text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  #
  # Copyright (c) 2021 Intel Corporation
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import multiprocessing
  import threading
  import subprocess
  import time
  import os
  import sys
  import argparse
  import array
  import logging
  import numpy as np
  import time
  from utils import TF_BERTDataSet
  
  logging.basicConfig(level=logging.INFO)
  log = logging.getLogger("DISTILBERTBASE")
  
  
  def get_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("--batch_size", default=8,
                          type=int, help="Batch size")
      parser.add_argument("--input_model", default="distilbert_base_uncased_mrpc.onnx",
                           type=str, help="input_model_path")
      parser.add_argument("--output_model", default="./ir/", type=str, help="output_model_path")
      parser.add_argument("--vocab_file", default="./data/vocab.txt",
                              type=str, help="vocab_file_path")
      parser.add_argument("--do_lower_case", type=bool, default=True,
                          help="vocab whether all lower case")
      parser.add_argument("--data_dir", default="./data/MRPC/", type=str,
                          help="The input data dir. Should contain the .tsv files.")
      parser.add_argument("--config", default="./bert.yaml", type=str, help="yaml path")
      parser.add_argument('--benchmark', action='store_true', default=False)
      parser.add_argument('--tune', action='store_true',
                          default=False, help="whether quantize the model")
      parser.add_argument('--mode', type=str, help="benchmark mode of performance or accuracy")
      args = parser.parse_args()
      return args
  
  def main():
  
      args = get_args()
      if args.benchmark:
          from neural_compressor.experimental import Benchmark, common
          ds = TF_BERTDataSet(args.data_dir, args.vocab_file, args.do_lower_case)
          evaluator = Benchmark(args.config)
          evaluator.model = common.Model(args.input_model)
          evaluator.b_dataloader = common.DataLoader(ds, args.batch_size)
          evaluator(args.mode)
  
      if args.tune:
          from neural_compressor.experimental import Quantization, common
          ds = TF_BERTDataSet(args.data_dir, args.vocab_file, args.do_lower_case)
          quantizer = Quantization(args.config)
          quantizer.model = common.Model(args.input_model)
          quantizer.eval_dataloader = common.DataLoader(ds, args.batch_size)
          quantizer.calib_dataloader = common.DataLoader(ds, args.batch_size)
          q_model = quantizer.fit()
          q_model.save(args.output_model)
  
  if __name__ == '__main__':
      main()
  