text: import torch
  from torch import nn
  from torch.nn import functional as F
  
  import distributed as dist_fn
  
  
  # Copyright 2018 The Sonnet Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  
  
  # Borrowed from https://github.com/deepmind/sonnet and ported it to PyTorch
  
  
  class Quantize(nn.Module):
      def __init__(self, dim, n_embed, decay=0.99, eps=1e-5):
          super().__init__()
  
          self.dim = dim
          self.n_embed = n_embed
          self.decay = decay
          self.eps = eps
  
          embed = torch.randn(dim, n_embed, dtype=torch.float32)
          self.register_buffer("embed", embed)
          self.register_buffer("cluster_size", torch.zeros(n_embed, dtype=torch.float32))
          self.register_buffer("embed_avg", embed.clone())
  
      @torch.cuda.amp.autocast(enabled=False)
      def forward(self, input):
          flatten = input.reshape(-1, self.dim).float()
          dist = (
              flatten.pow(2).sum(1, keepdim=True)
              - 2 * flatten @ self.embed
              + self.embed.pow(2).sum(0, keepdim=True)
          )
          _, embed_ind = (-dist).max(1)
          embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype)
          embed_ind = embed_ind.view(*input.shape[:-1])
          quantize = self.embed_code(embed_ind)
  
          if self.training:
              embed_onehot_sum = embed_onehot.sum(0)
              embed_sum = flatten.transpose(0, 1) @ embed_onehot
  
              dist_fn.all_reduce(embed_onehot_sum)
              dist_fn.all_reduce(embed_sum)
  
              self.cluster_size.data.mul_(self.decay).add_(
                  embed_onehot_sum, alpha=1 - self.decay
              )
              self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay)
              n = self.cluster_size.sum()
              cluster_size = (
                  (self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n
              )
              embed_normalized = self.embed_avg / cluster_size.unsqueeze(0)
              self.embed.data.copy_(embed_normalized)
  
          diff = (quantize.detach() - input).pow(2).mean()
          quantize = input + (quantize - input).detach()
  
          return quantize, diff, embed_ind
  
      def embed_code(self, embed_id):
          return F.embedding(embed_id, self.embed.transpose(0, 1))
  
  
  class ResBlock(nn.Module):
      def __init__(self, in_channel, channel):
          super().__init__()
  
          self.conv = nn.Sequential(
              nn.ReLU(),
              nn.Conv2d(in_channel, channel, 3, padding=1),
              nn.ReLU(inplace=True),
              nn.Conv2d(channel, in_channel, 1),
          )
  
      def forward(self, input):
          out = self.conv(input)
          out += input
  
          return out
  
  
  class Encoder(nn.Module):
      def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride):
          super().__init__()
  
          if stride == 4:
              blocks = [
                  nn.Conv2d(in_channel, channel // 2, 4, stride=2, padding=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(channel // 2, channel, 4, stride=2, padding=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(channel, channel, 3, padding=1),
              ]
  
          elif stride == 2:
              blocks = [
                  nn.Conv2d(in_channel, channel // 2, 4, stride=2, padding=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(channel // 2, channel, 3, padding=1),
              ]
  
          for i in range(n_res_block):
              blocks.append(ResBlock(channel, n_res_channel))
  
          blocks.append(nn.ReLU(inplace=True))
  
          self.blocks = nn.Sequential(*blocks)
  
      def forward(self, input):
          return self.blocks(input)
  
  
  class Decoder(nn.Module):
      def __init__(
          self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride
      ):
          super().__init__()
  
          blocks = [nn.Conv2d(in_channel, channel, 3, padding=1)]
  
          for i in range(n_res_block):
              blocks.append(ResBlock(channel, n_res_channel))
  
          blocks.append(nn.ReLU(inplace=True))
  
          if stride == 4:
              blocks.extend(
                  [
                      nn.ConvTranspose2d(channel, channel // 2, 4, stride=2, padding=1),
                      nn.ReLU(inplace=True),
                      nn.ConvTranspose2d(
                          channel // 2, out_channel, 4, stride=2, padding=1
                      ),
                  ]
              )
  
          elif stride == 2:
              blocks.append(
                  nn.ConvTranspose2d(channel, out_channel, 4, stride=2, padding=1)
              )
  
          self.blocks = nn.Sequential(*blocks)
  
      def forward(self, input):
          return self.blocks(input)
  
  
  class VQVAE(nn.Module):
      def __init__(
          self,
          in_channel=3,
          channel=128,
          n_res_block=2,
          n_res_channel=32,
          embed_dim=64,
          n_embed=512,
          decay=0.99,
      ):
          super().__init__()
  
          self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4)
          self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2)
          self.quantize_conv_t = nn.Conv2d(channel, embed_dim, 1)
          self.quantize_t = Quantize(embed_dim, n_embed)
          self.dec_t = Decoder(
              embed_dim, embed_dim, channel, n_res_block, n_res_channel, stride=2
          )
          self.quantize_conv_b = nn.Conv2d(embed_dim + channel, embed_dim, 1)
          self.quantize_b = Quantize(embed_dim, n_embed)
          self.upsample_t = nn.ConvTranspose2d(
              embed_dim, embed_dim, 4, stride=2, padding=1
          )
          self.dec = Decoder(
              embed_dim + embed_dim,
              in_channel,
              channel,
              n_res_block,
              n_res_channel,
              stride=4,
          )
  
      def forward(self, input):
          quant_t, quant_b, diff, _, _ = self.encode(input)
          dec = self.decode(quant_t, quant_b)
  
          return dec, diff
  
      def encode(self, input):
          enc_b = self.enc_b(input)
          enc_t = self.enc_t(enc_b)
  
          quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1)
          quant_t, diff_t, id_t = self.quantize_t(quant_t)
          quant_t = quant_t.permute(0, 3, 1, 2)
          diff_t = diff_t.unsqueeze(0)
  
          dec_t = self.dec_t(quant_t)
          enc_b = torch.cat([dec_t, enc_b], 1)
  
          quant_b = self.quantize_conv_b(enc_b).permute(0, 2, 3, 1)
          quant_b, diff_b, id_b = self.quantize_b(quant_b)
          quant_b = quant_b.permute(0, 3, 1, 2)
          diff_b = diff_b.unsqueeze(0)
  
          return quant_t, quant_b, diff_t + diff_b, id_t, id_b
  
      def decode(self, quant_t, quant_b):
          upsample_t = self.upsample_t(quant_t)
          quant = torch.cat([upsample_t, quant_b], 1)
          dec = self.dec(quant)
  
          return dec
  
      def decode_code(self, code_t, code_b):
          quant_t = self.quantize_t.embed_code(code_t)
          quant_t = quant_t.permute(0, 3, 1, 2)
          quant_b = self.quantize_b.embed_code(code_b)
          quant_b = quant_b.permute(0, 3, 1, 2)
  
          dec = self.decode(quant_t, quant_b)
  
          return dec
  