"""
论文：JEPA-Anything: Learning Predictive Models across Different Worlds
arXiv: 2609.20800
代码：https://github.com/Gen-Verse/JEPA-Anything

核心方法：正交预测分解(OPF)的域无关JEPA
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List

class OrthogonalPredictiveFactorization(nn.Module):
    """
    正交预测分解 (OPF)
    
    将潜在预测目标分解为互补因子，
    通过专用路径学习后重组
    """
    
    def __init__(self, 
                 latent_dim: int = 512,
                 num_factors: int = 4,
                 hidden_dim: int = 256):
        super().__init__()
        
        self.num_factors = num_factors
        
        # 因子分解器：将表征分解为正交因子
        self.factorizer = nn.ModuleList([
            nn.Sequential(
                nn.Linear(latent_dim, hidden_dim),
                nn.GELU(),
                nn.Linear(hidden_dim, hidden_dim),
                nn.GELU(),
                nn.Linear(hidden_dim, latent_dim // num_factors)
            ) for _ in range(num_factors)
        ])
        
        # 正交约束：确保因子之间正交
        self.orthogonal_weight = nn.Parameter(
            torch.randn(num_factors, latent_dim // num_factors, latent_dim // num_factors)
        )
        
        # 重组器：将因子重组为完整表征
        self.recombiner = nn.Sequential(
            nn.Linear(latent_dim, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, latent_dim)
        )
        
        # 预测器：从输入表征预测目标因子
        self.predictors = nn.ModuleList([
            nn.GRU(
                input_size=latent_dim,
                hidden_size=latent_dim // num_factors,
                batch_first=True
            ) for _ in range(num_factors)
        ])
    
    def factorize(self, z):
        """将表征分解为正交因子"""
        factors = []
        for i, factorizer in enumerate(self.factorizer):
            factor = factorizer(z)  # (B, latent_dim // num_factors)
            factors.append(factor)
        
        # 正交约束损失
        stacked = torch.stack(factors, dim=1)  # (B, num_factors, dim)
        gram = torch.bmm(stacked, stacked.transpose(1, 2))  # (B, num_factors, num_factors)
        identity = torch.eye(self.num_factors, device=z.device).unsqueeze(0)
        ortho_loss = F.mse_loss(gram, identity.expand_as(gram))
        
        return factors, ortho_loss
    
    def recombine(self, factors):
        """将因子重组为完整表征"""
        concatenated = torch.cat(factors, dim=-1)  # (B, latent_dim)
        return self.recombiner(concatenated)
    
    def predict(self, z_seq, factors):
        """从输入序列预测目标因子"""
        predicted_factors = []
        for i, predictor in enumerate(self.predictors):
            out, _ = predictor(z_seq)  # (B, T, dim//num_factors)
            predicted_factors.append(out[:, -1, :])  # 取最后时刻
        
        return predicted_factors
    
    def forward(self, z_input_seq, z_target):
        """
        Args:
            z_input_seq: (B, T, latent_dim) 输入序列
            z_target: (B, latent_dim) 目标表征
        
        Returns:
            predicted_z: 预测的目标表征
            total_loss: 总损失
        """
        # 1. 分解目标
        target_factors, ortho_loss = self.factorize(z_target)
        
        # 2. 预测各因子
        predicted_factors = self.predict(z_input_seq, target_factors)
        
        # 3. 重组
        predicted_z = self.recombine(predicted_factors)
        
        # 4. 预测损失
        pred_loss = F.mse_loss(predicted_z, z_target)
        
        # 5. 总损失
        total_loss = pred_loss + 0.1 * ortho_loss
        
        return predicted_z, total_loss


# 域适配器：将不同领域数据映射到统一表征空间
class DomainAdapter(nn.Module):
    """域适配器：为不同领域提供统一接口"""
    
    def __init__(self, domain: str, input_dim: int, latent_dim: int = 512):
        super().__init__()
        self.domain = domain
        
        if domain == "vision":
            self.encoder = VisionEncoder(input_dim, latent_dim)
        elif domain == "clinical":
            self.encoder = ClinicalEncoder(input_dim, latent_dim)
        elif domain == "molecular":
            self.encoder = MolecularEncoder(input_dim, latent_dim)
        elif domain == "cabin":  # 座舱领域(本文新增)
            self.encoder = CabinEncoder(input_dim, latent_dim)
        else:
            self.encoder = nn.Linear(input_dim, latent_dim)
    
    def forward(self, x):
        return self.encoder(x)


class VisionEncoder(nn.Module):
    def __init__(self, input_dim, latent_dim):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(input_dim, 64, 3, stride=2, padding=1),
            nn.GELU(),
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.GELU(),
            nn.Conv2d(128, 256, 3, stride=2, padding=1),
            nn.GELU(),
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(256, latent_dim)
        )
    
    def forward(self, x):
        return self.conv(x)


class CabinEncoder(nn.Module):
    """座舱领域编码器：编码DMS/OMS多传感器数据"""
    def __init__(self, input_dim, latent_dim):
        super().__init__()
        # 输入: [摄像头特征, 雷达特征, 传感器状态]
        self.mlp = nn.Sequential(
            nn.Linear(input_dim, 256),
            nn.GELU(),
            nn.Linear(256, latent_dim)
        )
    
    def forward(self, x):
        return self.mlp(x)


# 测试
if __name__ == "__main__":
    # 模拟跨域预测
    B, T, D = 8, 10, 512
    
    # 输入序列 + 目标
    z_seq = torch.randn(B, T, D)
    z_target = torch.randn(B, D)
    
    # OPF模型
    opf = OrthogonalPredictiveFactorization(latent_dim=D, num_factors=4)
    
    # 前向
    predicted_z, loss = opf(z_seq, z_target)
    
    print(f"输入序列: {z_seq.shape}")
    print(f"目标: {z_target.shape}")
    print(f"预测: {predicted_z.shape}")
    print(f"损失: {loss.item():.4f}")
    
    # 论文关键结果
    print("\n=== 论文实验结果 ===")
    print("10/10动力学任务提升")
    print("Interventional Pong预测误差降低34.8%")
    print("4/4分子系统100步预测最低误差")
    print("开普勒指数恢复: -1.4991 (理论值-1.5)")
    print("生物干预: 细胞+类器官+肿瘤+小鼠实验验证")

# 基于OPF的座舱世界模型
class CabinWorldModel(nn.Module):
    """
    座舱世界模型：预测未来座舱状态
    
    使用OPF将预测分解为正交因子：
    - 因子1: 疲劳发展轨迹
    - 因子2: 分心行为模式  
    - 因子3: 乘员姿态变化
    - 因子4: 环境状态变化
    """
    
    def __init__(self, latent_dim=512, num_factors=4):
        super().__init__()
        # 传感器融合编码器
        self.fusion_encoder = DomainAdapter("cabin", input_dim=256, latent_dim=latent_dim)
        # OPF预测核心
        self.opf = OrthogonalPredictiveFactorization(latent_dim, num_factors)
        # 状态解码器
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim, 256),
            nn.GELU(),
            nn.Linear(256, 128),
            nn.GELU(),
            nn.Linear(128, 64)
        )
    
    def forward(self, sensor_seq, target_state):
        """
        Args:
            sensor_seq: (B, T, 256) 多传感器时序
            target_state: (B, 512) 目标状态表征
        """
        # 编码
        z_seq = self.fusion_encoder(sensor_seq)  # (B, T, 512)
        z_target = self.fusion_encoder(target_state) if target_state.dim() == 2 else self.fusion_encoder(target_state.unsqueeze(1)).squeeze(1)
        
        # OPF预测
        predicted_z, loss = self.opf(z_seq, z_target)
        
        # 解码状态
        predicted_state = self.decoder(predicted_z)
        
        return predicted_state, loss