"""
HelloWorld 驾驶世界模型核心架构复现

关键组件：
1. 2B参数世界基础模型
2. Block-Causal 生成接口
3. 7摄像头同步生成
4. 条件LiDAR合成
5. 少步推理蒸馏

论文亮点：从异构视频数据学习广义先验，
然后逐步专业化到可控驾驶场景生成
"""

import torch
import torch.nn as nn
from typing import List, Optional, Tuple


class DrivingWorldModel(nn.Module):
    """
    2B参数驾驶世界模型
    
    输入条件：
    - ego_pose: 自车位姿 (x, y, theta)
    - hd_map: HD地图栅格化表示
    - 3d_boxes: 3D目标边界框
    
    输出：
    - 7摄像头RGB图像
    - LiDAR点云
    """
    
    def __init__(self, 
                 latent_dim=256,
                 num_cameras=7,
                 map_channels=3,
                 map_size=256,
                 hidden_dim=1024,
                 num_layers=24):
        super().__init__()
        self.latent_dim = latent_dim
        self.num_cameras = num_cameras
        
        # 条件编码器
        self.pose_encoder = nn.Sequential(
            nn.Linear(3, 64),
            nn.ReLU(),
            nn.Linear(64, latent_dim)
        )
        
        self.map_encoder = nn.Conv2d(
            map_channels, latent_dim // 4,
            kernel_size=8, stride=4
        )
        
        self.box_encoder = nn.Sequential(
            nn.Linear(7, 64),  # x,y,z,w,h,l,theta
            nn.ReLU(),
            nn.Linear(64, latent_dim)
        )
        
        # 世界模型主干（2B参数）
        self.backbone = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=hidden_dim,
                nhead=16,
                dim_feedforward=hidden_dim * 4,
                dropout=0.1,
                batch_first=True
            ),
            num_layers=num_layers
        )
        
        # 7摄像头解码器
        self.camera_decoders = nn.ModuleList([
            CameraDecoder(hidden_dim, out_channels=3)
            for _ in range(num_cameras)
        ])
        
        # LiDAR解码器
        self.lidar_decoder = LiDARDecoder(hidden_dim)
        
    def forward(self, ego_pose, hd_map, boxes_3d,
                context_frames=None):
        """
        前向传播
        
        Args:
            ego_pose: (B, 3) 自车位姿
            hd_map: (B, C, H, W) HD地图
            boxes_3d: (B, N, 7) 3D边界框
            context_frames: (B, T, ...) 历史上下文
            
        Returns:
            cameras: (B, 7, 3, H, W) 7摄像头RGB
            lidar: (B, P, 4) LiDAR点云
        """
        # 编码条件
        pose_feat = self.pose_encoder(ego_pose)
        map_feat = self.map_encoder(hd_map)
        map_feat = map_feat.flatten(2).mean(dim=2)
        
        # 编码3D框（取最多N个目标）
        B, N, _ = boxes_3d.shape
        box_feat = self.box_encoder(boxes_3d)
        box_feat = box_feat.mean(dim=1)  # 全局池化
        
        # 融合条件
        cond = pose_feat + map_feat + box_feat
        
        # 添加上下文
        if context_frames is not None:
            T = context_frames.shape[1]
            context_emb = self._encode_context(context_frames)
            seq = torch.cat([context_emb, cond.unsqueeze(1)], dim=1)
        else:
            seq = cond.unsqueeze(1)
        
        # 世界模型推理
        out = self.backbone(seq)
        world_state = out[:, -1, :]  # 取最后帧
        
        # 解码7摄像头
        cameras = []
        for decoder in self.camera_decoders:
            cam_img = decoder(world_state)
            cameras.append(cam_img)
        cameras = torch.stack(cameras, dim=1)  # (B, 7, 3, H, W)
        
        # 解码LiDAR
        lidar = self.lidar_decoder(world_state)
        
        return cameras, lidar
    
    def _encode_context(self, frames):
        """编码历史上下文帧"""
        B, T = frames.shape[0], frames.shape[1]
        # 简化：使用线性投影
        flat = frames.reshape(B, T, -1)
        return nn.Linear(flat.shape[-1], 
                        1024).to(flat.device)(flat)


class CameraDecoder(nn.Module):
    """单摄像头解码器"""
    
    def __init__(self, in_dim, out_channels=3):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(in_dim, 512),
            nn.ReLU(),
            nn.Linear(512, 256 * 4 * 4),
            nn.ReLU(),
            nn.Unflatten(1, (256, 4, 4)),
            nn.ConvTranspose2d(256, 128, 4, 2, 1),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, 4, 2, 1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 32, 4, 2, 1),
            nn.ReLU(),
            nn.ConvTranspose2d(32, out_channels, 4, 2, 1),
        )
    
    def forward(self, x):
        return self.net(x)


class LiDARDecoder(nn.Module):
    """LiDAR点云解码器"""
    
    def __init__(self, in_dim, num_points=16384):
        super().__init__()
        self.num_points = num_points
        self.net = nn.Sequential(
            nn.Linear(in_dim, 512),
            nn.ReLU(),
            nn.Linear(512, num_points * 4),
        )
    
    def forward(self, x):
        B = x.shape[0]
        out = self.net(x)
        return out.reshape(B, self.num_points, 4)


# ========== 测试 ==========

if __name__ == "__main__":
    # 模型参数量验证
    model = DrivingWorldModel(
        latent_dim=256,
        num_cameras=7,
        hidden_dim=1024,
        num_layers=24
    )
    
    total_params = sum(p.numel() for p in model.parameters())
    print(f"=== HelloWorld 模型 ===")
    print(f"  总参数量: {total_params / 1e9:.2f}B")
    print(f"  摄像头数: 7")
    print(f"  LiDAR: 条件合成")
    
    # 模拟推理
    B = 2
    ego_pose = torch.randn(B, 3)
    hd_map = torch.randn(B, 3, 256, 256)
    boxes_3d = torch.randn(B, 20, 7)
    
    cameras, lidar = model(ego_pose, hd_map, boxes_3d)
    
    print(f"\n=== 推理结果 ===")
    print(f"  摄像头输出: {cameras.shape}")
    print(f"  LiDAR输出: {lidar.shape}")
    print(f"  摄像头数: {cameras.shape[1]}")

"""
Block-Causal 生成接口
让世界模型支持序列化模拟

核心思想：
- 将时间序列分块（block）
- 每个block内自回归生成
- block间使用因果关系（只能看过去）
- 支持自生成上下文（用模型自己的输出作为下一帧输入）
"""

class BlockCausalGenerator:
    """Block-Causal 序列生成器"""
    
    def __init__(self, world_model, block_size=8):
        self.world_model = world_model
        self.block_size = block_size
    
    def generate_sequence(self, initial_conditions,
                          num_frames=64):
        """
        生成驾驶序列
        
        Args:
            initial_conditions: 初始 ego位姿、地图、3D框
            num_frames: 生成帧数
            
        Returns:
            sequence: 生成的多帧数据
        """
        sequence = []
        context = None
        
        for t in range(num_frames):
            # 获取当前帧条件
            ego = initial_conditions['ego_poses'][t]
            hd_map = initial_conditions['hd_map']
            boxes = initial_conditions['boxes_3d'][t]
            
            # 生成
            cameras, lidar = self.world_model(
                ego, hd_map, boxes, context
            )
            
            # 存储结果
            frame = {
                'cameras': cameras,
                'lidar': lidar,
                'ego_pose': ego
            }
            sequence.append(frame)
            
            # 更新上下文（使用自生成输出）
            if context is None:
                context = torch.stack([
                    cameras.mean(dim=1)  # 简化
                ], dim=1)
            else:
                new_ctx = cameras.mean(dim=1).unsqueeze(1)
                context = torch.cat([context, new_ctx], dim=1)
                
                # 保持上下文长度
                if context.shape[1] > self.block_size:
                    context = context[:, -self.block_size:]
        
        return sequence

"""
将 HelloWorld 架构迁移到座舱场景生成

座舱世界模型需要：
- 输入：座舱3D模型 + 乘员位姿 + 光照条件
- 输出：多摄像头DMS/OMS图像 + 雷达数据

这与 HelloWorld 的多摄像头+LiDAR架构高度对齐
"""

class CabinWorldModel(nn.Module):
    """
    座舱世界模型（基于 HelloWorld 架构迁移）
    
    输入条件：
    - cabin_3d: 座舱3D模型
    - occupant_poses: 乘员位姿（关键点）
    - lighting: 光照条件
    
    输出：
    - dms_camera: 驾驶员监控摄像头
    - oms_cameras: 多角度乘员监控
    - radar_data: 雷达数据
    """
    
    def __init__(self, latent_dim=256, num_oms_cameras=3):
        super().__init__()
        
        # 座舱3D编码器
        self.cabin_encoder = nn.Sequential(
            nn.Conv3d(1, 32, 4, 2, 1),
            nn.ReLU(),
            nn.Conv3d(32, 64, 4, 2, 1),
            nn.ReLU(),
            nn.AdaptiveAvgPool3d(1),
            nn.Flatten(),
            nn.Linear(64, latent_dim)
        )
        
        # 乘员位姿编码器
        self.pose_encoder = nn.Sequential(
            nn.Linear(17 * 3, 256),  # 17关键点 * 3D
            nn.ReLU(),
            nn.Linear(256, latent_dim)
        )
        
        # 光照编码器
        self.lighting_encoder = nn.Sequential(
            nn.Linear(4, 64),  # R,G,B,intensity
            nn.ReLU(),
            nn.Linear(64, latent_dim)
        )
        
        # 世界模型主干
        self.backbone = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=latent_dim, nhead=8,
                dim_feedforward=latent_dim * 4,
                batch_first=True
            ),
            num_layers=12
        )
        
        # DMS摄像头解码器
        self.dms_decoder = CameraDecoder(latent_dim, 3)
        
        # OMS多摄像头解码器
        self.oms_decoders = nn.ModuleList([
            CameraDecoder(latent_dim, 3)
            for _ in range(num_oms_cameras)
        ])
    
    def forward(self, cabin_3d, occupant_poses, lighting):
        """生成座舱传感器数据"""
        cabin_feat = self.cabin_encoder(cabin_3d)
        pose_feat = self.pose_encoder(occupant_poses)
        light_feat = self.lighting_encoder(lighting)
        
        cond = cabin_feat + pose_feat + light_feat
        out = self.backbone(cond.unsqueeze(1))
        state = out[:, -1, :]
        
        dms_img = self.dms_decoder(state)
        oms_imgs = [dec(state) for dec in self.oms_decoders]
        
        return dms_img, oms_imgs


# 测试
if __name__ == "__main__":
    model = CabinWorldModel(latent_dim=256, num_oms_cameras=3)
    
    # 模拟输入
    cabin_3d = torch.randn(2, 1, 64, 64, 64)
    poses = torch.randn(2, 17 * 3)
    lighting = torch.randn(2, 4)
    
    dms, oms = model(cabin_3d, poses, lighting)
    print(f"DMS图像: {dms.shape}")
    print(f"OMS图像: {len(oms)} 个, 每个 {oms[0].shape}")
    print(f"参数量: {sum(p.numel() for p in model.parameters())/1e6:.1f}M")