# 示意代码,不可直接运行 —— SensAI 官方未开源,以下为按项目页描述的假想编码器结构,非真实实现
import torch
import torch.nn as nn

class SensAIEncoder(nn.Module):
    """
    SensAI多模态编码器架构
    
    每个模态有专用编码器，
    输出统一维度嵌入向量，
    融合为认知状态数字孪生
    """
    
    def __init__(self, embed_dim: int = 256):
        super().__init__()
        
        # 视觉编码器（摄像头）
        self.vision_encoder = nn.Sequential(
            nn.Conv2d(3, 32, 3, stride=2, padding=1), nn.ReLU(),
            nn.Conv2d(32, 64, 3, stride=2, padding=1), nn.ReLU(),
            nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(128, embed_dim)
        )
        
        # 音频编码器
        self.audio_encoder = nn.Sequential(
            nn.Conv1d(1, 32, 5, stride=2), nn.ReLU(),
            nn.Conv1d(32, 64, 5, stride=2), nn.ReLU(),
            nn.Conv1d(64, 128, 5, stride=2), nn.ReLU(),
            nn.AdaptiveAvgPool1d(1),
            nn.Flatten(),
            nn.Linear(128, embed_dim)
        )
        
        # 生理信号编码器（rPPG/EDA）
        self.physio_encoder = nn.Sequential(
            nn.Linear(20, 64), nn.ReLU(),
            nn.Linear(64, 128), nn.ReLU(),
            nn.Linear(128, embed_dim)
        )
        
        # 车辆数据编码器
        self.vehicle_encoder = nn.Sequential(
            nn.Linear(10, 32), nn.ReLU(),
            nn.Linear(32, 64), nn.ReLU(),
            nn.Linear(64, embed_dim)
        )
        
        # 认知状态融合层
        self.fusion = nn.Sequential(
            nn.Linear(embed_dim * 4, 256), nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(256, 128), nn.ReLU(),
            nn.Linear(128, 3)  # 专注/轻度分心/深度走神
        )
    
    def forward(
        self,
        vision: torch.Tensor,
        audio: torch.Tensor,
        physio: torch.Tensor,
        vehicle: torch.Tensor
    ) -> dict:
        v_emb = self.vision_encoder(vision)
        a_emb = self.audio_encoder(audio)
        p_emb = self.physio_encoder(physio)
        veh_emb = self.vehicle_encoder(vehicle)
        
        # 数字孪生嵌入
        digital_twin = torch.cat([v_emb, a_emb, p_emb, veh_emb], dim=-1)
        
        # 认知状态评估
        cognitive_state = self.fusion(digital_twin)
        
        return {
            'cognitive_state': cognitive_state,
            'digital_twin': digital_twin,
            'embeddings': {
                'vision': v_emb,
                'audio': a_emb,
                'physio': p_emb,
                'vehicle': veh_emb
            }
        }


# 测试
if __name__ == "__main__":
    model = SensAIEncoder(embed_dim=128)
    
    # 模拟输入
    vision = torch.randn(4, 3, 64, 64)  # 座舱图像
    audio = torch.randn(4, 1, 16000)    # 1s音频@16kHz
    physio = torch.randn(4, 20)         # 生理特征
    vehicle = torch.randn(4, 10)        # 车辆数据
    
    out = model(vision, audio, physio, vehicle)
    
    print(f"认知状态: {out['cognitive_state'].shape}")
    print(f"数字孪生: {out['digital_twin'].shape}")
    print(f"模态嵌入维度:")
    for mod, emb in out['embeddings'].items():
        print(f"  {mod}: {emb.shape}")