def METHOD_NAME():
    parser = argparse.ArgumentParser(__doc__)
    parser.add_argument('--model_name_or_path', type=str, default='unified_transformer-12L-cn-luge', help='The path or shortcut name of the pre-trained model.')
    parser.add_argument('--output_path', type=str, default='./predict.txt', help='The file path where the infer result will be saved.')
    parser.add_argument('--logging_steps', type=int, default=100, help='Log every X updates steps.')
    parser.add_argument('--seed', type=int, default=2021, help='Random seed for initialization.')
    parser.add_argument('--batch_size', type=int, default=16, help='Batch size per GPU/CPU for training.')
    parser.add_argument('--max_seq_len', type=int, default=512, help='The maximum sequence length of training.')
    parser.add_argument('--max_response_len', type=int, default=128, help='The maximum response sequence length of training.')
    parser.add_argument('--max_knowledge_len', type=int, default=256, help='The maximum knowledge sequence length of training.')
    parser.add_argument('--min_dec_len', type=int, default=1, help='The minimum sequence length of generation.')
    parser.add_argument('--max_dec_len', type=int, default=64, help='The maximum sequence length of generation.')
    parser.add_argument('--num_return_sequences', type=int, default=20, help='The numbers of returned sequences for one input in generation.')
    parser.add_argument('--decode_strategy', type=str, default='sampling', help='The decode strategy in generation.')
    parser.add_argument('--top_k', type=int, default=0, help='The number of highest probability vocabulary tokens to keep for top-k sampling.')
    parser.add_argument('--temperature', type=float, default=1.0, help='The value used to module the next token probabilities.')
    parser.add_argument('--top_p', type=float, default=1.0, help='The cumulative probability for top-p sampling.')
    parser.add_argument('--num_beams', type=int, default=0, help='The number of beams for beam search.')
    parser.add_argument('--length_penalty', type=float, default=1.0, help='The exponential penalty to the sequence length for beam search.')
    parser.add_argument('--early_stopping', type=eval, default=False, help='Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.')
    parser.add_argument('--device', type=str, default='gpu', help='The device to select for training the model.')
    parser.add_argument('--faster', action='store_true', help='Whether to process inference using faster transformer. ')
    parser.add_argument('--use_fp16_decoding', action='store_true', help='Whether to use fp16 when using faster transformer. Only works when using faster transformer. ')
    args = parser.METHOD_NAME()
    return args