"""
TimeSformer-based Video Anomaly Detection Model
For deepfake detection via reconstruction
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange

class Factorized3DConv(nn.Module):
    def __init__(self, in_c, out_c, k=3, s=1, p=1):
        super().__init__()
        self.spatial = nn.Conv3d(in_c, out_c, (1,k,k), (1,s,s), (0,p,p), bias=False)
        self.temporal = nn.Conv3d(out_c, out_c, (k,1,1), (1,1,1), (p,0,0), bias=False)
        self.bn1 = nn.BatchNorm3d(out_c)
        self.bn2 = nn.BatchNorm3d(out_c)
        self.relu = nn.ReLU(inplace=True)
    
    def forward(self, x):
        return self.relu(self.bn2(self.temporal(self.relu(self.bn1(self.spatial(x))))))

class OpticalFlowEstimator(nn.Module):
    def __init__(self, in_c=3):
        super().__init__()
        self.conv1 = nn.Conv2d(in_c*2, 64, 7, 2, 3)
        self.conv2 = nn.Conv2d(64, 128, 5, 2, 2)
        self.conv3 = nn.Conv2d(128, 256, 3, 2, 1)
        self.flow_head = nn.Conv2d(256, 2, 1)
    
    def forward(self, f1, f2):
        x = torch.cat([f1, f2], 1)
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        return F.interpolate(self.flow_head(x), f1.shape[2:], mode='bilinear', align_corners=False)

class PatchEmbedding3D(nn.Module):
    def __init__(self, img_sz=224, p_sz=16, in_c=3, emb=768, t_sz=2):
        super().__init__()
        self.proj = nn.Conv3d(in_c, emb, (t_sz, p_sz, p_sz), (t_sz, p_sz, p_sz))
    
    def forward(self, x):
        x = self.proj(x)
        B, E, T, H, W = x.shape
        return rearrange(x, 'b e t h w -> b (t h w) e'), T*H*W

class MultiHeadAttention3D(nn.Module):
    def __init__(self, dim, heads=12, drop=0.):
        super().__init__()
        self.heads = heads
        self.scale = (dim // heads) ** -0.5
        self.qkv = nn.Linear(dim, dim * 3)
        self.proj = nn.Linear(dim, dim)
        self.drop = nn.Dropout(drop)
    
    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.heads, C // self.heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = self.drop(attn.softmax(-1))
        return self.proj((attn @ v).transpose(1, 2).reshape(B, N, C))

class TransformerBlock3D(nn.Module):
    def __init__(self, dim, heads, mlp_r=4., drop=0.):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = MultiHeadAttention3D(dim, heads, drop)
        self.norm2 = nn.LayerNorm(dim)
        self.mlp = nn.Sequential(
            nn.Linear(dim, int(dim * mlp_r)), 
            nn.GELU(), 
            nn.Dropout(drop), 
            nn.Linear(int(dim * mlp_r), dim), 
            nn.Dropout(drop)
        )
    
    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        return x + self.mlp(self.norm2(x))

class EnhancedTimeSformer(nn.Module):
    def __init__(self, img_sz=224, p_sz=16, in_c=3, n_fr=16, emb=768, depth=12, heads=12):
        super().__init__()
        self.img_sz = img_sz
        self.p_sz = p_sz
        self.stem = nn.Sequential(
            Factorized3DConv(in_c, 64, 7, 2, 3), 
            Factorized3DConv(64, 128, 3, 2, 1)
        )
        self.flow_est = OpticalFlowEstimator(in_c)
        self.flow_enc = nn.Sequential(
            nn.Conv2d(2, 64, 3, 1, 1), 
            nn.ReLU(), 
            nn.Conv2d(64, 128, 3, 1, 1)
        )
        self.patch_emb = PatchEmbedding3D(img_sz // 4, p_sz // 4, 128, emb, 2)
        self.cls_tok = nn.Parameter(torch.zeros(1, 1, emb))
        self.pos_emb = nn.Parameter(torch.zeros(1, 2048, emb))
        self.blocks = nn.ModuleList([
            TransformerBlock3D(emb, heads, 4., 0.1) for _ in range(depth)
        ])
        self.norm = nn.LayerNorm(emb)
        self.recon_grid = (img_sz // 4) // (p_sz // 4)
        self.dec = nn.ModuleDict({
            'frame': nn.Sequential(
                nn.Linear(emb, emb * 2), 
                nn.GELU(), 
                nn.Dropout(0.1), 
                nn.Linear(emb * 2, self.recon_grid ** 2 * in_c)
            ),
            'flow': nn.Sequential(
                nn.Linear(emb, emb), 
                nn.GELU(), 
                nn.Linear(emb, self.recon_grid ** 2 * 2)
            )
        })
        nn.init.trunc_normal_(self.pos_emb, std=0.02)
        nn.init.trunc_normal_(self.cls_tok, std=0.02)
    
    def forward(self, x):
        B, C, T, H, W = x.shape
        flows = [self.flow_est(x[:, :, t], x[:, :, t+1]) for t in range(T-1)]
        if flows:
            flows = torch.stack(flows, 2)
            flows = rearrange(
                self.flow_enc(rearrange(flows, 'b c t h w -> (b t) c h w')), 
                '(b t) c h w -> b c t h w', 
                b=B
            )
        else:
            flows = None
        x = self.stem(x)
        if flows is not None:
            flows = F.interpolate(
                rearrange(flows, 'b c t h w -> (b t) c h w'),
                size=(x.shape[3], x.shape[4]),
                mode='bilinear',
                align_corners=False
            )
            flows = rearrange(flows, '(b t) c h w -> b c t h w', b=B)
            if flows.shape[2] < x.shape[2]:
                flows = F.pad(flows, (0, 0, 0, 0, 0, x.shape[2] - flows.shape[2]))
            x = x + 0.1 * flows
        x, n_p = self.patch_emb(x)
        x = torch.cat([self.cls_tok.expand(B, -1, -1), x], 1)
        if x.shape[1] > self.pos_emb.shape[1]:
            pe = F.interpolate(
                self.pos_emb.permute(0, 2, 1), 
                x.shape[1], 
                mode='linear', 
                align_corners=False
            ).permute(0, 2, 1)
        else:
            pe = self.pos_emb[:, :x.shape[1]]
        x = x + pe
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)
        mid = n_p // 2
        tok = x[:, mid:mid+1]
        fr = rearrange(
            self.dec['frame'](tok), 
            'b 1 (p1 p2 c) -> b c p1 p2', 
            p1=self.recon_grid, 
            p2=self.recon_grid, 
            c=C
        )
        fl = rearrange(
            self.dec['flow'](tok), 
            'b 1 (p1 p2 c) -> b c p1 p2', 
            p1=self.recon_grid, 
            p2=self.recon_grid, 
            c=2
        )
        fr = F.interpolate(fr, (H, W), mode='bilinear', align_corners=False)
        fl = F.interpolate(fl, (H, W), mode='bilinear', align_corners=False)
        return fr, fl

def create_model():
    """Factory function to create the model"""
    return EnhancedTimeSformer(
        img_sz=224,
        p_sz=16,
        in_c=3,
        n_fr=16,
        emb=768,
        depth=12,
        heads=12
    )