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

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -17,8 +17,8 @@ import unittest
 import numpy as np
 import pytest
 from datasets import load_dataset
-
 from huggingface_hub import snapshot_download
+
 from transformers import (
     MODEL_FOR_CTC_MAPPING,
     MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
@@ -277,7 +277,6 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
     @require_torch
     @slow
     def test_torch_large(self):
-
         speech_recognizer = pipeline(
             task="automatic-speech-recognition",
             model="facebook/wav2vec2-base-960h",
@@ -645,7 +644,6 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
     @require_torch
     @require_torchaudio
     def test_simple_s2t(self):
-
         model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-it-st")
         tokenizer = AutoTokenizer.from_pretrained("facebook/s2t-small-mustc-en-it-st")
         feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/s2t-small-mustc-en-it-st")

tests/pipelines/test_pipelines_common.py

@@ -26,10 +26,10 @@ from unittest import skipIf
 
 import datasets
 import numpy as np
-
 import requests
 from huggingface_hub import HfFolder, Repository, create_repo, delete_repo, set_access_token
 from requests.exceptions import HTTPError
+
 from transformers import (
     AutoModelForSequenceClassification,
     AutoTokenizer,
@@ -119,7 +119,6 @@ def is_test_to_skip(test_casse_name, config_class, model_architecture, tokenizer
 
     # TODO: check and fix if possible
     if not to_skip and tokenizer_name is not None:
-
         if (
             test_casse_name == "QAPipelineTests"
             and not tokenizer_name.endswith("Fast")
@@ -196,7 +195,6 @@ def is_test_to_skip(test_casse_name, config_class, model_architecture, tokenizer
 
 
 def validate_test_components(test_case, model, tokenizer, processor):
-
     # TODO: Move this to tiny model creation script
     # head-specific (within a model type) necessary changes to the config
     # 1. for `BlenderbotForCausalLM`
@@ -296,7 +294,6 @@ class PipelineTestCaseMeta(type):
             mapping = dct.get(key, {})
             if mapping:
                 for config_class, model_architectures in mapping.items():
-
                     if not isinstance(model_architectures, tuple):
                         model_architectures = (model_architectures,)
 

tests/pipelines/test_pipelines_depth_estimation.py

@@ -44,7 +44,6 @@ def hashimage(image: Image) -> str:
 @require_timm
 @require_torch
 class DepthEstimationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
-
     model_mapping = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
 
     def get_test_pipeline(self, model, tokenizer, processor):

tests/pipelines/test_pipelines_image_segmentation.py

@@ -18,9 +18,9 @@ from typing import Dict
 
 import datasets
 import numpy as np
+import requests
 from datasets import load_dataset
 
-import requests
 from transformers import (
     MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
     MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,

tests/pipelines/test_pipelines_video_classification.py

@@ -15,6 +15,7 @@
 import unittest
 
 from huggingface_hub import hf_hub_download
+
 from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
 from transformers.pipelines import VideoClassificationPipeline, pipeline
 from transformers.testing_utils import (
@@ -47,7 +48,6 @@ class VideoClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
         return video_classifier, examples
 
     def run_pipeline_test(self, video_classifier, examples):
-
         for example in examples:
             outputs = video_classifier(example)
 

tests/pipelines/test_pipelines_zero_shot_object_detection.py

@@ -33,7 +33,6 @@ else:
 @require_vision
 @require_torch
 class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
-
     model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
 
     def get_test_pipeline(self, model, tokenizer, processor):