content: import torch
  import torch.nn as nn
  
  from taming.modules.losses.vqperceptual import *  # TODO: taming dependency yes/no?
  
  
  class LPIPSWithDiscriminator(nn.Module):
      def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
                   disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
                   perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
                   disc_loss="hinge"):
  
          super().__init__()
          assert disc_loss in ["hinge", "vanilla"]
          self.kl_weight = kl_weight
          self.pixel_weight = pixelloss_weight
          self.perceptual_loss = LPIPS().eval()
          self.perceptual_weight = perceptual_weight
          # output log variance
          self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
  
          self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
                                                   n_layers=disc_num_layers,
                                                   use_actnorm=use_actnorm
                                                   ).apply(weights_init)
          self.discriminator_iter_start = disc_start
          self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
          self.disc_factor = disc_factor
          self.discriminator_weight = disc_weight
          self.disc_conditional = disc_conditional
  
      def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
          if last_layer is not None:
              nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
              g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
          else:
              nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
              g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
  
          d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
          d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
          d_weight = d_weight * self.discriminator_weight
          return d_weight
  
      def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
                  global_step, last_layer=None, cond=None, split="train",
                  weights=None):
          rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
          if self.perceptual_weight > 0:
              p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
              rec_loss = rec_loss + self.perceptual_weight * p_loss
  
          nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
          weighted_nll_loss = nll_loss
          if weights is not None:
              weighted_nll_loss = weights*nll_loss
          weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
          nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
          kl_loss = posteriors.kl()
          kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
  
          # now the GAN part
          if optimizer_idx == 0:
              # generator update
              if cond is None:
                  assert not self.disc_conditional
                  logits_fake = self.discriminator(reconstructions.contiguous())
              else:
                  assert self.disc_conditional
                  logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
              g_loss = -torch.mean(logits_fake)
  
              if self.disc_factor > 0.0:
                  try:
                      d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
                  except RuntimeError:
                      assert not self.training
                      d_weight = torch.tensor(0.0)
              else:
                  d_weight = torch.tensor(0.0)
  
              disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
              loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
  
              log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
                     "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
                     "{}/rec_loss".format(split): rec_loss.detach().mean(),
                     "{}/d_weight".format(split): d_weight.detach(),
                     "{}/disc_factor".format(split): torch.tensor(disc_factor),
                     "{}/g_loss".format(split): g_loss.detach().mean(),
                     }
              return loss, log
  
          if optimizer_idx == 1:
              # second pass for discriminator update
              if cond is None:
                  logits_real = self.discriminator(inputs.contiguous().detach())
                  logits_fake = self.discriminator(reconstructions.contiguous().detach())
              else:
                  logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
                  logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
  
              disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
              d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
  
              log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
                     "{}/logits_real".format(split): logits_real.detach().mean(),
                     "{}/logits_fake".format(split): logits_fake.detach().mean()
                     }
              return d_loss, log
  
  