"""
Cosmos 3 Edge 座舱数据合成管道

核心能力:
- 生成多样化座舱场景（不同光照、乘客姿态、遮挡）
- 基于物理世界理解的运动模拟
- 端侧生成，无需云端GPU

适用于: 疲劳/分心/CPD/OOP训练数据增强
"""

# Cosmos 3 Edge 关键配置
cosmos_edge_config = {
    "模型": "Cosmos3-Edge (4B参数)",
    "推理器": "Nemotron 2B (语言+推理)",
    "视觉编码": "640×540, 15Hz",
    "内存": "~9GB BF16 (Jetson Thor 16GB)",
    "推理速度": "1.53s/action_chunk (32 actions @ 15Hz)",
    "训练数据": "同 Cosmos 3 Super (物理世界多模态数据)",
    "开源": "完全开源 (HuggingFace + GitHub)",
    "许可证": "NVIDIA Open Model License",
    
    "座舱合成能力": {
        "场景生成": "文本→视频/图像生成座舱场景",
        "动作预测": "给定当前帧→预测未来动作序列",
        "域适应": "post-train到特定座舱配置",
        "多视角": "支持多摄像头输入(wrist+exterior)",
    },
    
    "IMS合成场景示例": [
        "疲劳驾驶员闭眼+打哈欠+头部下垂序列",
        "儿童在不同座椅位置玩耍/睡眠", 
        "驾驶员分心看手机/操作中控",
        "乘客异常姿态(前倾/侧倾/半躺)",
        "夜间红外+白天RGB不同光照",
        "不同肤色/年龄/体型乘客"
    ]
}

# 管道示例
print("=== Cosmos 3 Edge 座舱数据合成管道 ===\n")
for k, v in cosmos_edge_config.items():
    if isinstance(v, dict):
        print(f"{k}:")
        for sk, sv in v.items():
            print(f"  {sk}: {sv}")
    elif isinstance(v, list):
        print(f"{k}:")
        for item in v:
            print(f"  - {item}")
    else:
        print(f"{k}: {v}")

"""
Cosmos 3 Edge 在 Jetson Thor 上的部署

Jetson AGX Thor T5000 关键参数:
- GPU: Blackwell架构, 1000 AI TOPS (INT8)
- CPU: 16核 Arm Cortex-A77AE
- 内存: 64GB LPDDR5 (统一内存)
- 功耗: 60-100W
- 车规: 工业级(非车规,但可评估)

Cosmos 3 Edge ~9GB BF16 可完全装入64GB内存
推理延迟: 1.53s生成32个action(覆盖2.13s)
→ 实时性满足: 下一chunk在当前chunk执行完前就绪
"""

jetson_thor_spec = {
    "平台": "Jetson AGX Thor T5000",
    "GPU": "Blackwell, 1000 AI TOPS (INT8)",
    "CPU": "16x Cortex-A77AE",
    "内存": "64GB LPDDR5",
    "功耗": "60-100W",
    "Cosmos Edge内存": "9GB BF16",
    "推理延迟": "1.53s/32-actions",
    "控制频率": "15Hz",
    "实时性": "✓ 满足(chunk覆盖2.13s > 1.53s生成时间)",
    "闭环评估": "RoboLab 22.9%成功率(120任务)"
}

# 部署代码示例(基于NVIDIA官方教程)
deploy_code = """
# Jetson Thor 部署 Cosmos 3 Edge
export HF_TOKEN=<your_token>
export TORCHDYNAMO_DISABLE=1  # 禁用dynamo, Thor不支持sm_110a

# 启动推理服务
python -m cosmos_framework.scripts.action_policy_server_robolab \\
    --checkpoint_path nvidia/Cosmos3-Edge-Policy-DROID \\
    --port 8000 \\
    --format-prompt-as-json True

# 健康检查
curl localhost:8000/healthz  # -> OK

# 客户端调用
from openpi_client.websocket_client_policy import WebsocketClientPolicy
client = WebsocketClientPolicy(host="localhost", port=8000)
result = client.infer(observation)
actions = result["action"]  # [32, 8]: 32步, 7关节+夹爪, 15Hz
"""

print("=== Jetson Thor 部署规格 ===")
for k, v in jetson_thor_spec.items():
    print(f"  {k}: {v}")

"""
Cosmos 3 Edge Post-Train 到座舱场景

参考: NVIDIA DROID post-training教程
适配: 从机器人操作→座舱监控

关键修改:
1. 数据格式: LeRobot Dataset v3 → 座舱监控Dataset
2. Action space: 7关节+夹爪 → 监控决策(警告等级/类型)
3. Camera layout: wrist+exterior → DMS+OMS摄像头
4. Task instruction: "put marker in basket" → "detect fatigue/CPD/OOP"
"""

post_train_config = {
    "基础模型": "Cosmos3-Edge (4B)",
    "训练数据": "座舱监控标注数据(LeRobot v3格式)",
    "训练硬件": "DGX Station (GB200) 或 GB300",
    "训练配置": {
        "Action space": "monitoring_decision: 5-D (疲劳等级0-3 + 触发flag)",
        "Observation": "DMS摄像头(640×480) + OMS摄像头(320×240) → 540×640",
        "Action chunk": "32 future decisions @ 15Hz",
        "Loss balance": "Vision flow-matching + action loss",
        "Learning rate": "2e-4",
        "Batch size": "8192 (global)",
        "Iterations": "60K (~68 hours on 64×GB200)",
    },
    "训练步骤": [
        "1. 收集座舱监控视频(多场景多乘员)",
        "2. 标注: 疲劳等级/分心类型/CPD/OOP标签",
        "3. 转为LeRobot v3格式(视频+joint states+actions+task)",
        "4. Post-train Cosmos 3 Edge",
        "5. 部署到Jetson Thor",
        "6. 闭环验证(Isaac Sim座舱仿真)"
    ],
    "预估成本": {
        "训练": "64节点×4×GB200, 68小时 ≈ 17.4K GB200-hours",
        "推理": "单台Jetson Thor, 60W, 实时",
        "数据": "需≥76K条标注轨迹(参考DROID规模)"
    }
}

print("=== Post-Train 座舱场景配置 ===")
for k, v in post_train_config.items():
    if isinstance(v, dict):
        print(f"\n{k}:")
        for sk, sv in v.items():
            if isinstance(sv, list):
                print(f"  {sk}:")
                for item in sv:
                    print(f"    - {item}")
            else:
                print(f"  {sk}: {sv}")
    else:
        print(f"{k}: {v}")

"""
Cosmos 3 Edge 推理客户端示例

基于NVIDIA官方openpi_client协议
适配座舱监控场景
"""

import numpy as np
from PIL import Image
from openpi_client.websocket_client_policy import WebsocketClientPolicy

class CabinMonitoringInference:
    """座舱监控推理客户端"""
    
    def __init__(self, host="localhost", port=8000):
        self.client = WebsocketClientPolicy(host=host, port=port)
    
    def infer(self, dms_image, oms_image, vehicle_state):
        """
        座舱监控推理
        
        Args:
            dms_image: DMS摄像头帧 (H,W,3), RGB
            oms_image: OMS摄像头帧 (H,W,3), RGB
            vehicle_state: 车辆状态 dict
            
        Returns:
            result: {
                'fatigue_level': 0-3,
                'distraction_type': str,
                'cpd_detected': bool,
                'oop_detected': bool,
                'warning_level': 0-3,
                'action_chunk': 32步预测
            }
        """
        observation = {
            "prompt": "monitor cabin safety: detect fatigue, distraction, CPD, OOP",
            "observation/dms_image": np.asarray(dms_image),
            "observation/oms_image": np.asarray(oms_image),
            "observation/vehicle_speed": np.float32(vehicle_state.get("speed", 0)),
            "observation/steering_angle": np.float32(vehicle_state.get("steering", 0)),
        }
        
        result = self.client.infer(observation)
        actions = result["action"]  # [32, 5]: 32步, 5维监控决策
        
        return {
            "fatigue_level": int(actions[0, 0]),
            "distraction_type": ["none", "phone", "conversation", "daydreaming"][int(actions[0, 1]) % 4],
            "cpd_detected": bool(actions[0, 2] > 0.5),
            "oop_detected": bool(actions[0, 3] > 0.5),
            "warning_level": int(actions[0, 4]),
            "action_chunk": actions
        }


# 冒烟测试(无需真实模型)
if __name__ == "__main__":
    print("=== Cosmos 3 Edge 座舱监控推理测试 ===\n")
    
    # 模拟输入
    dms_img = Image.new("RGB", (640, 480))
    oms_img = Image.new("RGB", (320, 240))
    vehicle = {"speed": 60.0, "steering": 0.5}
    
    print(f"DMS图像: {dms_img.size}")
    print(f"OMS图像: {oms_img.size}")
    print(f"车辆状态: {vehicle}")
    print(f"推理协议: WebSocket (openpi_client)")
    print(f"输出: 32步action chunk @ 15Hz")
    print(f"覆盖时间: {32/15:.2f}秒")
    print(f"\n注: 需先部署Cosmos 3 Edge到Jetson Thor")
    print(f"并启动policy server: python -m cosmos_framework.scripts.action_policy_server")