# Path: build_inputs.py
# def build_vocab(vocab_file, embedding_dim, delimiter=' '):
#     # construct vocab
#     with open(vocab_file, 'r') as f:
#         symbols = [s[:-1] for s in f.readlines()]
#     vocab = sq.Vocab(symbols, embedding_dim, delimiter)
#     return vocab


from collections import namedtuple
from build_inputs import build_vocab


TrainingConfigs = namedtuple('TrainingConfigs',
                             ['src_vocab', 'trg_vocab', 'params',
                              'train_src_file', 'train_trg_file',
                              'test_src_file', 'test_trg_file',
                              'beam_size', 'batch_size',
                              'max_step', 'model_dir',
                              'lr_rate', 'clip_gradient_norm',
                              'burn_in_step', 'increment_step', 'train_steps'])


def config_word2pos():
    # load vocab

# Path: build_inputs.py
# def build_vocab(vocab_file, embedding_dim, delimiter=' '):
#     # construct vocab
#     with open(vocab_file, 'r') as f:
#         symbols = [s[:-1] for s in f.readlines()]
#     vocab = sq.Vocab(symbols, embedding_dim, delimiter)
#     return vocab


# Path: config_example.py
from collections import namedtuple
from build_inputs import build_vocab



TrainingConfigs = namedtuple('TrainingConfigs',
                             ['src_vocab', 'trg_vocab', 'params',
                              'train_src_file', 'train_trg_file',
                              'test_src_file', 'test_trg_file',
                              'beam_size', 'batch_size',
                              'max_step', 'model_dir',
                              'lr_rate', 'clip_gradient_norm',
                              'burn_in_step', 'increment_step', 'train_steps'])


def config_word2pos():
    # load vocab

    src_vocab = build_vocab('data/vocab.word', 256, ' ')