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/quantization-qdqbert/evaluate-hf-trt-qa.py

@@ -21,19 +21,19 @@ import timeit
 
 import datasets
 import numpy as np
-import torch
-from absl import logging as absl_logging
-from datasets import load_dataset, load_metric
-from torch.utils.data import DataLoader
-
 import pycuda.autoinit  # noqa: F401
 import pycuda.driver as cuda
 import tensorrt as trt
-import transformers
+import torch
+from absl import logging as absl_logging
 from accelerate import Accelerator
+from datasets import load_dataset, load_metric
+from torch.utils.data import DataLoader
+from utils_qa import postprocess_qa_predictions
+
+import transformers
 from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
 from transformers.trainer_pt_utils import nested_concat, nested_truncate
-from utils_qa import postprocess_qa_predictions
 
 
 TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
@@ -395,7 +395,6 @@ logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
 with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
     f.read()
 ) as engine, engine.create_execution_context() as context:
-
     # setup for TRT inferrence
     for i in range(len(input_names)):
         context.set_binding_shape(i, INPUT_SHAPE)
@@ -427,7 +426,6 @@ with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.d
 
     all_preds = None
     for step, batch in enumerate(eval_dataloader):
-
         outputs, infer_time = model_infer(batch, context, d_inputs, h_output0, h_output1, d_output0, d_output1, stream)
         total_time += infer_time
         niter += 1

examples/research_projects/quantization-qdqbert/ort-infer-benchmark.py

@@ -2,7 +2,6 @@ import os
 import time
 
 import numpy as np
-
 import onnxruntime as ort
 
 

examples/research_projects/quantization-qdqbert/quant_trainer.py

@@ -16,10 +16,9 @@
 import logging
 import re
 
-import torch
-
 import pytorch_quantization
 import pytorch_quantization.nn as quant_nn
+import torch
 from pytorch_quantization import calib
 from pytorch_quantization.tensor_quant import QuantDescriptor
 

examples/research_projects/quantization-qdqbert/run_quant_qa.py

@@ -26,11 +26,12 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
-from datasets import load_dataset, load_metric
-
 import quant_trainer
-import transformers
+from datasets import load_dataset, load_metric
 from trainer_quant_qa import QuestionAnsweringTrainer
+from utils_qa import postprocess_qa_predictions
+
+import transformers
 from transformers import (
     AutoTokenizer,
     DataCollatorWithPadding,
@@ -46,7 +47,6 @@ from transformers import (
 from transformers.trainer_utils import SchedulerType, get_last_checkpoint
 from transformers.utils import check_min_version
 from transformers.utils.versions import require_version
-from utils_qa import postprocess_qa_predictions
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

examples/research_projects/quantization-qdqbert/trainer_quant_qa.py

@@ -20,10 +20,10 @@ A subclass of `Trainer` specific to Question-Answering tasks
 import logging
 import os
 
+import quant_trainer
 import torch
 from torch.utils.data import DataLoader
 
-import quant_trainer
 from transformers import Trainer, is_torch_tpu_available
 from transformers.trainer_utils import PredictionOutput