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

import gradio as gr

import utils
from config import config

import torch
import commons
from text import cleaned_text_to_sequence, get_bert

from tools.sentence import extrac, is_japanese, is_chinese, seconds_to_ass_time, extract_text_from_file, remove_annotations,extract_and_convert

from text.cleaner import clean_text
import utils

from tools.translate import translate

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

import random
import hashlib

from fugashi import Tagger
import jaconv
import unidic
import subprocess

import requests

from ebooklib import epub
import PyPDF2
from PyPDF2 import PdfReader
from bs4 import BeautifulSoup
import jieba 
import romajitable

webBase = {
    'pyopenjtalk-V2.3-Katakana': 'https://mahiruoshi-mygo-vits-bert.hf.space/',
    'fugashi-V2.3-Katakana': 'https://mahiruoshi-mygo-vits-bert.hf.space/',
}

languages = [ "Auto", "ZH", "JP"]
modelPaths = []
modes = ['pyopenjtalk-V2.3']
if torch.cuda.is_available():
    modes = ['pyopenjtalk-V2.3','fugashi-V2.3']
sentence_modes = ['sentence','paragraph']

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"]
}

# 推理工具
def download_unidic():
    try:
        Tagger()
        print("Tagger launch successfully.")
    except Exception as e:
        print("UNIDIC dictionary not found, downloading...")
        subprocess.run([sys.executable, "-m", "unidic", "download"])
        print("Download completed.")

def kanji_to_hiragana(text):
    global tagger
    output = ""

    # 更新正则表达式以更准确地区分文本和标点符号
    segments = re.findall(r'[一-龥ぁ-んァ-ン\w]+|[^\一-龥ぁ-んァ-ン\w\s]', text, re.UNICODE)

    for segment in segments:
        if re.match(r'[一-龥ぁ-んァ-ン\w]+', segment):
            # 如果是单词或汉字，转换为平假名
            for word in tagger(segment):
                kana = word.feature.kana or word.surface
                hiragana = jaconv.kata2hira(kana)  # 将片假名转换为平假名
                output += hiragana
        else:
            # 如果是标点符号，保持不变
            output += segment

    return output

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",
    mode = 'pyopenjtalk-V2.3',
    skip_start=False,
    skip_end=False,
):
    if style_text == None:
        style_text = ""
        style_weight=0,
    if mode == 'fugashi-V2.3':
        text = kanji_to_hiragana(text) if is_japanese(text) else text
    if language == "JP":
        text = translate(text,"jp")
    if language == "ZH":
        text = translate(text,"zh")
    if language == "Auto":
        language= 'JP' if is_japanese(text) else 'ZH'
    #print(f'{text}:{sdp_ratio}:{noise_scale}:{noise_scale_w}:{length_scale}:{length_scale}:{sid}:{language}:{mode}:{skip_start}:{skip_end}')            
    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
        text,
        language,
        hps,
        device,
        style_text=style_text,
        style_weight=style_weight,
    )
    if skip_start:
        phones = phones[3:]
        tones = tones[3:]
        lang_ids = lang_ids[3:]
        bert = bert[:, 3:]
        ja_bert = ja_bert[:, 3:]
        en_bert = en_bert[:, 3:]
    if skip_end:
        phones = phones[:-2]
        tones = tones[:-2]
        lang_ids = lang_ids[:-2]
        bert = bert[:, :-2]
        ja_bert = ja_bert[:, :-2]
        en_bert = en_bert[:, :-2]
    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()
    print("Success.")
    return audio

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,
    speakerList,
    silenceTime,
    language,
    mode,
    skip_start,
    skip_end,
    style_text,
    style_weight,
    ):
    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:
            if len(sentence) > 1:
                FakeSpeaker = sentence.split("|")[0]
                print(FakeSpeaker)
                SpeakersList = re.split('\n', speakerList)
                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':
                    text = (remove_annotations(sentence.split("|")[-1]).replace(" ","")+"。").replace("，。","。")
                    if mode == 'pyopenjtalk-V2.3' or mode == 'fugashi-V2.3':
                        #print(f'{text}:{sdp_ratio}:{noise_scale}:{noise_scale_w}:{length_scale}:{length_scale}:{speaker}:{language}:{mode}:{skip_start}:{skip_end}')
                        audio = infer(
                            text,
                            sdp_ratio,
                            noise_scale,
                            noise_scale_w,
                            length_scale,
                            speaker,
                            style_text,
                            style_weight,
                            language,
                            mode,
                            skip_start,
                            skip_end,
                        )
                    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, gr.processing_utils.convert_to_16_bit_wav(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, gr.processing_utils.convert_to_16_bit_wav(np.concatenate(audio_fin)))

def generate_audio(
    inputFile,
    groupSize,
    filepath,
    silenceTime,
    speakerList,
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    style_text=None,
    style_weight=0.7,
    language = "Auto",
    mode = 'pyopenjtalk-V2.3',
    sentence_mode = 'sentence',
    skip_start=False,
    skip_end=False,
):
    if inputFile:
        text = extract_text_from_file(inputFile.name)
        sentence_mode = 'paragraph'
    if mode == 'pyopenjtalk-V2.3' or mode == 'fugashi-V2.3':
        if sentence_mode == 'sentence':
            audio = infer(
                        text,
                        sdp_ratio,
                        noise_scale,
                        noise_scale_w,
                        length_scale,
                        sid,
                        style_text,
                        style_weight,
                        language,
                        mode,
                        skip_start,
                        skip_end,
                    )
            return (hps.data.sampling_rate,gr.processing_utils.convert_to_16_bit_wav(audio))
        if sentence_mode == 'paragraph':
            GROUP_SIZE = groupSize
            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 language == 'Auto':
                sentences = extrac(extract_and_convert(text))
            else:
                sentences = extrac(text)
            for i in range(0, len(sentences), GROUP_SIZE):
                group = sentences[i:i+GROUP_SIZE]
                if speakerList == "":
                    speakerList = "无"
                result = generate_audio_and_srt_for_group(
                    group,
                    directory_path,
                    i//GROUP_SIZE + 1,
                    44100,
                    sid,
                    sdp_ratio,
                    noise_scale,
                    noise_scale_w,
                    length_scale,
                    speakerList,
                    silenceTime,
                    language,
                    mode,
                    skip_start,
                    skip_end,
                    style_text,
                    style_weight,       
                    )
                if not torch.cuda.is_available():
                    return result
            return result
    #url = f'{webBase[mode]}?text={text}&speaker={sid}&sdp_ratio={sdp_ratio}&noise_scale={noise_scale}&noise_scale_w={noise_scale_w}&length_scale={length_scale}&language={language}&skip_start={skip_start}&skip_end={skip_end}'
    #print(url)
    #res = requests.get(url)
    #改用post
    res = requests.post(webBase[mode], json = {
        "groupSize": groupSize,
        "filepath": filepath,
        "silenceTime": silenceTime,
        "speakerList": speakerList,
        "text": text,
        "speaker": sid,
        "sdp_ratio": sdp_ratio,
        "noise_scale": noise_scale,
        "noise_scale_w": noise_scale_w,
        "length_scale": length_scale,
        "language": language,
        "skip_start": skip_start,
        "skip_end": skip_end,
        "mode": mode,
        "sentence_mode": sentence_mode,
        "style_text": style_text,
        "style_weight": style_weight
    })
    audio = res.content
    with open('output.wav', 'wb') as code:
        code.write(audio)
    file_path = "output.wav"
    return file_path

if __name__ == "__main__":
    if torch.cuda.is_available():
        download_unidic()
        tagger = Tagger()
    for dirpath, dirnames, filenames in os.walk('Data/BangDream/models/'):
        for filename in filenames:
            modelPaths.append(os.path.join(dirpath, filename))
    hps = utils.get_hparams_from_file('Data/BangDream/config.json')
    net_g = get_net_g(
        model_path=modelPaths[-1], 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>'
                                    )
                                with gr.Accordion(label="参数设定", open=False):
                                    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="Noise:感情调节"
                                    )
                                    noise_scale_w = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.667, step=0.01, label="Noise_W:音素长度"
                                    )
                                    skip_start = gr.Checkbox(label="skip_start")
                                    skip_end = gr.Checkbox(label="skip_end")
                                    speaker = gr.Dropdown(
                                        choices=speakers, value=name, label="说话人"
                                    )
                                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="语言选择,若不选自动则会将输入语言翻译为日语或中文"
                                    )
                                mode = gr.Dropdown(
                                        choices=modes, value="pyopenjtalk-V2.3", label="TTS模式，合成少歌角色需要切换成 pyopenjtalk-V2.3-Katakana "
                                    )
                                sentence_mode = gr.Dropdown(
                                        choices=sentence_modes, value="paragraph", label="文本合成模式"
                                    )
                                with gr.Accordion(label="扩展选项", open=False):
                                    inputFile = gr.UploadButton(label="txt文件输入")
                                    speakerList = gr.TextArea(
                                        label="角色对应表,如果你记不住角色名可以这样,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}",
                                        value = "ましろ|天音\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",
                                    )
                                    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句子数"
                                    )
                                    filepath = gr.TextArea(
                                        label="本地合成时的音频存储文件夹(会清空文件夹,别把C盘删了)",
                                        value = "D:/audiobook/book1",
                                    )
                                    silenceTime = gr.Slider(
                                        minimum=0, maximum=1, value=0.5, step=0.01, label="句子的间隔"
                                    )
                                    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,可用'|'分割说话人和文本,注意换行",
                                        info="输入纯日语或者中文",
                                        value=f"{name}|你是职业歌手吗\n天音|我觉得我是",
                                        placeholder=f"私は{name}です、あの子はだれ？ " 
                                    )
                                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(
                        generate_audio,
                        inputs=[
                            inputFile,
                            groupSize,
                            filepath,
                            silenceTime,
                            speakerList,
                            text,
                            sdp_ratio,
                            noise_scale,
                            noise_scale_w,
                            length_scale,
                            speaker,
                            style_text,
                            style_weight,
                            language,
                            mode,
                            sentence_mode,
                            skip_start,
                            skip_end
                        ],
                        outputs=[audio_output],
                    )
    print("推理页面已开启!")
    app.launch()