"""
mmMind 核心架构复现

论文 Section 3: Pose-Guided Kinematic Representation Learning
- 训练阶段：3D 姿态监督 → 雷达编码器学习运动学表征
- 推理阶段：移除姿态头，纯雷达输入 → 连续运动学 token → LLM 对齐
"""

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

class SpatioTemporalRadarEncoder(nn.Module):
    """
    时空雷达点云编码器
    
    输入：雷达点云序列 (B, T, N, F)
        - B: batch size
        - T: 时间帧数（5秒 clips, 75帧 @ 15fps）
        - N: 每帧点数（最多192个点, 3帧聚合）
        - F: 点特征维度 (x, y, z, intensity, vx, vy, vz = 7)
    
    输出：连续运动学 token (B, T, D)
        - D: token 嵌入维度
    """
    
    def __init__(
        self,
        point_dim: int = 7,
        hidden_dim: int = 256,
        token_dim: int = 512,
        num_heads: int = 8,
        num_layers: int = 4
    ):
        super().__init__()
        
        # 逐点特征提取
        self.point_mlp = nn.Sequential(
            nn.Linear(point_dim, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.GELU()
        )
        
        # 空间注意力（帧内）
        self.spatial_attn = nn.MultiheadAttention(
            embed_dim=hidden_dim,
            num_heads=num_heads,
            batch_first=True
        )
        
        # 时序 Transformer（帧间）
        temporal_layer = nn.TransformerEncoderLayer(
            d_model=hidden_dim,
            nhead=num_heads,
            dim_feedforward=hidden_dim * 4,
            batch_first=True
        )
        self.temporal_encoder = nn.TransformerEncoder(
            temporal_layer, num_layers=num_layers
        )
        
        # 投影到 token 维度
        self.proj = nn.Linear(hidden_dim, token_dim)
        
    def forward(
        self,
        radar_points: torch.Tensor,
        point_mask: torch.Tensor = None
    ) -> torch.Tensor:
        """
        Args:
            radar_points: (B, T, N, F) 雷达点云
            point_mask: (B, T, N) 有效点掩码
            
        Returns:
            kinematic_tokens: (B, T, D) 运动学 token
        """
        B, T, N, F = radar_points.shape
        
        # 逐点 MLP
        x = self.point_mlp(radar_points)  # (B, T, N, H)
        
        # 帧内空间注意力
        x = x.view(B * T, N, -1)  # (B*T, N, H)
        x, _ = self.spatial_attn(x, x, x, key_padding_mask=point_mask)
        
        # 帧级池化
        x = x.mean(dim=1)  # (B*T, H)
        x = x.view(B, T, -1)  # (B, T, H)
        
        # 时序 Transformer
        x = self.temporal_encoder(x)  # (B, T, H)
        
        # 投影
        tokens = self.proj(x)  # (B, T, D)
        
        return tokens


class PoseHead(nn.Module):
    """
    3D 姿态预测头（仅训练阶段使用）
    
    监督信号：同步的 3D 姿态序列
    训练后移除，推理时仅用雷达编码器
    """
    
    def __init__(self, token_dim: int = 512, num_joints: int = 17):
        super().__init__()
        self.joint_predictor = nn.Sequential(
            nn.Linear(token_dim, token_dim // 2),
            nn.GELU(),
            nn.Linear(token_dim // 2, num_joints * 3)  # 3D 坐标
        )
        
    def forward(self, tokens: torch.Tensor) -> torch.Tensor:
        """
        Args:
            tokens: (B, T, D)
        Returns:
            poses: (B, T, J, 3)
        """
        B, T, D = tokens.shape
        poses = self.joint_predictor(tokens)  # (B, T, J*3)
        poses = poses.view(B, T, -1, 3)  # (B, T, J, 3)
        return poses


class PoseGuidedPretrainLoss(nn.Module):
    """
    姿态引导预训练损失
    
    组合：
    1. 关节位置 MSE 损失
    2. 运动学一致性损失（速度平滑）
    3. 对比损失（增强鲁棒性）
    """
    
    def __init__(self, lambda_vel: float = 0.1, lambda_contrast: float = 0.05):
        super().__init__()
        self.lambda_vel = lambda_vel
        self.lambda_contrast = lambda_contrast
        
    def forward(
        self,
        pred_poses: torch.Tensor,    # (B, T, J, 3)
        gt_poses: torch.Tensor,        # (B, T, J, 3)
        tokens: torch.Tensor            # (B, T, D)
    ) -> Tuple[torch.Tensor, dict]:
        # 位置损失
        pos_loss = nn.functional.mse_loss(pred_poses, gt_poses)
        
        # 速度一致性损失
        pred_vel = pred_poses[:, 1:] - pred_poses[:, :-1]
        gt_vel = gt_poses[:, 1:] - gt_poses[:, :-1]
        vel_loss = nn.functional.mse_loss(pred_vel, gt_vel)
        
        # 对比损失（同一序列不同增强应相似）
        B = tokens.shape[0]
        if B > 1:
            sim = torch.cosine_similarity(
                tokens[::2].flatten(1),
                tokens[1::2].flatten(1),
                dim=-1
            )
            contrast_loss = -sim.mean()
        else:
            contrast_loss = torch.tensor(0.0, device=tokens.device)
        
        total = pos_loss + self.lambda_vel * vel_loss + self.lambda_contrast * contrast_loss
        
        return total, {
            'pos_loss': pos_loss.item(),
            'vel_loss': vel_loss.item(),
            'contrast_loss': contrast_loss.item()
        }


class mmMindModel(nn.Module):
    """
    完整 mmMind 模型
    
    训练阶段：
        radar → encoder → pose_head → loss（3D姿态监督）
        
    推理阶段：
        radar → encoder → projector → LLM → 行为描述/问答
    """
    
    def __init__(self, llm_dim: int = 4096):
        super().__init__()
        self.encoder = SpatioTemporalRadarEncoder()
        
        # 训练阶段使用
        self.pose_head = PoseHead()
        self.pretrain_loss = PoseGuidedPretrainLoss()
        
        # 推理阶段使用
        self.projector = nn.Sequential(
            nn.Linear(512, llm_dim),
            nn.GELU(),
            nn.Linear(llm_dim, llm_dim)
        )
        
    def train_forward(self, radar_points, gt_poses, point_mask=None):
        """训练前向"""
        tokens = self.encoder(radar_points, point_mask)
        pred_poses = self.pose_head(tokens)
        loss, metrics = self.pretrain_loss(pred_poses, gt_poses, tokens)
        return loss, metrics
    
    def inference_forward(self, radar_points, llm, point_mask=None):
        """推理前向（纯雷达输入）"""
        tokens = self.encoder(radar_points, point_mask)
        projected = self.projector(tokens)
        
        # 将运动学 token 注入 LLM
        # 这里用伪 LLM 接口示意
        # 实际实现中通过 cross-attention 或 prefix tuning
        behavior_text = llm.generate(
            inputs_embeds=projected,
            max_new_tokens=128
        )
        return behavior_text


# ============ 测试代码 ============
if __name__ == "__main__":
    torch.manual_seed(42)
    
    # 模拟雷达点云输入 (B=2, T=75, N=192, F=7)
    radar_input = torch.randn(2, 75, 192, 7)
    
    # 模拟 3D 姿态 GT (B=2, T=75, J=17, 3)
    pose_gt = torch.randn(2, 75, 17, 3)
    
    # 训练模式
    model = mmMindModel(llm_dim=4096)
    loss, metrics = model.train_forward(radar_input, pose_gt)
    print(f"训练损失: {loss.item():.4f}")
    print(f"  位置: {metrics['pos_loss']:.4f}")
    print(f"  速度: {metrics['vel_loss']:.4f}")
    print(f"  对比: {metrics['contrast_loss']:.4f}")
    
    # 验证编码器输出维度
    tokens = model.encoder(radar_input)
    print(f"\n运动学 token 形状: {tokens.shape}")
    print(f"  B={tokens.shape[0]}, T={tokens.shape[1]}, D={tokens.shape[2]}")

# mmMind-Bench 数据采集配置
data_config = {
    "radar_devices": {
        "60GHz": "TI IWR6843AGR (商用开发板)",
        "77GHz": "TI AWR1843 (车载级)"
    },
    "frame_rate": "15 Hz",
    "points_per_frame": "up to 64 (CFAR)",
    "superframe": "3帧聚合 → 192 points",
    "point_features": ["x", "y", "z", "intensity", "vx", "vy", "vz"],  # 7维
    "clip_length": "5 seconds = 75 frames",
    "rgb_d": "用于语言标注（训练后移除）",
    "pose_gt": "3D骨骼关节坐标（训练后移除）",
    "synchronization": "硬件触发同步"
}

# 信号处理管道
signal_pipeline = """
原始ADC数据
    → Range-Doppler FFT
    → CFAR 检测（保留 ≤64 点/帧）
    → 3帧聚合（superframe, ≤192 点）
    → 坐标变换到统一外参系
    → 运动学 token 提取
"""

# CPD 场景：检测后排是否有儿童/婴儿
cpd_scenario = {
    "sensor": "TI AWRL6432 60GHz",  # 已量产低成本雷达
    "mount": "车顶/B柱内侧",
    "input": "雷达点云序列",
    "mmMind_task": "行为描述 → '检测到后排小型人体，处于坐姿/躺姿'",
    "qa": "Q: 后排是否有人？ A: 是，检测到约80cm高度的人体反射",
    "advantage": "无需摄像头，隐私友好，支持夜间",
    "challenge": "车内多径干扰需要专门处理",
    "deployment": "模型量化到INT8，Hexagon NPU部署"
}

# OOP 场景：检测乘员是否处于异常坐姿
oop_detection = {
    "normal_states": ["正常坐姿", "前倾", "后仰", "左倾", "右倾"],
    "abnormal_states": ["跪姿", "蹲姿", "趴在仪表盘", "伸出手窗外"],
    "approach": "运动学token → 轻量分类器",
    "accuracy_target": ">95%（4类正常 vs 异常）",
    "latency": "<100ms（5秒clip处理）",
    "hardware": "QCS8255 Hexagon NPU, INT8量化"
}

# 认知分心：通过行为序列推断注意力状态
cognitive_distraction = {
    "indicators": [
        "长时间静止（走神）",
        "频繁微小调整（焦虑）",
        "手势减少（注意力下降）",
        "头部朝向变化频率降低"
    ],
    "mmMind_advantage": "LLM可以做时序推理，不只是分类",
    "example_qa": "Q: 驾驶员过去30秒行为模式？ A: 头部运动减少，手部活动降低，疑似注意力下降"
}