import torch
import torch.nn as nn

class ParallelMultiCropExtractor(nn.Module):
    """
    并行多裁剪特征提取
    
    原始3DB：串行处理N个裁剪区域 → N × T_crop
    Fast版：并行处理N个裁剪区域 → T_crop（与N无关）
    
    关键：解耦裁剪区域间的串行空间依赖
    """
    
    def __init__(self, backbone: nn.Module, num_crops: int = 4):
        super().__init__()
        self.backbone = backbone
        self.num_crops = num_crops
        
        # 共享backbone权重（减少参数量）
        # 使用batch维度并行处理多裁剪
    
    def forward(self, crops: torch.Tensor) -> torch.Tensor:
        """
        Args:
            crops: (B*N, C, H, W) N个裁剪区域
        Returns:
            features: (B, N, D) 各裁剪区域特征
        """
        B = crops.shape[0] // self.num_crops
        
        # 并行通过backbone（一次性处理所有裁剪）
        features = self.backbone(crops)  # (B*N, D, 1, 1)
        features = features.view(B, self.num_crops, -1)  # (B, N, D)
        
        return features


class StreamlinedTransformerDecoder(nn.Module):
    """
    精简Transformer解码器
    
    原始3DB：多层迭代Transformer，每层依赖前层输出
    Fast版：架构感知剪枝，减少层数和注意力头数
    
    通过分析各层贡献度，保留高贡献层，剪枝低贡献层
    """
    
    def __init__(self, dim: int = 1024, num_heads: int = 8, 
                 num_layers: int = 3):  # 原始6层→3层
        super().__init__()
        self.layers = nn.ModuleList([
            nn.TransformerDecoderLayer(
                d_model=dim,
                nhead=num_heads,
                dim_feedforward=dim * 2,
                batch_first=True
            ) for _ in range(num_layers)
        ])
    
    def forward(self, tgt: torch.Tensor, memory: torch.Tensor) -> torch.Tensor:
        for layer in self.layers:
            tgt = layer(tgt, memory)
        return tgt

class ArchitectureAwarePruner:
    """
    架构感知剪枝器
    
    分析步骤：
    1. 测量各层对最终输出的贡献度
    2. 保留高贡献层，剪枝低贡献层
    3. 微调保留层以补偿剪枝损失
    
    论文报告结果：
    - DINOv3 backbone: 24层 → 12层
    - 精度损失 < 2%
    - 速度提升 ~2x
    """
    
    def __init__(self, model: nn.Module):
        self.model = model
        self.layer_scores = {}
    
    def measure_contribution(self, x: torch.Tensor) -> dict:
        """测量各层贡献度"""
        contributions = {}
        
        for name, module in self.model.named_children():
            # 原始输出
            with torch.no_grad():
                original_out = module(x)
            
            # 跳过该层的输出
            with torch.no_grad():
                skipped_out = self._forward_skip(x, name)
            
            # 贡献度 = 输出差异
            contrib = (original_out - skipped_out).pow(2).mean().item()
            contributions[name] = contrib
        
        return contributions
    
    def _forward_skip(self, x: torch.Tensor, skip_name: str) -> torch.Tensor:
        """跳过指定层的前向传播"""
        # 简化实现
        return torch.zeros_like(x)
    
    def prune(self, ratio: float = 0.5) -> nn.Module:
        """剪枝指定比例的层"""
        # 按贡献度排序，剪枝低贡献层
        sorted_layers = sorted(
            self.layer_scores.items(), 
            key=lambda x: x[1]
        )
        num_to_prune = int(len(sorted_layers) * ratio)
        layers_to_prune = [name for name, _ in sorted_layers[:num_to_prune]]
        
        print(f"剪枝 {num_to_prune}/{len(sorted_layers)} 层")
        print(f"保留层: {[n for n, _ in sorted_layers[num_to_prune:]]}")
        
        return self.model

class FeedforwardSMPLMapping(nn.Module):
    """
    前馈SMPL映射网络
    
    原始3DB：迭代网格拟合（MHR → SMPL），每次迭代调整顶点位置
    → 耗时数秒，占总推理时间的80%+
    
    Fast版：直接从MHR特征前馈映射到SMPL参数
    → 单次前向传播，加速10,000倍以上
    
    SMPL参数：
    - pose: (24, 3) 24个关节的轴角旋转 → 72维
    - shape: (10,) 体型参数 → 10维
    - translation: (3,) 全局平移 → 3维
    总计: 85维参数
    """
    
    def __init__(self, input_dim: int = 1024, hidden_dim: int = 512):
        super().__init__()
        
        # 前馈网络：特征 → SMPL参数
        self.pose_head = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.GELU(),
            nn.LayerNorm(hidden_dim),
            nn.Linear(hidden_dim, hidden_dim),
            nn.GELU(),
            nn.LayerNorm(hidden_dim),
            nn.Linear(hidden_dim, 72)  # 24 joints × 3 axis-angle
        )
        
        self.shape_head = nn.Sequential(
            nn.Linear(input_dim, hidden_dim // 2),
            nn.GELU(),
            nn.Linear(hidden_dim // 2, 10)  # SMPL shape params
        )
        
        self.translation_head = nn.Sequential(
            nn.Linear(input_dim, hidden_dim // 2),
            nn.GELU(),
            nn.Linear(hidden_dim // 2, 3)
        )
    
    def forward(self, features: torch.Tensor) -> dict:
        """
        Args:
            features: (B, D) MHR特征向量
        
        Returns:
            dict with 'pose', 'shape', 'translation'
        """
        return {
            'pose': self.pose_head(features),               # (B, 72)
            'shape': self.shape_head(features),              # (B, 10)
            'translation': self.translation_head(features)  # (B, 3)
        }
    
    def to_smpl_mesh(self, pose: torch.Tensor, 
                     shape: torch.Tensor,
                     translation: torch.Tensor) -> torch.Tensor:
        """
        将SMPL参数转换为网格顶点
        
        实际实现需要SMPL模型，这里给出接口
        """
        # smpl_model = SMPL(model_path, ...)
        # vertices = smpl_model(pose, shape, translation)
        # return vertices  # (B, 6890, 3)
        pass


# TensorRT 加速配置
class TensorRTConfig:
    """TensorRT 优化配置"""
    
    # YOLO-Pose 前端
    YOLO_CONFIG = {
        'model': 'yolo11m-pose.pt',
        'imgsz': 640,
        'half': True,  # FP16
        'engine': 'yolo11m-pose.engine'
    }
    
    # MoGe 编码器
    MOGE_CONFIG = {
        'encoder': 'dinov2_encoder_fp16',
        'precision': 'fp16',
        'engine': 'moge_dinov2_encoder_fp16.engine'
    }
    
    # DINOv3 backbone
    BACKBONE_CONFIG = {
        'precision': 'fp16',
        'dynamic_batch': True
    }


# 测试
if __name__ == "__main__":
    mapping = FeedforwardSMPLMapping(input_dim=1024, hidden_dim=512)
    
    # 模拟MHR特征
    features = torch.randn(1, 1024)
    
    smpl_params = mapping(features)
    
    print("=== Fast SAM 3D Body 前馈映射 ===")
    print(f"输入特征: {features.shape}")
    print(f"pose参数: {smpl_params['pose'].shape} (24 joints × 3)")
    print(f"shape参数: {smpl_params['shape'].shape} (10 dims)")
    print(f"translation: {smpl_params['translation'].shape} (3 dims)")
    
    total_params = sum(p.numel() for p in mapping.parameters())
    print(f"\n映射网络参数量: {total_params:,}")
    print(f"模型大小: {total_params * 4 / 1024:.1f} KB (FP32)")
    print(f"FP16大小: {total_params * 2 / 1024:.1f} KB")

class OOPDetector:
    """
    基于 Fast SAM 3D Body 的OOP检测器
    
    优势：
    1. 全身3D网格 → 精确关节角度
    2. 实时65ms → 可用于安全气囊预判
    3. 单RGB摄像头 → 低成本
    4. 多人支持 → 适用于全座舱
    
    局限：
    1. 需要GPU（RTX级别）
    2. 光照敏感（但可配合IR摄像头）
    3. 未在车内场景训练（需迁移学习）
    """
    
    # SMPL关节索引
    JOINT_NAMES = [
        'pelvis', 'left_hip', 'right_hip', 'spine1',
        'left_knee', 'right_knee', 'spine2', 'left_ankle',
        'right_ankle', 'spine3', 'left_foot', 'right_foot',
        'neck', 'left_collar', 'right_collar', 'head',
        'left_shoulder', 'right_shoulder', 'left_elbow',
        'right_elbow', 'left_wrist', 'right_wrist',
        'left_hand', 'right_hand'
    ]
    
    def __init__(self, reference_pose: dict = None):
        # 正常坐姿参考姿态
        self.reference_pose = reference_pose or self._default_reference()
    
    def _default_reference(self) -> dict:
        """默认正常坐姿参考"""
        return {
            'torso_forward_angle': 15.0,  # 躯干前倾角度(度)
            'torso_side_angle': 5.0,     # 躯干侧倾角度
            'head_height': 1.2,           # 头部高度(m)
            'knee_height': 0.5,          # 膝盖高度
            'foot_height': 0.1,           # 脚部高度
        }
    
    def detect_oop(self, smpl_pose: dict, 
                   seat_position: tuple = None) -> dict:
        """
        检测OOP异常姿态
        
        Args:
            smpl_pose: SMPL参数 {'pose': (72,), 'shape': (10,), 
                                  'translation': (3,)}
            seat_position: 座椅位置参考
            
        Returns:
            {'is_oop': bool, 'oop_type': str, 'severity': float,
             'joint_angles': dict}
        """
        # 从SMPL参数提取关键关节角度
        pose = smpl_pose['pose']  # (72,) = 24 joints × 3
        joints = self._smpl_to_joints(pose)
        
        # 计算OOP指标
        torso_angle = self._compute_torso_forward(joints)
        side_angle = self._compute_torso_side(joints)
        head_height = joints['head'][1]  # y坐标
        knee_height = min(joints['left_knee'][1], 
                          joints['right_knee'][1])
        foot_height = max(joints['left_foot'][1],
                         joints['right_foot'][1])
        
        results = {}
        
        # 1. 前倾检测
        ref_forward = self.reference_pose['torso_forward_angle']
        if torso_angle > ref_forward + 15:
            results['forward_lean'] = {
                'detected': True,
                'angle': float(torso_angle),
                'severity': min((torso_angle - ref_forward) / 30, 1.0)
            }
        
        # 2. 侧倾检测
        ref_side = self.reference_pose['torso_side_angle']
        if abs(side_angle) > ref_side + 10:
            results['side_lean'] = {
                'detected': True,
                'angle': float(side_angle),
                'direction': 'left' if side_angle < 0 else 'right',
                'severity': min(abs(side_angle) / 30, 1.0)
            }
        
        # 3. 跪立检测（膝盖高度异常高）
        if knee_height > 0.8:
            results['kneeling'] = {
                'detected': True,
                'knee_height': float(knee_height),
                'severity': min(knee_height / 1.0, 1.0)
            }
        
        # 4. 脚部上抬检测
        if foot_height > 0.6:
            results['feet_on_dash'] = {
                'detected': True,
                'foot_height': float(foot_height),
                'severity': min(foot_height / 1.0, 1.0)
            }
        
        is_oop = len(results) > 0
        max_severity = max([r['severity'] for r in results.values()], default=0)
        
        return {
            'is_oop': is_oop,
            'oop_types': list(results.keys()),
            'severity': float(max_severity),
            'details': results,
            'joint_angles': {
                'torso_forward': float(torso_angle),
                'torso_side': float(side_angle),
                'head_height': float(head_height),
                'knee_height': float(knee_height),
                'foot_height': float(foot_height),
            }
        }
    
    def _smpl_to_joints(self, pose_params: torch.Tensor) -> dict:
        """SMPL参数 → 3D关节坐标（简化版）"""
        # 实际实现需要SMPL前向运动学
        # 这里返回模拟值用于演示
        np.random.seed(42)
        joints = {}
        for name in self.JOINT_NAMES:
            joints[name] = np.random.randn(3)
        return joints
    
    def _compute_torso_forward(self, joints: dict) -> float:
        """计算躯干前倾角度"""
        pelvis = joints['pelvis']
        neck = joints['neck']
        # 躯干向量与垂直方向的夹角
        torso_vec = neck - pelvis
        forward_angle = np.degrees(np.arctan2(
            torso_vec[2],  # z轴（前后）
            max(torso_vec[1], 0.01)  # y轴（上下）
        ))
        return abs(forward_angle)
    
    def _compute_torso_side(self, joints: dict) -> float:
        """计算躯干侧倾角度"""
        pelvis = joints['pelvis']
        neck = joints['neck']
        torso_vec = neck - pelvis
        side_angle = np.degrees(np.arctan2(
            torso_vec[0],  # x轴（左右）
            max(torso_vec[1], 0.01)
        ))
        return side_angle


# 测试
if __name__ == "__main__":
    detector = OOPDetector()
    
    # 模拟正常坐姿
    normal_pose = {
        'pose': torch.randn(72),
        'shape': torch.randn(10),
        'translation': torch.randn(3)
    }
    
    result = detector.detect_oop(normal_pose)
    print("=== OOP检测结果 ===")
    print(f"OOP检测: {result['is_oop']}")
    print(f"OOP类型: {result['oop_types']}")
    print(f"严重度: {result['severity']:.2f}")
    print(f"关节角度: {result['joint_angles']}")

class AirbagSuppressionSystem:
    """
    基于OOP检测的安全气囊抑制系统
    
    Euro NCAP OOP场景要求：
    - 检测到前倾 → 降低气囊展开力度
    - 检测到跪立 → 抑制气囊
    - 检测到儿童 → 抑制副驾气囊
    """
    
    SUPPRESSION_RULES = {
        'forward_lean': {
            'mild': 'reduce_force_50',
            'severe': 'reduce_force_80',
        },
        'kneeling': {
            'any': 'suppress_airbag',
        },
        'side_lean': {
            'mild': 'reduce_force_30',
            'severe': 'suppress_airbag',
        },
        'feet_on_dash': {
            'any': 'suppress_airbag',
        }
    }
    
    def get_airbag_action(self, oop_result: dict) -> dict:
        """根据OOP检测结果决定气囊策略"""
        if not oop_result['is_oop']:
            return {'action': 'normal_deploy', 'reason': '正常坐姿'}
        
        actions = []
        for oop_type, details in oop_result['details'].items():
            rule = self.SUPPRESSION_RULES.get(oop_type, {})
            severity = 'severe' if details['severity'] > 0.6 else 'mild'
            action = rule.get(severity, rule.get('any', 'normal_deploy'))
            actions.append((oop_type, action))
        
        # 取最保守的动作
        if any(a == 'suppress_airbag' for _, a in actions):
            return {'action': 'suppress', 'reason': f'OOP: {[t for t, a in actions if a == "suppress_airbag"]}'}
        elif any('reduce_force_80' in a for _, a in actions):
            return {'action': 'reduce_80', 'reason': f'OOP前倾严重'}
        elif any('reduce_force_50' in a for _, a in actions):
            return {'action': 'reduce_50', 'reason': f'OOP前倾中度'}
        else:
            return {'action': 'normal_deploy', 'reason': 'OOP程度低'}


# 测试
if __name__ == "__main__":
    system = AirbagSuppressionSystem()
    
    test_cases = [
        {'is_oop': False, 'details': {}, 'severity': 0},
        {'is_oop': True, 'details': {'forward_lean': {'severity': 0.4}}, 'severity': 0.4},
        {'is_oop': True, 'details': {'kneeling': {'severity': 0.8}}, 'severity': 0.8},
        {'is_oop': True, 'details': {'forward_lean': {'severity': 0.9}, 'side_lean': {'severity': 0.3}}, 'severity': 0.9},
    ]
    
    for tc in test_cases:
        result = system.get_airbag_action(tc)
        print(f"OOP={tc['is_oop']}, 严重度={tc['severity']:.1f} → {result['action']}: {result['reason']}")