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 logging
  import os
  import warnings
  
  from torch import Tensor
  from torch import nn
  
  
  logger = logging.getLogger("dinov2")
  
  
  XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
  try:
      if XFORMERS_ENABLED:
          from xformers.ops import memory_efficient_attention, unbind
  
          XFORMERS_AVAILABLE = True
          warnings.warn("xFormers is available (Attention)")
      else:
          warnings.warn("xFormers is disabled (Attention)")
          raise ImportError
  except ImportError:
      XFORMERS_AVAILABLE = False
      warnings.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
  