magvits_data / code /module /models_zzzzzz.py
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import math

from module.aligner import AlignmentEncoder
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F

from module import commons
from module import modules
from module import attentions
import monotonic_align

from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm

from module.commons import init_weights, get_padding
from text import symbols

from torch.autograd.function import InplaceFunction
from numba import jit, prange
from module.mrte_model import MRTE

@jit(nopython=True)
def mas_width1(attn_map):
    """mas with hardcoded width=1"""
    # assumes mel x text
    opt = np.zeros_like(attn_map)
    attn_map = np.log(attn_map)
    attn_map[0, 1:] = -np.inf
    log_p = np.zeros_like(attn_map)
    log_p[0, :] = attn_map[0, :]
    prev_ind = np.zeros_like(attn_map, dtype=np.int64)
    for i in range(1, attn_map.shape[0]):
        for j in range(attn_map.shape[1]): # for each text dim
            prev_log = log_p[i - 1, j]
            prev_j = j

            if j - 1 >= 0 and log_p[i - 1, j - 1] >= log_p[i - 1, j]:
                prev_log = log_p[i - 1, j - 1]
                prev_j = j - 1

            log_p[i, j] = attn_map[i, j] + prev_log
            prev_ind[i, j] = prev_j

    # now backtrack
    curr_text_idx = attn_map.shape[1] - 1
    for i in range(attn_map.shape[0] - 1, -1, -1):
        opt[i, curr_text_idx] = 1
        curr_text_idx = prev_ind[i, curr_text_idx]
    opt[0, curr_text_idx] = 1
    return opt


@jit(nopython=True, parallel=True)
def b_mas(b_attn_map, in_lens, out_lens, width=1):
    assert width == 1
    attn_out = np.zeros_like(b_attn_map)

    for b in prange(b_attn_map.shape[0]):
        out = mas_width1(b_attn_map[b, 0, : out_lens[b], : in_lens[b]])
        attn_out[b, 0, : out_lens[b], : in_lens[b]] = out
    return attn_out

def binarize_attention_parallel(attn, in_lens, out_lens):
    """For training purposes only. Binarizes attention with MAS.
    These will no longer recieve a gradient.
    Args:
        attn: B x 1 x max_mel_len x max_text_len
    """
    with torch.no_grad():
        attn_cpu = attn.data.cpu().numpy()
        attn_out = b_mas(attn_cpu, in_lens.cpu().numpy(), out_lens.cpu().numpy(), width=1)
    return torch.from_numpy(attn_out).to(attn.device)

 
class scalar_func9(InplaceFunction):
    @staticmethod
    def forward(ctx, input):
        ctx.input = input
        return torch.round(9*input)/9
    @staticmethod
    def backward(ctx, grad_output):
        grad_input = grad_output.clone()
        return grad_input
    


class TextEncoder(nn.Module):
    def __init__(self,
                 out_channels,
                 hidden_channels,
                 filter_channels,
                 n_heads,
                 n_layers,
                 kernel_size,
                 p_dropout):
        super().__init__()
        self.out_channels = out_channels
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout

        self.text_emb = nn.Embedding(len(symbols), hidden_channels)
        nn.init.normal_(self.text_emb.weight, 0.0, hidden_channels ** -0.5)

        # self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
        # self.encoder_ssl = attentions.Encoder(
        #     hidden_channels,
        #     filter_channels,
        #     n_heads,
        #     n_layers//2,
        #     kernel_size,
        #     p_dropout)

        self.encoder_text = attentions.Encoder(
            hidden_channels,
            filter_channels,
            n_heads,
            n_layers,
            kernel_size,
            p_dropout)
        
        # self.mrte = MRTE()


        # self.vq_dim = 8
        # self.vq_down = nn.Conv1d(hidden_channels, self.vq_dim, 1)
        # self.vq = scalar_func9()
        # self.vq_up = nn.Conv1d(self.vq_dim, hidden_channels, 1)


        self.encoder2 = attentions.Encoder(
            hidden_channels,
            filter_channels,
            n_heads,
            n_layers//2,
            kernel_size,
            p_dropout)
        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(self,y, y_mask, x, x_mask):

        # y = self.ssl_proj(y * y_mask) * y_mask
        # y = self.encoder_ssl(y * y_mask, y_mask)


        x = (self.text_emb(x)) * math.sqrt(self.hidden_channels)  # [b, t, h]
        x = torch.transpose(x, 1, -1)  # [b, h, t]
        x = self.encoder_text(x * x_mask, x_mask)

        # prosody = self.mrte(x, x_mask, y, y_mask)
        # prosody = self.vq_down(prosody)
        # prosody =  F.tanh(prosody)
        # prosody_code = self.vq.apply(prosody)
        # z_q = self.vq_up(prosody_code)
        prosody_code = torch.zeros_like(x)


        # x = x + z_q
        x = self.encoder2(x * x_mask, x_mask)

        stats = self.proj(x) * x_mask
        m, logs = torch.split(stats, self.out_channels, dim=1)
        return x, m, logs, prosody_code

class ResidualCouplingBlock(nn.Module):
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        n_flows=4,
        gin_channels=0,
    ):
        super(ResidualCouplingBlock, self).__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.n_flows = n_flows
        self.gin_channels = gin_channels

        self.flows = nn.ModuleList()
        for i in range(n_flows):
            self.flows.append(
                modules.ResidualCouplingLayer(
                    channels,
                    hidden_channels,
                    kernel_size,
                    dilation_rate,
                    n_layers,
                    gin_channels=gin_channels,
                    mean_only=True,
                )
            )
            self.flows.append(modules.Flip())

    def forward(
        self,
        x: torch.Tensor,
        x_mask: torch.Tensor,
        g=None,
        reverse: bool = False,
    ):
        if not reverse:
            for flow in self.flows:
                x, _ = flow(x, x_mask, g=g, reverse=reverse)
        else:
            for flow in self.flows[::-1]:
                x, _ = flow.forward(x, x_mask, g=g, reverse=reverse)
        return x

    def remove_weight_norm(self):
        for i in range(self.n_flows):
            self.flows[i * 2].remove_weight_norm()

    def __prepare_scriptable__(self):
        for i in range(self.n_flows):
            for hook in self.flows[i * 2]._forward_pre_hooks.values():
                if (
                    hook.__module__ == "torch.nn.utils.weight_norm"
                    and hook.__class__.__name__ == "WeightNorm"
                ):
                    torch.nn.utils.remove_weight_norm(self.flows[i * 2])

        return self



class PosteriorEncoder(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 hidden_channels,
                 kernel_size,
                 dilation_rate,
                 n_layers,
                 gin_channels=0):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.gin_channels = gin_channels

        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(self, x, x_lengths, g=None):
        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
        x = self.pre(x) * x_mask
        x = self.enc(x, x_mask, g=g)
        stats = self.proj(x) * x_mask
        m, logs = torch.split(stats, self.out_channels, dim=1)
        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
        return z, m, logs, x_mask


class Generator(torch.nn.Module):
    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
                 upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
        super(Generator, self).__init__()
        self.num_kernels = len(resblock_kernel_sizes)
        self.num_upsamples = len(upsample_rates)
        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2

        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
            self.ups.append(weight_norm(
                ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
                                k, u, padding=(k - u) // 2)))

        self.resblocks = nn.ModuleList()
        for i in range(len(self.ups)):
            ch = upsample_initial_channel // (2 ** (i + 1))
            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
                self.resblocks.append(resblock(ch, k, d))

        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
        self.ups.apply(init_weights)

        if gin_channels != 0:
            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)

    def forward(self, x, g=None):
        x = self.conv_pre(x)
        if g is not None:
            x = x + self.cond(g)

        for i in range(self.num_upsamples):
            x = F.leaky_relu(x, modules.LRELU_SLOPE)
            x = self.ups[i](x)
            xs = None
            for j in range(self.num_kernels):
                if xs is None:
                    xs = self.resblocks[i * self.num_kernels + j](x)
                else:
                    xs += self.resblocks[i * self.num_kernels + j](x)
            x = xs / self.num_kernels
        x = F.leaky_relu(x)
        x = self.conv_post(x)
        x = torch.tanh(x)

        return x

    def remove_weight_norm(self):
        print('Removing weight norm...')
        for l in self.ups:
            remove_weight_norm(l)
        for l in self.resblocks:
            l.remove_weight_norm()


class DiscriminatorP(torch.nn.Module):
    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
        super(DiscriminatorP, self).__init__()
        self.period = period
        self.use_spectral_norm = use_spectral_norm
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
        ])
        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))

    def forward(self, x):
        fmap = []

        # 1d to 2d
        b, c, t = x.shape
        if t % self.period != 0:  # pad first
            n_pad = self.period - (t % self.period)
            x = F.pad(x, (0, n_pad), "reflect")
            t = t + n_pad
        x = x.view(b, c, t // self.period, self.period)

        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, modules.LRELU_SLOPE)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class DiscriminatorS(torch.nn.Module):
    def __init__(self, use_spectral_norm=False):
        super(DiscriminatorS, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
        ])
        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))

    def forward(self, x):
        fmap = []

        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, modules.LRELU_SLOPE)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class MultiPeriodDiscriminator(torch.nn.Module):
    def __init__(self, use_spectral_norm=False):
        super(MultiPeriodDiscriminator, self).__init__()
        periods = [2, 3, 5, 7, 11]

        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
        self.discriminators = nn.ModuleList(discs)

    def forward(self, y, y_hat):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        for i, d in enumerate(self.discriminators):
            y_d_r, fmap_r = d(y)
            y_d_g, fmap_g = d(y_hat)
            y_d_rs.append(y_d_r)
            y_d_gs.append(y_d_g)
            fmap_rs.append(fmap_r)
            fmap_gs.append(fmap_g)

        return y_d_rs, y_d_gs, fmap_rs, fmap_gs


class ReferenceEncoder(nn.Module):
    '''
    inputs --- [N, Ty/r, n_mels*r]  mels
    outputs --- [N, ref_enc_gru_size]
    '''

    def __init__(self, spec_channels, gin_channels=0):

        super().__init__()
        self.spec_channels = spec_channels
        ref_enc_filters = [32, 32, 64, 64, 128, 128]
        K = len(ref_enc_filters)
        filters = [1] + ref_enc_filters
        convs = [weight_norm(nn.Conv2d(in_channels=filters[i],
                                       out_channels=filters[i + 1],
                                       kernel_size=(3, 3),
                                       stride=(2, 2),
                                       padding=(1, 1))) for i in range(K)]
        self.convs = nn.ModuleList(convs)
        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])

        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
        self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels,
                          hidden_size=256 // 2,
                          batch_first=True)
        self.proj = nn.Linear(128, gin_channels)

    def forward(self, inputs, mask=None):
        N = inputs.size(0)
        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
        for conv in self.convs:
            out = conv(out)
            # out = wn(out)
            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]

        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
        T = out.size(1)
        N = out.size(0)
        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]

        self.gru.flatten_parameters()
        memory, out = self.gru(out)  # out --- [1, N, 128]

        return self.proj(out.squeeze(0)).unsqueeze(-1)

    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
        for i in range(n_convs):
            L = (L - kernel_size + 2 * pad) // stride + 1
        return L


class FramePriorNet(nn.Module):
    def __init__(self,
                 out_channels,
                 hidden_channels,
                 filter_channels,
                 n_heads,
                 n_layers,
                 kernel_size,
                 p_dropout):
        super().__init__()

        self.enc = attentions.Encoder(
            hidden_channels,
            filter_channels,
            n_heads,
            6,
            kernel_size,
            p_dropout)
        self.out_proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
        self.out_channels = out_channels

    def forward(self, x, x_mask):
        x = self.enc(x, x_mask)
        stats = self.out_proj(x) * x_mask
        m, logs = torch.split(stats, self.out_channels, dim=1)
        return m, logs

class ProsodyEncoder(nn.Module):
    def __init__(self,
                 in_channels,
                 hidden_channels,
                 kernel_size,
                 dilation_rate,
                 n_layers,
                 gin_channels,
                 vq_dim=16,
                 ):
        super().__init__()

        self.in_proj = nn.Conv1d(in_channels, hidden_channels, 1)
        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0)
        # from dac
        self.duration_dec = modules.WN(hidden_channels, kernel_size, dilation_rate, 3, gin_channels=0)
        self.duration_proj = nn.Linear(hidden_channels, 512, False)

        self.vq_dim = vq_dim
        self.vq_down = nn.Conv1d(hidden_channels, self.vq_dim, 1)
        self.vq = scalar_func9()
        self.vq_up = nn.Conv1d(self.vq_dim, hidden_channels, 1)


    def calc_dur_loss(self, x, x_mask, dur):
        dur = dur.clamp(0, 509)
        x = self.duration_dec(x, x_mask)
        logits = self.duration_proj(x.transpose(1, 2))
        assert logits.shape[:2] == dur.shape[:2], (logits.shape, dur.shape)
        x_mask_bool = x_mask.squeeze(1).bool()
        dur = dur[x_mask_bool].view(-1)
        logits = logits[x_mask_bool].view(-1, 512)
        dur_loss = F.cross_entropy(logits, dur, reduction='mean')
        acc = (logits.argmax(-1) == dur).float().mean() * 100
        # print('dur acc', acc.item())
        pred_dur = logits.argmax(-1)

        return dur_loss, pred_dur

    def decode_dur(self, x, x_mask):
        x = self.duration_dec(x, x_mask)
        logits = self.duration_proj(x.transpose(1, 2))
        logits = logits.view(-1, 512)
        pred_dur = logits.argmax(-1).reshape(-1, x.size(2))
        return pred_dur

    def forward(self, y, x_mask, attn, dur):
        y = self.in_proj(y)
        s = attn.sum(axis=2).unsqueeze(2)
        s[s == 0] = 1
        attn_pool = attn / s
        x = torch.matmul(attn_pool.squeeze(1).transpose(1, 2), y.transpose(1, 2)).transpose(1, 2)

        x = self.enc(x, x_mask, g=None)
        x = self.vq_down(x)
        x =  F.tanh(x)
        z = self.vq.apply(x)
        # print('z.std', z.std())
        z_q = self.vq_up(z)

        quantize_loss = torch.tensor(0.)
        dur_loss, pred_dur = self.calc_dur_loss(z_q, x_mask, dur)
        return z_q, quantize_loss, z, dur_loss, pred_dur

    def decode(self, z_pred, x_mask):
        z = self.vq.apply(z_pred)
        z_q = self.vq_up(z)
        dur = self.decode_dur(z_q, x_mask)
        return z_q, dur


class DP(nn.Module):
    def __init__(self,
                 hidden_channels,
                 kernel_size,
                 dilation_rate,
                 ):
        super().__init__()

        self.duration_dec = modules.WN(hidden_channels, kernel_size, dilation_rate, 3, gin_channels=0)
        self.duration_proj = nn.Linear(hidden_channels, 512, False)



    def forward(self, x, x_mask, dur):
        dur = dur.clamp(0, 509)
        x = self.duration_dec(x, x_mask)
        logits = self.duration_proj(x.transpose(1, 2))
        assert logits.shape[:2] == dur.shape[:2], (logits.shape, dur.shape)
        x_mask_bool = x_mask.squeeze(1).bool()
        dur = dur[x_mask_bool].view(-1)
        logits = logits[x_mask_bool].view(-1, 512)
        dur_loss = F.cross_entropy(logits, dur, reduction='mean')
        acc = (logits.argmax(-1) == dur).float().mean() * 100
        print('dur acc', acc.item())
        pred_dur = logits.argmax(-1)

        return dur_loss, pred_dur

    def decode_dur(self, x, x_mask):
        x = self.duration_dec(x, x_mask)
        logits = self.duration_proj(x.transpose(1, 2))
        logits = logits.view(-1, 512)
        pred_dur = logits.argmax(-1).reshape(-1, x.size(2))
        return pred_dur



def mono_align_and_get_loss(m_p, logs_p, z_p,x_mask,y_mask):
    
    with torch.no_grad():
      # negative cross-entropy
      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4

      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()

    w = attn.sum(2).squeeze(1).long()

    # print('w', w.shape, 'attn_hard', attn_hard.shape, 'g', g.shape)
    return attn, w
    

class SynthesizerTrn(nn.Module):
    """
      Synthesizer for Training
    """

    def __init__(self,
                 spec_channels,
                 segment_size,
                 inter_channels,
                 hidden_channels,
                 filter_channels,
                 n_heads,
                 n_layers,
                 kernel_size,
                 p_dropout,
                 resblock,
                 resblock_kernel_sizes,
                 resblock_dilation_sizes,
                 upsample_rates,
                 upsample_initial_channel,
                 upsample_kernel_sizes,
                 n_speakers=0,
                 gin_channels=0,
                 use_sdp=True,
                 freeze_quantizer=False,
                 **kwargs):
        super().__init__()
        self.spec_channels = spec_channels
        self.inter_channels = inter_channels
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.resblock = resblock
        self.resblock_kernel_sizes = resblock_kernel_sizes
        self.resblock_dilation_sizes = resblock_dilation_sizes
        self.upsample_rates = upsample_rates
        self.upsample_initial_channel = upsample_initial_channel
        self.upsample_kernel_sizes = upsample_kernel_sizes
        self.segment_size = segment_size
        self.n_speakers = n_speakers

        gin_channels = hidden_channels
        self.gin_channels = gin_channels

        self.use_sdp = use_sdp

        self.enc_p = TextEncoder(inter_channels,
                                 hidden_channels,
                                 filter_channels,
                                 n_heads,
                                 n_layers,
                                 kernel_size,
                                 p_dropout)
        print('n_param of enc_p', sum(p.numel() for p in self.enc_p.parameters() if p.requires_grad) / 1e6, 'M')

        self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
                             upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
        self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
                                      gin_channels=gin_channels)
        self.flow = ResidualCouplingBlock(
            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
        )
        self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
        # self.dp = DP(hidden_channels, 5, 1)

    # def get_codes(self, x, x_lengths, y, y_lengths, ssl, attn_priors):
    #     ph = x.clone()
    #     y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
    #     g = self.ref_enc(y * y_mask, y_mask)

    #     x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)

    #     x, x_align = self.enc_p(x, x_mask)
    #     z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)

    #     attn_out, attn, duration = mono_align_and_get_loss(self.aligner, x_align, z.detach(), x_mask, y_mask, attn_priors, x_lengths, y_lengths, g)

    #     _, _, pred_prosody_codes,_ , _ = self.prosody_encoder(ssl, x_mask, attn, duration)
    #     return pred_prosody_codes
    
    def forward(self, x, x_lengths, y, y_lengths, mel, ssl, attn_priors, bert, spk_emb, ):
        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
        g = self.ref_enc(y * y_mask, y_mask)

        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)

        x, m_p, logs_p, prosody = self.enc_p(ssl, y_mask, x, x_mask)
        assert not torch.isnan(x).any(), 'x nan'

        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
        z_p = self.flow(z, y_mask, g=g)
        assert not torch.isnan(z_p).any(), 'z_p nan'

        attn, duration = mono_align_and_get_loss(m_p, logs_p, z_p, x_mask, y_mask)
        assert not torch.isnan(attn).any(), 'attn nan'
        assert not torch.isnan(duration).any(), 'duration nan'

        # dur_loss, pred_dur = self.dp(x, x_mask, duration)
        dur_loss = torch.tensor(0.).to(x.device)
        print('dur loss', dur_loss.item())
        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
        assert not torch.isnan(m_p).any(), 'm_p nan'

        z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
        o = self.dec(z_slice, g=g)
        return o, attn, ids_slice, x_mask, y_mask, (
        z, z_p, m_p, logs_p, m_q, logs_q), prosody, dur_loss

    def reconstruct(self, x, x_lengths, y, y_lengths,mel, ssl, attn_priors, bert, spk_emb, noise_scale=.4):
        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
        g = self.ref_enc(y * y_mask, y_mask)

        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)

        x, m_p, logs_p, prosody = self.enc_p(ssl, y_mask, x, x_mask)

        duration = self.dp.decode_dur(x, x_mask)

        total_duration = torch.sum(duration, dim=1)
        y_lengths = total_duration.long()
        print('y_lengths', y_lengths, 'duration', duration)

        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y_lengths.max()), 1).float()
        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)

        attn = commons.generate_path(duration.unsqueeze(1), attn_mask)


        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)

        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
        z = self.flow(z_p, y_mask, g=g, reverse=True)
        o = self.dec((z * y_mask), g=g)
        return o, attn, y_mask, (z, z_p, m_p, logs_p)




    # def encode(self, x, x_lengths, y, y_lengths,ssl, attn_priors, bert, spk_emb):
    #     y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
    #     g = self.ref_enc(y * y_mask, y_mask)

    #     x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)

    #     x, x_align = self.enc_p(x, x_mask)
    #     z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)

    #     attn_out, attn, duration = mono_align_and_get_loss(self.aligner, x_align, z.detach(), x_mask, y_mask, attn_priors, x_lengths, y_lengths, g)

    #     _, _, pred_prosody_codes,_ , _ = self.prosody_encoder(ssl, x_mask, attn, duration)
    #     return pred_prosody_codes, x, x_mask

    # def decode(self,pred_prosody_codes,  x, x_lengths, y, y_lengths, noise_scale=.4):
    #     y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
    #     g = self.ref_enc(y * y_mask, y_mask)

    #     x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)
    #     x, x_align = self.enc_p(x, x_mask)

    #     z_q, duration = self.prosody_encoder.decode(pred_prosody_codes, x_mask)

    #     total_duration = torch.sum(duration, dim=1)
    #     y_lengths = total_duration.long()

    #     y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y_lengths.max()), 1).float()
    #     attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)

    #     attn = commons.generate_path(duration.unsqueeze(1), attn_mask)

    #     x = x + z_q

    #     frame_x = torch.matmul(attn.squeeze(1), x.transpose(1, 2)).transpose(1, 2)
    #     m_p, logs_p = self.frame_prior_net(frame_x, y_mask)

    #     z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
    #     z = self.flow(z_p, y_mask, g=g, reverse=True)
    #     o = self.dec((z * y_mask), g=g)
    #     return o