content: import torch
  from torch import nn
  import torch.nn.functional as F
  from einops import repeat
  
  from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
  from taming.modules.losses.lpips import LPIPS
  from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
  
  
  def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
      assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
      loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
      loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
      loss_real = (weights * loss_real).sum() / weights.sum()
      loss_fake = (weights * loss_fake).sum() / weights.sum()
      d_loss = 0.5 * (loss_real + loss_fake)
      return d_loss
  
  def adopt_weight(weight, global_step, threshold=0, value=0.):
      if global_step < threshold:
          weight = value
      return weight
  
  
  def measure_perplexity(predicted_indices, n_embed):
      # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
      # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
      encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
      avg_probs = encodings.mean(0)
      perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
      cluster_use = torch.sum(avg_probs > 0)
      return perplexity, cluster_use
  
  def l1(x, y):
      return torch.abs(x-y)
  
  
  def l2(x, y):
      return torch.pow((x-y), 2)
  
  
  class VQLPIPSWithDiscriminator(nn.Module):
      def __init__(self, disc_start, codebook_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_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
                   pixel_loss="l1"):
          super().__init__()
          assert disc_loss in ["hinge", "vanilla"]
          assert perceptual_loss in ["lpips", "clips", "dists"]
          assert pixel_loss in ["l1", "l2"]
          self.codebook_weight = codebook_weight
          self.pixel_weight = pixelloss_weight
          if perceptual_loss == "lpips":
              print(f"{self.__class__.__name__}: Running with LPIPS.")
              self.perceptual_loss = LPIPS().eval()
          else:
              raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
          self.perceptual_weight = perceptual_weight
  
          if pixel_loss == "l1":
              self.pixel_loss = l1
          else:
              self.pixel_loss = l2
  
          self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
                                                   n_layers=disc_num_layers,
                                                   use_actnorm=use_actnorm,
                                                   ndf=disc_ndf
                                                   ).apply(weights_init)
          self.discriminator_iter_start = disc_start
          if disc_loss == "hinge":
              self.disc_loss = hinge_d_loss
          elif disc_loss == "vanilla":
              self.disc_loss = vanilla_d_loss
          else:
              raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
          print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
          self.disc_factor = disc_factor
          self.discriminator_weight = disc_weight
          self.disc_conditional = disc_conditional
          self.n_classes = n_classes
  
      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, codebook_loss, inputs, reconstructions, optimizer_idx,
                  global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
          if not exists(codebook_loss):
              codebook_loss = torch.tensor([0.]).to(inputs.device)
          #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
          rec_loss = self.pixel_loss(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
          else:
              p_loss = torch.tensor([0.0])
  
          nll_loss = rec_loss
          #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
          nll_loss = torch.mean(nll_loss)
  
          # 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)
  
              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)
  
              disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
              loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
  
              log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
                     "{}/quant_loss".format(split): codebook_loss.detach().mean(),
                     "{}/nll_loss".format(split): nll_loss.detach().mean(),
                     "{}/rec_loss".format(split): rec_loss.detach().mean(),
                     "{}/p_loss".format(split): p_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(),
                     }
              if predicted_indices is not None:
                  assert self.n_classes is not None
                  with torch.no_grad():
                      perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
                  log[f"{split}/perplexity"] = perplexity
                  log[f"{split}/cluster_usage"] = cluster_usage
              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
  