import os

os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
os.system('pip install pypinyin Cython==0.29.21 librosa==0.8.0 matplotlib==3.3.1 numpy==1.18.5 phonemizer==2.2.1 scipy==1.5.2 Unidecode==1.1.1 >log.log')
os.system('sudo apt-get install espeak -y >log.log')
os.system('pip install gdown >log.log')
os.system('pip install pyopenjtalk janome > log.log')
os.system('pip install cloud-tpu-client > log.log')

import logging

numba_logger = logging.getLogger('numba')
numba_logger.setLevel(logging.WARNING)

import librosa

import matplotlib.pyplot as plt
import IPython.display as ipd

import os
import json
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader

import commons
import utils
from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
from models import SynthesizerTrn
from text.symbols import symbols
from text.cleaners import japanese_phrase_cleaners
from text import cleaned_text_to_sequence
from pypinyin import lazy_pinyin, Style

from scipy.io.wavfile import write

def get_text(text, hps):
    text_norm = cleaned_text_to_sequence(text)
    if hps.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    text_norm = torch.LongTensor(text_norm)
    return text_norm
# hps_ms = utils.get_hparams_from_file("./configs/vctk_base.json")


hps = utils.get_hparams_from_file("./configs/tokaiteio.json")
# net_g_ms = SynthesizerTrn(
#     len(symbols),
#     hps_ms.data.filter_length // 2 + 1,
#     hps_ms.train.segment_size // hps.data.hop_length,
#     n_speakers=hps_ms.data.n_speakers,
#     **hps_ms.model)

net_g = SynthesizerTrn(
    len(symbols),
    hps.data.filter_length // 2 + 1,
    hps.train.segment_size // hps.data.hop_length,
    **hps.model)
_ = net_g.eval()


def tts(text):
    if len(text) > 150:
        return "Error: Text is too long", None
    stn_tst = get_text(text, hps)
    with torch.no_grad():
        x_tst = stn_tst.unsqueeze(0)
        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
        audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.float().numpy()
    ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate))


def tts_fn(text, speaker_id):
    if len(text) > 150:
        return "Error: Text is too long", None
    stn_tst = get_text(text, hps)
    with torch.no_grad():
        x_tst = stn_tst.unsqueeze(0)
        x_tst_lengths = LongTensor([stn_tst.size(0)])
        audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][
            0, 0].data.cpu().float().numpy()
    return "Success", (hps.data.sampling_rate, audio)


if __name__ == '__main__':
    _ = utils.load_checkpoint("G_50000.pth", net_g, None)

    app = gr.Blocks()

    with app:
        with gr.Tabs():
            with gr.Column():
                tts_input1 = gr.TextArea(label="Text (150 words limitation)", value="こんにちは。")
                tts_submit = gr.Button("Generate", variant="primary")
                tts_output1 = gr.Textbox(label="Output Message")
                tts_output2 = gr.Audio(label="Output Audio")

        tts_submit.click(tts_fn, [tts_input1, tts_input2], [tts_output1, tts_output2])

    app.launch()