content: # Copyright (c) Meta Platforms, Inc. and affiliates.
  #
  # This source code is licensed under the Apache License, Version 2.0
  # found in the LICENSE file in the root directory of this source tree.
  
  # References:
  #   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
  #   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
  
  import os
  import logging
  
  from torch import Tensor
  from torch import nn
  from .utils import setup_logger
  
  setup_logger('', logging.INFO, [logging.INFO, logging.WARNING], [logging.ERROR, logging.CRITICAL], logging.Formatter('%(message)s'))
  
  XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
  try:
      if XFORMERS_ENABLED:
          from xformers.ops import memory_efficient_attention, unbind
  
          XFORMERS_AVAILABLE = True
          logging.warn("xFormers is available (Attention)")
      else:
          logging.warn("xFormers is disabled (Attention)")
          raise ImportError
  except ImportError:
      XFORMERS_AVAILABLE = False
      logging.warn("xFormers is not available (Attention)")
  
  class Attention(nn.Module):
      def __init__(
          self,
          dim: int,
          num_heads: int = 8,
          qkv_bias: bool = False,
          proj_bias: bool = True,
          attn_drop: float = 0.0,
          proj_drop: float = 0.0,
      ) -> None:
          super().__init__()
          self.num_heads = num_heads
          head_dim = dim // num_heads
          self.scale = head_dim**-0.5
  
          self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
          self.attn_drop = nn.Dropout(attn_drop)
          self.proj = nn.Linear(dim, dim, bias=proj_bias)
          self.proj_drop = nn.Dropout(proj_drop)
  
      def forward(self, x: Tensor) -> Tensor:
          B, N, C = x.shape
          qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
  
          q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
          attn = q @ k.transpose(-2, -1)
  
          attn = attn.softmax(dim=-1)
          attn = self.attn_drop(attn)
  
          x = (attn @ v).transpose(1, 2).reshape(B, N, C)
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  
  
  class MemEffAttention(Attention):
      def forward(self, x: Tensor, attn_bias=None) -> Tensor:
          if not XFORMERS_AVAILABLE:
              if attn_bias is not None:
                  raise AssertionError("xFormers is required for using nested tensors")
              return super().forward(x)
  
          B, N, C = x.shape
          qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
  
          q, k, v = unbind(qkv, 2)
  
          x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
          x = x.reshape([B, N, C])
  
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  
  
  class CrossAttention(nn.Module):
      def __init__(
          self,
          dim: int,
          dim_q: int,
          dim_k: int,
          dim_v: int,
          num_heads: int = 8,
          qkv_bias: bool = False,
          proj_bias: bool = True,
          attn_drop: float = 0.0,
          proj_drop: float = 0.0,
      ) -> None:
          super().__init__()
          self.dim = dim
          self.num_heads = num_heads
          head_dim = dim // num_heads
          self.scale = head_dim**-0.5
  
          self.to_q = nn.Linear(dim_q, dim, bias=qkv_bias)
          self.to_k = nn.Linear(dim_k, dim, bias=qkv_bias)
          self.to_v = nn.Linear(dim_v, dim, bias=qkv_bias)
          self.attn_drop = nn.Dropout(attn_drop)
          self.proj = nn.Linear(dim, dim, bias=proj_bias)
          self.proj_drop = nn.Dropout(proj_drop)
  
      def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
          # q: [B, N, Cq]
          # k: [B, M, Ck]
          # v: [B, M, Cv]
          # return: [B, N, C]
  
          B, N, _ = q.shape
          M = k.shape[1]
          
          q = self.scale * self.to_q(q).reshape(B, N, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, N, C/nh]
          k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, M, C/nh]
          v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, M, C/nh]
  
          attn = q @ k.transpose(-2, -1) # [B, nh, N, M]
  
          attn = attn.softmax(dim=-1) # [B, nh, N, M]
          attn = self.attn_drop(attn)
  
          x = (attn @ v).transpose(1, 2).reshape(B, N, -1) # [B, nh, N, M] @ [B, nh, M, C/nh] --> [B, nh, N, C/nh] --> [B, N, nh, C/nh] --> [B, N, C]
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  
  
  class MemEffCrossAttention(CrossAttention):
      def forward(self, q: Tensor, k: Tensor, v: Tensor, attn_bias=None) -> Tensor:
          if not XFORMERS_AVAILABLE:
              if attn_bias is not None:
                  raise AssertionError("xFormers is required for using nested tensors")
              return super().forward(x)
  
          B, N, _ = q.shape
          M = k.shape[1]
  
          q = self.scale * self.to_q(q).reshape(B, N, self.num_heads, self.dim // self.num_heads) # [B, N, nh, C/nh]
          k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads) # [B, M, nh, C/nh]
          v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads) # [B, M, nh, C/nh]
  
          x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
          x = x.reshape(B, N, -1)
  
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  