"""
Auto Dialogue Evaluation.
"""

import argparse
import six

def parse_args():
    """
    Auto Dialogue Evaluation Config
    """
    parser = argparse.ArgumentParser('Automatic Dialogue Evaluation.')
    parser.add_argument(
        '--do_train', type=bool, default=False, help='Whether to perform training.')
    parser.add_argument(
        '--do_val', type=bool, default=False, help='Whether to perform evaluation.')
    parser.add_argument(
        '--do_infer', type=bool, default=False, help='Whether to perform inference.')
    parser.add_argument(
        '--loss_type', type=str, default='CLS', help='Loss type, CLS or L2.')

    #data path
    parser.add_argument(
        '--train_path', type=str, default=None, help='Path of training data')
    parser.add_argument(
        '--val_path', type=str, default=None, help='Path of validation data')
    parser.add_argument(
        '--test_path', type=str, default=None, help='Path of validation data')
    parser.add_argument(
        '--save_path', type=str, default='tmp', help='Save path')

    #step fit for data size
    parser.add_argument(
        '--print_step', type=int, default=50, help='Print step')
    parser.add_argument(
        '--save_step', type=int, default=400, help='Save step')
    parser.add_argument(
        '--num_scan_data', type=int, default=20, help='Save step')

    parser.add_argument(
        '--word_emb_init', type=str, default=None, help='Path to the initial word embedding')
    parser.add_argument(
        '--init_model', type=str, default=None, help='Path to the init model')

    parser.add_argument(
        '--use_cuda',
        action='store_true',
        help='If set, use cuda for training.')
    parser.add_argument(
        '--batch_size', type=int, default=256, help='Batch size')
    parser.add_argument(
        '--hidden_size', type=int, default=256, help='Hidden size')
    parser.add_argument(
        '--emb_size', type=int, default=256, help='Embedding size')
    parser.add_argument(
        '--vocab_size', type=int, default=484016, help='Vocabulary size')
    parser.add_argument(
        '--learning_rate', type=float, default=0.001, help='Learning rate')
    parser.add_argument(
        '--sample_pro', type=float, default=0.1, help='Sample probability for training data')
    parser.add_argument(
        '--max_len', type=int, default=50, help='Max length for sentences')

    args = parser.parse_args()
    return args


def print_arguments(args):
    """
    Print Config
    """
    print('-----------  Configuration Arguments -----------')
    for arg, value in sorted(six.iteritems(vars(args))):
        print('%s: %s' % (arg, value))
    print('------------------------------------------------')