import torch
import torch.nn as nn

class SpatioTemporalRadarEncoder(nn.Module):
    """
    时空雷达编码器
    
    捕获体态配置和运动动态
    论文 Section 3.1
    
    输入：雷达张量序列 (B, T, C, D_r, D_d, D_a)
    输出：时空特征序列 (B, T, hidden)
    """
    def __init__(self, in_channels=4, hidden=256, 
                 num_heads=4, num_layers=4):
        super().__init__()
        
        # 空间编码：3D CNN 处理每帧雷达张量
        self.spatial_encoder = nn.Sequential(
            nn.Conv3d(in_channels, hidden // 4, 3, stride=2, padding=1),
            nn.BatchNorm3d(hidden // 4),
            nn.ReLU(),
            nn.Conv3d(hidden // 4, hidden // 2, 3, stride=2, padding=1),
            nn.BatchNorm3d(hidden // 2),
            nn.ReLU(),
            nn.Conv3d(hidden // 2, hidden, 3, stride=2, padding=1),
            nn.BatchNorm3d(hidden),
            nn.ReLU(),
            nn.AdaptiveAvgPool3d(1)
        )
        
        # 时间编码：Transformer 处理序列
        self.temporal_pos_embed = nn.Parameter(torch.randn(1, 256, hidden))
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=hidden,
            nhead=num_heads,
            dim_feedforward=hidden * 4,
            dropout=0.1,
            activation='gelu',
            batch_first=True
        )
        self.temporal_encoder = nn.TransformerEncoder(encoder_layer, num_layers)
        
    def forward(self, radar_sequence):
        """
        Args:
            radar_sequence: (B, T, C, D_r, D_d, D_a)
        Returns:
            features: (B, T, hidden) - 时空特征
        """
        B, T = radar_sequence.shape[:2]
        
        # 空间编码每帧
        spatial_feats = []
        for t in range(T):
            feat = self.spatial_encoder(radar_sequence[:, t])  # (B, hidden, 1, 1, 1)
            spatial_feats.append(feat.flatten(1))
        spatial_feats = torch.stack(spatial_feats, dim=1)  # (B, T, hidden)
        
        # 加入时间位置编码
        spatial_feats = spatial_feats + self.temporal_pos_embed[:, :T, :]
        
        # 时间编码
        temporal_feats = self.temporal_encoder(spatial_feats)
        
        return temporal_feats


class PoseGuidedPretraining(nn.Module):
    """
    Stage 1: 姿态引导预训练
    
    训练时用 3D 姿态作为监督
    推理时移除姿态头，仅用雷达
    
    论文 Section 3.2
    """
    def __init__(self, hidden=256, pose_dim=66):  # 22 joints * 3
        super().__init__()
        self.encoder = SpatioTemporalRadarEncoder()
        
        # 姿态预测头（训练时使用，推理时移除）
        self.pose_head = nn.Sequential(
            nn.Linear(hidden, hidden),
            nn.GELU(),
            nn.Linear(hidden, pose_dim)
        )
        
    def forward(self, radar_sequence, return_pose=False):
        features = self.encoder(radar_sequence)
        
        if return_pose:
            # 训练时预测姿态
            pose_pred = self.pose_head(features)
            return features, pose_pred
        else:
            # 推理时仅返回特征
            return features


class mmMindModel(nn.Module):
    """
    mmMind: 完整的雷达-语言模型
    
    论文：arXiv:2608.04127
    """
    def __init__(self, hidden=256, llm_dim=4096, 
                 num_radar_tokens=32):
        super().__init__()
        
        # 雷达编码器（姿态引导预训练后冻结）
        self.radar_encoder = PoseGuidedPretraining(hidden=hidden)
        for param in self.radar_encoder.parameters():
            param.requires_grad = False
        
        # 投影层：雷达特征 -> LLM 空间
        self.projection = nn.Sequential(
            nn.Linear(hidden, llm_dim),
            nn.GELU(),
            nn.Linear(llm_dim, llm_dim)
        )
        
        # 可学习的查询 Token
        self.query_tokens = nn.Parameter(
            torch.randn(num_radar_tokens, llm_dim)
        )
        
        # LLM（使用 LoRA 微调）
        # 假设使用 LLaMA-3 8B
        # 实际使用 transformers 库
        self.llm_dim = llm_dim
        
    def forward(self, radar_sequence, text_input=None):
        """
        Args:
            radar_sequence: (B, T, C, D_r, D_d, D_a)
            text_input: 文本输入（问答模式）
        Returns:
            radar_tokens: (B, num_tokens, llm_dim) - 用于 LLM
        """
        # 雷达编码
        features = self.radar_encoder(radar_sequence)  # (B, T, hidden)
        
        # 投影到 LLM 空间
        projected = self.projection(features)  # (B, T, llm_dim)
        
        # 扩展查询 Token
        B = projected.shape[0]
        queries = self.query_tokens.unsqueeze(0).expand(B, -1, -1)
        
        # 简化的 Token 融合
        radar_tokens = torch.cat([queries, projected], dim=1)
        
        return radar_tokens

class mmMindBench:
    """mmMind-Bench 基准数据集说明"""
    
    TASKS = {
        'captioning': {
            'description': '根据雷达信号描述人体行为',
            'example_input': '雷达流（10s, 30fps）',
            'example_output': '一个人从客厅走入厨房，打开冰箱，取出一瓶水后关闭冰箱门。',
            'evaluation': 'BLEU, ROUGE, CIDEr'
        },
        'qa': {
            'description': '时空问答',
            'example_q': '这个人在第5秒时在做什么？',
            'example_a': '他正坐在沙发上，看起来在用右手操作手机。',
            'evaluation': '准确率, F1'
        },
        'generalization': {
            'description': '未见动作泛化',
            'test': '训练集不含的动作类别',
            'evaluation': 'zero-shot 准确率'
        }
    }
    
    ACTIONS = [
        'walking', 'sitting', 'standing', 'waving', 
        'picking_up', 'placing_down', 'opening_door',
        'drinking', 'phone_use', 'reading',
        'cleaning', 'cooking', 'exercising',
        'falling', 'lying_down', 'searching'
    ]

"""
mmMind 推理管道复现
论文：arXiv:2608.04127
"""

import torch
import numpy as np

def test_mmmind():
    print("=" * 60)
    print("mmMind 雷达-语言模型测试")
    print("=" * 60)
    
    # 模型配置
    hidden = 256
    llm_dim = 4096  # LLaMA-3 8B hidden dim
    num_radar_tokens = 32
    
    model = mmMindModel(hidden=hidden, llm_dim=llm_dim, 
                         num_radar_tokens=num_radar_tokens)
    
    # 模拟雷达流：10秒，30fps，4通道，64x16x32
    T = 10  # 10 帧（每帧 ~1s 的雷达数据）
    radar_stream = torch.randn(1, T, 4, 64, 16, 32)
    
    # 推理
    radar_tokens = model(radar_stream)
    
    print(f"输入: {radar_stream.shape} (B, T, C, D_r, D_d, D_a)")
    print(f"输出: {radar_tokens.shape} (B, num_tokens + T, llm_dim)")
    print(f"Token 数: {radar_tokens.shape[1]} (32 查询 + {T} 时空)")
    
    # 参数量
    total = sum(p.numel() for p in model.parameters())
    trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print(f"\n总参数量: {total / 1e6:.2f}M")
    print(f"可训练参数: {trainable / 1e6:.2f}M")
    print(f"冻结参数: {(total - trainable) / 1e6:.2f}M")
    
    # 推理速度
    import time
    model.eval()
    with torch.no_grad():
        for _ in range(5):
            _ = model(radar_stream)
        times = []
        for _ in range(20):
            start = time.perf_counter()
            _ = model(radar_stream)
            times.append((time.perf_counter() - start) * 1000)
    
    print(f"\n雷达编码延迟: {np.mean(times):.2f} ms")
    print(f"理论帧率: {1000 / np.mean(times):.1f} fps")
    
    return model


def test_action_recognition():
    """行为识别测试"""
    print("\n" + "=" * 60)
    print("行为识别测试")
    print("=" * 60)
    
    model = mmMindModel()
    model.eval()
    
    # 模拟不同行为的雷达流
    actions = {
        'sitting': torch.randn(1, 10, 4, 64, 16, 32) * 0.5,  # 低动态
        'walking': torch.randn(1, 10, 4, 64, 16, 32) * 3.0,  # 高动态
        'falling': torch.randn(1, 10, 4, 64, 16, 32) * 5.0,  # 突变动态
    }
    
    with torch.no_grad():
        for name, radar in actions.items():
            tokens = model(radar)
            # 计算动态特征
            dynamic_energy = torch.std(tokens, dim=1).mean().item()
            print(f"{name:12s}: token 能量 = {dynamic_energy:.4f}")


if __name__ == "__main__":
    model = test_mmmind()
    test_action_recognition()

class mmMindCabinIntegration:
    """mmMind 与座舱 DMS 的集成方案"""
    
    def __init__(self):
        self.scenarios = {
            'driver_monitoring': {
                'radar_input': 'A柱 mmWave 雷达',
                'current_method': 'IR 摄像头',
                'mmind_advantage': '隐私友好 + 穿遮挡',
                'implementation': '姿态引导预训练 + 座舱动作微调'
            },
            'rear_occupant': {
                'radar_input': '车顶 mmWave 雷达',
                'current_method': '无/座椅传感器',
                'mmind_advantage': '行为理解 + 描述',
                'implementation': 'mmMind + 问答接口'
            },
            'cpd_enhanced': {
                'radar_input': 'UWB + 60GHz',
                'current_method': '二分类（有人/无人）',
                'mmind_advantage': '行为描述（"婴儿在呼吸" vs "婴儿在动"）',
                'implementation': 'mmMind 微调 + CPD 数据'
            },
            'safety_event': {
                'radar_input': '座舱多雷达',
                'current_method': '规则触发',
                'mmind_advantage': '语义理解（"驾驶员突然倒向右侧"）',
                'implementation': 'mmMind + 紧急事件微调'
            }
        }