# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license 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

from torch import Tensor
from torch import nn
from shared.attention import pay_attention

logger = logging.getLogger("dinov2")

XFORMERS_AVAILABLE = False


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, 1, 3, 4)
        # Preserve FP32 depth inference without materializing the quadratic attention matrix.
        x = pay_attention(list(qkv.unbind(0)), force_attention="sdpa").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:
            assert attn_bias is None, "xFormers is required for nested tensors usage"
            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