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

class GazeDiT(nn.Module):
    """
    GazeDiT: 注视精确扩散图像生成模型
    
    论文: arXiv:2609.17814
    核心创新:
    1. 4D注视标签 → 物理眼渲染器 → 注视一致几何
    2. 冻结SegFormer提取瞳孔/虹膜结构作为空间条件
    3. 扩散模型在空间条件引导下生成图像
    
    输入: 4D注视标签 + 随机噪声
    输出: 注视精确的人脸/眼部图像
    """
    
    def __init__(self, gaze_dim=4, feat_dim=256):
        super().__init__()
        
        # 1. 物理眼渲染器（不可学习，参数化）
        self.eye_renderer = PhysicalEyeRenderer()
        
        # 2. 冻结的SegFormer（提取瞳孔/虹膜几何）
        self.segformer = self._load_frozen_segformer()
        
        # 3. 空间条件编码
        self.spatial_encoder = nn.Sequential(
            nn.Linear(feat_dim * 2, feat_dim),  # 左右眼特征
            nn.GELU(),
            nn.Linear(feat_dim, feat_dim)
        )
        
        # 4. 扩散模型UNet（简化版）
        self.denoising_unet = GazeConditionedUNet(
            condition_dim=feat_dim,
            gaze_dim=gaze_dim
        )
    
    def _load_frozen_segformer(self):
        """加载冻结的SegFormer"""
        # 简化：用小型分割网络模拟
        seg = nn.Sequential(
            nn.Conv2d(3, 64, 3, 1, 1), nn.ReLU(),
            nn.Conv2d(64, 128, 3, 2, 1), nn.ReLU(),
            nn.Conv2d(128, 256, 3, 2, 1), nn.ReLU(),
            nn.AdaptiveAvgPool2d(1), nn.Flatten()
        )
        for p in seg.parameters():
            p.requires_grad = False
        return seg
    
    def forward(self, noise, gaze_label, timesteps):
        """
        Args:
            noise: (B, 3, H, W) 噪声图像
            gaze_label: (B, 4) 4D双眼注视 [left_x, left_y, right_x, right_y]
            timesteps: (B,) 扩散时间步
        Returns:
            denoised: (B, 3, H, W) 去噪图像
        """
        # 1. 物理渲染器生成注视一致的眼部几何
        eye_geometry = self.eye_renderer(gaze_label)  # (B, 3, H_eye, W_eye)
        
        # 2. SegFormer提取空间条件
        with torch.no_grad():
            spatial_feat = self.segformer(eye_geometry)  # (B, feat_dim)
        
        # 3. 编码空间条件
        # 模拟左右眼特征拼接
        spatial_cond = self.spatial_encoder(
            torch.cat([spatial_feat, spatial_feat], dim=-1)
        )  # (B, feat_dim)
        
        # 4. 条件扩散去噪
        denoised = self.denoising_unet(noise, spatial_cond, gaze_label, timesteps)
        
        return denoised


class PhysicalEyeRenderer:
    """
    物理眼渲染器
    
    根据注视角度生成注视一致的眼部几何
    
    参数化模型：
    - 瞳孔位置随注视角度偏移
    - 虹膜变形随角度变化
    - 眼睑开度随角度调整
    """
    
    def __call__(self, gaze_label):
        """
        Args:
            gaze_label: (B, 4) [left_yaw, left_pitch, right_yaw, right_pitch]
        Returns:
            eye_image: (B, 3, 64, 128) 双眼渲染图
        """
        B = gaze_label.shape[0]
        # 简化：生成参数化的眼部图像
        eye_image = torch.zeros(B, 3, 64, 128)
        # 左眼区域 [0:64, 0:64]
        # 右眼区域 [0:64, 64:128]
        for i in range(B):
            # 瞳孔偏移 = 注视角度 × 缩放
            left_x = 32 + gaze_label[i, 0] * 10
            left_y = 32 + gaze_label[i, 1] * 8
            right_x = 32 + gaze_label[i, 2] * 10 + 64
            right_y = 32 + gaze_label[i, 3] * 8
            
            # 绘制瞳孔（简化）
            eye_image[i, :, int(left_y), int(left_x)] = 0  # 黑色瞳孔
            eye_image[i, :, int(right_y), int(right_x)] = 0
        
        return eye_image


class GazeConditionedUNet(nn.Module):
    """注视条件UNet扩散模型（简化版）"""
    
    def __init__(self, condition_dim=256, gaze_dim=4):
        super().__init__()
        
        # 时间嵌入
        self.time_embed = nn.Sequential(
            nn.Linear(1, 64), nn.GELU(),
            nn.Linear(64, condition_dim)
        )
        
        # 注视嵌入
        self.gaze_embed = nn.Sequential(
            nn.Linear(gaze_dim, 64), nn.GELU(),
            nn.Linear(64, condition_dim)
        )
        
        # 条件融合
        self.cond_fuse = nn.Linear(condition_dim * 3, condition_dim)
        
        # UNet编码器
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 3, 1, 1), nn.GroupNorm(8, 64), nn.SiLU(),
            nn.Conv2d(64, 128, 3, 2, 1), nn.GroupNorm(8, 128), nn.SiLU(),
            nn.Conv2d(128, 256, 3, 2, 1), nn.GroupNorm(8, 256), nn.SiLU(),
        )
        
        # 条件注入（FiLM）
        self.film = nn.Linear(condition_dim, 256 * 2)
        
        # UNet解码器
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(256, 128, 4, 2, 1), nn.GroupNorm(8, 128), nn.SiLU(),
            nn.ConvTranspose2d(128, 64, 4, 2, 1), nn.GroupNorm(8, 64), nn.SiLU(),
            nn.Conv2d(64, 3, 3, 1, 1)
        )
    
    def forward(self, x, cond, gaze, t):
        # 编码
        feat = self.encoder(x)  # (B, 256, H/4, W/4)
        
        # 条件注入
        t_emb = self.time_embed(t.unsqueeze(-1).float())
        g_emb = self.gaze_embed(gaze)
        combined = self.cond_fuse(torch.cat([cond, t_emb, g_emb], dim=-1))
        
        # FiLM调制
        film_params = self.film(combined)  # (B, 512)
        scale, shift = film_params.chunk(2, dim=-1)
        feat = feat * (1 + scale.unsqueeze(-1).unsqueeze(-1)) + shift.unsqueeze(-1).unsqueeze(-1)
        
        # 解码
        out = self.decoder(feat)
        return out


# 测试
if __name__ == "__main__":
    model = GazeDiT(gaze_dim=4, feat_dim=256)
    
    noise = torch.randn(4, 3, 64, 128)
    gaze = torch.randn(4, 4) * 0.5  # 4D注视
    t = torch.randint(0, 1000, (4,))
    
    out = model(noise, gaze, t)
    print(f"Noise input: {noise.shape}")
    print(f"Gaze label: {gaze.shape}")
    print(f"Generated image: {out.shape}")
    print(f"Parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}")