text: import augmentation as aug
  from fairfil import fairfil_trainer
  from finetune import sst_trainer
  
  import argparse
  import collections
  import logging
  import json
  import re
  from tqdm import tqdm
  
  import os
  
  def main():
      parser = argparse.ArgumentParser()
  
      ## Required parameters
      parser.add_argument("--output_file", default='feature.json', type=str)
      parser.add_argument("--bert_model", default='bert-base-uncased', type=str,
                          help="Bert pre-trained model selected in the list: bert-base-uncased, "
                               "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
  
      ## Other parameters
      parser.add_argument("--layers", default="-1,-2,-3,-4", type=str)
      parser.add_argument("--filter_nlayer", default=1, type=int)
      parser.add_argument("--max_seq_length", default=128, type=int,
                          help="The maximum total input sequence length after WordPiece tokenization. Sequences longer "
                              "than this will be truncated, and sequences shorter than this will be padded.")
      parser.add_argument("--batch_size", default=16, type=int, help="Batch size for predictions.")
      parser.add_argument("--epochs", default=10, type=int, help="num of epochs.")
      parser.add_argument("--log_step", default=10, type=int, help="")
      parser.add_argument("--temperature", default=0.07, type=float, help="temperature scaling")
      parser.add_argument("--log_dir", default="./log", type=str, help="log directory")
      parser.add_argument("--local_rank",
                          type=int,
                          default=-1,
                          help = "local_rank for distributed training on gpus")
      parser.add_argument("--no_cuda",
                          action='store_true',
                          help="Whether not to use CUDA when available")
      parser.add_argument("--dr", action='store_true')
  
  
      args = parser.parse_args()
  
      aug_data = aug.get_data()
      f = open('aug_data.json','w')
      json.dump(aug_data, f)
  
      if not os.path.exists(args.log_dir):
          os.makedirs(args.log_dir)
  
      fairfil_trainer(input_file=aug_data, args=args)
      #finetuning
      sst_trainer()
      #seat 계산하기
  
  
  if __name__ == "__main__":
      main()
  