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class ReAttention(nn.Module):
    """
    It is observed that similarity along same batch of data is extremely large. 
    Thus can reduce the bs dimension when calculating the attention map.
    """
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,expansion_ratio = 3, apply_transform=True, transform_scale=False):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.apply_transform = apply_transform
        
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim ** -0.5
        if apply_transform:
            self.reatten_matrix = nn.Conv2d(self.num_heads,self.num_heads, 1, 1)
            self.var_norm = nn.BatchNorm2d(self.num_heads)
            self.qkv = nn.Linear(dim, dim * expansion_ratio, bias=qkv_bias)
            self.reatten_scale = self.scale if transform_scale else 1.0
        else:
            self.qkv = nn.Linear(dim, dim * expansion_ratio, bias=qkv_bias)
        
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
    def forward(self, x, **atten**=None):
        B, N, C = x.shape
        # x = self.fc(x)
        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], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        if self.apply_transform:
            attn = self.var_norm(self.reatten_matrix(attn)) * self.reatten_scale
        attn_next = attn
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x, attn_next