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

src/transformers/pipelines/__init__.py

@@ -24,9 +24,8 @@ import warnings
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
-from numpy import isin
-
 from huggingface_hub import model_info
+from numpy import isin
 
 from ..configuration_utils import PretrainedConfig
 from ..dynamic_module_utils import get_class_from_dynamic_module

src/transformers/pipelines/audio_classification.py

@@ -15,7 +15,6 @@ import subprocess
 from typing import Union
 
 import numpy as np
-
 import requests
 
 from ..utils import add_end_docstrings, is_torch_available, logging

src/transformers/pipelines/automatic_speech_recognition.py

@@ -15,7 +15,6 @@ from collections import defaultdict
 from typing import TYPE_CHECKING, Dict, Optional, Union
 
 import numpy as np
-
 import requests
 
 from ..utils import is_torch_available, logging
@@ -302,7 +301,7 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         feature_extractor: Union["SequenceFeatureExtractor", str],
         *,
         decoder: Optional[Union["BeamSearchDecoderCTC", str]] = None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(**kwargs)
         self.feature_extractor = feature_extractor

src/transformers/pipelines/image_segmentation.py

@@ -165,7 +165,6 @@ class ImageSegmentationPipeline(Pipeline):
     def postprocess(
         self, model_outputs, subtask=None, threshold=0.9, mask_threshold=0.5, overlap_mask_area_threshold=0.5
     ):
-
         fn = None
         if subtask in {"panoptic", None} and hasattr(self.image_processor, "post_process_panoptic_segmentation"):
             fn = self.image_processor.post_process_panoptic_segmentation

src/transformers/pipelines/question_answering.py

@@ -307,7 +307,7 @@ class QuestionAnsweringPipeline(ChunkPipeline):
         max_question_len=None,
         handle_impossible_answer=None,
         align_to_words=None,
-        **kwargs
+        **kwargs,
     ):
         # Set defaults values
         preprocess_params = {}

src/transformers/pipelines/table_question_answering.py

@@ -23,7 +23,6 @@ if is_torch_available():
 
 if is_tf_available() and is_tensorflow_probability_available():
     import tensorflow as tf
-
     import tensorflow_probability as tfp
 
     from ..models.auto.modeling_tf_auto import (

src/transformers/pipelines/text2text_generation.py

@@ -78,7 +78,7 @@ class Text2TextGenerationPipeline(Pipeline):
         clean_up_tokenization_spaces=None,
         truncation=None,
         stop_sequence=None,
-        **generate_kwargs
+        **generate_kwargs,
     ):
         preprocess_params = {}
         if truncation is not None:

src/transformers/pipelines/text_generation.py

@@ -97,7 +97,7 @@ class TextGenerationPipeline(Pipeline):
         prefix=None,
         handle_long_generation=None,
         stop_sequence=None,
-        **generate_kwargs
+        **generate_kwargs,
     ):
         preprocess_params = {}
         if prefix is not None:

src/transformers/pipelines/token_classification.py

@@ -22,7 +22,6 @@ class TokenClassificationArgumentHandler(ArgumentHandler):
     """
 
     def __call__(self, inputs: Union[str, List[str]], **kwargs):
-
         if inputs is not None and isinstance(inputs, (list, tuple)) and len(inputs) > 0:
             inputs = list(inputs)
             batch_size = len(inputs)
@@ -141,7 +140,6 @@ class TokenClassificationPipeline(Pipeline):
         aggregation_strategy: Optional[AggregationStrategy] = None,
         offset_mapping: Optional[List[Tuple[int, int]]] = None,
     ):
-
         preprocess_params = {}
         if offset_mapping is not None:
             preprocess_params["offset_mapping"] = offset_mapping

src/transformers/pipelines/video_classification.py

@@ -9,7 +9,6 @@ from .base import PIPELINE_INIT_ARGS, Pipeline
 
 if is_decord_available():
     import numpy as np
-
     from decord import VideoReader
 
 
@@ -85,7 +84,6 @@ class VideoClassificationPipeline(Pipeline):
         return super().__call__(videos, **kwargs)
 
     def preprocess(self, video, num_frames=None, frame_sampling_rate=1):
-
         if num_frames is None:
             num_frames = self.model.config.num_frames
 

src/transformers/pipelines/zero_shot_object_detection.py

@@ -66,7 +66,7 @@ class ZeroShotObjectDetectionPipeline(ChunkPipeline):
         self,
         image: Union[str, "Image.Image", List[Dict[str, Any]]],
         candidate_labels: Union[str, List[str]] = None,
-        **kwargs
+        **kwargs,
     ):
         """
         Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
@@ -168,7 +168,6 @@ class ZeroShotObjectDetectionPipeline(ChunkPipeline):
         return model_outputs
 
     def postprocess(self, model_outputs, threshold=0.1, top_k=None):
-
         results = []
         for model_output in model_outputs:
             label = model_output["candidate_label"]