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/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 = []