwargs
+    **extra_train_kwargs,
 ):
     pl.seed_everything(args.seed)
 

examples/legacy/pytorch-lightning/run_glue.py

@@ -7,21 +7,19 @@ from argparse import Namespace
 
 import numpy as np
 import torch
+from lightning_base import BaseTransformer, add_generic_args, generic_train
 from torch.utils.data import DataLoader, TensorDataset
 
-from lightning_base import BaseTransformer, add_generic_args, generic_train
 from transformers import glue_compute_metrics as compute_metrics
 from transformers import glue_convert_examples_to_features as convert_examples_to_features
-from transformers import glue_output_modes
+from transformers import glue_output_modes, glue_tasks_num_labels
 from transformers import glue_processors as processors
-from transformers import glue_tasks_num_labels
 
 
 logger = logging.getLogger(__name__)
 
 
 class GLUETransformer(BaseTransformer):
-
     mode = "sequence-classification"
 
     def __init__(self, hparams):

examples/legacy/pytorch-lightning/run_ner.py

@@ -7,11 +7,10 @@ from importlib import import_module
 
 import numpy as np
 import torch
+from lightning_base import BaseTransformer, add_generic_args, generic_train
 from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
 from torch.nn import CrossEntropyLoss
 from torch.utils.data import DataLoader, TensorDataset
-
-from lightning_base import BaseTransformer, add_generic_args, generic_train
 from utils_ner import TokenClassificationTask
 
 

examples/legacy/question-answering/run_squad.py

@@ -172,7 +172,6 @@ def train(args, train_dataset, model, tokenizer):
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-
             # Skip past any already trained steps if resuming training
             if steps_trained_in_current_epoch > 0:
                 steps_trained_in_current_epoch -= 1

examples/legacy/question-answering/run_squad_trainer.py

@@ -30,9 +30,10 @@ from transformers import (
     DataCollatorWithPadding,
     HfArgumentParser,
     SquadDataset,
+    Trainer,
+    TrainingArguments,
 )
 from transformers import SquadDataTrainingArguments as DataTrainingArguments
-from transformers import Trainer, TrainingArguments
 from transformers.trainer_utils import is_main_process
 
 

examples/legacy/run_chinese_ref.py

@@ -4,6 +4,7 @@ import json
 from typing import List
 
 from ltp import LTP
+
 from transformers import BertTokenizer
 
 
@@ -93,7 +94,6 @@ def prepare_ref(lines: List[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokeni
 
     ref_ids = []
     for input_ids, chinese_word in zip(bert_res, ltp_res):
-
         input_tokens = []
         for id in input_ids:
             token = bert_tokenizer._convert_id_to_token(id)

examples/legacy/seq2seq/finetune_trainer.py

@@ -19,9 +19,10 @@ import sys
 from dataclasses import dataclass, field
 from typing import Optional
 
-import transformers
 from seq2seq_trainer import Seq2SeqTrainer
 from seq2seq_training_args import Seq2SeqTrainingArguments
+
+import transformers
 from transformers import (
     AutoConfig,
     AutoModelForSeq2SeqLM,
@@ -337,7 +338,6 @@ def main():
         metrics["val_loss"] = round(metrics["val_loss"], 4)
 
         if trainer.is_world_process_zero():
-
             handle_metrics("val", metrics, training_args.output_dir)
             all_metrics.update(metrics)
 

examples/legacy/seq2seq/old_test_calculate_rouge.py

@@ -16,8 +16,8 @@ from collections import defaultdict
 from pathlib import Path
 
 import pandas as pd
-
 from rouge_cli import calculate_rouge_path
+
 from utils import calculate_rouge
 
 
@@ -87,7 +87,6 @@ def test_single_sent_scores_dont_depend_on_newline_sep():
 
 
 def test_pegasus_newline():
-
     pred = [
         """" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" """
     ]

examples/legacy/seq2seq/old_test_datasets.py

@@ -17,11 +17,11 @@ from pathlib import Path
 
 import numpy as np
 import pytest
-from torch.utils.data import DataLoader
-
 from pack_dataset import pack_data_dir
 from parameterized import parameterized
 from save_len_file import save_len_file
+from torch.utils.data import DataLoader
+
 from transformers import AutoTokenizer
 from transformers.models.mbart.modeling_mbart import shift_tokens_right
 from transformers.testing_utils import TestCasePlus, slow

examples/legacy/seq2seq/old_test_fsmt_bleu_score.py

@@ -18,6 +18,7 @@ import json
 import unittest
 
 from parameterized import parameterized
+
 from transformers import FSMTForConditionalGeneration, FSMTTokenizer
 from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
 from utils import calculate_bleu

examples/legacy/seq2seq/old_test_seq2seq_examples.py

@@ -21,6 +21,7 @@ from unittest.mock import patch
 from parameterized import parameterized
 from run_eval import run_generate
 from run_eval_search import run_search
+
 from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
 from utils import ROUGE_KEYS
 

examples/legacy/seq2seq/pack_dataset.py

@@ -29,7 +29,6 @@ from transformers import AutoTokenizer
 
 
 def pack_examples(tok, src_examples, tgt_examples, max_tokens=1024):
-
     finished_src, finished_tgt = [], []
 
     sorted_examples = list(zip(src_examples, tgt_examples))

examples/legacy/seq2seq/run_eval_search.py

@@ -20,6 +20,7 @@ import sys
 from collections import OrderedDict
 
 from run_eval import datetime_now, run_generate
+
 from utils import ROUGE_KEYS
 
 

examples/legacy/seq2seq/seq2seq_training_args.py

@@ -17,6 +17,7 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 from seq2seq_trainer import arg_to_scheduler
+
 from transformers import TrainingArguments
 
 

examples/legacy/seq2seq/utils.py

@@ -29,10 +29,10 @@ import torch
 import torch.distributed as dist
 from rouge_score import rouge_scorer, scoring
 from sacrebleu import corpus_bleu
+from sentence_splitter import add_newline_to_end_of_each_sentence
 from torch import nn
 from torch.utils.data import Dataset, Sampler
 
-from sentence_splitter import add_newline_to_end_of_each_sentence
 from transformers import BartTokenizer, EvalPrediction, PreTrainedTokenizer, T5Tokenizer
 from transformers.models.bart.modeling_bart import shift_tokens_right
 from transformers.utils import cached_property
@@ -132,7 +132,7 @@ class AbstractSeq2SeqDataset(Dataset):
         type_path="train",
         n_obs=None,
         prefix="",
-        **dataset_kwargs
+        **dataset_kwargs,
     ):
         super().__init__()
         self.src_file = Path(data_dir).joinpath(type_path + ".source")

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)

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()

examples/research_projects/adversarial/run_hans.py

@@ -22,6 +22,7 @@ from typing import Dict, List, Optional
 
 import numpy as np
 import torch
+from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
 
 import transformers
 from transformers import (
@@ -35,7 +36,6 @@ from transformers import (
     set_seed,
 )
 from transformers.trainer_utils import is_main_process
-from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
 
 
 logger = logging.getLogger(__name__)

examples/research_projects/adversarial/utils_hans.py

@@ -20,8 +20,8 @@ from dataclasses import dataclass
 from typing import List, Optional, Union
 
 import tqdm
-
 from filelock import FileLock
+
 from transformers import (
     BartTokenizer,
     BartTokenizerFast,
@@ -134,7 +134,6 @@ 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)

examples/research_projects/bert-loses-patience/run_glue_with_pabee.py

@@ -25,14 +25,14 @@ import random
 
 import numpy as np
 import torch
+from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
+from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
 from torch import nn
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
 import transformers
-from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
-from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -173,7 +173,6 @@ def train(args, train_dataset, model, tokenizer):
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-
             # Skip past any already trained steps if resuming training
             if steps_trained_in_current_epoch > 0:
                 steps_trained_in_current_epoch -= 1
@@ -263,7 +262,6 @@ def train(args, train_dataset, model, tokenizer):
 
 
 def evaluate(args, model, tokenizer, prefix="", patience=0):
-
     if args.model_type == "albert":
         model.albert.set_regression_threshold(args.regression_threshold)
         model.albert.set_patience(patience)
@@ -736,7 +734,6 @@ def main():
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
 
         for checkpoint in checkpoints:
-
             global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
             prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
 

examples/research_projects/bert-loses-patience/test_run_glue_with_pabee.py

@@ -4,6 +4,7 @@ import sys
 from unittest.mock import patch
 
 import run_glue_with_pabee
+
 from transformers.testing_utils import TestCasePlus
 
 

examples/research_projects/bertabs/convert_bertabs_original_pytorch_checkpoint.py

@@ -24,9 +24,9 @@ import logging
 from collections import namedtuple
 
 import torch
-
 from model_bertabs import BertAbsSummarizer
 from models.model_builder import AbsSummarizer  # The authors' implementation
+
 from transformers import BertTokenizer
 
 

examples/research_projects/bertabs/modeling_bertabs.py

@@ -24,10 +24,10 @@ import math
 
 import numpy as np
 import torch
+from configuration_bertabs import BertAbsConfig
 from torch import nn
 from torch.nn.init import xavier_uniform_
 
-from configuration_bertabs import BertAbsConfig
 from transformers import BertConfig, BertModel, PreTrainedModel
 
 

examples/research_projects/bertabs/run_summarization.py

@@ -6,10 +6,10 @@ import sys
 from collections import namedtuple
 
 import torch
+from modeling_bertabs import BertAbs, build_predictor
 from torch.utils.data import DataLoader, SequentialSampler
 from tqdm import tqdm
 
-from modeling_bertabs import BertAbs, build_predictor
 from transformers import BertTokenizer
 
 from .utils_summarization import (
@@ -45,7 +45,6 @@ def evaluate(args):
         generated_summaries = []
 
         import nltk
-
         import rouge
 
         nltk.download("punkt")

examples/research_projects/codeparrot/examples/train_complexity_predictor.py

@@ -3,8 +3,8 @@ from copy import deepcopy
 
 import numpy as np
 from datasets import ClassLabel, DatasetDict, load_dataset
-
 from evaluate import load
+
 from transformers import (
     AutoModelForSequenceClassification,
     AutoTokenizer,

examples/research_projects/codeparrot/scripts/bpe_training.py

@@ -1,7 +1,7 @@
+from arguments import TokenizerTrainingArguments
 from datasets import load_dataset
 from tqdm import tqdm
 
-from arguments import TokenizerTrainingArguments
 from transformers import AutoTokenizer, HfArgumentParser
 from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
 

examples/research_projects/codeparrot/scripts/codeparrot_training.py

@@ -6,16 +6,16 @@ from pathlib import Path
 
 import datasets
 import torch
+from accelerate import Accelerator, DistributedType
+from arguments import TrainingArguments
 from datasets import load_dataset
+from huggingface_hub import Repository
 from torch.optim import AdamW
 from torch.utils.data import IterableDataset
 from torch.utils.data.dataloader import DataLoader
 from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
 
 import transformers
-from accelerate import Accelerator, DistributedType
-from arguments import TrainingArguments
-from huggingface_hub import Repository
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
 
 

examples/research_projects/codeparrot/scripts/human_eval.py

@@ -5,15 +5,15 @@ import re
 from collections import defaultdict
 
 import torch
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from arguments import HumanEvalArguments
 from datasets import load_dataset, load_metric
 from torch.utils.data import IterableDataset
 from torch.utils.data.dataloader import DataLoader
 from tqdm import tqdm
 
 import transformers
-from accelerate import Accelerator
-from accelerate.utils import set_seed
-from arguments import HumanEvalArguments
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
 
 

examples/research_projects/codeparrot/scripts/initialize_model.py

@@ -1,4 +1,5 @@
 from arguments import InitializationArguments
+
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
 
 

examples/research_projects/codeparrot/scripts/minhash_deduplication.py

@@ -6,10 +6,9 @@ from functools import partial
 from typing import Dict, List, Optional, Set, Tuple, Type
 
 from datasets import Dataset
-from tqdm import tqdm
-
 from datasketch import MinHash, MinHashLSH
 from dpu_utils.utils.iterators import ThreadedIterator
+from tqdm import tqdm
 
 
 NON_ALPHA = re.compile("[^A-Za-z_0-9]")

examples/research_projects/codeparrot/scripts/preprocessing.py

@@ -9,10 +9,10 @@ import time
 from pathlib import Path
 
 import numpy as np
-from datasets import load_dataset
-
 from arguments import PreprocessingArguments
+from datasets import load_dataset
 from minhash_deduplication import deduplicate_dataset
+
 from transformers import AutoTokenizer, HfArgumentParser
 
 

examples/research_projects/codeparrot/scripts/pretokenizing.py

@@ -1,9 +1,9 @@
 import multiprocessing
 import time
 
+from arguments import PretokenizationArguments
 from datasets import load_dataset
 
-from arguments import PretokenizationArguments
 from transformers import AutoTokenizer, HfArgumentParser
 
 

examples/research_projects/codeparrot/scripts/tests/test_deduplicate.py

@@ -1,7 +1,6 @@
 from unittest import TestCase
 
 from datasets import Dataset
-
 from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
 
 

examples/research_projects/codeparrot/scripts/validation_loss.py

@@ -1,12 +1,12 @@
 import logging
 
 import torch
+from accelerate import Accelerator
+from arguments import EvaluationArguments
 from datasets import load_dataset
 from torch.utils.data import IterableDataset
 from torch.utils.data.dataloader import DataLoader
 
-from accelerate import Accelerator
-from arguments import EvaluationArguments
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
 
 

examples/research_projects/decision_transformer/run_decision_transformer.py

@@ -1,8 +1,8 @@
+import gym
 import numpy as np
 import torch
-
-import gym
 from mujoco_py import GlfwContext
+
 from transformers import DecisionTransformerModel
 
 

examples/research_projects/deebert/src/modeling_highway_bert.py

@@ -229,7 +229,10 @@ class DeeBertModel(BertPreTrainedModel):
         sequence_output = encoder_outputs[0]
         pooled_output = self.pooler(sequence_output)
 
-        outputs = (sequence_output, pooled_output,) + encoder_outputs[
+        outputs = (
+            sequence_output,
+            pooled_output,
+        ) + encoder_outputs[
             1:
         ]  # add hidden_states and attentions if they are here
         return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

examples/research_projects/deebert/src/modeling_highway_roberta.py

@@ -19,7 +19,6 @@ from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayExc
     ROBERTA_START_DOCSTRING,
 )
 class DeeRobertaModel(DeeBertModel):
-
     config_class = RobertaConfig
     base_model_prefix = "roberta"
 
@@ -36,7 +35,6 @@ class DeeRobertaModel(DeeBertModel):
     ROBERTA_START_DOCSTRING,
 )
 class DeeRobertaForSequenceClassification(BertPreTrainedModel):
-
     config_class = RobertaConfig
     base_model_prefix = "roberta"
 

examples/research_projects/deebert/test_glue_deebert.py

@@ -4,6 +4,7 @@ import sys
 from unittest.mock import patch
 
 import run_glue_deebert
+
 from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
 
 
@@ -45,7 +46,6 @@ class DeeBertTests(TestCasePlus):
     @slow
     @require_torch_non_multi_gpu
     def test_glue_deebert_train(self):
-
         train_args = """
             --model_type roberta
             --model_name_or_path roberta-base

examples/research_projects/distillation/distiller.py

@@ -21,14 +21,14 @@ import time
 
 import psutil
 import torch
+from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
+from lm_seqs_dataset import LmSeqsDataset
 from torch import nn
 from torch.optim import AdamW
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm
 
-from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
-from lm_seqs_dataset import LmSeqsDataset
 from transformers import get_linear_schedule_with_warmup
 from utils import logger
 

examples/research_projects/distillation/run_squad_w_distillation.py

@@ -189,7 +189,6 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-
             # Skip past any already trained steps if resuming training
             if steps_trained_in_current_epoch > 0:
                 steps_trained_in_current_epoch -= 1

examples/research_projects/distillation/train.py

@@ -24,9 +24,9 @@ import shutil
 
 import numpy as np
 import torch
-
 from distiller import Distiller
 from lm_seqs_dataset import LmSeqsDataset
+
 from transformers import (
     BertConfig,
     BertForMaskedLM,

examples/research_projects/information-gain-filtration/igf/igf.py

@@ -5,13 +5,13 @@ import copy
 import logging
 import random
 
+import joblib
 import numpy as np
 import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
-import joblib
 from transformers import AdamW, GPT2LMHeadModel, get_linear_schedule_with_warmup
 
 
@@ -119,7 +119,6 @@ def recopy_gpt2(orig_model, device, max_steps):
 
 
 def intermittent_save(contexts, real_perps, past_perps, filename):
-
     """
     save the perplexity differences to filename
 
@@ -152,7 +151,6 @@ def collect_objective_set(
     filename="dev.jbl",
     recopy_model=recopy_gpt2,
 ):
-
     """
     Collect individual IGF values from pre-trained transformer model
     max_steps samples of training data to train secondary model
@@ -271,7 +269,6 @@ def generate_datasets(
 def train_secondary_learner(
     secondary_learner, train_dataset, max_epochs, batch_size, eval_freq=50, igf_model_path="secondary_learner.pt"
 ):
-
     """
     Train the secondary learner (igf_model)
 

examples/research_projects/information-gain-filtration/run_clm_igf.py

@@ -28,11 +28,9 @@ Last, a plot is generated to compare the performance of IGF to standard fine-tun
 import argparse
 import random
 
+import joblib
 import numpy as np
 import torch
-from torch.utils.data import DataLoader, RandomSampler
-
-import joblib
 from igf.igf import (
     SecondaryLearner,
     collect_objective_set,
@@ -43,6 +41,8 @@ from igf.igf import (
     set_seed,
     train_secondary_learner,
 )
+from torch.utils.data import DataLoader, RandomSampler
+
 from transformers import GPT2LMHeadModel
 
 
@@ -55,7 +55,6 @@ def generate_n_pairs(
     data_file="data/tokenized_stories_train_wikitext103.jbl",
     igf_data_file="igf_context_pairs.jbl",
 ):
-
     """
     Collecting *n* pairs for training the secondary learner
     Args:

examples/research_projects/jax-projects/big_bird/bigbird_flax.py

@@ -4,8 +4,6 @@ from dataclasses import dataclass
 from functools import partial
 from typing import Callable
 
-from tqdm.auto import tqdm
-
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
@@ -16,6 +14,8 @@ from flax import jax_utils, struct, traverse_util
 from flax.serialization import from_bytes, to_bytes
 from flax.training import train_state
 from flax.training.common_utils import shard
+from tqdm.auto import tqdm
+
 from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
 from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
 
@@ -98,7 +98,6 @@ class Args:
 
 @dataclass
 class DataCollator:
-
     pad_id: int
     max_length: int = 4096  # no dynamic padding on TPUs
 

examples/research_projects/jax-projects/big_bird/evaluate.py

@@ -1,8 +1,8 @@
-from datasets import load_from_disk
-
 import jax
 import jax.numpy as jnp
 from bigbird_flax import FlaxBigBirdForNaturalQuestions
+from datasets import load_from_disk
+
 from transformers import BigBirdTokenizerFast
 
 

examples/research_projects/jax-projects/big_bird/prepare_natural_questions.py

@@ -1,10 +1,9 @@
 import os
 
+import jsonlines
 import numpy as np
 from tqdm import tqdm
 
-import jsonlines
-
 
 DOC_STRIDE = 2048
 MAX_LENGTH = 4096

examples/research_projects/jax-projects/big_bird/train.py

@@ -1,12 +1,12 @@
 import os
 from dataclasses import replace
 
-from datasets import load_dataset
-
 import jax
 import wandb
 from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
+from datasets import load_dataset
 from flax import jax_utils
+
 from transformers import BigBirdTokenizerFast
 
 

examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py

@@ -32,17 +32,17 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import datasets
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
 import flax
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
+from datasets import load_dataset
 from flax import jax_utils, traverse_util
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
+from tqdm import tqdm
+
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,

examples/research_projects/jax-projects/hybrid_clip/modeling_hybrid_clip.py

@@ -20,6 +20,7 @@ import jax
 import jax.numpy as jnp
 from configuration_hybrid_clip import HybridCLIPConfig
 from flax.core.frozen_dict import FrozenDict
+
 from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
 from transformers.modeling_flax_utils import FlaxPreTrainedModel
 from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
@@ -132,7 +133,7 @@ class FlaxHybridCLIP(FlaxPreTrainedModel):
         input_shape: Optional[Tuple] = None,
         seed: int = 0,
         dtype: jnp.dtype = jnp.float32,
-        **kwargs
+        **kwargs,
     ):
         if input_shape is None:
             input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

examples/research_projects/jax-projects/hybrid_clip/run_hybrid_clip.py

@@ -32,22 +32,22 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Callable, Optional
 
+import jax
+import jax.numpy as jnp
+import optax
 import torch
+from flax import jax_utils
+from flax.jax_utils import unreplicate
+from flax.training import train_state
+from flax.training.common_utils import get_metrics, shard, shard_prng_key
+from modeling_hybrid_clip import FlaxHybridCLIP
 from torchvision.datasets import VisionDataset
 from torchvision.io import ImageReadMode, read_image
 from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
 from torchvision.transforms.functional import InterpolationMode
 from tqdm import tqdm
 
-import jax
-import jax.numpy as jnp
-import optax
 import transformers
-from flax import jax_utils
-from flax.jax_utils import unreplicate
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, shard, shard_prng_key
-from modeling_hybrid_clip import FlaxHybridCLIP
 from transformers import AutoTokenizer, HfArgumentParser, TrainingArguments, is_tensorboard_available, set_seed
 
 

examples/research_projects/jax-projects/model_parallel/run_clm_mp.py

@@ -28,19 +28,19 @@ from pathlib import Path
 from typing import Callable, Optional
 
 import datasets
-import numpy as np
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
-import transformers
+from datasets import Dataset, load_dataset
 from flax.core.frozen_dict import freeze, unfreeze
 from flax.training.common_utils import onehot, stack_forest
 from jax.experimental.maps import mesh
 from jax.experimental.pjit import pjit
 from partitions import set_partitions
+from tqdm import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,

examples/research_projects/jax-projects/wav2vec2/run_wav2vec2_pretrain_flax.py

@@ -6,18 +6,18 @@ from dataclasses import field
 from pathlib import Path
 from typing import Dict, List, Optional, Union
 
-import numpy as np
-from datasets import DatasetDict, load_dataset
-from tqdm import tqdm
-
 import flax
 import jax
 import jax.numpy as jnp
 import librosa
+import numpy as np
 import optax
+from datasets import DatasetDict, load_dataset
 from flax import jax_utils, traverse_util
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
+from tqdm import tqdm
+
 from transformers import (
     FlaxWav2Vec2ForPreTraining,
     HfArgumentParser,