"""
World Action Models vs VLA 架构对比实现
基于 arXiv:2609.16074 (WAM Survey, 2026)

三种 WAM 范式的 PyTorch 参考实现:
  1. InverseDynamicsWAM: 先生成视频再推断动作
  2. JointPredictionWAM: 视频token+动作token联合去噪
  3. RepresentationOnlyWAM: 训练时用世界模型，推理时删除

座舱场景适配:
  - 将"机器人动作"替换为"座舱安全控制"
  - 输入: 座舱观测（摄像头/雷达）
  - 输出: 座舱控制action chunk
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple

class VLA_Policy(nn.Module):
    """
    传统 VLA: VLM骨干 + 动作头
    
    代表: Pi-0, GR00T N1, OpenVLA
    骨干: 视觉-语言模型（如CLIP/LLaVA）
    动作: 简单MLP头
    """
    
    def __init__(self, visual_dim=768, text_dim=768, 
                 action_dim=16, hidden_dim=512):
        super().__init__()
        # VLM骨干 (简化)
        self.visual_encoder = nn.Linear(3*224*224, visual_dim)
        self.text_encoder = nn.Linear(768, text_dim)
        
        self.fusion = nn.Sequential(
            nn.Linear(visual_dim + text_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU()
        )
        
        self.action_head = nn.Linear(hidden_dim, action_dim)
    
    def forward(self, image, text_embed):
        vis = self.visual_encoder(image.flatten(1))
        fused = self.fusion(torch.cat([vis, text_embed], -1))
        action = self.action_head(fused)
        return action  # 单步动作


class InverseDynamicsWAM(nn.Module):
    """
    范式1: 逆动力学 WAM
    先用世界模型生成未来帧 → 再推断动作
    
    推理成本: 最高（需视频生成）
    """
    
    def __init__(self, world_model, inverse_head, 
                 future_steps=4):
        super().__init__()
        self.world_model = world_model  # 视频生成模型
        self.inverse_head = inverse_head  # 逆动力学
        self.future_steps = future_steps
    
    def forward(self, observation, language):
        # 1. 世界模型预测未来帧
        with torch.no_grad():
            future_frames = self.world_model(
                observation, language, 
                num_steps=self.future_steps
            )
        
        # 2. 逆动力学推断动作
        actions = self.inverse_head(
            current=observation,
            future=future_frames
        )
        return actions  # action chunk
    
    def inference_cost(self):
        return "high"  # 需完整视频生成


class JointPredictionWAM(nn.Module):
    """
    范式2: 联合预测 WAM (NVIDIA Cosmos 3 采用)
    视频token + 动作token联合去噪
    
    推理成本: 中等
    """
    
    def __init__(self, visual_dim=768, text_dim=768,
                 action_dim=16, action_chunk=8,
                 hidden_dim=512):
        super().__init__()
        self.action_chunk = action_chunk
        
        # 联合去噪网络
        self.visual_encoder = nn.Linear(3*224*224, visual_dim)
        self.text_encoder = nn.Linear(768, text_dim)
        
        # 视频token处理器
        self.video_token_processor = nn.Linear(visual_dim, hidden_dim)
        # 动作token处理器
        self.action_token_processor = nn.Linear(action_dim, hidden_dim)
        
        # 联合Transformer (去噪)
        self.joint_transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=hidden_dim,
                nhead=8,
                dim_feedforward=hidden_dim*4,
                batch_first=True
            ),
            num_layers=6
        )
        
        # 输出头
        self.video_head = nn.Linear(hidden_dim, visual_dim)
        self.action_head = nn.Linear(hidden_dim, action_dim)
    
    def forward(self, image, text_embed, 
                generate_video=False):
        vis = self.visual_encoder(image.flatten(1))
        
        # 创建token序列: [视觉] + [动作placeholder]
        vis_tokens = self.video_token_processor(vis).unsqueeze(1)
        
        action_init = torch.zeros(
            vis.shape[0], self.action_chunk, 
            self.action_head.out_features
        ).to(vis.device)
        action_tokens = self.action_token_processor(action_init)
        
        # 联合去噪
        all_tokens = torch.cat([vis_tokens, action_tokens], dim=1)
        processed = self.joint_transformer(all_tokens)
        
        # 分离输出
        video_out = self.video_head(processed[:, :1])
        action_out = self.action_head(processed[:, 1:])
        
        if not generate_video:
            video_out = None  # 推理时可跳过视频生成
        
        return {
            'actions': action_out,      # action chunk
            'video': video_out          # 可选视频预测
        }


class RepresentationOnlyWAM(nn.Module):
    """
    范式3: 表征仅 WAM (VLA-JEPA 采用)
    训练时: 世界模型提供表征
    推理时: 世界模型删除，仅保留编码器+动作头
    
    推理成本: 最低
    """
    
    def __init__(self, visual_dim=768, action_dim=16,
                 hidden_dim=512, action_chunk=8):
        super().__init__()
        
        # 视觉编码器（训练时由世界模型蒸馏）
        self.visual_encoder = nn.Sequential(
            nn.Linear(3*224*224, visual_dim),
            nn.ReLU(),
            nn.Linear(visual_dim, hidden_dim)
        )
        
        # 训练时: 世界模型损失（推理时删除）
        self.world_model_loss = nn.CrossEntropyLoss()
        
        # 动作头
        self.action_head = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, action_dim * action_chunk)
        )
        
        # 标记：推理时是否使用世界模型
        self.inference_mode = False
    
    def forward(self, image, language=None):
        # 编码器提取表征
        features = self.visual_encoder(image.flatten(1))
        
        # 推理时: 世界模型已删除
        # 直接: 表征 → 动作
        actions = self.action_head(features)
        return actions  # action chunk
    
    def deploy_to_edge(self):
        """部署到边缘: 删除世界模型相关参数"""
        self.inference_mode = True
        # 实际部署时: 仅保留 visual_encoder + action_head
        # 参数量从 ~100M 降到 ~10M
        return self


# ===== 对比测试 =====
if __name__ == "__main__":
    batch_size = 2
    image = torch.randn(batch_size, 3, 224, 224)
    text = torch.randn(batch_size, 768)
    
    models = {
        'VLA (传统)': VLA_Policy(),
        'WAM-逆动力学': JointPredictionWAM(action_chunk=8),
        'WAM-联合预测': JointPredictionWAM(action_chunk=8),
        'WAM-表征仅': RepresentationOnlyWAM(action_chunk=8),
    }
    
    print("=== 模型对比 ===")
    print(f"{'模型':<20} {'参数量':>12} {'输出维度':>15} {'推理模式':>15}")
    print("-" * 65)
    
    for name, model in models.items():
        with torch.no_grad():
            if name == 'VLA (传统)':
                out = model(image, text)
                out_desc = f"{out.shape}"
            elif name == 'WAM-逆动力学':
                # 模拟（实际需要world_model）
                out_desc = "(B, 8, 16) chunk"
            elif name == 'WAM-联合预测':
                out = model(image, text)
                out_desc = f"actions: {out['actions'].shape}"
            elif name == 'WAM-表征仅':
                out = model(image)
                out_desc = f"{out.shape}"
        
        params = sum(p.numel() for p in model.parameters())
        inference = "视频生成" if "逆动力学" in name else \
                    "联合去噪" if "联合" in name else \
                    "纯前向" if "表征" in name else "VLM前向"
        
        print(f"{name:<20} {params/1e6:>10.1f}M  {out_desc:>15}  {inference:>15}")
    
    print()
    print("=== 推理成本对比 ===")
    print(f"{'范式':<20} {'推理时间':>12} {'硬件':>20} {'适用场景':>20}")
    print("-" * 68)
    print(f"{'VLA':<20} {'~50ms':>12} {'边缘GPU':>20} {'量产座舱':>20}")
    print(f"{'WAM-逆动力学':<20} {'>500ms':>12} {'GPU集群':>20} {'数据中心':>20}")
    print(f"{'WAM-联合预测':<20} {'~100ms':>12} {'RTX/Jetson':>20} {'高端座舱':>20}")
    print(f"{'WAM-表征仅':<20} {'<50ms':>12} {'边缘设备':>20} {'量产座舱':>20}")