# flake8: noqa: E402

import sys, os

import logging

logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

logging.basicConfig(
    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
)

logger = logging.getLogger(__name__)
import torch
import argparse
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
import gradio as gr
import webbrowser
import numpy as np


net_g = None

if sys.platform == "darwin" and torch.backends.mps.is_available():
    device = "mps"
    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
else:
    device = "cpu"


def get_text(text, language_str, hps):
    norm_text, phone, tone, word2ph = clean_text(text, language_str)
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)

    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
    bert = get_bert(norm_text, word2ph, language_str, device)
    del word2ph
    assert bert.shape[-1] == len(phone), phone

    if language_str == "ZH":
        bert = bert
        ja_bert = torch.zeros(768, len(phone))
    elif language_str == "JP":
        ja_bert = bert
        bert = torch.zeros(1024, len(phone))
    else:
        bert = torch.zeros(1024, len(phone))
        ja_bert = torch.zeros(768, len(phone))

    assert bert.shape[-1] == len(
        phone
    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"

    phone = torch.LongTensor(phone)
    tone = torch.LongTensor(tone)
    language = torch.LongTensor(language)
    return bert, ja_bert, phone, tone, language


def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
    global net_g
    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
    with torch.no_grad():
        x_tst = phones.to(device).unsqueeze(0)
        tones = tones.to(device).unsqueeze(0)
        lang_ids = lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        ja_bert = ja_bert.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        del phones
        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
        audio = (
            net_g.infer(
                x_tst,
                x_tst_lengths,
                speakers,
                tones,
                lang_ids,
                bert,
                ja_bert,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )[0][0, 0]
                .data.cpu()
                .float()
                .numpy()
        )
        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
        torch.cuda.empty_cache()
        return audio


def tts_fn(
        text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language
):
    print(f"text: {text}, speaker: {speaker}")
    slices = text.split("\n")
    audio_list = []
    with torch.no_grad():
        for slice in slices:
            audio = infer(
                slice,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
                sid=speaker,
                language=language,
            )
            audio_list.append(audio)
            silence = np.zeros(hps.data.sampling_rate)  # 生成1秒的静音
            audio_list.append(silence)  # 将静音添加到列表中
    audio_concat = np.concatenate(audio_list)
    return "Success", (hps.data.sampling_rate, audio_concat)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-m", "--model", default="./models/G_270K.pth", help="path of your model"
    )
    parser.add_argument(
        "-c",
        "--config",
        default="./models/config.json",
        help="path of your config file",
    )
    parser.add_argument(
        "--share", default=False, help="make link public", action="store_true"
    )
    parser.add_argument(
        "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
    )
    parser.add_argument(
        "--info_md", default='./info.md', help="info markdown file"
    )

    args = parser.parse_args()
    if args.debug:
        logger.info("Enable DEBUG-LEVEL log")
        logging.basicConfig(level=logging.DEBUG)
    hps = utils.get_hparams_from_file(args.config)

    device = (
        "cuda:0"
        if torch.cuda.is_available()
        else (
            "mps"
            if sys.platform == "darwin" and torch.backends.mps.is_available()
            else "cpu"
        )
    )
    net_g = SynthesizerTrn(
        len(symbols),
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model,
    ).to(device)
    _ = net_g.eval()

    _ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)

    speaker_ids = hps.data.spk2id
    speakers = list(speaker_ids.keys())
    languages = ["JP"]
    with gr.Blocks(title="Umamusume-DeBERTa-VITS2") as app:
        with gr.Row():
            with gr.Column():
                text = gr.TextArea(
                    label="Text",
                    placeholder="Input Text Here",
                    value="サイーゲームス！。ウマ娘、プリティーダービー！",
                )
                speaker = gr.Dropdown(
                    choices=speakers, value=speakers[0], label="Speaker"
                )
                sdp_ratio = gr.Slider(
                    minimum=0, maximum=1, value=0.2, step=0.05, label="SDP Ratio (个人认为更大的SDP Ratio会产生\"感情更强烈的\"语音) (I personally believe that a larger SDP Ratio will make the generated voice \"emotionally stronger\")"
                )
                noise_scale = gr.Slider(
                    minimum=0.1, maximum=2, value=0.6, step=0.05, label="Noise Scale"
                )
                noise_scale_w = gr.Slider(
                    minimum=0.1, maximum=2, value=0.8, step=0.05, label="Noise Scale W"
                )
                length_scale = gr.Slider(
                    minimum=0.1, maximum=2, value=1, step=0.05, label="Length Scale (控制生成语音的长度) (Controlling the length of the generated audio)"
                )
                language = gr.Dropdown(
                    choices=languages, value=languages[0], label="Language"
                )
                btn = gr.Button("Generate!", variant="primary")
            with gr.Column():
                text_output = gr.Textbox(label="Message")
                audio_output = gr.Audio(label="Output Audio")
                samples = gr.Textbox(label="WEIRD Samples Given By GPT-4")
                samples.value = "⚠ 强烈不建议将所有内容扔进输入，这会导致相当久的推理时间 ⚠\n" \
                                "⚠ すべての内容をお入りになることがお勧めしませんで、生成時間が非常に長くなるでしょう ⚠\n" \
                                "⚠ Throwing Everything into text input leads to unexpected long inference time ⚠\n" \
                                "おはよう、今日も一緒に頑張りましょうね！\n" \
                                + "ねえねえ、あなたの好きなお料理作ってあげるよ！\n" \
                                + "きゃー！びっくりさせないでよ～！\n" \
                                + "あのね、新しいドレス買ったの。どう思う？\n" \
                                + "あっ、遅くなっちゃった！ごめんなさい！\n" \
                                + "今日のデート、すごく楽しかったよ！また行きましょうね！\n" \
                                + "私のこと、好き？\n" \
                                + "あなたといると、時間があっという間に過ぎちゃうね。\n" \
                                + "あたし、あなたが大好きだよ。\n" \
                                + "ねえ、もっと話して！あなたの話、大好きなの！\n" \
                                + "あっ、それ可愛いね！私に似合うかな？\n" \
                                + "あなたのこと、ずっと考えてたんだよ。\n" \
                                + "今日はどんな一日だった？私にも話して！\n" \
                                + "あなたの笑顔、大好き！もっと見せて！\n" \
                                + "おやすみ、いい夢見てね！"
                with open(args.info_md, 'r', encoding='UTF-8') as file:
                    data = file.read()
                    md_info = gr.Markdown(data)

        btn.click(
            tts_fn,
            inputs=[
                text,
                speaker,
                sdp_ratio,
                noise_scale,
                noise_scale_w,
                length_scale,
                language,
            ],
            outputs=[text_output, audio_output],
        )
    app.launch()