import argparse
import os
from pathlib import Path

import logging
import re_matching

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 shutil
from scipy.io.wavfile import write
import librosa
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from tools.sentence import extrac, is_japanese, is_chinese, seconds_to_ass_time, extract_text_from_file, remove_annotations,extract_and_convert


import gradio as gr

import utils
from config import config

import torch
import commons
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
import utils

from models import SynthesizerTrn
from text.symbols import symbols
import sys
import re
from tools.translate import translate

net_g = None

device = (
        "cuda:0"
        if torch.cuda.is_available()
        else (
            "mps"
            if sys.platform == "darwin" and torch.backends.mps.is_available()
            else "cpu"
        )
    )

#device = "cpu"
BandList = {
        "PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
        "Afterglow":["蘭","モカ","ひまり","巴","つぐみ"],
        "HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"],
        "PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"],
        "Roselia":["友希那","紗夜","リサ","燐子","あこ"],
        "RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"],
        "Morfonica":["ましろ","瑠唯","つくし","七深","透子"],
        "MyGo":["燈","愛音","そよ","立希","楽奈"],
        "AveMujica":["祥子","睦","海鈴","にゃむ","初華"],
        "圣翔音乐学园":["華戀","光","香子","雙葉","真晝","純那","克洛迪娜","真矢","奈奈"],
        "凛明馆女子学校":["珠緒","壘","文","悠悠子","一愛"],
        "弗隆提亚艺术学校":["艾露","艾露露","菈樂菲","司","靜羽"],
        "西克菲尔特音乐学院":["晶","未知留","八千代","栞","美帆"]
}

def get_net_g(model_path: str,  device: str, hps):
    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(model_path, net_g, None, skip_optimizer=True)
    return net_g

def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
    style_text = None if style_text == "" else style_text
    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_ori = get_bert(
        norm_text, word2ph, language_str, device, style_text, style_weight
    )
    del word2ph
    assert bert_ori.shape[-1] == len(phone), phone

    if language_str == "ZH":
        bert = bert_ori
        ja_bert = torch.randn(1024, len(phone))
        en_bert = torch.randn(1024, len(phone))
    elif language_str == "JP":
        bert = torch.randn(1024, len(phone))
        ja_bert = bert_ori
        en_bert = torch.randn(1024, len(phone))
    elif language_str == "EN":
        bert = torch.randn(1024, len(phone))
        ja_bert = torch.randn(1024, len(phone))
        en_bert = bert_ori
    else:
        raise ValueError("language_str should be ZH, JP or EN")

    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, en_bert, phone, tone, language


def infer(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    style_text=None,
    style_weight=0.7,
    language = "Auto",
):
    if language == "Auto":
        language= 'JP' if is_japanese(text) else 'ZH'
    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
        text,
        language,
        hps,
        device,
        style_text=style_text,
        style_weight=style_weight,
    )
    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)
        en_bert = en_bert.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        # emo = emo.to(device).unsqueeze(0)
        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,
                en_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,
            ja_bert,
            en_bert,
        )  # , emo
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return (hps.data.sampling_rate,gr.processing_utils.convert_to_16_bit_wav(audio))

def is_japanese(string):
        for ch in string:
            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
                return True
        return False

def loadmodel(model):
    _ = net_g.eval()
    _ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True)
    return "success"

def generate_audio_and_srt_for_group(group, outputPath, group_index, sampling_rate, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime):
    audio_fin = []
    ass_entries = []
    start_time = 0
    #speaker = random.choice(cara_list)
    ass_header = """[Script Info]
; 我没意见
Title: Audiobook
ScriptType: v4.00+
WrapStyle: 0
PlayResX: 640
PlayResY: 360
ScaledBorderAndShadow: yes
[V4+ Styles]
Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
Style: Default,Arial,20,&H00FFFFFF,&H000000FF,&H00000000,&H00000000,0,0,0,0,100,100,0,0,1,1,1,2,10,10,10,1
[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
"""

    for sentence in group:
        try:
            FakeSpeaker = sentence.split("|")[0]
            print(FakeSpeaker)
            SpeakersList = re.split('\n', spealerList)
            if FakeSpeaker in list(hps.data.spk2id.keys()):
                speaker = FakeSpeaker
            for i in SpeakersList:
                if FakeSpeaker == i.split("|")[1]:
                    speaker = i.split("|")[0]
            if sentence != '\n':
                audio = infer_simple((remove_annotations(sentence.split("|")[-1]).replace(" ","")+"。").replace("，。","。").replace("。。","。"), sdp_ratio, noise_scale, noise_scale_w, length_scale,speaker)
                silence_frames = int(silenceTime * 44010) if is_chinese(sentence) else int(silenceTime * 44010)
                silence_data = np.zeros((silence_frames,), dtype=audio.dtype)
                audio_fin.append(audio)
                audio_fin.append(silence_data)

                duration = len(audio) / sampling_rate
                print(duration)
                end_time = start_time + duration + silenceTime
                ass_entries.append("Dialogue: 0,{},{},".format(seconds_to_ass_time(start_time), seconds_to_ass_time(end_time)) + "Default,,0,0,0,,{}".format(sentence.replace("|","：")))
                start_time = end_time
        except:
            pass
    wav_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.wav')
    ass_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.ass')

    write(wav_filename, sampling_rate, np.concatenate(audio_fin))

    with open(ass_filename, 'w', encoding='utf-8') as f:
        f.write(ass_header + '\n'.join(ass_entries))
    return (hps.data.sampling_rate, np.concatenate(audio_fin))

def audiobook(inputFile, groupsize, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime,filepath,raw_text):
    directory_path = filepath if torch.cuda.is_available() else "books"
 
    if os.path.exists(directory_path):
        shutil.rmtree(directory_path)

    os.makedirs(directory_path)
    if inputFile:
        text = extract_text_from_file(inputFile.name)
    else:
        text = raw_text
    sentences = extrac(extract_and_convert(text))
    GROUP_SIZE = groupsize
    for i in range(0, len(sentences), GROUP_SIZE):
        group = sentences[i:i+GROUP_SIZE]
        if spealerList == "":
            spealerList = "无"
        result = generate_audio_and_srt_for_group(group,directory_path, i//GROUP_SIZE + 1, 44100, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime)
        if not torch.cuda.is_available():
            return result
    return result

def infer_simple(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    style_text=None,
    style_weight=0.7,
):
    if is_chinese(text) or is_japanese(text):
        if len(text) > 1:
            language= 'JP' if is_japanese(text) else 'ZH'
            bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
                text,
                language,
                hps,
                device,
                style_text="",
                style_weight=0,
            )
            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)
                en_bert = en_bert.to(device).unsqueeze(0)
                x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
                # emo = emo.to(device).unsqueeze(0)
                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,
                        en_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,
                    ja_bert,
                    en_bert,
                )  # , emo
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()
                return audio

if __name__ == "__main__":
    languages = [ "Auto", "ZH", "JP"]
    modelPaths = []
    for dirpath, dirnames, filenames in os.walk('Data/Chinese/models/'):
        for filename in filenames:
            modelPaths.append(os.path.join(dirpath, filename))
    hps = utils.get_hparams_from_file('Data/Chinese/config.json')
    net_g = get_net_g(
        model_path="Data/Chinese/models/G_80000.pth", device=device, hps=hps
    )
    speaker_ids = hps.data.spk2id
    speakers = list(speaker_ids.keys())
    with gr.Blocks() as app:
        gr.Markdown(value="""
            [日语特化版(推荐)](https://huggingface.co/spaces/Mahiruoshi/BangStarlight),国内可用连接: https://mahiruoshi-BangStarlight.hf.space/\n
            [假名标注版](https://huggingface.co/spaces/Mahiruoshi/MyGO_VIts-bert)，国内可用连接: https://mahiruoshi-MyGO-VIts-bert.hf.space/\n
            该界面的真实链接(国内可用): https://mahiruoshi-bangdream-bert-vits2.hf.space/\n
            ([Bert-Vits2](https://github.com/Stardust-minus/Bert-VITS2) V2.3)少歌邦邦全员在线语音合成\n
            [好玩的](http://love.soyorin.top/)\n
            API: https://mahiruoshi-bert-vits2-api.hf.space/ \n
            调用方式: https://mahiruoshi-bert-vits2-api.hf.space/?text={{speakText}}&speaker=chosen_speaker\n
            推荐搭配[Legado开源阅读](https://github.com/gedoor/legado)或[聊天bot](https://github.com/Paraworks/BangDreamAi)使用\n
            二创请标注作者：B站@Mahiroshi: https://space.bilibili.com/19874615\n
            训练数据集归属：BangDream及少歌手游,提取自BestDori,[数据集获取流程](https://nijigaku.top/2023/09/29/Bestbushiroad%E8%AE%A1%E5%88%92-vits-%E9%9F%B3%E9%A2%91%E6%8A%93%E5%8F%96%E5%8F%8A%E6%95%B0%E6%8D%AE%E9%9B%86%E5%AF%B9%E9%BD%90/)\n
            BangDream数据集下载[链接](https://huggingface.co/spaces/Mahiruoshi/BangDream-Bert-VITS2/blob/main/%E7%88%AC%E8%99%AB/SortPathUrl.txt)\n
            ！！！注意：huggingface容器仅用作展示，建议在右上角更多选项中克隆本项目或Docker运行app.py/server.py,环境参考requirements.txt\n""")
        for band in BandList:
            with gr.TabItem(band):
                for name in BandList[band]:
                    with gr.TabItem(name):
                        with gr.Row():
                            with gr.Column():
                                with gr.Row():
                                    gr.Markdown(
                                        '<div align="center">'
                                        f'<img style="width:auto;height:400px;" src="https://mahiruoshi-bangdream-bert-vits2.hf.space/file/image/{name}.png">' 
                                        '</div>'
                                    )
                                length_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
                                    )
                                language = gr.Dropdown(
                                        choices=languages, value="Auto", label="语言"
                                    )
                                with gr.Accordion(label="参数设定", open=True):
                                    sdp_ratio = gr.Slider(
                                    minimum=0, maximum=1, value=0.5, step=0.01, label="SDP/DP混合比"
                                    )
                                    noise_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节"
                                    )
                                    noise_scale_w = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.667, step=0.01, label="音素长度"
                                    )
                                    speaker = gr.Dropdown(
                                        choices=speakers, value=name, label="说话人"
                                    ) 
                                with gr.Accordion(label="切换模型", open=False):
                                    modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
                                    btnMod = gr.Button("载入模型")
                                    statusa = gr.TextArea(label = "模型加载状态")
                                    btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa])
                            with gr.Column():
                                text = gr.TextArea(
                                    label="文本输入",
                                    info="输入纯日语或者中文",
                                    value="我是来结束这个乐队的。",
                                )
                                style_text = gr.Textbox(
                                    label="情感辅助文本",
                                    info="语言保持跟主文本一致,文本可以参考训练集:https://huggingface.co/spaces/Mahiruoshi/BangDream-Bert-VITS2/blob/main/filelists/Mygo.list)",
                                    placeholder="使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
                        "**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）"
                                                        )
                                style_weight = gr.Slider(
                                        minimum=0,
                                        maximum=1,
                                        value=0.7,
                                        step=0.1,
                                        label="Weight",
                                        info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本",
                                    )
                                btn = gr.Button("点击生成", variant="primary")
                                audio_output = gr.Audio(label="Output Audio")
                                btntran = gr.Button("快速中翻日")
                                translateResult = gr.TextArea(label="使用百度翻译",placeholder="从这里复制翻译后的文本")
                                btntran.click(translate, inputs=[text], outputs = [translateResult])
                                
                    btn.click(
                        infer,
                        inputs=[
                            text,
                            sdp_ratio,
                            noise_scale,
                            noise_scale_w,
                            length_scale,
                            speaker,
                            style_text,
                            style_weight,
                            language,
                        ],
                        outputs=[audio_output],
                    )
        with gr.Tab('拓展功能'):
            with gr.Row():
                with gr.Column():
                    gr.Markdown(
                                    f"从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看自制galgame使用说明\n</a>"
                                )
                    inputFile = gr.UploadButton(label="txt文件输入")
                    raw_text = gr.TextArea(
                                        label="文本输入",
                                        info="输入纯日语或者中文",
                                        value="つくし|我是来结束这个乐队的。",
                    )
                    groupSize = gr.Slider(
                    minimum=10, maximum=1000 if  torch.cuda.is_available() else 50,value = 50, step=1, label="单个音频文件包含的最大字数"
                    )
                    silenceTime = gr.Slider(
                    minimum=0, maximum=1, value=0.5, step=0.01, label="句子的间隔"
                    )
                    filepath = gr.TextArea(
                                        label="本地合成时的音频存储文件夹(会清空文件夹)",
                                        value = "D:/audiobook/book1",
                    )
                    spealerList = gr.TextArea(
                                        label="角色对应表,左边是你想要在每一句话合成中用到的speaker(见角色清单)右边是你上传文本时分隔符左边设置的说话人:{ChoseSpeakerFromConfigList}|{SeakerInUploadText}",
                                        placeholder = "ましろ|真白\n七深|七深\n透子|透子\nつくし|筑紫\n瑠唯|瑠唯\nそよ|素世\n祥子|祥子",
                    )                  
                    speaker = gr.Dropdown(
                        choices=speakers, value = "ましろ", label="选择默认说话人"
                    )
                with gr.Column():
                    sdp_ratio = gr.Slider(
                    minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比"
                    )
                    noise_scale = gr.Slider(
                        minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节"
                    )
                    noise_scale_w = gr.Slider(
                        minimum=0.1, maximum=2, value=0.667, step=0.01, label="音素长度"
                    )
                    length_scale = gr.Slider(
                        minimum=0.1, maximum=2, value=1, step=0.01, label="生成长度"
                    )
                    LastAudioOutput = gr.Audio(label="当使用cuda时才能在本地文件夹浏览全部文件")
                    btn2 = gr.Button("点击生成", variant="primary")
                btn2.click(
                    audiobook,
                    inputs=[
                        inputFile,
                        groupSize,
                        speaker,
                        sdp_ratio,
                        noise_scale,
                        noise_scale_w,
                        length_scale,
                        spealerList,
                        silenceTime,
                        filepath,
                        raw_text
                    ],
                    outputs=[LastAudioOutput],
                )
    print("推理页面已开启!")
    app.launch(share=True)