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/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,