import os, re, logging
import LangSegment
import pdb
import torch
import gradio as gr
from transformers import AutoModelForMaskedLM, AutoTokenizer
import numpy as np
import librosa
from feature_extractor import cnhubert
from module.models import SynthesizerTrn
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
from time import time as ttime
from module.mel_processing import spectrogram_torch
from tools.my_utils import load_audio
from tools.i18n.i18n import I18nAuto
import scipy.io.wavfile as wavfile

device = "cuda" if torch.cuda.is_available() else "cpu"

i18n = I18nAuto()

dict_language = {
    i18n("中文"): "all_zh",  # 全部按中文识别
    i18n("英文"): "en",  # 全部按英文识别#######不变
    i18n("日文"): "all_ja",  # 全部按日文识别
    i18n("中英混合"): "zh",  # 按中英混合识别####不变
    i18n("日英混合"): "ja",  # 按日英混合识别####不变
    i18n("多语种混合"): "auto",  # 多语种启动切分识别语种
}

is_share = os.environ.get("is_share", "False")
is_share = eval(is_share)
if "_CUDA_VISIBLE_DEVICES" in os.environ:
    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
half_precision = True
is_half = half_precision and torch.cuda.is_available()
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
punctuation = set(['!', '?', '…', ',', '.', '-', " "])


class DictToAttrRecursive(dict):
    def __init__(self, input_dict):
        super().__init__(input_dict)
        for key, value in input_dict.items():
            if isinstance(value, dict):
                value = DictToAttrRecursive(value)
            self[key] = value
            setattr(self, key, value)

    def __getattr__(self, item):
        try:
            return self[item]
        except KeyError:
            raise AttributeError(f"Attribute {item} not found")

    def __setattr__(self, key, value):
        if isinstance(value, dict):
            value = DictToAttrRecursive(value)
        super(DictToAttrRecursive, self).__setitem__(key, value)
        super().__setattr__(key, value)

    def __delattr__(self, item):
        try:
            del self[item]
        except KeyError:
            raise AttributeError(f"Attribute {item} not found")


def replace_consecutive_punctuation(text):
    punctuations = ''.join(re.escape(p) for p in punctuation)
    pattern = f'([{punctuations}])([{punctuations}])+'
    result = re.sub(pattern, r'\1', text)
    return result


def get_first(text):
    pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
    text = re.split(pattern, text)[0].strip()
    return text


def split(todo_text):
    todo_text = todo_text.replace("……", "。").replace("——", "，")
    if todo_text[-1] not in splits:
        todo_text += "。"
    i_split_head = i_split_tail = 0
    len_text = len(todo_text)
    todo_texts = []
    while 1:
        if i_split_head >= len_text:
            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
        if todo_text[i_split_head] in splits:
            i_split_head += 1
            todo_texts.append(todo_text[i_split_tail:i_split_head])
            i_split_tail = i_split_head
        else:
            i_split_head += 1
    return todo_texts


# 四句一切
def cut1(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    split_idx = list(range(0, len(inps), 4))
    split_idx[-1] = None
    if len(split_idx) > 1:
        opts = []
        for idx in range(len(split_idx) - 1):
            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
    else:
        opts = [inp]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


# 句号切
def cut3(inp):
    inp = inp.strip("\n")
    opts = ["%s" % item for item in inp.strip("。").split("。")]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


def process_text(texts):
    _text = []
    if all(text in [None, " ", "\n", ""] for text in texts):
        raise ValueError(i18n("请输入有效文本"))
    for text in texts:
        if text in [None, " ", ""]:
            pass
        else:
            _text.append(text)
    return _text


def merge_short_text_in_array(texts, threshold):
    if (len(texts)) < 2:
        return texts
    result = []
    text = ""
    for ele in texts:
        text += ele
        if len(text) >= threshold:
            result.append(text)
            text = ""
    if (len(text) > 0):
        if len(result) == 0:
            result.append(text)
        else:
            result[len(result) - 1] += text
    return result


def clean_text_inf(text, language):
    phones, word2ph, norm_text = clean_text(text, language)
    phones = cleaned_text_to_sequence(phones)
    return phones, word2ph, norm_text


def get_bert_feature(text, word2ph):
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors="pt")
        for i in inputs:
            inputs[i] = inputs[i].to(device)
        res = bert_model(**inputs, output_hidden_states=True)
        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
    assert len(word2ph) == len(text)
    phone_level_feature = []
    for i in range(len(word2ph)):
        repeat_feature = res[i].repeat(word2ph[i], 1)
        phone_level_feature.append(repeat_feature)
    phone_level_feature = torch.cat(phone_level_feature, dim=0)
    return phone_level_feature.T


dtype = torch.float16 if is_half else torch.float32


def get_bert_inf(phones, word2ph, norm_text, language):
    language = language.replace("all_", "")
    if language == "zh":
        bert = get_bert_feature(norm_text, word2ph).to(device)  # .to(dtype)
    else:
        bert = torch.zeros(
            (1024, len(phones)),
            dtype=torch.float16 if is_half else torch.float32,
        ).to(device)

    return bert


def get_spepc(hps, filename):
    audio = load_audio(filename, int(hps.data.sampling_rate))
    audio = torch.FloatTensor(audio)
    audio_norm = audio
    audio_norm = audio_norm.unsqueeze(0)
    spec = spectrogram_torch(
        audio_norm,
        hps.data.filter_length,
        hps.data.sampling_rate,
        hps.data.hop_length,
        hps.data.win_length,
        center=False,
    )
    return spec


def get_phones_and_bert(text, language):
    if language in {"en", "all_zh", "all_ja"}:
        language = language.replace("all_", "")
        if language == "en":
            LangSegment.setfilters(["en"])
            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
        else:
            # 因无法区别中日文汉字,以用户输入为准
            formattext = text
        while "  " in formattext:
            formattext = formattext.replace("  ", " ")
        phones, word2ph, norm_text = clean_text_inf(formattext, language)
        if language == "zh":
            bert = get_bert_feature(norm_text, word2ph).to(device)
        else:
            bert = torch.zeros(
                (1024, len(phones)),
                dtype=torch.float16 if is_half == True else torch.float32,
            ).to(device)
    elif language in {"zh", "ja", "auto"}:
        textlist = []
        langlist = []
        LangSegment.setfilters(["zh", "ja", "en", "ko"])
        if language == "auto":
            for tmp in LangSegment.getTexts(text):
                if tmp["lang"] == "ko":
                    langlist.append("zh")
                    textlist.append(tmp["text"])
                else:
                    langlist.append(tmp["lang"])
                    textlist.append(tmp["text"])
        else:
            for tmp in LangSegment.getTexts(text):
                if tmp["lang"] == "en":
                    langlist.append(tmp["lang"])
                else:
                    # 因无法区别中日文汉字,以用户输入为准
                    langlist.append(language)
                textlist.append(tmp["text"])
        print(textlist)
        print(langlist)
        phones_list = []
        bert_list = []
        norm_text_list = []
        for i in range(len(textlist)):
            lang = langlist[i]
            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
            bert = get_bert_inf(phones, word2ph, norm_text, lang)
            phones_list.append(phones)
            norm_text_list.append(norm_text)
            bert_list.append(bert)
        bert = torch.cat(bert_list, dim=1)
        phones = sum(phones_list, [])
        norm_text = ''.join(norm_text_list)

    return phones, bert.to(dtype), norm_text


def set_gpt_weights(gpt_path):
    global hz, max_sec, t2s_model, config
    hz = 50
    dict_s1 = torch.load(gpt_path, map_location="cpu")
    config = dict_s1["config"]
    max_sec = config["data"]["max_sec"]
    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
    t2s_model.load_state_dict(dict_s1["weight"])
    if is_half:
        t2s_model = t2s_model.half()
    t2s_model = t2s_model.to(device)
    t2s_model.eval()
    total = sum([param.nelement() for param in t2s_model.parameters()])
    print("Number of parameter: %.2fM" % (total / 1e6))
    with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)


def set_sovits_weights(sovits_path):
    global vq_model, hps
    dict_s2 = torch.load(sovits_path, map_location="cpu")
    hps = dict_s2["config"]
    hps = DictToAttrRecursive(hps)
    hps.model.semantic_frame_rate = "25hz"
    vq_model = SynthesizerTrn(
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model
    )
    if "pretrained" not in sovits_path:
        del vq_model.enc_q
    if is_half:
        vq_model = vq_model.half().to(device)
    else:
        vq_model = vq_model.to(device)
    vq_model.eval()
    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
    with open("./sweight.txt", "w", encoding="utf-8") as f:
        f.write(sovits_path)


def gen_audio(ref_wav_path, prompt_text, text_to_speak, output_file, top_k=20, top_p=0.6, temperature=0.6, ref_free=False):
    if prompt_text is None or len(prompt_text) == 0:
        ref_free = True
    t0 = ttime()
    prompt_language = "zh"
    text_language = "zh"
    if not ref_free:
        prompt_text = prompt_text.strip("\n")
        if prompt_text[-1] not in splits:
            prompt_text += "。" if prompt_language != "en" else "."
        print(i18n("实际输入的参考文本:"), prompt_text)
    text_to_speak = text_to_speak.strip("\n")
    text_to_speak = replace_consecutive_punctuation(text_to_speak)
    if text_to_speak[0] not in splits and len(get_first(text_to_speak)) < 4:
        text_to_speak = "。" + text_to_speak if text_language != "en" else "." + text_to_speak

    print(i18n("实际输入的目标文本:"), text_to_speak)
    zero_wav = np.zeros(
        int(hps.data.sampling_rate * 0.3),
        dtype=np.float16 if is_half == True else np.float32,
    )
    if not ref_free:
        with torch.no_grad():
            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
            if wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000:
                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
            wav16k = torch.from_numpy(wav16k)
            zero_wav_torch = torch.from_numpy(zero_wav)
            if is_half:
                wav16k = wav16k.half().to(device)
                zero_wav_torch = zero_wav_torch.half().to(device)
            else:
                wav16k = wav16k.to(device)
                zero_wav_torch = zero_wav_torch.to(device)
            wav16k = torch.cat([wav16k, zero_wav_torch])
            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
                "last_hidden_state"
            ].transpose(
                1, 2
            )  # .float()
            codes = vq_model.extract_latent(ssl_content)
            prompt_semantic = codes[0, 0]
            prompt = prompt_semantic.unsqueeze(0).to(device)

    t1 = ttime()
    # text_to_speak = cut1(text_to_speak)
    text_to_speak = cut3(text_to_speak)
    while "\n\n" in text_to_speak:
        text_to_speak = text_to_speak.replace("\n\n", "\n")
    print(i18n("实际输入的目标文本(切句后):"), text_to_speak)
    texts = text_to_speak.split("\n")
    texts = process_text(texts)
    texts = merge_short_text_in_array(texts, 5)
    audio_opt = []
    if not ref_free:
        phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language)

    for text_to_speak in texts:
        # 解决输入目标文本的空行导致报错的问题
        if len(text_to_speak.strip()) == 0:
            continue
        if text_to_speak[-1] not in splits:
            text_to_speak += "。" if text_language != "en" else "."
        print(i18n("实际输入的目标文本(每句):"), text_to_speak)
        phones2, bert2, norm_text2 = get_phones_and_bert(text_to_speak, text_language)
        print(i18n("前端处理后的文本(每句):"), norm_text2)
        if not ref_free:
            bert = torch.cat([bert1, bert2], 1)
            all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
        else:
            bert = bert2
            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)

        bert = bert.to(device).unsqueeze(0)
        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)

        t2 = ttime()
        with torch.no_grad():
            # pred_semantic = t2s_model.model.infer(
            pred_semantic, idx = t2s_model.model.infer_panel(
                all_phoneme_ids,
                all_phoneme_len,
                None if ref_free else prompt,
                bert,
                # prompt_phone_len=ph_offset,
                top_k=top_k,
                top_p=top_p,
                temperature=temperature,
                early_stop_num=hz * max_sec,
            )
        t3 = ttime()
        # print(pred_semantic.shape,idx)
        pred_semantic = pred_semantic[:, -idx:].unsqueeze(
            0
        )  # .unsqueeze(0)#mq要多unsqueeze一次
        refer = get_spepc(hps, ref_wav_path)  # .to(device)
        if is_half:
            refer = refer.half().to(device)
        else:
            refer = refer.to(device)
        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
        audio = (
            vq_model.decode(
                pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
            )
            .detach()
            .cpu()
            .numpy()[0, 0]
        )  # 试试重建不带上prompt部分
        max_audio = np.abs(audio).max()  # 简单防止16bit爆音
        if max_audio > 1:
            audio /= max_audio
        audio_opt.append(audio)
        audio_opt.append(zero_wav)
        t4 = ttime()

    # 将音频数据合并
    audio_data = np.concatenate(audio_opt, 0) * 32768
    audio_data = audio_data.astype(np.int16)
    wavfile.write(output_file, hps.data.sampling_rate, audio_data)

    print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))


cnhubert_base_path = os.environ.get(
    "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
)
bert_path = os.environ.get(
    "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
)

cnhubert.cnhubert_base_path = cnhubert_base_path
ssl_model = cnhubert.get_model()
if is_half:
    ssl_model = ssl_model.half().to(device)
else:
    ssl_model = ssl_model.to(device)

tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
if is_half:
    bert_model = bert_model.half().to(device)
else:
    bert_model = bert_model.to(device)


def speak(text_to_speak):
    sovits_path = "SoVITS_weights/阿贝多_e12_s2748.pth"
    set_sovits_weights(sovits_path)
    gpt_path = "GPT_weights/阿贝多-e10.ckpt"
    set_gpt_weights(gpt_path)
    ref_wav_path = "audio/首先，先看看这不明来源的元素力，究竟是如何对外流动的.wav"
    prompt_text = "首先，先看看这不明来源的元素力，究竟是如何对外流动的。"
    # text_to_speak = "我...我...我不知道你在说什么，我们之间没有秘密呀。可能你弄错了，我们平时关系很好的，请不要误会。"
    # 创建一个时间戳的文件名
    output_file = "outputs/" + str(int(ttime())) + ".wav"
    gen_audio(ref_wav_path, prompt_text, text_to_speak, output_file)
    return output_file


def main():
    speak("放学了，我该回家了，你叫我留下来干什么？")


if __name__ == '__main__':
    main()