text: #from https://github.com/CompVis/taming-transformers
  # pytorch_diffusion + derived encoder decoder
  import math
  import torch
  import torch.nn as nn
  import numpy as np
  
  
  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.type_as(timesteps)
      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):
      return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
  
  
  class Upsample(nn.Module):
      def __init__(self, in_channels, with_conv, stride=2):
          super().__init__()
          self.with_conv = with_conv
          self.stride = stride
          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=self.stride, mode="nearest")
          if self.with_conv:
              x = self.conv(x)         
          return x
  
  
  class Downsample(nn.Module):
      def __init__(self, in_channels, with_conv, stride=2):
          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)
              if stride == 4:
                  self.conv_2 = torch.nn.Conv2d(in_channels,
                                          in_channels,
                                          kernel_size=3,
                                          stride=2,
                                          padding=0)                
          self.stride = stride
  
      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)
              if self.stride == 4: 
                  x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
                  x = self.conv_2(x)                
          else:
              x = torch.nn.functional.avg_pool2d(x, kernel_size=self.stride, stride=self.stride)
          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 Encoder(nn.Module):
      def __init__(self, *, hidden_dim, in_channels, ch_mult=(1,2,4,8), num_res_blocks,
                   attn_resolutions, dropout=0.0, resamp_with_conv=True, 
                   resolution, z_channels, double_z=True, use_attn=False, **ignore_kwargs):
          super().__init__()
          self.hidden_dim = hidden_dim
          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.use_attn = use_attn
          # downsampling
          self.conv_in = torch.nn.Conv2d(in_channels,
                                         self.hidden_dim,
                                         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()
              if self.use_attn:
                  attn = nn.ModuleList()
              block_in = hidden_dim*in_ch_mult[i_level]
              block_out = hidden_dim*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 self.use_attn:
                      if curr_res in attn_resolutions:
                          attn.append(AttnBlock(block_in))
              down = nn.Module()
              down.block = block
              if self.use_attn:
                  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)
          if self.use_attn:                               
              self.mid.attn_1 = AttnBlock(block_in)
          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):
          #assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution)
  
          # 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 self.use_attn:
                      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)
          if self.use_attn:
              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, *, hidden_dim, out_channels, 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, use_attn=False, **ignorekwargs):
          super().__init__()
          self.hidden_dim = hidden_dim
          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.use_attn = use_attn
          # compute in_ch_mult, block_in and curr_res at lowest res
          in_ch_mult = (1,)+tuple(ch_mult)
          block_in = hidden_dim*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)
          if self.use_attn:                                       
              self.mid.attn_1 = AttnBlock(block_in)
          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()
              if self.use_attn:        
                  attn = nn.ModuleList()
              block_out = hidden_dim*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 self.use_attn:                
                      if curr_res in attn_resolutions:
                          attn.append(AttnBlock(block_in))
              up = nn.Module()
              up.block = block
              if self.use_attn:           
                  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_channels,
                                          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)
          if self.use_attn:    
              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 self.use_attn:    
                      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)
          return h
  
  