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/tensorflow/question-answering/run_qa.py

@@ -26,10 +26,11 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Optional
 
+import evaluate
 import tensorflow as tf
 from datasets import load_dataset
+from utils_qa import postprocess_qa_predictions
 
-import evaluate
 import transformers
 from transformers import (
     AutoConfig,
@@ -44,7 +45,6 @@ from transformers import (
     set_seed,
 )
 from transformers.utils import CONFIG_NAME, TF2_WEIGHTS_NAME, check_min_version, send_example_telemetry
-from utils_qa import postprocess_qa_predictions
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
@@ -214,6 +214,7 @@ class DataTrainingArguments:
 
 # endregion
 
+
 # region Helper classes
 class SavePretrainedCallback(tf.keras.callbacks.Callback):
     # Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
@@ -610,7 +611,6 @@ def main():
     # endregion
 
     with training_args.strategy.scope():
-
         dataset_options = tf.data.Options()
         dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
         num_replicas = training_args.strategy.num_replicas_in_sync
@@ -628,7 +628,6 @@ def main():
             use_auth_token=True if model_args.use_auth_token else None,
         )
         if training_args.do_train:
-
             training_dataset = model.prepare_tf_dataset(
                 processed_datasets["train"],
                 shuffle=True,