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  # *****************************************************************************
  import copy
  import torch
  from torch.autograd import Variable
  import torch.nn.functional as F
  
  
  @torch.jit.script
  def fused_add_tanh_sigmoid_multiply(input_a, input_b, input_s, n_channels):
      n_channels_int = n_channels[0]
      in_act = input_a+input_b+input_s
      t_act = torch.tanh(in_act[:, :n_channels_int, :])
      s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
      acts = t_act * s_act
      return acts
  
  
  class WaveGlowLoss(torch.nn.Module):
      def __init__(self, sigma=1.0):
          super(WaveGlowLoss, self).__init__()
          self.sigma = sigma
  
      def forward(self, model_output):
          z, log_s_list, log_det_W_list = model_output
          for i, log_s in enumerate(log_s_list):
              if i == 0:
                  log_s_total = torch.sum(log_s)
                  log_det_W_total = log_det_W_list[i]
              else:
                  log_s_total = log_s_total + torch.sum(log_s)
                  log_det_W_total += log_det_W_list[i]
  
          loss = torch.sum(z*z)/(2*self.sigma*self.sigma) - log_s_total - log_det_W_total
          return loss/(z.size(0)*z.size(1)*z.size(2))
  
  
  class Invertible1x1Conv(torch.nn.Module):
      """
      The layer outputs both the convolution, and the log determinant
      of its weight matrix.  If reverse=True it does convolution with
      inverse
      """
      def __init__(self, c):
          super(Invertible1x1Conv, self).__init__()
          self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0,
                                      bias=False)
  
          # Sample a random orthonormal matrix to initialize weights
          W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
  
          # Ensure determinant is 1.0 not -1.0
          if torch.det(W) < 0:
              W[:, 0] = -1*W[:, 0]
          W = W.view(c, c, 1)
          self.conv.weight.data = W
  
      def forward(self, z, reverse=False):
          # shape
          batch_size, group_size, n_of_groups = z.size()
  
          W = self.conv.weight.squeeze()
  
          if reverse:
              if not hasattr(self, 'W_inverse'):
                  # Reverse computation
                  W_inverse = W.float().inverse()
                  W_inverse = Variable(W_inverse[..., None])
                  if z.type() == 'torch.cuda.HalfTensor':
                      W_inverse = W_inverse.half()
                  self.W_inverse = W_inverse
              z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
              return z
          else:
              # Forward computation
              log_det_W = batch_size * n_of_groups * torch.logdet(W)
              z = self.conv(z)
              return z, log_det_W
  
  
  class WN(torch.nn.Module):
      """
      This is the WaveNet like layer for the affine coupling.  The primary difference
      from WaveNet is the convolutions need not be causal.  There is also no dilation
      size reset.  The dilation only doubles on each layer
      """
      def __init__(self, n_in_channels, n_mel_channels, speaker_emb_size,
                   n_layers, n_channels, kernel_size):
          super(WN, self).__init__()
          assert(kernel_size % 2 == 1)
          assert(n_channels % 2 == 0)
          self.n_layers = n_layers
          self.n_channels = n_channels
          self.in_layers = torch.nn.ModuleList()
          self.res_skip_layers = torch.nn.ModuleList()
  
          start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
          start = torch.nn.utils.weight_norm(start, name='weight')
          self.start = start
  
          # Initializing last layer to 0 makes the affine coupling layers
          # do nothing at first.  This helps with training stability
          end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
          end.weight.data.zero_()
          end.bias.data.zero_()
          self.end = end
  
          cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels*n_layers, 1)
          self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
  
          speaker_layer = torch.nn.Conv1d(speaker_emb_size, 2*n_channels, 1)
          self.speaker_layer = torch.nn.utils.weight_norm(speaker_layer, name='weight')
  
          for i in range(n_layers):
              dilation = 2 ** i
              padding = int((kernel_size*dilation - dilation)/2)
              in_layer = torch.nn.Conv1d(n_channels, 2*n_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*n_channels
              else:
                  res_skip_channels = n_channels
              res_skip_layer = torch.nn.Conv1d(n_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, forward_input):
          audio, spect, speaker_emb = forward_input
          audio = self.start(audio)
          output = torch.zeros_like(audio)
          n_channels_tensor = torch.IntTensor([self.n_channels])
  
          spect = self.cond_layer(spect)
          speaker_emb = self.speaker_layer(speaker_emb)
  
          for i in range(self.n_layers):
              spect_offset = i*2*self.n_channels
              acts = fused_add_tanh_sigmoid_multiply(
                  self.in_layers[i](audio),
                  spect[:, spect_offset:spect_offset+2*self.n_channels, :],
                  speaker_emb,
                  n_channels_tensor)
  
              res_skip_acts = self.res_skip_layers[i](acts)
              if i < self.n_layers - 1:
                  audio = audio + res_skip_acts[:, :self.n_channels, :]
                  output = output + res_skip_acts[:, self.n_channels:, :]
              else:
                  output = output + res_skip_acts
  
          return self.end(output)
  
  
  class WaveGlow(torch.nn.Module):
      def __init__(self, speaker_emb_size, n_mel_channels, n_flows, n_group, n_early_every,
                   n_early_size, WN_config):
          super(WaveGlow, self).__init__()
  
          self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
                                                   n_mel_channels,
                                                   1024, stride=256)
          assert(n_group % 2 == 0)
          self.n_flows = n_flows
          self.n_group = n_group
          self.n_early_every = n_early_every
          self.n_early_size = n_early_size
          self.WN = torch.nn.ModuleList()
          self.convinv = torch.nn.ModuleList()
  
          n_half = int(n_group/2)
  
          # Set up layers with the right sizes based on how many dimensions
          # have been output already
          n_remaining_channels = n_group
          for k in range(n_flows):
              if k % self.n_early_every == 0 and k > 0:
                  n_half = n_half - int(self.n_early_size/2)
                  n_remaining_channels = n_remaining_channels - self.n_early_size
              self.convinv.append(Invertible1x1Conv(n_remaining_channels))
              self.WN.append(WN(n_half, n_mel_channels*n_group, speaker_emb_size, **WN_config))
          self.n_remaining_channels = n_remaining_channels  # Useful during inference
  
      def forward(self, forward_input):
          """
          forward_input[0] = mel_spectrogram:  batch x n_mel_channels x frames
          forward_input[1] = audio: batch x time
          UPD
          forward_input[2] = speaker embeddings: batch x speaker_embedding_size
          """
          spect, audio, speaker_emb = forward_input
  
          #  Upsample spectrogram to size of audio
          spect = self.upsample(spect)
          assert(spect.size(2) >= audio.size(1))
          if spect.size(2) > audio.size(1):
              spect = spect[:, :, :audio.size(1)]
  
          spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
          spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
  
          audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
          speaker_emb = speaker_emb[:, :, None].repeat(1, 1, audio.size(2))
          output_audio = []
          log_s_list = []
          log_det_W_list = []
  
          for k in range(self.n_flows):
              if k % self.n_early_every == 0 and k > 0:
                  output_audio.append(audio[:, :self.n_early_size, :])
                  audio = audio[:, self.n_early_size:, :]
  
              audio, log_det_W = self.convinv[k](audio)
              log_det_W_list.append(log_det_W)
  
              n_half = int(audio.size(1)/2)
              audio_0 = audio[:, :n_half, :]
              audio_1 = audio[:, n_half:, :]
  
              output = self.WN[k]((audio_0, spect, speaker_emb))
              log_s = output[:, n_half:, :]
              b = output[:, :n_half, :]
              audio_1 = torch.exp(log_s)*audio_1 + b
              log_s_list.append(log_s)
  
              audio = torch.cat([audio_0, audio_1], 1)
  
          output_audio.append(audio)
          return torch.cat(output_audio, 1), log_s_list, log_det_W_list
  
      def infer(self, spect, sigma=1.0, speaker_emb=None):
          spect = self.upsample(spect)
          # trim conv artifacts. maybe pad spec to kernel multiple
          time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
          spect = spect[:, :, :-time_cutoff]
  
          spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
          spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
  
          if spect.type() == 'torch.cuda.HalfTensor':
              audio = torch.cuda.HalfTensor(spect.size(0),
                                            self.n_remaining_channels,
                                            spect.size(2)).normal_()
          else:
              audio = torch.cuda.FloatTensor(spect.size(0),
                                             self.n_remaining_channels,
                                             spect.size(2)).normal_()
  
          audio = torch.autograd.Variable(sigma*audio)
          speaker_emb = speaker_emb[:, :, None].repeat(1, 1, audio.size(2))
  
          for k in reversed(range(self.n_flows)):
              n_half = int(audio.size(1)/2)
              audio_0 = audio[:, :n_half, :]
              audio_1 = audio[:, n_half:, :]
  
              output = self.WN[k]((audio_0, spect, speaker_emb))  # forward?
  
              s = output[:, n_half:, :]
              b = output[:, :n_half, :]
              audio_1 = (audio_1 - b)/torch.exp(s)
              audio = torch.cat([audio_0, audio_1], 1)
  
              audio = self.convinv[k](audio, reverse=True)
  
              if k % self.n_early_every == 0 and k > 0:
                  if spect.type() == 'torch.cuda.HalfTensor':
                      z = torch.cuda.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
                  else:
                      z = torch.cuda.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
                  audio = torch.cat((sigma*z, audio), 1)
  
          audio = audio.permute(0, 2, 1).contiguous().view(audio.size(0), -1).data
          return audio
  
      @staticmethod
      def remove_weightnorm(model):
          waveglow = model
          for WN in waveglow.WN:
              WN.start = torch.nn.utils.remove_weight_norm(WN.start)
              WN.in_layers = remove(WN.in_layers)
              WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer)
              WN.speaker_layer = torch.nn.utils.remove_weight_norm(WN.speaker_layer)
              WN.res_skip_layers = remove(WN.res_skip_layers)
          return waveglow
  
  
  def remove(conv_list):
      new_conv_list = torch.nn.ModuleList()
      for old_conv in conv_list:
          old_conv = torch.nn.utils.remove_weight_norm(old_conv)
          new_conv_list.append(old_conv)
      return new_conv_list
  