# 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.
#

import torch
import torch.nn as nn
import torch.nn.functional as F


class MLP(nn.Module):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop=0.
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.,
        proj_drop=0.,
        use_sdpa=True
    ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or 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)
        self.proj_drop_prob = proj_drop
        self.proj_drop = nn.Dropout(proj_drop)
        self.use_sdpa = use_sdpa

    def forward(self, x, mask=None):
        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], qkv[1], qkv[2]  # [B, num_heads, N, D]

        if self.use_sdpa:
            with torch.backends.cuda.sdp_kernel():
                x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.proj_drop_prob)
                attn = None
        else:
            attn = (q @ k.transpose(-2, -1)) * self.scale  # [B, num_heads, D, D]
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = (attn @ v)
        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x, attn


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.,
        qkv_bias=False,
        qk_scale=None,
        drop=0.,
        attn_drop=0.,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        grid_size=None,
        grid_depth=None,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop)

        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MLP(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop)

    def forward(self, x, return_attention=False, mask=None):
        y, attn = self.attn(self.norm1(x), mask=mask)
        if return_attention:
            return attn
        x = x + y
        x = x + self.mlp(self.norm2(x))
        return x


class CrossAttention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=12,
        qkv_bias=False,
        use_sdpa=True
    ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5
        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, int(dim*2), bias=qkv_bias)
        self.proj = nn.Linear(dim, dim)
        self.use_sdpa = use_sdpa

    def forward(self, q, x):
        B, n, C = q.shape
        q = self.q(q).reshape(B, n, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        B, N, C = x.shape # Batch is the batch size. N is the number of tokens (spatial/temporal tokens). D is the embedding dimension (from the encoder).
        kv = self.kv(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]  # (batch_size, num_heads, seq_len, feature_dim_per_head)

        if self.use_sdpa:
            with torch.backends.cuda.sdp_kernel():
                q = F.scaled_dot_product_attention(q, k, v)
        else:
            xattn = (q @ k.transpose(-2, -1)) * self.scale
            xattn = xattn.softmax(dim=-1)  # (batch_size, num_heads, query_len, seq_len)
            q = (xattn @ v)

        q = q.transpose(1, 2).reshape(B, n, C)
        q = self.proj(q)
    
        return q


class CrossAttentionBlock(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.,
        qkv_bias=False,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.xattn = CrossAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer)

    def forward(self, q, x):
        y = self.xattn(q, self.norm1(x))
        q = q + y
        q = q + self.mlp(self.norm2(q))
        return q