text: import argparse
  
  from trainer import Trainer
  from utils import init_logger, load_tokenizer, set_seed, MODEL_CLASSES, MODEL_PATH_MAP
  from data_loader import load_and_cache_examples
  
  
  def main(args):
      init_logger()
      set_seed(args)
  
      tokenizer = load_tokenizer(args)
      train_dataset = load_and_cache_examples(args, tokenizer, mode="train")
      dev_dataset = None
      test_dataset = load_and_cache_examples(args, tokenizer, mode="test")
      trainer = Trainer(args, train_dataset, dev_dataset, test_dataset)
  
      if args.do_train:
          trainer.train()
  
      if args.do_eval:
          trainer.load_model()
          trainer.evaluate("test")
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
  
      parser.add_argument("--task", default="nsmc", type=str, help="The name of the task to train")
      parser.add_argument("--model_dir", default="./model", type=str, help="Path to save, load model")
      parser.add_argument("--data_dir", default="./data", type=str, help="The input data dir")
      parser.add_argument("--train_file", default="ratings_train.txt", type=str, help="Train file")
      parser.add_argument("--test_file", default="ratings_test.txt", type=str, help="Test file")
  
      parser.add_argument("--model_type", default="kobert", type=str, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
  
      parser.add_argument('--seed', type=int, default=42, help="random seed for initialization")
      parser.add_argument("--train_batch_size", default=32, type=int, help="Batch size for training.")
      parser.add_argument("--eval_batch_size", default=64, type=int, help="Batch size for evaluation.")
      parser.add_argument("--max_seq_len", default=50, type=int, help="The maximum total input sequence length after tokenization.")
      parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
      parser.add_argument("--num_train_epochs", default=5.0, type=float, help="Total number of training epochs to perform.")
      parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
      parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
                          help="Number of updates steps to accumulate before performing a backward/update pass.")
      parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
      parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
      parser.add_argument("--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
      parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
  
      parser.add_argument('--logging_steps', type=int, default=100, help="Log every X updates steps.")
      parser.add_argument('--save_steps', type=int, default=100, help="Save checkpoint every X updates steps.")
  
      parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
      parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the test set.")
      parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
  
      args = parser.parse_args()
  
      args.model_name_or_path = MODEL_PATH_MAP[args.model_type]
      main(args)
  