content: # Copyright (c) 2023, Zexin He
  #
  # 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
  #
  #     https://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.
  
  
  import torch
  import torch.nn as nn
  
  
  class BasicTransformerBlock(nn.Module):
      """
      Transformer block that takes in a cross-attention condition and another modulation vector applied to sub-blocks.
      """
      # use attention from torch.nn.MultiHeadAttention
      # Block contains a cross-attention layer, a self-attention layer, and a MLP
      def __init__(
          self, 
          inner_dim: int, 
          cond_dim: int, 
          num_heads: int, 
          eps: float,
          attn_drop: float = 0., 
          attn_bias: bool = False,
          mlp_ratio: float = 4., 
          mlp_drop: float = 0.,
      ):
          super().__init__()
  
          self.norm1 = nn.LayerNorm(inner_dim)
          self.cross_attn = nn.MultiheadAttention(
              embed_dim=inner_dim, num_heads=num_heads, kdim=cond_dim, vdim=cond_dim,
              dropout=attn_drop, bias=attn_bias, batch_first=True)
          self.norm2 = nn.LayerNorm(inner_dim)
          self.self_attn = nn.MultiheadAttention(
              embed_dim=inner_dim, num_heads=num_heads,
              dropout=attn_drop, bias=attn_bias, batch_first=True)
          self.norm3 = nn.LayerNorm(inner_dim)
          self.mlp = nn.Sequential(
              nn.Linear(inner_dim, int(inner_dim * mlp_ratio)),
              nn.GELU(),
              nn.Dropout(mlp_drop),
              nn.Linear(int(inner_dim * mlp_ratio), inner_dim),
              nn.Dropout(mlp_drop),
          )
  
      def forward(self, x, cond):
          # x: [N, L, D]
          # cond: [N, L_cond, D_cond]
          x = x + self.cross_attn(self.norm1(x), cond, cond)[0]
          before_sa = self.norm2(x)
          x = x + self.self_attn(before_sa, before_sa, before_sa)[0]
          x = x + self.mlp(self.norm3(x))
          return x
  
  
  class TriplaneTransformer(nn.Module):
      """
      Transformer with condition that generates a triplane representation.
      
      Reference:
      Timm: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L486
      """
      def __init__(
          self, 
          inner_dim: int, 
          image_feat_dim: int,
          triplane_low_res: int, 
          triplane_high_res: int, 
          triplane_dim: int,
          num_layers: int, 
          num_heads: int,
          eps: float = 1e-6,
      ):
          super().__init__()
  
          # attributes
          self.triplane_low_res = triplane_low_res
          self.triplane_high_res = triplane_high_res
          self.triplane_dim = triplane_dim
  
          # modules
          # initialize pos_embed with 1/sqrt(dim) * N(0, 1)
          self.pos_embed = nn.Parameter(torch.randn(1, 3*triplane_low_res**2, inner_dim) * (1. / inner_dim) ** 0.5)
          self.layers = nn.ModuleList([
              BasicTransformerBlock(
                  inner_dim=inner_dim, cond_dim=image_feat_dim, num_heads=num_heads, eps=eps)
              for _ in range(num_layers)
          ])
          self.norm = nn.LayerNorm(inner_dim, eps=eps)
          self.deconv = nn.ConvTranspose2d(inner_dim, triplane_dim, kernel_size=2, stride=2, padding=0)
  
      def forward(self, image_feats):
          # image_feats: [N, L_cond, D_cond]
  
          N = image_feats.shape[0]
          H = W = self.triplane_low_res
          L = 3 * H * W
  
          x = self.pos_embed.repeat(N, 1, 1)  # [N, L, D]
          for layer in self.layers:
              x = layer(x, image_feats)
          x = self.norm(x)
  
          # separate each plane and apply deconv
          x = x.view(N, 3, H, W, -1)
          x = torch.einsum('nihwd->indhw', x)  # [3, N, D, H, W]
          x = x.contiguous().view(3*N, -1, H, W)  # [3*N, D, H, W]
          x = self.deconv(x)  # [3*N, D', H', W']
          x = x.view(3, N, *x.shape[-3:])  # [3, N, D', H', W']
          x = torch.einsum('indhw->nidhw', x)  # [N, 3, D', H', W']
          x = x.contiguous()
  
          return x
  