class Cosmos3IMSConfig:
    """
    Cosmos 3 在 IMS 数据合成中的能力映射
    
    关键优势：
    1. 文本→视频：直接生成驾驶场景视频
    2. 物理推理：理解驾驶员-座椅-方向盘空间关系
    3. 声音生成：合成车内环境声（引擎、风噪、对话）
    4. 动作轨迹：生成驾驶员头部/手部运动轨迹
    5. 多视角一致性：多摄像头同步生成
    """
    
    # Cosmos 3 三版本对比
    MODELS = {
        "Super": {
            "参数量": "大（未公开具体数字）",
            "精度": "最高物理准确性",
            "速度": "较慢",
            "用途": "训练数据生成、精细场景合成",
            "部署": "云端/DGX"
        },
        "Nano": {
            "参数量": "小",
            "精度": "高质量",
            "速度": "亚秒级生成",
            "用途": "快速原型、迭代验证",
            "部署": "云端/本地GPU"
        },
        "Edge": {
            "参数量": "最小（量化）",
            "精度": "良好",
            "速度": "实时推理",
            "用途": "车载实时推理（未来）",
            "部署": "QCS8255/Orin"
        }
    }
    
    # IMS 数据合成场景模板
    SCENE_TEMPLATES = {
        "fatigue_normal": {
            "prompt": "Driver yawning, eyes closing slowly, head nodding forward, daytime highway driving, frontal camera view",
            "modalities": ["video", "depth", "segmentation", "sound"],
            "labels": ["PERCLOS>30%", "yawning", "head_nod"],
            "duration_sec": 30,
        },
        "distraction_phone": {
            "prompt": "Driver looking down at phone, typing, eyes off road for 3 seconds, urban driving, IR camera view",
            "modalities": ["video", "depth", "segmentation", "action_trajectory"],
            "labels": ["gaze_off_road>3s", "phone_use", "manual_distraction"],
            "duration_sec": 10,
        },
        "oop_forward_lean": {
            "prompt": "Passenger leaning forward excessively, torso angle >30 degrees, hands reaching to dashboard, side camera view",
            "modalities": ["video", "depth", "segmentation", "skeleton"],
            "labels": ["OOP-01", "forward_lean", "torso_angle>30"],
            "duration_sec": 15,
        },
        "cpd_child_rear": {
            "prompt": "Child left alone in rear seat, irregular breathing, lying on seat, rear camera with depth sensing",
            "modalities": ["video", "depth", "radar_simulation"],
            "labels": ["CPD", "child_detected", "irregular_posture"],
            "duration_sec": 60,
        },
        "alcohol_impaired": {
            "prompt": "Driver with flushed face, drooping eyelids, delayed blink response, erratic steering movements, nighttime driving",
            "modalities": ["video", "depth", "sound", "action_trajectory"],
            "labels": ["BAC>0.08", "facial_flush", "ptosis", "erratic_steering"],
            "duration_sec": 30,
        },
    }

import numpy as np
from typing import Dict, List, Tuple
from dataclasses import dataclass

@dataclass
class IMSSyntheticDataPipeline:
    """
    Cosmos 3 + Isaac Sim IMS 合成数据管道
    
    工作流：
    1. Cosmos 3 生成多样化驾驶场景视频
    2. Isaac Sim 精确物理仿真验证
    3. Omniverse Replicator 自动标注
    4. 输出 IMS 训练数据集
    
    硬件需求：
    | 组件 | 型号 | 用途 |
    |------|------|------|
    | GPU | RTX PRO 6000 Blackwell | Cosmos 3 推理 |
    | 仿真 | Isaac Sim 5.0 | 物理仿真 |
    | 编排 | NVIDIA OSMO | 工作流管理 |
    | 存储 | 50TB+ NVMe | 数据集存储 |
    """
    
    model: str = "cosmos-3-super"
    simulator: str = "isaac-sim-5.0"
    
    def generate_scenario(self, prompt: str, num_variations: int = 100) -> List[dict]:
        """
        从文本 prompt 生成多样化的驾驶场景
        
        Args:
            prompt: 场景描述
            num_variations: 变体数量
            
        Returns:
            scenarios: 生成的场景元数据列表
        """
        # 域随机化参数
        domain_randomization = {
            "lighting": ["daylight", "dusk", "night", "tunnel", "glare"],
            "weather": ["clear", "rain", "fog", "snow"],
            "subject": {
                "gender": ["male", "female"],
                "age": [20, 30, 40, 50, 60],
                "ethnicity": ["asian", "caucasian", "african", "latino"],
                "glasses": [True, False],
                "facial_hair": [True, False],
            },
            "vehicle_interior": ["sedan", "suv", "truck", "luxury"],
            "camera_position": ["A-pillar", "dashboard", "rearview", "roof"],
            "camera_type": ["RGB", "IR", "RGB-IR", "depth"],
        }
        
        scenarios = []
        for i in range(num_variations):
            # 为每个变体随机选择域参数
            config = {
                "id": f"synth_{i:04d}",
                "prompt": prompt,
                "lighting": np.random.choice(domain_randomization["lighting"]),
                "weather": np.random.choice(domain_randomization["weather"]),
                "subject_gender": np.random.choice(domain_randomization["subject"]["gender"]),
                "subject_age": np.random.choice(domain_randomization["subject"]["age"]),
                "subject_ethnicity": np.random.choice(domain_randomization["subject"]["ethnicity"]),
                "camera_position": np.random.choice(domain_randomization["camera_position"]),
                "camera_type": np.random.choice(domain_randomization["camera_type"]),
                "duration_sec": np.random.randint(10, 60),
                "fps": 30,
            }
            scenarios.append(config)
        
        return scenarios
    
    def estimate_cost(self, num_samples: int, resolution: str = "1080p") -> dict:
        """
        估算合成数据生成成本
        
        | 分辨率 | 每样本GPU秒 | RTX PRO 6000 成本 |
        |--------|------------|-------------------|
        | 720p | ~5s | $0.01 |
        | 1080p | ~15s | $0.03 |
        | 4K | ~60s | $0.12 |
        """
        gpu_seconds_per_sample = {"720p": 5, "1080p": 15, "4k": 60}
        gpu_cost_per_hour = 2.0  # RTX PRO 6000 云端租用
        
        total_gpu_seconds = num_samples * gpu_seconds_per_sample.get(resolution, 15)
        total_cost = (total_gpu_seconds / 3600) * gpu_cost_per_hour
        total_time_hours = total_gpu_seconds / 3600
        
        return {
            "samples": num_samples,
            "resolution": resolution,
            "total_gpu_hours": round(total_time_hours, 1),
            "estimated_cost_usd": round(total_cost, 2),
            "cost_per_1k_samples": round(total_cost / num_samples * 1000, 2),
        }


# 测试
if __name__ == "__main__":
    pipeline = IMSSyntheticDataPipeline()
    
    # 生成疲劳场景
    scenarios = pipeline.generate_scenario(
        "Driver yawning, PERCLOS > 30%, daytime highway",
        num_variations=1000
    )
    print(f"生成场景数: {len(scenarios)}")
    print(f"示例: {scenarios[0]}")
    
    # 成本估算
    cost = pipeline.estimate_cost(10000, "1080p")
    print(f"\n=== 10K 样本成本 ===")
    for k, v in cost.items():
        print(f"  {k}: {v}")

class FatigueDataAugmentation:
    """
    使用 Cosmos 3 增强疲劳检测训练数据
    
    当前痛点：
    - 真实疲劳数据采集成本高（每受试者 $500+）
    - PERCLOS 标注耗时（每分钟视频 10 分钟标注）
    - 多人种/多光照覆盖不足
    
    Cosmos 3 方案：
    - 生成 10K+ 疲劳场景视频
    - 自动 PERCLOS 标注（从动作轨迹推断）
    - 域随机化覆盖所有光照/人种
    """
    
    def __init__(self):
        self.prompt_templates = [
            "Driver with heavy eyelids, slow blinking, PERCLOS 30%, {lighting}, {interior}",
            "Driver yawning widely, head nodding, micro-sleep episode, {lighting}, {interior}",
            "Driver with drooping posture, reduced steering activity, {lighting}, {interior}",
            "Driver rubbing eyes frequently, delayed response, {lighting}, {interior}",
        ]
    
    def generate_balanced_dataset(self, target_per_class: int = 2000) -> dict:
        """
        生成平衡的疲劳检测数据集
        
        Returns:
            dataset_stats: 数据集统计
        """
        classes = ["alert", "mild_fatigue", "severe_fatigue", "micro_sleep"]
        
        # 每类生成 target_per_class 个样本
        # 域随机化确保覆盖：
        # - 5 种光照 × 4 种内饰 × 2 性别 × 5 年龄段 × 4 人种 = 800 组合
        # 2000 样本 / 800 组合 ≈ 2.5 样本/组合（足够覆盖）
        
        return {
            "total_samples": target_per_class * len(classes),
            "classes": classes,
            "samples_per_class": target_per_class,
            "coverage": {
                "lighting": 5,
                "interior": 4,
                "gender": 2,
                "age_groups": 5,
                "ethnicity": 4,
                "total_combinations": 800,
            },
            "estimated_cost": target_per_class * len(classes) * 0.03,  # ~$240
        }