# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
# reference: https://github.com/lifeiteng/vall-e
import argparse
from typing import Optional
from my_utils import load_audio
from text import cleaned_text_to_sequence
import torch
import torchaudio

from torch import IntTensor, LongTensor, Tensor, nn
from torch.nn import functional as F

from transformers import AutoModelForMaskedLM, AutoTokenizer
from feature_extractor import cnhubert

from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from module.models_onnx import SynthesizerTrn

from inference_webui import get_phones_and_bert

import os
import soundfile

default_config = {
    "embedding_dim": 512,
    "hidden_dim": 512,
    "num_head": 8,
    "num_layers": 12,
    "num_codebook": 8,
    "p_dropout": 0.0,
    "vocab_size": 1024 + 1,
    "phoneme_vocab_size": 512,
    "EOS": 1024,
}

def get_raw_t2s_model(dict_s1) -> Text2SemanticLightningModule: 
    config = dict_s1["config"]
    config["model"]["dropout"] = float(config["model"]["dropout"])
    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
    t2s_model.load_state_dict(dict_s1["weight"])
    t2s_model = t2s_model.eval()
    return t2s_model

@torch.jit.script
def logits_to_probs(
    logits,
    previous_tokens: Optional[torch.Tensor] = None,
    temperature: float = 1.0,
    top_k: Optional[int] = None,
    top_p: Optional[int] = None,
    repetition_penalty: float = 1.0,
):
    # if previous_tokens is not None:
    #     previous_tokens = previous_tokens.squeeze()
    # print(logits.shape,previous_tokens.shape)
    # pdb.set_trace()
    if previous_tokens is not None and repetition_penalty != 1.0:
        previous_tokens = previous_tokens.long()
        score = torch.gather(logits, dim=1, index=previous_tokens)
        score = torch.where(
            score < 0, score * repetition_penalty, score / repetition_penalty
        )
        logits.scatter_(dim=1, index=previous_tokens, src=score)

    if top_p is not None and top_p < 1.0:
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        cum_probs = torch.cumsum(
            torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
        )
        sorted_indices_to_remove = cum_probs > top_p
        sorted_indices_to_remove[:, 0] = False  # keep at least one option
        indices_to_remove = sorted_indices_to_remove.scatter(
            dim=1, index=sorted_indices, src=sorted_indices_to_remove
        )
        logits = logits.masked_fill(indices_to_remove, -float("Inf"))

    logits = logits / max(temperature, 1e-5)

    if top_k is not None:
        v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
        pivot = v[: , -1].unsqueeze(-1)
        logits = torch.where(logits < pivot, -float("Inf"), logits)

    probs = torch.nn.functional.softmax(logits, dim=-1)
    return probs

@torch.jit.script
def multinomial_sample_one_no_sync(probs_sort):  
    # Does multinomial sampling without a cuda synchronization
    q = torch.randn_like(probs_sort)
    return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)

@torch.jit.script
def sample(
    logits,
    previous_tokens,
    temperature: float = 1.0,
    top_k: Optional[int] = None,
    top_p: Optional[int] = None,
    repetition_penalty: float = 1.0,
):
    probs = logits_to_probs(
        logits=logits, previous_tokens=previous_tokens, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty
    )
    idx_next = multinomial_sample_one_no_sync(probs)
    return idx_next, probs


@torch.jit.script
def spectrogram_torch(y:Tensor, n_fft:int, sampling_rate:int, hop_size:int, win_size:int, center:bool=False):
    hann_window = torch.hann_window(win_size,device=y.device,dtype=y.dtype)
    y = torch.nn.functional.pad(
        y.unsqueeze(1),
        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
        mode="reflect",
    )
    y = y.squeeze(1)
    spec = torch.stft(
        y,
        n_fft,
        hop_length=hop_size,
        win_length=win_size,
        window=hann_window,
        center=center,
        pad_mode="reflect",
        normalized=False,
        onesided=True,
        return_complex=False,
    )
    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
    return spec


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")

@torch.jit.script
class T2SMLP:
    def __init__(self, w1, b1, w2, b2):
        self.w1 = w1
        self.b1 = b1
        self.w2 = w2
        self.b2 = b2

    def forward(self, x):
        x = F.relu(F.linear(x, self.w1, self.b1))
        x = F.linear(x, self.w2, self.b2)
        return x

@torch.jit.script
class T2SBlock:
    def __init__(
            self,
            num_heads: int,
            hidden_dim: int,
            mlp: T2SMLP,
            qkv_w,
            qkv_b,
            out_w,
            out_b,
            norm_w1,
            norm_b1,
            norm_eps1: float,
            norm_w2,
            norm_b2,
            norm_eps2: float,
    ):
        self.num_heads = num_heads
        self.mlp = mlp
        self.hidden_dim: int = hidden_dim
        self.qkv_w = qkv_w
        self.qkv_b = qkv_b
        self.out_w = out_w
        self.out_b = out_b
        self.norm_w1 = norm_w1
        self.norm_b1 = norm_b1
        self.norm_eps1 = norm_eps1
        self.norm_w2 = norm_w2
        self.norm_b2 = norm_b2
        self.norm_eps2 = norm_eps2

        self.false = torch.tensor(False, dtype=torch.bool)

    @torch.jit.ignore
    def to_mask(self, x:torch.Tensor, padding_mask:Optional[torch.Tensor]):
        if padding_mask is None:
            return x
        
        if padding_mask.dtype == torch.bool:
            return x.masked_fill(padding_mask, 0)
        else:
            return x * padding_mask
        
    def process_prompt(self, x:torch.Tensor, attn_mask : torch.Tensor, padding_mask:Optional[torch.Tensor]=None):
        q, k, v = F.linear(self.to_mask(x, padding_mask), self.qkv_w, self.qkv_b).chunk(3, dim=-1)

        batch_size = q.shape[0]
        q_len = q.shape[1]
        kv_len = k.shape[1]
        
        q = self.to_mask(q, padding_mask)
        k_cache = self.to_mask(k, padding_mask)
        v_cache = self.to_mask(v, padding_mask)

        q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
        k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
        v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)

        attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask)

        attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
        attn = F.linear(self.to_mask(attn, padding_mask), self.out_w, self.out_b)

        if padding_mask is not None:
            for i in range(batch_size):
                # mask = padding_mask[i,:,0]
                if self.false.device!= padding_mask.device:
                    self.false = self.false.to(padding_mask.device)
                idx = torch.where(padding_mask[i,:,0]==self.false)[0]
                x_item = x[i,idx,:].unsqueeze(0)
                attn_item = attn[i,idx,:].unsqueeze(0)
                x_item = x_item + attn_item
                x_item = F.layer_norm(
                    x_item, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
                )
                x_item = x_item + self.mlp.forward(x_item)
                x_item = F.layer_norm(
                    x_item,
                    [self.hidden_dim],
                    self.norm_w2,
                    self.norm_b2,
                    self.norm_eps2,
                )
                x[i,idx,:] = x_item.squeeze(0)
            x = self.to_mask(x, padding_mask)
        else:
            x = x + attn
            x = F.layer_norm(
                x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
            )
            x = x + self.mlp.forward(x)
            x = F.layer_norm(
                x,
                [self.hidden_dim],
                self.norm_w2,
                self.norm_b2,
                self.norm_eps2,
            )
        return x, k_cache, v_cache
    
    def decode_next_token(self, x:torch.Tensor, k_cache:torch.Tensor, v_cache:torch.Tensor):
        q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)

        k_cache = torch.cat([k_cache, k], dim=1)
        v_cache = torch.cat([v_cache, v], dim=1)
        
        batch_size = q.shape[0]
        q_len = q.shape[1]
        kv_len = k_cache.shape[1]

        q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
        k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
        v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)

        attn = F.scaled_dot_product_attention(q, k, v)

        attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
        attn = F.linear(attn, self.out_w, self.out_b)

        x = x + attn
        x = F.layer_norm(
            x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
        )
        x = x + self.mlp.forward(x)
        x = F.layer_norm(
            x,
            [self.hidden_dim],
            self.norm_w2,
            self.norm_b2,
            self.norm_eps2,
        )
        return x, k_cache, v_cache

@torch.jit.script
class T2STransformer:
    def __init__(self, num_blocks : int, blocks: list[T2SBlock]):
        self.num_blocks : int = num_blocks
        self.blocks = blocks

    def process_prompt(
        self, x:torch.Tensor, attn_mask : torch.Tensor,padding_mask : Optional[torch.Tensor]=None):
        k_cache : list[torch.Tensor] = []
        v_cache : list[torch.Tensor] = []
        for i in range(self.num_blocks):
            x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask, padding_mask)
            k_cache.append(k_cache_)
            v_cache.append(v_cache_)
        return x, k_cache, v_cache

    def decode_next_token(
        self, x:torch.Tensor, 
        k_cache: list[torch.Tensor], 
        v_cache: list[torch.Tensor]):
        for i in range(self.num_blocks):
            x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i])
        return x, k_cache, v_cache
       
class VitsModel(nn.Module):
    def __init__(self, vits_path):
        super().__init__()
        dict_s2 = torch.load(vits_path,map_location="cpu")
        self.hps = dict_s2["config"]
        if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
            self.hps["model"]["version"] = "v1"
        else:
            self.hps["model"]["version"] = "v2"
        
        self.hps = DictToAttrRecursive(self.hps)
        self.hps.model.semantic_frame_rate = "25hz"
        self.vq_model = SynthesizerTrn(
            self.hps.data.filter_length // 2 + 1,
            self.hps.train.segment_size // self.hps.data.hop_length,
            n_speakers=self.hps.data.n_speakers,
            **self.hps.model
        )
        self.vq_model.eval()
        self.vq_model.load_state_dict(dict_s2["weight"], strict=False)

    def forward(self, text_seq, pred_semantic, ref_audio, speed=1.0):
        refer = spectrogram_torch(
            ref_audio,
            self.hps.data.filter_length,
            self.hps.data.sampling_rate,
            self.hps.data.hop_length,
            self.hps.data.win_length,
            center=False
        )
        return self.vq_model(pred_semantic, text_seq, refer, speed)[0, 0]

class T2SModel(nn.Module):
    def __init__(self,raw_t2s:Text2SemanticLightningModule):
        super(T2SModel, self).__init__()
        self.model_dim = raw_t2s.model.model_dim
        self.embedding_dim = raw_t2s.model.embedding_dim
        self.num_head = raw_t2s.model.num_head
        self.num_layers = raw_t2s.model.num_layers
        self.vocab_size = raw_t2s.model.vocab_size
        self.phoneme_vocab_size = raw_t2s.model.phoneme_vocab_size
        # self.p_dropout = float(raw_t2s.model.p_dropout)
        self.EOS:int = int(raw_t2s.model.EOS)
        self.norm_first = raw_t2s.model.norm_first
        assert self.EOS == self.vocab_size - 1
        self.hz = 50

        self.bert_proj = raw_t2s.model.bert_proj
        self.ar_text_embedding = raw_t2s.model.ar_text_embedding
        self.ar_text_position = raw_t2s.model.ar_text_position
        self.ar_audio_embedding = raw_t2s.model.ar_audio_embedding
        self.ar_audio_position = raw_t2s.model.ar_audio_position
        
        # self.t2s_transformer = T2STransformer(self.num_layers, blocks)
        # self.t2s_transformer = raw_t2s.model.t2s_transformer

        blocks = []
        h = raw_t2s.model.h

        for i in range(self.num_layers):
            layer = h.layers[i]
            t2smlp = T2SMLP(
                layer.linear1.weight,
                layer.linear1.bias,
                layer.linear2.weight,
                layer.linear2.bias
            )

            block = T2SBlock(
                self.num_head,
                self.model_dim,
                t2smlp,
                layer.self_attn.in_proj_weight,
                layer.self_attn.in_proj_bias,
                layer.self_attn.out_proj.weight,
                layer.self_attn.out_proj.bias,
                layer.norm1.weight,
                layer.norm1.bias,
                layer.norm1.eps,
                layer.norm2.weight,
                layer.norm2.bias,
                layer.norm2.eps
            )

            blocks.append(block)
        
        self.t2s_transformer = T2STransformer(self.num_layers, blocks)

        # self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
        self.ar_predict_layer = raw_t2s.model.ar_predict_layer
        # self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
        self.max_sec = raw_t2s.config["data"]["max_sec"]
        self.top_k = int(raw_t2s.config["inference"]["top_k"])
        self.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
    
    def forward(self,prompts:LongTensor, ref_seq:LongTensor, text_seq:LongTensor, ref_bert:torch.Tensor, text_bert:torch.Tensor):
        bert = torch.cat([ref_bert.T, text_bert.T], 1)
        all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
        bert = bert.unsqueeze(0)

        x = self.ar_text_embedding(all_phoneme_ids)
        x = x + self.bert_proj(bert.transpose(1, 2))
        x:torch.Tensor = self.ar_text_position(x)

        early_stop_num = self.early_stop_num


        #[1,N,512] [1,N]
        # y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
        y = prompts
        # x_example = x[:,:,0] * 0.0

        x_len = x.shape[1]
        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)

        y_emb = self.ar_audio_embedding(y)
        y_len = y_emb.shape[1]
        prefix_len = y.shape[1]
        y_pos = self.ar_audio_position(y_emb)
        xy_pos = torch.concat([x, y_pos], dim=1)

        bsz = x.shape[0]
        src_len = x_len + y_len
        x_attn_mask_pad = F.pad(
            x_attn_mask,
            (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
            value=True,
        )
        y_attn_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
            (x_len, 0),
            value=False,
        )
        xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)\
                                                .unsqueeze(0)\
                                                .expand(bsz*self.num_head, -1, -1)\
                                                .view(bsz, self.num_head, src_len, src_len)\
                                                .to(device=x.device, dtype=torch.bool)
        
        idx = 0
        
        xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)

        logits = self.ar_predict_layer(xy_dec[:, -1])
        logits = logits[:, :-1]
        samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
        y = torch.concat([y, samples], dim=1)
        y_emb = self.ar_audio_embedding(y[:, -1:])
        xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)

        stop = False
        # for idx in range(1, 50):
        for idx in range(1, 1500):
            #[1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
            # y, k, v, y_emb, logits, samples = self.stage_decoder(y, k, v, y_emb, x_example)
            xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
            logits = self.ar_predict_layer(xy_dec[:, -1])

            if(idx<11):###至少预测出10个token不然不给停止（0.4s）
                logits = logits[:, :-1]
            
            samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]

            y = torch.concat([y, samples], dim=1)
            
            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
                stop = True
            if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
                stop = True
            if stop:
                if y.shape[1] == 0:
                    y = torch.concat([y, torch.zeros_like(samples)], dim=1)
                break

            y_emb = self.ar_audio_embedding(y[:, -1:])
            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
        
        y[0,-1] = 0
        
        return y[:, -idx:].unsqueeze(0)

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

@torch.jit.script
def build_phone_level_feature(res:Tensor, word2ph:IntTensor):
    phone_level_feature = []
    for i in range(word2ph.shape[0]):
        repeat_feature = res[i].repeat(word2ph[i].item(), 1)
        phone_level_feature.append(repeat_feature)
    phone_level_feature = torch.cat(phone_level_feature, dim=0)
    # [sum(word2ph), 1024]
    return phone_level_feature
    
class MyBertModel(torch.nn.Module):
    def __init__(self, bert_model):
        super(MyBertModel, self).__init__()
        self.bert = bert_model

    def forward(self, input_ids:torch.Tensor, attention_mask:torch.Tensor, token_type_ids:torch.Tensor, word2ph:IntTensor):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
        res = torch.cat(outputs["hidden_states"][-3:-2], -1)[0][1:-1]
        return build_phone_level_feature(res, word2ph)

class SSLModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.ssl = cnhubert.get_model().model

    def forward(self, ref_audio_16k)-> torch.Tensor:
        ssl_content = self.ssl(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
        return ssl_content

class ExportSSLModel(torch.nn.Module):
    def __init__(self,ssl:SSLModel):
        super().__init__()
        self.ssl = ssl

    def forward(self, ref_audio:torch.Tensor):
        return self.ssl(ref_audio)
    
    @torch.jit.export
    def resample(self,ref_audio:torch.Tensor,src_sr:int,dst_sr:int)->torch.Tensor:
        audio = resamplex(ref_audio,src_sr,dst_sr).float()
        return audio

def export_bert(ref_bert_inputs):
    tokenizer = AutoTokenizer.from_pretrained(bert_path)
    
    ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt")
    ref_bert_inputs['word2ph'] = torch.Tensor([2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,2,2]).int()

    bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
    my_bert_model = MyBertModel(bert_model)

    ref_bert_inputs = {
        'input_ids': ref_bert_inputs['input_ids'],
        'attention_mask': ref_bert_inputs['attention_mask'],
        'token_type_ids': ref_bert_inputs['token_type_ids'],
        'word2ph': ref_bert_inputs['word2ph']
    }

    my_bert_model = torch.jit.trace(my_bert_model,example_kwarg_inputs=ref_bert_inputs)
    my_bert_model.save("onnx/bert_model.pt")
    print('#### exported bert ####')
    
def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path):
    # export_bert(ref_bert_inputs)

    if not os.path.exists(output_path):
        os.makedirs(output_path)
        print(f"目录已创建: {output_path}")
    else:
        print(f"目录已存在: {output_path}")
    
    ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float()
    ssl = SSLModel()
    s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(ref_audio)))
    ssl_path = os.path.join(output_path, "ssl_model.pt")
    torch.jit.script(s).save(ssl_path)
    print('#### exported ssl ####')

    ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2')
    ref_seq = torch.LongTensor([ref_seq_id])
    ref_bert = ref_bert_T.T.to(ref_seq.device)
    text_seq_id,text_bert_T,norm_text = get_phones_and_bert("这是一条测试语音，说什么无所谓，只是给它一个例子","all_zh",'v2')
    text_seq = torch.LongTensor([text_seq_id])
    text_bert = text_bert_T.T.to(text_seq.device)

    ssl_content = ssl(ref_audio)

     # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
    vits = VitsModel(vits_path)
    vits.eval()

    # gpt_path = "GPT_weights_v2/xw-e15.ckpt"
    dict_s1 = torch.load(gpt_path, map_location="cpu")
    raw_t2s = get_raw_t2s_model(dict_s1)
    print('#### get_raw_t2s_model ####')
    print(raw_t2s.config)
    t2s_m = T2SModel(raw_t2s)
    t2s_m.eval()
    t2s = torch.jit.script(t2s_m)
    print('#### script t2s_m ####')
    
    print("vits.hps.data.sampling_rate:",vits.hps.data.sampling_rate)
    gpt_sovits = GPT_SoVITS(t2s,vits)
    gpt_sovits.eval()
    ref_audio_sr = s.resample(ref_audio,16000,32000)
    ref_audio_sr = s.resample(ref_audio,16000,32000)
    print('ref_audio_sr:',ref_audio_sr.shape)
    
    ref_audio_sr = s.resample(ref_audio,16000,32000)    
    print('ref_audio_sr:',ref_audio_sr.shape)
    
    gpt_sovits_export = torch.jit.trace(
        gpt_sovits,
        example_inputs=(
            ssl_content,
            ref_audio_sr,
            ref_seq,
            text_seq,
            ref_bert,
            text_bert))
    
    gpt_sovits_path = os.path.join(output_path, "gpt_sovits_model.pt")
    gpt_sovits_export.save(gpt_sovits_path)
    print('#### exported gpt_sovits ####')

@torch.jit.script
def parse_audio(ref_audio):
    ref_audio_16k = torchaudio.functional.resample(ref_audio,48000,16000).float()#.to(ref_audio.device)
    ref_audio_sr = torchaudio.functional.resample(ref_audio,48000,32000).float()#.to(ref_audio.device)
    return ref_audio_16k,ref_audio_sr

@torch.jit.script
def resamplex(ref_audio:torch.Tensor,src_sr:int,dst_sr:int)->torch.Tensor:
    return torchaudio.functional.resample(ref_audio,src_sr,dst_sr).float()

class GPT_SoVITS(nn.Module):
    def __init__(self, t2s:T2SModel,vits:VitsModel):
        super().__init__()
        self.t2s = t2s
        self.vits = vits

    def forward(self, ssl_content:torch.Tensor, ref_audio_sr:torch.Tensor, ref_seq:Tensor, text_seq:Tensor, ref_bert:Tensor, text_bert:Tensor, speed=1.0):
        codes = self.vits.vq_model.extract_latent(ssl_content)
        prompt_semantic = codes[0, 0]
        prompts = prompt_semantic.unsqueeze(0)

        pred_semantic = self.t2s(prompts, ref_seq, text_seq, ref_bert, text_bert)
        audio = self.vits(text_seq, pred_semantic, ref_audio_sr, speed)
        return audio

def test():
    parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
    parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file")
    parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file")
    parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
    parser.add_argument('--ref_text', required=True, help="Path to the reference text file")

    args = parser.parse_args()
    gpt_path = args.gpt_model
    vits_path = args.sovits_model
    ref_audio_path = args.ref_audio
    ref_text = args.ref_text

    tokenizer = AutoTokenizer.from_pretrained(bert_path)
    bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
    bert = MyBertModel(bert_model)
    # bert = torch.jit.load("onnx/bert_model.pt",map_location='cuda')

    # gpt_path = "GPT_weights_v2/xw-e15.ckpt"
    dict_s1 = torch.load(gpt_path, map_location="cpu")
    raw_t2s = get_raw_t2s_model(dict_s1)
    t2s = T2SModel(raw_t2s)
    t2s.eval()
    # t2s = torch.jit.load("onnx/xw/t2s_model.pt",map_location='cuda')

    # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
    vits = VitsModel(vits_path)
    vits.eval()

    ssl = ExportSSLModel(SSLModel())
    ssl.eval()

    gpt_sovits = GPT_SoVITS(t2s,vits)

    # vits = torch.jit.load("onnx/xw/vits_model.pt",map_location='cuda')
    # ssl = torch.jit.load("onnx/xw/ssl_model.pt",map_location='cuda')

    ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2')
    ref_seq = torch.LongTensor([ref_seq_id])
    ref_bert = ref_bert_T.T.to(ref_seq.device)
    text_seq_id,text_bert_T,norm_text = get_phones_and_bert("问木兰在想什么？问木兰在惦记什么？木兰答道,我也没有在想什么，也没有在惦记什么。","all_zh",'v2')
    text_seq = torch.LongTensor([text_seq_id])
    print('text_seq:',text_seq_id)
    text_bert = text_bert_T.T.to(text_seq.device)
    # text_bert = torch.zeros(text_bert.shape, dtype=text_bert.dtype).to(text_bert.device)
    print('text_seq:',text_seq.shape)
    print('text_bert:',text_bert.shape)

    #[1,N]
    ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float()
    print('ref_audio:',ref_audio.shape)
    
    ref_audio_sr = ssl.resample(ref_audio,16000,32000)
    print('start ssl')
    ssl_content = ssl(ref_audio)

    print('start gpt_sovits:')
    with torch.no_grad():
        audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, text_bert)
    print('start write wav')
    soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)

    # audio = vits(text_seq, pred_semantic1, ref_audio)
    # soundfile.write("out.wav", audio, 32000)

import text
import json

def export_symbel(version='v2'):
    if version=='v1':
        symbols = text._symbol_to_id_v1
        with open(f"onnx/symbols_v1.json", "w") as file:
            json.dump(symbols, file, indent=4)
    else:
        symbols = text._symbol_to_id_v2
        with open(f"onnx/symbols_v2.json", "w") as file:
            json.dump(symbols, file, indent=4)

def main():
    parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
    parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file")
    parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file")
    parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
    parser.add_argument('--ref_text', required=True, help="Path to the reference text file")
    parser.add_argument('--output_path', required=True, help="Path to the output directory")

    args = parser.parse_args()
    export(gpt_path=args.gpt_model, vits_path=args.sovits_model, ref_audio_path=args.ref_audio, ref_text=args.ref_text, output_path=args.output_path)

import inference_webui
if __name__ == "__main__":
    inference_webui.is_half=False
    inference_webui.dtype=torch.float32
    main()