text: # pytorch_diffusion + derived encoder decoder
  import math
  import torch
  import torch.nn as nn
  import numpy as np
  from einops import rearrange
  
  from ldm.util import instantiate_from_config
  from ldm.modules.attention import LinearAttention
  
  verbose = False
  
  
  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 LinAttnBlock(LinearAttention):
      """to match AttnBlock usage"""
      def __init__(self, in_channels):
          super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
  
  
  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_
  
  
  def make_attn(in_channels, attn_type="vanilla"):
      assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
      if verbose:
          print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
      if attn_type == "vanilla":
          return AttnBlock(in_channels)
      elif attn_type == "none":
          return nn.Identity(in_channels)
      else:
          return LinAttnBlock(in_channels)
  
  
  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)
          if verbose:
              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.+ (out_size % in_size)
          if verbose:
              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:
              if verbose:
                  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
  
  class FirstStagePostProcessor(nn.Module):
  
      def __init__(self, ch_mult:list, in_channels,
                   pretrained_model:nn.Module=None,
                   reshape=False,
                   n_channels=None,
                   dropout=0.,
                   pretrained_config=None):
          super().__init__()
          if pretrained_config is None:
              assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
              self.pretrained_model = pretrained_model
          else:
              assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
              self.instantiate_pretrained(pretrained_config)
  
          self.do_reshape = reshape
  
          if n_channels is None:
              n_channels = self.pretrained_model.encoder.ch
  
          self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
          self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
                              stride=1,padding=1)
  
          blocks = []
          downs = []
          ch_in = n_channels
          for m in ch_mult:
              blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
              ch_in = m * n_channels
              downs.append(Downsample(ch_in, with_conv=False))
  
          self.model = nn.ModuleList(blocks)
          self.downsampler = nn.ModuleList(downs)
  
  
      def instantiate_pretrained(self, config):
          model = instantiate_from_config(config)
          self.pretrained_model = model.eval()
          # self.pretrained_model.train = False
          for param in self.pretrained_model.parameters():
              param.requires_grad = False
  
  
      @torch.no_grad()
      def encode_with_pretrained(self,x):
          c = self.pretrained_model.encode(x)
          if isinstance(c, DiagonalGaussianDistribution):
              c = c.mode()
          return  c
  
      def forward(self,x):
          z_fs = self.encode_with_pretrained(x)
          z = self.proj_norm(z_fs)
          z = self.proj(z)
          z = nonlinearity(z)
  
          for submodel, downmodel in zip(self.model,self.downsampler):
              z = submodel(z,temb=None)
              z = downmodel(z)
  
          if self.do_reshape:
              z = rearrange(z,'b c h w -> b (h w) c')
          return z
  
  