content: # pytorch_diffusion + derived encoder decoder
  import math
  import torch
  import torch.nn as nn
  import numpy as np
  from einops import rearrange
  from typing import Optional, Any
  
  from ..attention import MemoryEfficientCrossAttention
  
  try:
      import xformers
      import xformers.ops
  
      XFORMERS_IS_AVAILBLE = True
  except:
      XFORMERS_IS_AVAILBLE = False
      print("No module 'xformers'. Proceeding without it.")
  
  
  def get_timestep_embedding(timesteps, embedding_dim):
      """
      This matches the implementation in Denoising Diffusion Probabilistic Models:
      From Fairseq.
      Build sinusoidal embeddings.
      This matches the implementation in tensor2tensor, but differs slightly
      from the description in Section 3.5 of "Attention Is All You Need".
      """
      assert len(timesteps.shape) == 1
  
      half_dim = embedding_dim // 2
      emb = math.log(10000) / (half_dim - 1)
      emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
      emb = emb.to(device=timesteps.device)
      emb = timesteps.float()[:, None] * emb[None, :]
      emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
      if embedding_dim % 2 == 1:  # zero pad
          emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
      return emb
  
  
  def nonlinearity(x):
      # swish
      return x * torch.sigmoid(x)
  
  
  def Normalize(in_channels, num_groups=32):
      return torch.nn.GroupNorm(
          num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
      )
  
  
  class Upsample(nn.Module):
      def __init__(self, in_channels, with_conv):
          super().__init__()
          self.with_conv = with_conv
          if self.with_conv:
              self.conv = torch.nn.Conv2d(
                  in_channels, in_channels, kernel_size=3, stride=1, padding=1
              )
  
      def forward(self, x):
          x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
          if self.with_conv:
              x = self.conv(x)
          return x
  
  
  class Downsample(nn.Module):
      def __init__(self, in_channels, with_conv):
          super().__init__()
          self.with_conv = with_conv
          if self.with_conv:
              # no asymmetric padding in torch conv, must do it ourselves
              self.conv = torch.nn.Conv2d(
                  in_channels, in_channels, kernel_size=3, stride=2, padding=0
              )
  
      def forward(self, x):
          if self.with_conv:
              pad = (0, 1, 0, 1)
              x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
              x = self.conv(x)
          else:
              x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
          return x
  
  
  class ResnetBlock(nn.Module):
      def __init__(
          self,
          *,
          in_channels,
          out_channels=None,
          conv_shortcut=False,
          dropout,
          temb_channels=512,
      ):
          super().__init__()
          self.in_channels = in_channels
          out_channels = in_channels if out_channels is None else out_channels
          self.out_channels = out_channels
          self.use_conv_shortcut = conv_shortcut
  
          self.norm1 = Normalize(in_channels)
          self.conv1 = torch.nn.Conv2d(
              in_channels, out_channels, kernel_size=3, stride=1, padding=1
          )
          if temb_channels > 0:
              self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
          self.norm2 = Normalize(out_channels)
          self.dropout = torch.nn.Dropout(dropout)
          self.conv2 = torch.nn.Conv2d(
              out_channels, out_channels, kernel_size=3, stride=1, padding=1
          )
          if self.in_channels != self.out_channels:
              if self.use_conv_shortcut:
                  self.conv_shortcut = torch.nn.Conv2d(
                      in_channels, out_channels, kernel_size=3, stride=1, padding=1
                  )
              else:
                  self.nin_shortcut = torch.nn.Conv2d(
                      in_channels, out_channels, kernel_size=1, stride=1, padding=0
                  )
  
      def forward(self, x, temb):
          h = x
          h = self.norm1(h)
          h = nonlinearity(h)
          h = self.conv1(h)
  
          if temb is not None:
              h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
  
          h = self.norm2(h)
          h = nonlinearity(h)
          h = self.dropout(h)
          h = self.conv2(h)
  
          if self.in_channels != self.out_channels:
              if self.use_conv_shortcut:
                  x = self.conv_shortcut(x)
              else:
                  x = self.nin_shortcut(x)
  
          return x + h
  
  
  class AttnBlock(nn.Module):
      def __init__(self, in_channels):
          super().__init__()
          self.in_channels = in_channels
  
          self.norm = Normalize(in_channels)
          self.q = torch.nn.Conv2d(
              in_channels, in_channels, kernel_size=1, stride=1, padding=0
          )
          self.k = torch.nn.Conv2d(
              in_channels, in_channels, kernel_size=1, stride=1, padding=0
          )
          self.v = torch.nn.Conv2d(
              in_channels, in_channels, kernel_size=1, stride=1, padding=0
          )
          self.proj_out = torch.nn.Conv2d(
              in_channels, in_channels, kernel_size=1, stride=1, padding=0
          )
  
      def forward(self, x):
          h_ = x
          h_ = self.norm(h_)
          q = self.q(h_)
          k = self.k(h_)
          v = self.v(h_)
  
          # compute attention
          b, c, h, w = q.shape
          q = q.reshape(b, c, h * w)
          q = q.permute(0, 2, 1)  # b,hw,c
          k = k.reshape(b, c, h * w)  # b,c,hw
          w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
          w_ = w_ * (int(c) ** (-0.5))
          w_ = torch.nn.functional.softmax(w_, dim=2)
  
          # attend to values
          v = v.reshape(b, c, h * w)
          w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
          h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
          h_ = h_.reshape(b, c, h, w)
  
          h_ = self.proj_out(h_)
  
          return x + h_
  
  
  class MemoryEfficientAttnBlock(nn.Module):
      """
      Uses xformers efficient implementation,
      see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
      Note: this is a single-head self-attention operation
      """
  
      #
      def __init__(self, in_channels):
          super().__init__()
          self.in_channels = in_channels
  
          self.norm = Normalize(in_channels)
          self.q = torch.nn.Conv2d(
              in_channels, in_channels, kernel_size=1, stride=1, padding=0
          )
          self.k = torch.nn.Conv2d(
              in_channels, in_channels, kernel_size=1, stride=1, padding=0
          )
          self.v = torch.nn.Conv2d(
              in_channels, in_channels, kernel_size=1, stride=1, padding=0
          )
          self.proj_out = torch.nn.Conv2d(
              in_channels, in_channels, kernel_size=1, stride=1, padding=0
          )
          self.attention_op: Optional[Any] = None
  
      def forward(self, x):
          h_ = x
          h_ = self.norm(h_)
          q = self.q(h_)
          k = self.k(h_)
          v = self.v(h_)
  
          # compute attention
          B, C, H, W = q.shape
          q, k, v = map(lambda x: rearrange(x, "b c h w -> b (h w) c"), (q, k, v))
  
          q, k, v = map(
              lambda t: t.unsqueeze(3)
              .reshape(B, t.shape[1], 1, C)
              .permute(0, 2, 1, 3)
              .reshape(B * 1, t.shape[1], C)
              .contiguous(),
              (q, k, v),
          )
          out = xformers.ops.memory_efficient_attention(
              q, k, v, attn_bias=None, op=self.attention_op
          )
  
          out = (
              out.unsqueeze(0)
              .reshape(B, 1, out.shape[1], C)
              .permute(0, 2, 1, 3)
              .reshape(B, out.shape[1], C)
          )
          out = rearrange(out, "b (h w) c -> b c h w", b=B, h=H, w=W, c=C)
          out = self.proj_out(out)
          return x + out
  
  
  class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
      def forward(self, x, context=None, mask=None):
          b, c, h, w = x.shape
          x = rearrange(x, "b c h w -> b (h w) c")
          out = super().forward(x, context=context, mask=mask)
          out = rearrange(out, "b (h w) c -> b c h w", h=h, w=w, c=c)
          return x + out
  
  
  def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
      assert attn_type in [
          "vanilla",
          "vanilla-xformers",
          "memory-efficient-cross-attn",
          "linear",
          "none",
      ], f"attn_type {attn_type} unknown"
      if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
          attn_type = "vanilla-xformers"
      print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
      if attn_type == "vanilla":
          assert attn_kwargs is None
          return AttnBlock(in_channels)
      elif attn_type == "vanilla-xformers":
          print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
          return MemoryEfficientAttnBlock(in_channels)
      elif type == "memory-efficient-cross-attn":
          attn_kwargs["query_dim"] = in_channels
          return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
      elif attn_type == "none":
          return nn.Identity(in_channels)
      else:
          raise NotImplementedError()
  
  
  class Model(nn.Module):
      def __init__(
          self,
          *,
          ch,
          out_ch,
          ch_mult=(1, 2, 4, 8),
          num_res_blocks,
          attn_resolutions,
          dropout=0.0,
          resamp_with_conv=True,
          in_channels,
          resolution,
          use_timestep=True,
          use_linear_attn=False,
          attn_type="vanilla",
      ):
          super().__init__()
          if use_linear_attn:
              attn_type = "linear"
          self.ch = ch
          self.temb_ch = self.ch * 4
          self.num_resolutions = len(ch_mult)
          self.num_res_blocks = num_res_blocks
          self.resolution = resolution
          self.in_channels = in_channels
  
          self.use_timestep = use_timestep
          if self.use_timestep:
              # timestep embedding
              self.temb = nn.Module()
              self.temb.dense = nn.ModuleList(
                  [
                      torch.nn.Linear(self.ch, self.temb_ch),
                      torch.nn.Linear(self.temb_ch, self.temb_ch),
                  ]
              )
  
          # downsampling
          self.conv_in = torch.nn.Conv2d(
              in_channels, self.ch, kernel_size=3, stride=1, padding=1
          )
  
          curr_res = resolution
          in_ch_mult = (1,) + tuple(ch_mult)
          self.down = nn.ModuleList()
          for i_level in range(self.num_resolutions):
              block = nn.ModuleList()
              attn = nn.ModuleList()
              block_in = ch * in_ch_mult[i_level]
              block_out = ch * ch_mult[i_level]
              for i_block in range(self.num_res_blocks):
                  block.append(
                      ResnetBlock(
                          in_channels=block_in,
                          out_channels=block_out,
                          temb_channels=self.temb_ch,
                          dropout=dropout,
                      )
                  )
                  block_in = block_out
                  if curr_res in attn_resolutions:
                      attn.append(make_attn(block_in, attn_type=attn_type))
              down = nn.Module()
              down.block = block
              down.attn = attn
              if i_level != self.num_resolutions - 1:
                  down.downsample = Downsample(block_in, resamp_with_conv)
                  curr_res = curr_res // 2
              self.down.append(down)
  
          # middle
          self.mid = nn.Module()
          self.mid.block_1 = ResnetBlock(
              in_channels=block_in,
              out_channels=block_in,
              temb_channels=self.temb_ch,
              dropout=dropout,
          )
          self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
          self.mid.block_2 = ResnetBlock(
              in_channels=block_in,
              out_channels=block_in,
              temb_channels=self.temb_ch,
              dropout=dropout,
          )
  
          # upsampling
          self.up = nn.ModuleList()
          for i_level in reversed(range(self.num_resolutions)):
              block = nn.ModuleList()
              attn = nn.ModuleList()
              block_out = ch * ch_mult[i_level]
              skip_in = ch * ch_mult[i_level]
              for i_block in range(self.num_res_blocks + 1):
                  if i_block == self.num_res_blocks:
                      skip_in = ch * in_ch_mult[i_level]
                  block.append(
                      ResnetBlock(
                          in_channels=block_in + skip_in,
                          out_channels=block_out,
                          temb_channels=self.temb_ch,
                          dropout=dropout,
                      )
                  )
                  block_in = block_out
                  if curr_res in attn_resolutions:
                      attn.append(make_attn(block_in, attn_type=attn_type))
              up = nn.Module()
              up.block = block
              up.attn = attn
              if i_level != 0:
                  up.upsample = Upsample(block_in, resamp_with_conv)
                  curr_res = curr_res * 2
              self.up.insert(0, up)  # prepend to get consistent order
  
          # end
          self.norm_out = Normalize(block_in)
          self.conv_out = torch.nn.Conv2d(
              block_in, out_ch, kernel_size=3, stride=1, padding=1
          )
  
      def forward(self, x, t=None, context=None):
          # assert x.shape[2] == x.shape[3] == self.resolution
          if context is not None:
              # assume aligned context, cat along channel axis
              x = torch.cat((x, context), dim=1)
          if self.use_timestep:
              # timestep embedding
              assert t is not None
              temb = get_timestep_embedding(t, self.ch)
              temb = self.temb.dense[0](temb)
              temb = nonlinearity(temb)
              temb = self.temb.dense[1](temb)
          else:
              temb = None
  
          # downsampling
          hs = [self.conv_in(x)]
          for i_level in range(self.num_resolutions):
              for i_block in range(self.num_res_blocks):
                  h = self.down[i_level].block[i_block](hs[-1], temb)
                  if len(self.down[i_level].attn) > 0:
                      h = self.down[i_level].attn[i_block](h)
                  hs.append(h)
              if i_level != self.num_resolutions - 1:
                  hs.append(self.down[i_level].downsample(hs[-1]))
  
          # middle
          h = hs[-1]
          h = self.mid.block_1(h, temb)
          h = self.mid.attn_1(h)
          h = self.mid.block_2(h, temb)
  
          # upsampling
          for i_level in reversed(range(self.num_resolutions)):
              for i_block in range(self.num_res_blocks + 1):
                  h = self.up[i_level].block[i_block](
                      torch.cat([h, hs.pop()], dim=1), temb
                  )
                  if len(self.up[i_level].attn) > 0:
                      h = self.up[i_level].attn[i_block](h)
              if i_level != 0:
                  h = self.up[i_level].upsample(h)
  
          # end
          h = self.norm_out(h)
          h = nonlinearity(h)
          h = self.conv_out(h)
          return h
  
      def get_last_layer(self):
          return self.conv_out.weight
  
  
  class Encoder(nn.Module):
      def __init__(
          self,
          *,
          ch,
          out_ch,
          ch_mult=(1, 2, 4, 8),
          num_res_blocks,
          attn_resolutions,
          dropout=0.0,
          resamp_with_conv=True,
          in_channels,
          resolution,
          z_channels,
          double_z=True,
          use_linear_attn=False,
          attn_type="vanilla",
          **ignore_kwargs,
      ):
          super().__init__()
          if use_linear_attn:
              attn_type = "linear"
          self.ch = ch
          self.temb_ch = 0
          self.num_resolutions = len(ch_mult)
          self.num_res_blocks = num_res_blocks
          self.resolution = resolution
          self.in_channels = in_channels
  
          # downsampling
          self.conv_in = torch.nn.Conv2d(
              in_channels, self.ch, kernel_size=3, stride=1, padding=1
          )
  
          curr_res = resolution
          in_ch_mult = (1,) + tuple(ch_mult)
          self.in_ch_mult = in_ch_mult
          self.down = nn.ModuleList()
          for i_level in range(self.num_resolutions):
              block = nn.ModuleList()
              attn = nn.ModuleList()
              block_in = ch * in_ch_mult[i_level]
              block_out = ch * ch_mult[i_level]
              for i_block in range(self.num_res_blocks):
                  block.append(
                      ResnetBlock(
                          in_channels=block_in,
                          out_channels=block_out,
                          temb_channels=self.temb_ch,
                          dropout=dropout,
                      )
                  )
                  block_in = block_out
                  if curr_res in attn_resolutions:
                      attn.append(make_attn(block_in, attn_type=attn_type))
              down = nn.Module()
              down.block = block
              down.attn = attn
              if i_level != self.num_resolutions - 1:
                  down.downsample = Downsample(block_in, resamp_with_conv)
                  curr_res = curr_res // 2
              self.down.append(down)
  
          # middle
          self.mid = nn.Module()
          self.mid.block_1 = ResnetBlock(
              in_channels=block_in,
              out_channels=block_in,
              temb_channels=self.temb_ch,
              dropout=dropout,
          )
          self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
          self.mid.block_2 = ResnetBlock(
              in_channels=block_in,
              out_channels=block_in,
              temb_channels=self.temb_ch,
              dropout=dropout,
          )
  
          # end
          self.norm_out = Normalize(block_in)
          self.conv_out = torch.nn.Conv2d(
              block_in,
              2 * z_channels if double_z else z_channels,
              kernel_size=3,
              stride=1,
              padding=1,
          )
  
      def forward(self, x):
          # timestep embedding
          temb = None
  
          # downsampling
          hs = [self.conv_in(x)]
          for i_level in range(self.num_resolutions):
              for i_block in range(self.num_res_blocks):
                  h = self.down[i_level].block[i_block](hs[-1], temb)
                  if len(self.down[i_level].attn) > 0:
                      h = self.down[i_level].attn[i_block](h)
                  hs.append(h)
              if i_level != self.num_resolutions - 1:
                  hs.append(self.down[i_level].downsample(hs[-1]))
  
          # middle
          h = hs[-1]
          h = self.mid.block_1(h, temb)
          h = self.mid.attn_1(h)
          h = self.mid.block_2(h, temb)
  
          # end
          h = self.norm_out(h)
          h = nonlinearity(h)
          h = self.conv_out(h)
          return h
  
  
  class Decoder(nn.Module):
      def __init__(
          self,
          *,
          ch,
          out_ch,
          ch_mult=(1, 2, 4, 8),
          num_res_blocks,
          attn_resolutions,
          dropout=0.0,
          resamp_with_conv=True,
          in_channels,
          resolution,
          z_channels,
          give_pre_end=False,
          tanh_out=False,
          use_linear_attn=False,
          attn_type="vanilla",
          **ignorekwargs,
      ):
          super().__init__()
          if use_linear_attn:
              attn_type = "linear"
          self.ch = ch
          self.temb_ch = 0
          self.num_resolutions = len(ch_mult)
          self.num_res_blocks = num_res_blocks
          self.resolution = resolution
          self.in_channels = in_channels
          self.give_pre_end = give_pre_end
          self.tanh_out = tanh_out
  
          # compute in_ch_mult, block_in and curr_res at lowest res
          in_ch_mult = (1,) + tuple(ch_mult)
          block_in = ch * ch_mult[self.num_resolutions - 1]
          curr_res = resolution // 2 ** (self.num_resolutions - 1)
          self.z_shape = (1, z_channels, curr_res, curr_res)
          print(
              "Working with z of shape {} = {} dimensions.".format(
                  self.z_shape, np.prod(self.z_shape)
              )
          )
  
          # z to block_in
          self.conv_in = torch.nn.Conv2d(
              z_channels, block_in, kernel_size=3, stride=1, padding=1
          )
  
          # middle
          self.mid = nn.Module()
          self.mid.block_1 = ResnetBlock(
              in_channels=block_in,
              out_channels=block_in,
              temb_channels=self.temb_ch,
              dropout=dropout,
          )
          self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
          self.mid.block_2 = ResnetBlock(
              in_channels=block_in,
              out_channels=block_in,
              temb_channels=self.temb_ch,
              dropout=dropout,
          )
  
          # upsampling
          self.up = nn.ModuleList()
          for i_level in reversed(range(self.num_resolutions)):
              block = nn.ModuleList()
              attn = nn.ModuleList()
              block_out = ch * ch_mult[i_level]
              for i_block in range(self.num_res_blocks + 1):
                  block.append(
                      ResnetBlock(
                          in_channels=block_in,
                          out_channels=block_out,
                          temb_channels=self.temb_ch,
                          dropout=dropout,
                      )
                  )
                  block_in = block_out
                  if curr_res in attn_resolutions:
                      attn.append(make_attn(block_in, attn_type=attn_type))
              up = nn.Module()
              up.block = block
              up.attn = attn
              if i_level != 0:
                  up.upsample = Upsample(block_in, resamp_with_conv)
                  curr_res = curr_res * 2
              self.up.insert(0, up)  # prepend to get consistent order
  
          # end
          self.norm_out = Normalize(block_in)
          self.conv_out = torch.nn.Conv2d(
              block_in, out_ch, kernel_size=3, stride=1, padding=1
          )
  
      def forward(self, z):
          # assert z.shape[1:] == self.z_shape[1:]
          self.last_z_shape = z.shape
  
          # timestep embedding
          temb = None
  
          # z to block_in
          h = self.conv_in(z)
  
          # middle
          h = self.mid.block_1(h, temb)
          h = self.mid.attn_1(h)
          h = self.mid.block_2(h, temb)
  
          # upsampling
          for i_level in reversed(range(self.num_resolutions)):
              for i_block in range(self.num_res_blocks + 1):
                  h = self.up[i_level].block[i_block](h, temb)
                  if len(self.up[i_level].attn) > 0:
                      h = self.up[i_level].attn[i_block](h)
              if i_level != 0:
                  h = self.up[i_level].upsample(h)
  
          # end
          if self.give_pre_end:
              return h
  
          h = self.norm_out(h)
          h = nonlinearity(h)
          h = self.conv_out(h)
          if self.tanh_out:
              h = torch.tanh(h)
          return h
  
  
  class SimpleDecoder(nn.Module):
      def __init__(self, in_channels, out_channels, *args, **kwargs):
          super().__init__()
          self.model = nn.ModuleList(
              [
                  nn.Conv2d(in_channels, in_channels, 1),
                  ResnetBlock(
                      in_channels=in_channels,
                      out_channels=2 * in_channels,
                      temb_channels=0,
                      dropout=0.0,
                  ),
                  ResnetBlock(
                      in_channels=2 * in_channels,
                      out_channels=4 * in_channels,
                      temb_channels=0,
                      dropout=0.0,
                  ),
                  ResnetBlock(
                      in_channels=4 * in_channels,
                      out_channels=2 * in_channels,
                      temb_channels=0,
                      dropout=0.0,
                  ),
                  nn.Conv2d(2 * in_channels, in_channels, 1),
                  Upsample(in_channels, with_conv=True),
              ]
          )
          # end
          self.norm_out = Normalize(in_channels)
          self.conv_out = torch.nn.Conv2d(
              in_channels, out_channels, kernel_size=3, stride=1, padding=1
          )
  
      def forward(self, x):
          for i, layer in enumerate(self.model):
              if i in [1, 2, 3]:
                  x = layer(x, None)
              else:
                  x = layer(x)
  
          h = self.norm_out(x)
          h = nonlinearity(h)
          x = self.conv_out(h)
          return x
  
  
  class UpsampleDecoder(nn.Module):
      def __init__(
          self,
          in_channels,
          out_channels,
          ch,
          num_res_blocks,
          resolution,
          ch_mult=(2, 2),
          dropout=0.0,
      ):
          super().__init__()
          # upsampling
          self.temb_ch = 0
          self.num_resolutions = len(ch_mult)
          self.num_res_blocks = num_res_blocks
          block_in = in_channels
          curr_res = resolution // 2 ** (self.num_resolutions - 1)
          self.res_blocks = nn.ModuleList()
          self.upsample_blocks = nn.ModuleList()
          for i_level in range(self.num_resolutions):
              res_block = []
              block_out = ch * ch_mult[i_level]
              for i_block in range(self.num_res_blocks + 1):
                  res_block.append(
                      ResnetBlock(
                          in_channels=block_in,
                          out_channels=block_out,
                          temb_channels=self.temb_ch,
                          dropout=dropout,
                      )
                  )
                  block_in = block_out
              self.res_blocks.append(nn.ModuleList(res_block))
              if i_level != self.num_resolutions - 1:
                  self.upsample_blocks.append(Upsample(block_in, True))
                  curr_res = curr_res * 2
  
          # end
          self.norm_out = Normalize(block_in)
          self.conv_out = torch.nn.Conv2d(
              block_in, out_channels, kernel_size=3, stride=1, padding=1
          )
  
      def forward(self, x):
          # upsampling
          h = x
          for k, i_level in enumerate(range(self.num_resolutions)):
              for i_block in range(self.num_res_blocks + 1):
                  h = self.res_blocks[i_level][i_block](h, None)
              if i_level != self.num_resolutions - 1:
                  h = self.upsample_blocks[k](h)
          h = self.norm_out(h)
          h = nonlinearity(h)
          h = self.conv_out(h)
          return h
  
  
  class LatentRescaler(nn.Module):
      def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
          super().__init__()
          # residual block, interpolate, residual block
          self.factor = factor
          self.conv_in = nn.Conv2d(
              in_channels, mid_channels, kernel_size=3, stride=1, padding=1
          )
          self.res_block1 = nn.ModuleList(
              [
                  ResnetBlock(
                      in_channels=mid_channels,
                      out_channels=mid_channels,
                      temb_channels=0,
                      dropout=0.0,
                  )
                  for _ in range(depth)
              ]
          )
          self.attn = AttnBlock(mid_channels)
          self.res_block2 = nn.ModuleList(
              [
                  ResnetBlock(
                      in_channels=mid_channels,
                      out_channels=mid_channels,
                      temb_channels=0,
                      dropout=0.0,
                  )
                  for _ in range(depth)
              ]
          )
  
          self.conv_out = nn.Conv2d(
              mid_channels,
              out_channels,
              kernel_size=1,
          )
  
      def forward(self, x):
          x = self.conv_in(x)
          for block in self.res_block1:
              x = block(x, None)
          x = torch.nn.functional.interpolate(
              x,
              size=(
                  int(round(x.shape[2] * self.factor)),
                  int(round(x.shape[3] * self.factor)),
              ),
          )
          x = self.attn(x)
          for block in self.res_block2:
              x = block(x, None)
          x = self.conv_out(x)
          return x
  
  
  class MergedRescaleEncoder(nn.Module):
      def __init__(
          self,
          in_channels,
          ch,
          resolution,
          out_ch,
          num_res_blocks,
          attn_resolutions,
          dropout=0.0,
          resamp_with_conv=True,
          ch_mult=(1, 2, 4, 8),
          rescale_factor=1.0,
          rescale_module_depth=1,
      ):
          super().__init__()
          intermediate_chn = ch * ch_mult[-1]
          self.encoder = Encoder(
              in_channels=in_channels,
              num_res_blocks=num_res_blocks,
              ch=ch,
              ch_mult=ch_mult,
              z_channels=intermediate_chn,
              double_z=False,
              resolution=resolution,
              attn_resolutions=attn_resolutions,
              dropout=dropout,
              resamp_with_conv=resamp_with_conv,
              out_ch=None,
          )
          self.rescaler = LatentRescaler(
              factor=rescale_factor,
              in_channels=intermediate_chn,
              mid_channels=intermediate_chn,
              out_channels=out_ch,
              depth=rescale_module_depth,
          )
  
      def forward(self, x):
          x = self.encoder(x)
          x = self.rescaler(x)
          return x
  
  
  class MergedRescaleDecoder(nn.Module):
      def __init__(
          self,
          z_channels,
          out_ch,
          resolution,
          num_res_blocks,
          attn_resolutions,
          ch,
          ch_mult=(1, 2, 4, 8),
          dropout=0.0,
          resamp_with_conv=True,
          rescale_factor=1.0,
          rescale_module_depth=1,
      ):
          super().__init__()
          tmp_chn = z_channels * ch_mult[-1]
          self.decoder = Decoder(
              out_ch=out_ch,
              z_channels=tmp_chn,
              attn_resolutions=attn_resolutions,
              dropout=dropout,
              resamp_with_conv=resamp_with_conv,
              in_channels=None,
              num_res_blocks=num_res_blocks,
              ch_mult=ch_mult,
              resolution=resolution,
              ch=ch,
          )
          self.rescaler = LatentRescaler(
              factor=rescale_factor,
              in_channels=z_channels,
              mid_channels=tmp_chn,
              out_channels=tmp_chn,
              depth=rescale_module_depth,
          )
  
      def forward(self, x):
          x = self.rescaler(x)
          x = self.decoder(x)
          return x
  
  
  class Upsampler(nn.Module):
      def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
          super().__init__()
          assert out_size >= in_size
          num_blocks = int(np.log2(out_size // in_size)) + 1
          factor_up = 1.0 + (out_size % in_size)
          print(
              f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}"
          )
          self.rescaler = LatentRescaler(
              factor=factor_up,
              in_channels=in_channels,
              mid_channels=2 * in_channels,
              out_channels=in_channels,
          )
          self.decoder = Decoder(
              out_ch=out_channels,
              resolution=out_size,
              z_channels=in_channels,
              num_res_blocks=2,
              attn_resolutions=[],
              in_channels=None,
              ch=in_channels,
              ch_mult=[ch_mult for _ in range(num_blocks)],
          )
  
      def forward(self, x):
          x = self.rescaler(x)
          x = self.decoder(x)
          return x
  
  
  class Resize(nn.Module):
      def __init__(self, in_channels=None, learned=False, mode="bilinear"):
          super().__init__()
          self.with_conv = learned
          self.mode = mode
          if self.with_conv:
              print(
                  f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode"
              )
              raise NotImplementedError()
              assert in_channels is not None
              # no asymmetric padding in torch conv, must do it ourselves
              self.conv = torch.nn.Conv2d(
                  in_channels, in_channels, kernel_size=4, stride=2, padding=1
              )
  
      def forward(self, x, scale_factor=1.0):
          if scale_factor == 1.0:
              return x
          else:
              x = torch.nn.functional.interpolate(
                  x, mode=self.mode, align_corners=False, scale_factor=scale_factor
              )
          return x
  