text: import argparse
  import os
  global PAD_ID
  global UNK_ID
  def parse_args():
      parser = argparse.ArgumentParser(description=__doc__)
      parser.add_argument("--raw-path",type=str,default="dureader_data/dataset",help="raw data path.")
      parser.add_argument("--processed-path",type=str,default="dureader_data/processed",help="Processed data path.")
      parser.add_argument("--device",type=str,default="gpu",help="Device for selecting for the training.")
      parser.add_argument("--model-type",type=str,default="rnet",help="Model for training dataset.")
      parser.add_argument("--batch-size",type=int,default=4,help="Batch size for the train.")
      parser.add_argument("--emb-name",type=str,default="w2v.baidu_encyclopedia.target.word-word.dim300",help="Embedding name.")
      parser.add_argument("--dev-batch-size", type=int, default=4, help="Batch size for the dev.")
      parser.add_argument("--seed",type=int,default=1234,help="The seeds of the model.")
      parser.add_argument("--doc_stride",type=int,default=128,
                          help="When splitting up a long document into chunks, how much stride to take between chunks.")
      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("--num_train_epochs",default=3,type=int,
                          help="Total number of training epochs to perform.")
      parser.add_argument("--max_seq_length",default=128,type=int,
                          help="The maximum total input sequence length after tokenization. Sequences longer "+\
                                  "than this will be truncated, sequences shorter will be padded.")
      parser.add_argument("--do_train", action='store_true', help="Whether to train the model.")
      parser.add_argument("--do_predict", action='store_true', help="Whether to predict.")
  
      parser.add_argument("--learning_rate",default=5e-5,type=float,help="The initial learning rate for Adam.")
      parser.add_argument("--optimizer", default="adam", type=str, help="The method for gradients .")
      parser.add_argument("--warmup_proportion",default=0.0,type=float,
                          help="Proportion of training steps to perform linear learning rate warmup for.")
      parser.add_argument("--adam_epsilon",default=1e-8,type=float,help="Epsilon for Adam optimizer.")
      parser.add_argument("--weight_decay",default=0.0,type=float,help="Weight decay if we apply some.")
      parser.add_argument("--dropout-rate",default=0.2,type=float,help="Dropout for attention dropout.")
      parser.add_argument("--dropout-rnn",default=0.1,type=float,help="Dropout for rnn layer attention dropout.")
      parser.add_argument("-normalize",action='store_true',help='Whether to normalize the network.')
      args = parser.parse_args()
      return args
  def set_default_args(args):
      if not os.path.exists(args.processed_path):
          os.mkdir(args.processed_path)
      args.raw_train_file = os.path.join(args.raw_path, "train.json")
      args.raw_dev_file = os.path.join(args.raw_path, "dev.json")
      args.raw_test_file = os.path.join(args.raw_path, "test.json")
      args.train_file = os.path.join(args.processed_path,"train.json")
      args.dev_file = os.path.join(args.processed_path,"dev.json")
      args.test_file = os.path.join(args.processed_path,"test.json")
  
      args.embedding_name = args.emb_name
  def override_model_args(args, new_args):
      pass