import io
import sys

sys.path.append('src/tts/vits')

import soundfile
import os
os.environ["PYTORCH_JIT"] = "0"
import torch

import src.tts.vits.commons as commons
import src.tts.vits.utils as utils

from src.tts.vits.models import SynthesizerTrn
from src.tts.vits.text.symbols import symbols
from src.tts.vits.text import text_to_sequence

import logging
logging.basicConfig(level=logging.INFO)

dirbaspath = __file__.split("\\")[1:-1]
dirbaspath= "C://" + "/".join(dirbaspath)
config = {
    'paimon': {
        'cfg': dirbaspath + '/models/paimon6k.json',
        'model': dirbaspath + '/models/paimon6k_390k.pth',
        'char': 'character_paimon',
        'speed': 1
    },
    'yunfei': {
        'cfg': dirbaspath + '/tts/models/yunfeimix2.json',
        'model': dirbaspath + '/models/yunfeimix2_53k.pth',
        'char': 'character_yunfei',
        'speed': 1.1
    },
    'catmaid': {
        'cfg': dirbaspath + '/models/catmix.json',
        'model': dirbaspath + '/models/catmix_107k.pth',
        'char': 'character_catmaid',
        'speed': 1.2
    },
}

class TTService():
    
    def __init__(self, model_name="catmaid"):
        cfg = config[model_name]
        logging.info('Initializing TTS Service for %s...' % cfg["char"])
        self.hps = utils.get_hparams_from_file(cfg["cfg"])
        self.speed = cfg["speed"]
        self.net_g = SynthesizerTrn(
            len(symbols),
            self.hps.data.filter_length // 2 + 1,
            self.hps.train.segment_size // self.hps.data.hop_length,
            **self.hps.model).cuda()
        _ = self.net_g.eval()
        _ = utils.load_checkpoint(cfg["model"], self.net_g, None)

    def get_text(self, text, hps):
        text_norm = text_to_sequence(text, hps.data.text_cleaners)
        if hps.data.add_blank:
            text_norm = commons.intersperse(text_norm, 0)
        text_norm = torch.LongTensor(text_norm)
        return text_norm
    
    def read(self, text, format="wav") -> io.BytesIO:
        text = text.replace('~', '！')
        stn_tst = self.get_text(text, self.hps)
        
        with torch.no_grad():
            x_tst = stn_tst.cuda().unsqueeze(0)
            x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()
            # tp = self.net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.2, length_scale=self.speed)
            audio = self.net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.2, length_scale=self.speed)[0][
                0, 0].data.cpu().float().numpy()
            f = io.BytesIO()
            soundfile.write(f, audio, self.hps.data.sampling_rate, format=format)
            f.seek(0)
        return f