import torch
from torch.utils.data import DataLoader

import commons
import utils
from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
from tqdm import tqdm

from text import cleaned_text_to_sequence, get_bert

config_path = 'configs/config.json'
hps = utils.get_hparams_from_file(config_path)

# train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
# eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
#
# collate_fn = TextAudioSpeakerCollate()
# train_loader = DataLoader(train_dataset, num_workers=12, shuffle=False,
#                          batch_size=32, pin_memory=True,
#                          drop_last=False, collate_fn=collate_fn)
# eval_loader = DataLoader(eval_dataset, num_workers=12, shuffle=False,
#                          batch_size=32, pin_memory=True,
#                          drop_last=False, collate_fn=collate_fn)
# for _ in tqdm(train_loader):
#     pass
# for _ in tqdm(eval_loader):
#     pass

for line in tqdm( open(hps.data.training_files).readlines()):
    _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
    phone = phones.split(" ")
    tone = [int(i) for i in tone.split(" ")]
    word2ph = [int(i) for i in word2ph.split(" ")]
    # print(text, word2ph,phone, tone, language_str)
    w2pho = [i for i in word2ph]
    word2ph = [i for i in word2ph]
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
    pold2 = phone

    if hps.data.add_blank:
        phone = commons.intersperse(phone, 0)
        tone = commons.intersperse(tone, 0)
        language = commons.intersperse(language, 0)
        for i in range(len(word2ph)):
            word2ph[i] = word2ph[i] * 2
        word2ph[0] += 1
    wav_path = f'dataset/{spk}/{_id}.wav'

    bert_path = wav_path.replace(".wav", ".bert.pt")
    try:
        bert = torch.load(bert_path)
        assert bert.shape[-1] == len(phone)
    except:
        bert = get_bert(text, word2ph, language_str)
        assert bert.shape[-1] == len(phone)
        torch.save(bert, bert_path)