import copy
import math
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import functional as F

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

import commons
from commons import init_weights, get_padding
from transforms import piecewise_rational_quadratic_transform
from typing import Optional, Tuple
from numba import jit, prange
from scipy import signal as sig

LRELU_SLOPE = 0.1





class TMEncoder(nn.Module):
    def __init__(self, tm_start_size, enc_sizes):
        super().__init__()
        enc_layers = [torch.nn.Linear(tm_start_size,enc_sizes[0])]
    
        last_size = enc_sizes[0]
        for ly_size in enc_sizes:
            enc_layers.append(torch.nn.ReLU())
            enc_layers.append(torch.nn.Linear(last_size,ly_size))
            last_size = ly_size
    
        self.net = torch.nn.Sequential(*enc_layers)
  
    def forward(self, x):
        return self.net(x)



class CoMBD(torch.nn.Module):

    def __init__(self, filters, kernels, groups, strides, use_spectral_norm=False):
        super(CoMBD, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList()
        init_channel = 1
        for i, (f, k, g, s) in enumerate(zip(filters, kernels, groups, strides)):
            self.convs.append(norm_f(Conv1d(init_channel, f, k, s, padding=get_padding(k, 1), groups=g)))
            init_channel = f
        self.conv_post = norm_f(Conv1d(filters[-1], 1, 3, 1, padding=get_padding(3, 1)))

    def forward(self, x):
        fmap = []
        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, 0.1)
            fmap.append(x)
        x = self.conv_post(x)
        #fmap.append(x)
        x = torch.flatten(x, 1, -1)
        return x, fmap


class MDC(torch.nn.Module):

    def __init__(self, in_channel, channel, kernel, stride, dilations, use_spectral_norm=False):
        super(MDC, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = torch.nn.ModuleList()
        self.num_dilations = len(dilations)
        for d in dilations:
            self.convs.append(norm_f(Conv1d(in_channel, channel, kernel, stride=1, padding=get_padding(kernel, d),
                                            dilation=d)))

        self.conv_out = norm_f(Conv1d(channel, channel, 3, stride=stride, padding=get_padding(3, 1)))

    def forward(self, x):
        xs = None
        for l in self.convs:
            if xs is None:
                xs = l(x)
            else:
                xs += l(x)

        x = xs / self.num_dilations

        x = self.conv_out(x)
        x = F.leaky_relu(x, 0.1)
        return x


class SubBandDiscriminator(torch.nn.Module):

    def __init__(self, init_channel, channels, kernel, strides, dilations, use_spectral_norm=False):
        super(SubBandDiscriminator, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm

        self.mdcs = torch.nn.ModuleList()

        for c, s, d in zip(channels, strides, dilations):
            self.mdcs.append(MDC(init_channel, c, kernel, s, d))
            init_channel = c
        self.conv_post = norm_f(Conv1d(init_channel, 1, 3, padding=get_padding(3, 1)))

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

        for l in self.mdcs:
            x = l(x)
            fmap.append(x)
        x = self.conv_post(x)
        #fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap




# adapted from
# https://github.com/kan-bayashi/ParallelWaveGAN/tree/master/parallel_wavegan
class PQMF(torch.nn.Module):
    def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
        super(PQMF, self).__init__()

        self.N = N
        self.taps = taps
        self.cutoff = cutoff
        self.beta = beta

        QMF = sig.firwin(taps + 1, cutoff, window=('kaiser', beta))
        H = np.zeros((N, len(QMF)))
        G = np.zeros((N, len(QMF)))
        for k in range(N):
            constant_factor = (2 * k + 1) * (np.pi /
                                             (2 * N)) * (np.arange(taps + 1) -
                                                         ((taps - 1) / 2))  # TODO: (taps - 1) -> taps
            phase = (-1)**k * np.pi / 4
            H[k] = 2 * QMF * np.cos(constant_factor + phase)

            G[k] = 2 * QMF * np.cos(constant_factor - phase)

        H = torch.from_numpy(H[:, None, :]).float()
        G = torch.from_numpy(G[None, :, :]).float()

        self.register_buffer("H", H)
        self.register_buffer("G", G)

        updown_filter = torch.zeros((N, N, N)).float()
        for k in range(N):
            updown_filter[k, k, 0] = 1.0
        self.register_buffer("updown_filter", updown_filter)
        self.N = N

        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)

    def forward(self, x):
        return self.analysis(x)

    def analysis(self, x):
        return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N)

    def synthesis(self, x):
        x = F.conv_transpose1d(x,
                               self.updown_filter * self.N,
                               stride=self.N)
        x = F.conv1d(x, self.G, padding=self.taps // 2)
        return 

@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)
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




class PartialConv1d(torch.nn.Conv1d):
    """
    Zero padding creates a unique identifier for where the edge of the data is, such that the model can almost always identify
    exactly where it is relative to either edge given a sufficient receptive field. Partial padding goes to some lengths to remove 
    this affect.
    """

    def __init__(self, *args, **kwargs):
        super(PartialConv1d, self).__init__(*args, **kwargs)
        weight_maskUpdater = torch.ones(1, 1, self.kernel_size[0])
        self.register_buffer("weight_maskUpdater", weight_maskUpdater, persistent=False)
        slide_winsize = torch.tensor(self.weight_maskUpdater.shape[1] * self.weight_maskUpdater.shape[2])
        self.register_buffer("slide_winsize", slide_winsize, persistent=False)

        if self.bias is not None:
            bias_view = self.bias.view(1, self.out_channels, 1)
            self.register_buffer('bias_view', bias_view, persistent=False)
        # caching part
        self.last_size = (-1, -1, -1)

        update_mask = torch.ones(1, 1, 1)
        self.register_buffer('update_mask', update_mask, persistent=False)
        mask_ratio = torch.ones(1, 1, 1)
        self.register_buffer('mask_ratio', mask_ratio, persistent=False)
        self.partial: bool = True

    def calculate_mask(self, input: torch.Tensor, mask_in: Optional[torch.Tensor]):
        with torch.no_grad():
            if mask_in is None:
                mask = torch.ones(1, 1, input.shape[2], dtype=input.dtype, device=input.device)
            else:
                mask = mask_in
            update_mask = F.conv1d(
                mask,
                self.weight_maskUpdater,
                bias=None,
                stride=self.stride,
                padding=self.padding,
                dilation=self.dilation,
                groups=1,
            )
            # for mixed precision training, change 1e-8 to 1e-6
            mask_ratio = self.slide_winsize / (update_mask + 1e-6)
            update_mask = torch.clamp(update_mask, 0, 1)
            mask_ratio = torch.mul(mask_ratio.to(update_mask), update_mask)
            return torch.mul(input, mask), mask_ratio, update_mask

    def forward_aux(self, input: torch.Tensor, mask_ratio: torch.Tensor, update_mask: torch.Tensor) -> torch.Tensor:
        assert len(input.shape) == 3

        raw_out = self._conv_forward(input, self.weight, self.bias)

        if self.bias is not None:
            output = torch.mul(raw_out - self.bias_view, mask_ratio) + self.bias_view
            output = torch.mul(output, update_mask)
        else:
            output = torch.mul(raw_out, mask_ratio)

        return output

    @torch.jit.ignore
    def forward_with_cache(self, input: torch.Tensor, mask_in: Optional[torch.Tensor] = None) -> torch.Tensor:
        use_cache = not (torch.jit.is_tracing() or torch.onnx.is_in_onnx_export())
        cache_hit = use_cache and mask_in is None and self.last_size == input.shape
        if cache_hit:
            mask_ratio = self.mask_ratio
            update_mask = self.update_mask
        else:
            input, mask_ratio, update_mask = self.calculate_mask(input, mask_in)
            if use_cache:
                # if a mask is input, or tensor shape changed, update mask ratio
                self.last_size = tuple(input.shape)
                self.update_mask = update_mask
                self.mask_ratio = mask_ratio
        return self.forward_aux(input, mask_ratio, update_mask)

    def forward_no_cache(self, input: torch.Tensor, mask_in: Optional[torch.Tensor] = None) -> torch.Tensor:
        if self.partial:
            input, mask_ratio, update_mask = self.calculate_mask(input, mask_in)
            return self.forward_aux(input, mask_ratio, update_mask)
        else:
            if mask_in is not None:
                input = torch.mul(input, mask_in)
            return self._conv_forward(input, self.weight, self.bias)

    def forward(self, input: torch.Tensor, mask_in: Optional[torch.Tensor] = None) -> torch.Tensor:
        if self.partial:
            return self.forward_with_cache(input, mask_in)
        else:
            if mask_in is not None:
                input = torch.mul(input, mask_in)
            return self._conv_forward(input, self.weight, self.bias)


class ConvNorm(torch.nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size=1,
        stride=1,
        padding=None,
        dilation=1,
        bias=True,
        w_init_gain='linear',
        init_gain_param=None,
        use_partial_padding: bool = False,
        use_weight_norm: bool = False,
        norm_fn=None,
    ):
        super(ConvNorm, self).__init__()
        if padding is None:
            assert kernel_size % 2 == 1
            padding = int(dilation * (kernel_size - 1) / 2)
        self.use_partial_padding: bool = use_partial_padding
        conv = PartialConv1d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            bias=bias,
        )
        conv.partial = use_partial_padding
        torch.nn.init.xavier_uniform_(conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain,init_gain_param))
        if use_weight_norm:
            conv = torch.nn.utils.weight_norm(conv)
        if norm_fn is not None:
            self.norm = norm_fn(out_channels, affine=True)
        else:
            self.norm = None
        self.conv = conv

    def forward(self, input: torch.Tensor, mask_in: Optional[torch.Tensor] = None) -> torch.Tensor:
        ret = self.conv(input, mask_in)
        if self.norm is not None:
            ret = self.norm(ret)
        return ret

def binarize_attention_parallel(attn, in_lens, out_lens):
    """For training purposes only. Binarizes attention with MAS.
           These will no longer receive 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 AlignmentEncoder(torch.nn.Module):
    """Module for alignment text and mel spectrogram. """

    def __init__(
        self, n_mel_channels=80, n_text_channels=512, n_att_channels=80, temperature=0.0005,lr_slope=0.05,
    ):
        super().__init__()
        self.temperature = temperature
        self.softmax = torch.nn.Softmax(dim=3)
        self.log_softmax = torch.nn.LogSoftmax(dim=3)

        self.key_proj = nn.Sequential(
            ConvNorm(n_text_channels, n_text_channels * 2, kernel_size=3, bias=True, w_init_gain='leaky_relu',init_gain_param=lr_slope),
            torch.nn.LeakyReLU(lr_slope),
            ConvNorm(n_text_channels * 2, n_att_channels, kernel_size=1, bias=True),
        )

        self.query_proj = nn.Sequential(
            ConvNorm(n_mel_channels, n_mel_channels * 2, kernel_size=3, bias=True, w_init_gain='leaky_relu',init_gain_param=lr_slope),
            torch.nn.LeakyReLU(lr_slope),
            ConvNorm(n_mel_channels * 2, n_mel_channels, kernel_size=1, bias=True),
            torch.nn.LeakyReLU(lr_slope),
            ConvNorm(n_mel_channels, n_att_channels, kernel_size=1, bias=True),
        )

    def get_dist(self, keys, queries, mask=None):
        """Calculation of distance matrix.
        Args:
            queries (torch.tensor): B x C x T1 tensor (probably going to be mel data).
            keys (torch.tensor): B x C2 x T2 tensor (text data).
            mask (torch.tensor): B x T2 x 1 tensor, binary mask for variable length entries and also can be used
                for ignoring unnecessary elements from keys in the resulting distance matrix (True = mask element, False = leave unchanged).
        Output:
            dist (torch.tensor): B x T1 x T2 tensor.
        """
        keys_enc = self.key_proj(keys)  # B x n_attn_dims x T2
        queries_enc = self.query_proj(queries)  # B x n_attn_dims x T1
        attn = (queries_enc[:, :, :, None] - keys_enc[:, :, None]) ** 2  # B x n_attn_dims x T1 x T2
        dist = attn.sum(1, keepdim=True)  # B x 1 x T1 x T2

        if mask is not None:
            dist.data.masked_fill_(mask.permute(0, 2, 1).unsqueeze(2), float("inf"))

        return dist.squeeze(1)

    @staticmethod
    def get_durations(attn_soft, text_len, spect_len):
        """Calculation of durations.
        Args:
            attn_soft (torch.tensor): B x 1 x T1 x T2 tensor.
            text_len (torch.tensor): B tensor, lengths of text.
            spect_len (torch.tensor): B tensor, lengths of mel spectrogram.
        """
        attn_hard = binarize_attention_parallel(attn_soft, text_len, spect_len)
        durations = attn_hard.sum(2)[:, 0, :]
        assert torch.all(torch.eq(durations.sum(dim=1), spect_len))
        return durations

    @staticmethod
    def get_mean_dist_by_durations(dist, durations, mask=None):
        """Select elements from the distance matrix for the given durations and mask and return mean distance.
        Args:
            dist (torch.tensor): B x T1 x T2 tensor.
            durations (torch.tensor): B x T2 tensor. Dim T2 should sum to T1.
            mask (torch.tensor): B x T2 x 1 binary mask for variable length entries and also can be used
                for ignoring unnecessary elements in dist by T2 dim (True = mask element, False = leave unchanged).
        Output:
            mean_dist (torch.tensor): B x 1 tensor.
        """
        batch_size, t1_size, t2_size = dist.size()
        assert torch.all(torch.eq(durations.sum(dim=1), t1_size))

        if mask is not None:
            dist = dist.masked_fill(mask.permute(0, 2, 1).unsqueeze(2), 0)

        # TODO(oktai15): make it more efficient
        mean_dist_by_durations = []
        for dist_idx in range(batch_size):
            mean_dist_by_durations.append(
                torch.mean(
                    dist[
                        dist_idx,
                        torch.arange(t1_size),
                        torch.repeat_interleave(torch.arange(t2_size), repeats=durations[dist_idx]),
                    ]
                )
            )

        return torch.tensor(mean_dist_by_durations, dtype=dist.dtype, device=dist.device)

    @staticmethod
    def get_mean_distance_for_word(l2_dists, durs, start_token, num_tokens):
        """Calculates the mean distance between text and audio embeddings given a range of text tokens.
        Args:
            l2_dists (torch.tensor): L2 distance matrix from Aligner inference. T1 x T2 tensor.
            durs (torch.tensor): List of durations corresponding to each text token. T2 tensor. Should sum to T1.
            start_token (int): Index of the starting token for the word of interest.
            num_tokens (int): Length (in tokens) of the word of interest.
        Output:
            mean_dist_for_word (float): Mean embedding distance between the word indicated and its predicted audio frames.
        """
        # Need to calculate which audio frame we start on by summing all durations up to the start token's duration
        start_frame = torch.sum(durs[:start_token]).data

        total_frames = 0
        dist_sum = 0

        # Loop through each text token
        for token_ind in range(start_token, start_token + num_tokens):
            # Loop through each frame for the given text token
            for frame_ind in range(start_frame, start_frame + durs[token_ind]):
                # Recall that the L2 distance matrix is shape [spec_len, text_len]
                dist_sum += l2_dists[frame_ind, token_ind]

            # Update total frames so far & the starting frame for the next token
            total_frames += durs[token_ind]
            start_frame += durs[token_ind]

        return dist_sum / total_frames

    def forward(self, queries, keys, mask=None, attn_prior=None, conditioning=None):
        """Forward pass of the aligner encoder.
        Args:
            queries (torch.tensor): B x C x T1 tensor (probably going to be mel data).
            keys (torch.tensor): B x C2 x T2 tensor (text data).
            mask (torch.tensor): B x T2 x 1 tensor, binary mask for variable length entries (True = mask element, False = leave unchanged).
            attn_prior (torch.tensor): prior for attention matrix.
            conditioning (torch.tensor): B x T2 x 1 conditioning embedding
        Output:
            attn (torch.tensor): B x 1 x T1 x T2 attention mask. Final dim T2 should sum to 1.
            attn_logprob (torch.tensor): B x 1 x T1 x T2 log-prob attention mask.
        """
        if conditioning is not None:
            keys = keys + conditioning
            
        keys_enc = self.key_proj(keys)  # B x n_attn_dims x T2
        queries_enc = self.query_proj(queries)  # B x n_attn_dims x T1

        # Simplistic Gaussian Isotopic Attention
        attn = (queries_enc[:, :, :, None] - keys_enc[:, :, None]) ** 2  # B x n_attn_dims x T1 x T2
        attn = -self.temperature * attn.sum(1, keepdim=True)

        if attn_prior is not None:
            attn = self.log_softmax(attn) + torch.log(attn_prior[:, None] + 1e-8)

        attn_logprob = attn.clone()

        if mask is not None:
            attn.data.masked_fill_(mask.permute(0, 2, 1).unsqueeze(2), -float("inf"))

        attn = self.softmax(attn)  # softmax along T2
        return attn, attn_logprob



class LayerNorm(nn.Module):
  def __init__(self, channels, eps=1e-5):
    super().__init__()
    self.channels = channels
    self.eps = eps

    self.gamma = nn.Parameter(torch.ones(channels))
    self.beta = nn.Parameter(torch.zeros(channels))

  def forward(self, x):
    x = x.transpose(1, -1)
    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
    return x.transpose(1, -1)

 
class ConvReluNorm(nn.Module):
  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
    super().__init__()
    self.in_channels = in_channels
    self.hidden_channels = hidden_channels
    self.out_channels = out_channels
    self.kernel_size = kernel_size
    self.n_layers = n_layers
    self.p_dropout = p_dropout
    assert n_layers > 1, "Number of layers should be larger than 0."

    self.conv_layers = nn.ModuleList()
    self.norm_layers = nn.ModuleList()
    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
    self.norm_layers.append(LayerNorm(hidden_channels))
    self.relu_drop = nn.Sequential(
        nn.ReLU(),
        nn.Dropout(p_dropout))
    for _ in range(n_layers-1):
      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
      self.norm_layers.append(LayerNorm(hidden_channels))
    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
    self.proj.weight.data.zero_()
    self.proj.bias.data.zero_()

  def forward(self, x, x_mask):
    x_org = x
    for i in range(self.n_layers):
      x = self.conv_layers[i](x * x_mask)
      x = self.norm_layers[i](x)
      x = self.relu_drop(x)
    x = x_org + self.proj(x)
    return x * x_mask


class DDSConv(nn.Module):
  """
  Dialted and Depth-Separable Convolution
  """
  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
    super().__init__()
    self.channels = channels
    self.kernel_size = kernel_size
    self.n_layers = n_layers
    self.p_dropout = p_dropout

    self.drop = nn.Dropout(p_dropout)
    self.convs_sep = nn.ModuleList()
    self.convs_1x1 = nn.ModuleList()
    self.norms_1 = nn.ModuleList()
    self.norms_2 = nn.ModuleList()
    for i in range(n_layers):
      dilation = kernel_size ** i
      padding = (kernel_size * dilation - dilation) // 2
      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
          groups=channels, dilation=dilation, padding=padding
      ))
      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
      self.norms_1.append(LayerNorm(channels))
      self.norms_2.append(LayerNorm(channels))

  def forward(self, x, x_mask, g=None):
    if g is not None:
      x = x + g
    for i in range(self.n_layers):
      y = self.convs_sep[i](x * x_mask)
      y = self.norms_1[i](y)
      y = F.gelu(y)
      y = self.convs_1x1[i](y)
      y = self.norms_2[i](y)
      y = F.gelu(y)
      y = self.drop(y)
      x = x + y
    return x * x_mask


class WN(torch.nn.Module):
  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
    super(WN, self).__init__()
    assert(kernel_size % 2 == 1)
    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.p_dropout = p_dropout

    self.in_layers = torch.nn.ModuleList()
    self.res_skip_layers = torch.nn.ModuleList()
    self.drop = nn.Dropout(p_dropout)

    if gin_channels != 0:
      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')

    for i in range(n_layers):
      dilation = dilation_rate ** i
      padding = int((kernel_size * dilation - dilation) / 2)
      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
                                 dilation=dilation, padding=padding)
      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
      self.in_layers.append(in_layer)

      # last one is not necessary
      if i < n_layers - 1:
        res_skip_channels = 2 * hidden_channels
      else:
        res_skip_channels = hidden_channels

      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
      self.res_skip_layers.append(res_skip_layer)

  def forward(self, x, x_mask, g=None, **kwargs):
    output = torch.zeros_like(x)
    n_channels_tensor = torch.IntTensor([self.hidden_channels])

    if g is not None:
      g = self.cond_layer(g)

    for i in range(self.n_layers):
      x_in = self.in_layers[i](x)
      if g is not None:
        cond_offset = i * 2 * self.hidden_channels
        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
      else:
        g_l = torch.zeros_like(x_in)

      acts = commons.fused_add_tanh_sigmoid_multiply(
          x_in,
          g_l,
          n_channels_tensor)
      acts = self.drop(acts)

      res_skip_acts = self.res_skip_layers[i](acts)
      if i < self.n_layers - 1:
        res_acts = res_skip_acts[:,:self.hidden_channels,:]
        x = (x + res_acts) * x_mask
        output = output + res_skip_acts[:,self.hidden_channels:,:]
      else:
        output = output + res_skip_acts
    return output * x_mask

  def remove_weight_norm(self):
    if self.gin_channels != 0:
      torch.nn.utils.remove_weight_norm(self.cond_layer)
    for l in self.in_layers:
      torch.nn.utils.remove_weight_norm(l)
    for l in self.res_skip_layers:
     torch.nn.utils.remove_weight_norm(l)


class ResBlock1(torch.nn.Module):
    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
        super(ResBlock1, self).__init__()
        self.convs1 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
                               padding=get_padding(kernel_size, dilation[2])))
        ])
        self.convs1.apply(init_weights)

        self.convs2 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1)))
        ])
        self.convs2.apply(init_weights)

    def forward(self, x, x_mask=None):
        for c1, c2 in zip(self.convs1, self.convs2):
            xt = F.leaky_relu(x, LRELU_SLOPE)
            if x_mask is not None:
                xt = xt * x_mask
            xt = c1(xt)
            xt = F.leaky_relu(xt, LRELU_SLOPE)
            if x_mask is not None:
                xt = xt * x_mask
            xt = c2(xt)
            x = xt + x
        if x_mask is not None:
            x = x * x_mask
        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)


class ResBlock2(torch.nn.Module):
    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
        super(ResBlock2, self).__init__()
        self.convs = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1])))
        ])
        self.convs.apply(init_weights)

    def forward(self, x, x_mask=None):
        for c in self.convs:
            xt = F.leaky_relu(x, LRELU_SLOPE)
            if x_mask is not None:
                xt = xt * x_mask
            xt = c(xt)
            x = xt + x
        if x_mask is not None:
            x = x * x_mask
        return x

    def remove_weight_norm(self):
        for l in self.convs:
            remove_weight_norm(l)


class Log(nn.Module):
  def forward(self, x, x_mask, reverse=False, **kwargs):
    if not reverse:
      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
      logdet = torch.sum(-y, [1, 2])
      return y, logdet
    else:
      x = torch.exp(x) * x_mask
      return x
    

class Flip(nn.Module):
  def forward(self, x, *args, reverse=False, **kwargs):
    x = torch.flip(x, [1])
    if not reverse:
      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
      return x, logdet
    else:
      return x


class ElementwiseAffine(nn.Module):
  def __init__(self, channels):
    super().__init__()
    self.channels = channels
    self.m = nn.Parameter(torch.zeros(channels,1))
    self.logs = nn.Parameter(torch.zeros(channels,1))

  def forward(self, x, x_mask, reverse=False, **kwargs):
    if not reverse:
      y = self.m + torch.exp(self.logs) * x
      y = y * x_mask
      logdet = torch.sum(self.logs * x_mask, [1,2])
      return y, logdet
    else:
      x = (x - self.m) * torch.exp(-self.logs) * x_mask
      return x


class ResidualCouplingLayer(nn.Module):
  def __init__(self,
      channels,
      hidden_channels,
      kernel_size,
      dilation_rate,
      n_layers,
      p_dropout=0,
      gin_channels=0,
      mean_only=False):
    assert channels % 2 == 0, "channels should be divisible by 2"
    super().__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.half_channels = channels // 2
    self.mean_only = mean_only

    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
    self.post.weight.data.zero_()
    self.post.bias.data.zero_()

  def forward(self, x, x_mask, g=None, reverse=False):
    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
    h = self.pre(x0) * x_mask
    h = self.enc(h, x_mask, g=g)
    stats = self.post(h) * x_mask
    if not self.mean_only:
      m, logs = torch.split(stats, [self.half_channels]*2, 1)
    else:
      m = stats
      logs = torch.zeros_like(m)

    if not reverse:
      x1 = m + x1 * torch.exp(logs) * x_mask
      x = torch.cat([x0, x1], 1)
      logdet = torch.sum(logs, [1,2])
      return x, logdet
    else:
      x1 = (x1 - m) * torch.exp(-logs) * x_mask
      x = torch.cat([x0, x1], 1)
      return x


class ConvFlow(nn.Module):
  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
    super().__init__()
    self.in_channels = in_channels
    self.filter_channels = filter_channels
    self.kernel_size = kernel_size
    self.n_layers = n_layers
    self.num_bins = num_bins
    self.tail_bound = tail_bound
    self.half_channels = in_channels // 2

    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
    self.proj.weight.data.zero_()
    self.proj.bias.data.zero_()

  def forward(self, x, x_mask, g=None, reverse=False):
    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
    h = self.pre(x0)
    h = self.convs(h, x_mask, g=g)
    h = self.proj(h) * x_mask

    b, c, t = x0.shape
    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]

    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
    unnormalized_derivatives = h[..., 2 * self.num_bins:]

    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
        unnormalized_widths,
        unnormalized_heights,
        unnormalized_derivatives,
        inverse=reverse,
        tails='linear',
        tail_bound=self.tail_bound
    )

    x = torch.cat([x0, x1], 1) * x_mask
    logdet = torch.sum(logabsdet * x_mask, [1,2])
    if not reverse:
        return x, logdet
    else:
        return x


class PositionalEmbedding(nn.Module):
    def __init__(self, demb):
        super(PositionalEmbedding, self).__init__()
        self.demb = demb
        inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
        self.register_buffer('inv_freq', inv_freq)

    def forward(self, pos_seq, bsz=None):
        #        sinusoid_inp = torch.ger(pos_seq, self.inv_freq)
        sinusoid_inp = torch.matmul(torch.unsqueeze(pos_seq, -1), torch.unsqueeze(self.inv_freq, 0))

        pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=1)
        if bsz is not None:
            return pos_emb[None, :, :].repeat(bsz, 1, 1)
        else:
            return pos_emb[None, :, :]


class SelfAttentionModule(nn.Module):
    """Self-attention for lm tokens and text. """

    def __init__(self, n_text_channels=384, n_lm_tokens_channels=128):
        super().__init__()

        self.text_pos_emb = PositionalEmbedding(n_text_channels)
        self.lm_pos_emb = PositionalEmbedding(n_lm_tokens_channels)

        self.query_proj = nn.Sequential(
            ConvNorm(n_text_channels, n_text_channels, kernel_size=3, bias=True, w_init_gain='relu'),
            torch.nn.ReLU(),
            ConvNorm(n_text_channels, n_text_channels, kernel_size=1, bias=True),
        )

        self.key_proj = nn.Sequential(
            ConvNorm(n_lm_tokens_channels, n_text_channels, kernel_size=3, bias=True, w_init_gain='relu'),
            torch.nn.ReLU(),
            ConvNorm(n_text_channels, n_text_channels, kernel_size=1, bias=True),
        )

        self.value_proj = nn.Sequential(
            ConvNorm(n_lm_tokens_channels, n_text_channels, kernel_size=3, bias=True, w_init_gain='relu'),
            torch.nn.ReLU(),
            ConvNorm(n_text_channels, n_text_channels, kernel_size=1, bias=True),
        )

        self.scale = math.sqrt(n_text_channels)
     
    def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
        """Forward pass of self-attention.
        Args:
            queries (torch.tensor): B x T1 x C1 tensor
            keys (torch.tensor): B x T2 x C2 tensor
            values (torch.tensor): B x T2 x C2 tensor
            q_mask (torch.tensor): B x T1 tensor, bool mask for variable length entries
            kv_mask (torch.tensor): B x T2 tensor, bool mask for variable length entries
        Output:
            attn_out (torch.tensor): B x T1 x C1 tensor
        """
        pos_q_seq = torch.arange(queries.size(-2), device=queries.device).to(queries.dtype)
        pos_kv_seq = torch.arange(keys.size(-2), device=queries.device).to(queries.dtype)

        pos_q_emb = self.text_pos_emb(pos_q_seq)
        pos_kv_emb = self.lm_pos_emb(pos_kv_seq)

        if q_mask is not None:
            pos_q_emb = pos_q_emb * q_mask.unsqueeze(2)

        if kv_mask is not None:
            pos_kv_emb = pos_kv_emb * kv_mask.unsqueeze(2)

        queries = (queries + pos_q_emb).transpose(1, 2)
        keys = (keys + pos_kv_emb).transpose(1, 2)
        values = (values + pos_kv_emb).transpose(1, 2)

        queries_enc = self.query_proj(queries).transpose(-2, -1)  # B x T1 x C1
        keys_enc = self.key_proj(keys)  # B x C1 x T2
        values_enc = self.value_proj(values).transpose(-2, -1)  # B x T2 x C1

        scores = torch.matmul(queries_enc, keys_enc) / self.scale  # B x T1 x T2

        if kv_mask is not None:
            scores.masked_fill_(~kv_mask.unsqueeze(-2), -float("inf"))

        return torch.matmul(torch.softmax(scores, dim=-1), values_enc)  # B x T1 x C1