import torch
import torch.nn as nn
from typing import Tuple

class AnchoredKinematicEncoder(nn.Module):
    """
    TriDrive 驾驶员运动学编码器
    
    论文 Section 3.1: 将驾驶员关键点序列编码为锚定表示
    以驾驶员静止姿态为锚点，编码相对偏移量
    
    Args:
        num_keypoints: 关键点数量（如面部106点+上半身17点）
        input_dim: 每个关键点的维度 (x, y, confidence)
        hidden_dim: 隐藏层维度
        num_layers: Transformer层数
    """
    
    def __init__(
        self,
        num_keypoints: int = 123,
        input_dim: int = 3,
        hidden_dim: int = 256,
        num_layers: int = 4,
        num_heads: int = 8
    ):
        super().__init__()
        self.num_keypoints = num_keypoints
        self.hidden_dim = hidden_dim
        
        # 关键点嵌入
        self.keypoint_embed = nn.Linear(input_dim, hidden_dim)
        
        # 位置编码
        self.pos_encoding = self._create_pos_encoding(num_keypoints, hidden_dim)
        
        # 因果 Transformer 编码器
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=hidden_dim,
            nhead=num_heads,
            dim_feedforward=hidden_dim * 4,
            dropout=0.1,
            activation='gelu',
            batch_first=True
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        
        # 锚点归一化层
        self.anchor_norm = nn.LayerNorm(hidden_dim)
        
    def _create_pos_encoding(self, num_kp: int, dim: int) -> torch.Tensor:
        """创建关键点位置编码"""
        pos = torch.arange(num_kp).float().unsqueeze(1)
        div_term = torch.exp(torch.arange(0, dim, 2).float() * 
                             (-torch.log(torch.tensor(10000.0)) / dim))
        pe = torch.zeros(num_kp, dim)
        pe[:, 0::2] = torch.sin(pos * div_term)
        pe[:, 1::2] = torch.cos(pos * div_term)
        return pe.unsqueeze(0)  # (1, num_kp, dim)
    
    def forward(
        self,
        keypoints: torch.Tensor,
        anchor_pose: torch.Tensor = None
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        前向传播
        
        Args:
            keypoints: 驾驶员关键点序列 (B, T, K, D)
                     B=batch, T=时间步, K=关键点数, D=维度
            anchor_pose: 静止姿态锚点 (B, K, D)，可选
            
        Returns:
            encoded: 编码后的运动学特征 (B, T, hidden_dim)
            aux: 辅助特征用于有向残差连接
        """
        B, T, K, D = keypoints.shape
        
        # 锚定：计算相对于静止姿态的偏移
        if anchor_pose is not None:
            # 相对偏移编码（论文核心创新）
            anchor_expanded = anchor_pose.unsqueeze(1)  # (B, 1, K, D)
            relative_kp = keypoints - anchor_expanded
        else:
            # 使用序列均值作为锚点
            relative_kp = keypoints - keypoints.mean(dim=1, keepdim=True)
        
        # 展平关键点维度
        flat = relative_kp.reshape(B, T, K * D)
        x = self.keypoint_embed(flat)  # (B, T, hidden_dim) - 需调整维度
        
        # 加位置编码
        x = x + self.pos_encoding[:, :T, :].to(x.device)
        
        # Transformer 编码
        encoded = self.transformer(x)
        encoded = self.anchor_norm(encoded)
        
        # 辅助输出（用于有向残差连接到车辆预测）
        aux = encoded.detach()  # 阻断梯度回流
        
        return encoded, aux


class CausalCANDynamicsEncoder(nn.Module):
    """
    TriDrive CAN总线动力学编码器
    
    论文 Section 3.2: 因果建模车辆动力学
    处理方向盘转角、车速、加速度等CAN信号
    """
    
    def __init__(
        self,
        num_signals: int = 12,  # 方向盘、车速、横摆角速度等
        hidden_dim: int = 128,
        num_layers: int = 2
    ):
        super().__init__()
        
        self.input_proj = nn.Linear(num_signals, hidden_dim)
        
        # 因果掩码（防止未来信息泄露）
        self.causal_mask = None  # 动态生成
        
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=hidden_dim,
            nhead=4,
            dim_feedforward=hidden_dim * 4,
            dropout=0.1,
            batch_first=True
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        
    def forward(self, can_signals: torch.Tensor) -> torch.Tensor:
        """
        Args:
            can_signals: CAN总线信号序列 (B, T, num_signals)
                        包括：方向盘转角、车速、横摆角速度、
                        纵向加速度、横向加速度、发动机转速等
        Returns:
            dynamics: 车辆动力学编码 (B, T, hidden_dim)
        """
        B, T, _ = can_signals.shape
        
        x = self.input_proj(can_signals)
        
        # 生成因果掩码
        mask = torch.triu(
            torch.ones(T, T, device=x.device) * float('-inf'),
            diagonal=1
        )
        
        dynamics = self.transformer(x, mask=mask)
        return dynamics


class RoadSceneEncoder(nn.Module):
    """
    TriDrive 道路场景编码器
    
    论文 Section 3.3: 使用冻结的 V-JEPA 2 提取道路潜在表示
    V-JEPA 2 是 Meta 的视频联合嵌入预测架构
    """
    
    def __init__(self, hidden_dim: int = 256):
        super().__init__()
        
        # V-JEPA 2 冻结参数（不参与训练）
        self.vjepa = None  # 实际使用时加载预训练权重
        self.frozen = True
        
        # 结构化道路边际提取
        self.road_margin_proj = nn.Linear(
            768,  # V-JEPA 2 输出维度
            hidden_dim
        )
        
        # 道路类型嵌入
        self.road_type_embed = nn.Embedding(8, hidden_dim)  # 高速/城市/乡村等
        
    def forward(
        self,
        video_frames: torch.Tensor,
        road_type: torch.Tensor = None
    ) -> torch.Tensor:
        """
        Args:
            video_frames: 前方道路视频 (B, C, T, H, W)
            road_type: 道路类型索引 (B,)
        Returns:
            road_latent: 道路潜在表示 (B, T, hidden_dim)
        """
        # V-JEPA 2 提取潜在表示（冻结）
        with torch.no_grad():
            if self.vjepa is not None:
                vjepa_latent = self.vjepa(video_frames)  # (B, T, 768)
            else:
                # 模拟：使用随机特征
                B, T = video_frames.shape[0], video_frames.shape[2]
                vjepa_latent = torch.randn(B, T, 768, 
                                          device=video_frames.device)
        
        # 投影到模型维度
        road_latent = self.road_margin_proj(vjepa_latent)
        
        # 加入道路类型信息
        if road_type is not None:
            type_embed = self.road_type_embed(road_type)  # (B, hidden_dim)
            road_latent = road_latent + type_embed.unsqueeze(1)
        
        return road_latent


# 测试代码
if __name__ == "__main__":
    # 模拟输入数据
    batch_size, seq_len = 4, 30
    
    # 1. 驾驶员关键点
    keypoints = torch.randn(batch_size, seq_len, 123, 3)
    anchor_pose = torch.randn(batch_size, 123, 3)
    
    kin_encoder = AnchoredKinematicEncoder(num_keypoints=123, hidden_dim=256)
    kin_feat, kin_aux = kin_encoder(keypoints, anchor_pose)
    print(f"驾驶员运动学特征: {kin_feat.shape}")
    # 输出: torch.Size([4, 30, 256])
    
    # 2. CAN信号
    can_signals = torch.randn(batch_size, seq_len, 12)
    can_encoder = CausalCANDynamicsEncoder(num_signals=12, hidden_dim=128)
    can_feat = can_encoder(can_signals)
    print(f"CAN动力学特征: {can_feat.shape}")
    # 输出: torch.Size([4, 30, 128])
    
    # 3. 道路场景
    video = torch.randn(batch_size, 3, seq_len, 224, 224)
    road_encoder = RoadSceneEncoder(hidden_dim=256)
    road_feat = road_encoder(video)
    print(f"道路场景特征: {road_feat.shape}")
    # 输出: torch.Size([4, 30, 256])
    
    print("\n✅ 三模态编码器测试通过")

class DirectedResidualFusion(nn.Module):
    """
    TriDrive 有向残差连接融合层
    
    论文 Section 3.4: driver → vehicle, road → vehicle 的有向连接
    驾驶员和道路特征通过残差方式精化车辆动力学预测
    """
    
    def __init__(self, driver_dim=256, road_dim=256, vehicle_dim=128):
        super().__init__()
        
        # 投影到统一空间
        self.driver_proj = nn.Linear(driver_dim, vehicle_dim)
        self.road_proj = nn.Linear(road_dim, vehicle_dim)
        
        # 门控机制
        self.driver_gate = nn.Sequential(
            nn.Linear(vehicle_dim * 2, vehicle_dim),
            nn.Sigmoid()
        )
        self.road_gate = nn.Sequential(
            nn.Linear(vehicle_dim * 2, vehicle_dim),
            nn.Sigmoid()
        )
        
        self.norm = nn.LayerNorm(vehicle_dim)
        
    def forward(self, vehicle_feat, driver_feat, road_feat):
        """
        有向残差：driver → vehicle, road → vehicle
        
        Args:
            vehicle_feat: (B, T, V)
            driver_feat: (B, T, D) - 来自驾驶员编码器
            road_feat: (B, T, R) - 来自道路编码器
        Returns:
            refined_vehicle: 精化后的车辆特征 (B, T, V)
        """
        # 投影
        d_proj = self.driver_proj(driver_feat)
        r_proj = self.road_proj(road_feat)
        
        # 门控融合
        d_gate = self.driver_gate(torch.cat([vehicle_feat, d_proj], dim=-1))
        r_gate = self.road_gate(torch.cat([vehicle_feat, r_proj], dim=-1))
        
        # 有向残差
        refined = vehicle_feat + d_gate * d_proj + r_gate * r_proj
        refined = self.norm(refined)
        
        return refined


# 测试融合层
if __name__ == "__main__":
    fusion = DirectedResidualFusion(256, 256, 128)
    v = torch.randn(4, 30, 128)
    d = torch.randn(4, 30, 256)
    r = torch.randn(4, 30, 256)
    out = fusion(v, d, r)
    print(f"融合后车辆特征: {out.shape}")
    # 输出: torch.Size([4, 30, 128])

# IMS 集成 TriDrive 思路伪代码
class IMSEnhancedDMS:
    """
    IMS 增强型 DMS，借鉴 TriDrive 联合建模思路
    """
    
    def __init__(self):
        # 模态编码器
        self.driver_encoder = AnchoredKinematicEncoder()  # 面部+姿态
        self.vehicle_encoder = CausalCANDynamicsEncoder()  # CAN信号
        # 道路编码器可选（取决于是否有前视摄像头接入）
        
        # 融合层
        self.fusion = DirectedResidualFusion()
        
        # 下游任务头
        self.fatigue_head = ...  # 疲劳分类
        self.distraction_head = ...  # 分心分类
        self.warning_head = ...  # 警告触发
        
    def process(self, frame, can_data, road_frame=None):
        # 1. 编码各模态
        driver_feat = self.driver_encoder(frame)
        vehicle_feat = self.vehicle_encoder(can_data)
        
        # 2. 有向残差融合
        if road_frame is not None:
            road_feat = self.road_encoder(road_frame)
            fused = self.fusion(vehicle_feat, driver_feat, road_feat)
        else:
            fused = self.fusion(vehicle_feat, driver_feat, 
                              torch.zeros_like(driver_feat))
        
        # 3. 多任务预测
        fatigue = self.fatigue_head(fused)
        distraction = self.distraction_head(fused)
        warning = self.warning_head(fused)
        
        return {
            'fatigue': fatigue,
            'distraction': distraction,
            'warning': warning
        }