"""
NVIDIA Cosmos 3 座舱DMS合成数据管道
从少量真实数据生成大规模物理合理的座舱场景

依赖: torch, transformers, diffusers
模型: nvidia/cosmos-3-nano (8B) 或 nvidia/cosmos-3-super (32B)
"""

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

@dataclass
class CabinSyntheticConfig:
    """座舱合成数据配置"""
    # Cosmos 3 模型
    model_name: str = "nvidia/cosmos-3-nano"  # 8B, RTX PRO 6000
    # model_name: str = "nvidia/cosmos-3-super"  # 32B, Hopper/Blackwell
    
    # 生成参数
    resolution: tuple = (1920, 1080)  # Full HD
    fps: int = 30
    duration_sec: int = 10
    
    # 座舱场景定义
    cabin_environments = [
        "sedan_daytime_normal",
        "sedan_night_ir",
        "suv_tunnel_transition",
        "sedan_backlit_sunset",
        "truck_cab_low_light",
    ]
    
    # 驾驶员状态
    driver_states = [
        "normal_driving",
        "fatigue_moderate",
        "fatigue_severe",
        "cognitive_distraction",
        "visual_distraction_left",
        "visual_distraction_right",
        "phone_use_texting",
        "phone_use_calling",
        "eating_drinking",
        "smoking",
        "talking_to_passenger",
        "reaching_backseat",
    ]
    
    # 乘客配置
    passenger_configs = [
        "empty",
        "adult_front_passenger",
        "child_rear_facing_seat",
        "child_booster_seat",
        "two_adults_rear",
    ]
    
    # 光照条件
    lighting_conditions = [
        "day_500lux",
        "dusk_100lux",
        "night_ir_940nm",
        "tunnel_entry",
        "tunnel_exit",
        "parking_garage",
    ]


class Cosmos3CabinGenerator:
    """
    使用NVIDIA Cosmos 3生成座舱DMS合成数据
    
    工作流程:
    1. 描述目标场景（文本prompt）
    2. Cosmos 3推理物理约束（人体姿态、光照、遮挡）
    3. 生成物理合理的座舱图像/视频
    4. 自动标注（使用Cosmos 3的推理能力）
    """
    
    def __init__(self, config: CabinSyntheticConfig):
        self.config = config
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        
        # 模型加载 (伪代码 - 实际使用transformers/diffusers API)
        self.model = None  # CosmosModel.from_pretrained(config.model_name)
        self.tokenizer = None  # CosmosTokenizer.from_pretrained(config.model_name)
        
    def generate_cabin_scene(self,
                             environment: str,
                             driver_state: str,
                             passenger: str,
                             lighting: str,
                             n_samples: int = 100) -> Dict:
        """
        生成座舱场景合成数据
        
        Args:
            environment: 座舱环境类型
            driver_state: 驾驶员状态
            passenger: 乘客配置
            lighting: 光照条件
            n_samples: 生成样本数
            
        Returns:
            data: dict with 'images', 'labels', 'annotations'
        """
        # 构建推理prompt
        prompt = self._build_prompt(environment, driver_state, passenger, lighting)
        
        # Cosmos 3先推理物理约束
        # - 人体骨骼约束（坐姿→关节角度范围）
        # - 光照物理（IR vs RGB→反射模型）
        # - 遮挡关系（方向盘→面部部分遮挡）
        # - 材质反射（仪表盘反光、眼镜反光）
        
        # 生成图像/视频
        # images = self.model.generate(prompt, n_samples=n_samples, ...)
        
        # 自动标注
        # - EAR/MAR关键点
        # - 头部姿态
        # - 视线方向
        # - 手部位置
        # - 物体识别（手机/食物/香烟）
        
        return {
            'prompt': prompt,
            'n_samples': n_samples,
            'environment': environment,
            'driver_state': driver_state,
            'passenger': passenger,
            'lighting': lighting,
        }
    
    def _build_prompt(self, env, state, passenger, lighting):
        """构建物理约束prompt"""
        prompts = {
            'sedan_daytime_normal': "Interior view of a sedan cabin, daytime, "
                                    "natural sunlight through windshield, "
                                    "driver in normal driving posture",
            'sedan_night_ir': "Interior view of a sedan cabin, night, "
                              "940nm infrared illumination, "
                              "no visible light, IR reflectance model",
            'suv_tunnel_transition': "SUV cabin, entering tunnel, "
                                      "lighting transition from bright to dark",
        }
        
        state_desc = {
            'fatigue_severe': "driver eyes closed, head tilting forward, "
                              "yawning, slumped posture, PERCLOS > 30%",
            'phone_use_texting': "driver looking down at phone, "
                                  "right hand holding phone, "
                                  "eyes off road, gaze downward",
            'cognitive_distraction': "driver eyes on road but gaze fixed, "
                                      "reduced blink rate, tunnel vision, "
                                      "hands on wheel but stiff",
        }
        
        base = prompts.get(env, prompts['sedan_daytime_normal'])
        state_text = state_desc.get(state, "")
        
        return f"{base}. {state_text}. Passenger: {passenger}. Lighting: {lighting}."
    
    def generate_dataset(self, total_samples=10000) -> Dict:
        """
        生成完整合成数据集
        
        Returns:
            dataset_stats: 统计信息
        """
        configs = []
        
        for env in self.config.cabin_environments:
            for state in self.config.driver_states:
                for passenger in self.config.passenger_configs:
                    for light in self.config.lighting_conditions:
                        n = max(1, total_samples // (
                            len(self.config.cabin_environments) *
                            len(self.config.driver_states) *
                            len(self.config.passenger_configs) *
                            len(self.config.lighting_conditions)
                        ))
                        configs.append({
                            'environment': env,
                            'driver_state': state,
                            'passenger': passenger,
                            'lighting': light,
                            'n_samples': n,
                        })
        
        total_combos = len(configs)
        samples_per_combo = total_samples // total_combos
        
        print(f"合成数据集配置:")
        print(f"  总样本数: {total_samples}")
        print(f"  组合数: {total_combos}")
        print(f"  每组合样本: {samples_per_combo}")
        print(f"  环境: {len(self.config.cabin_environments)}种")
        print(f"  驾驶员状态: {len(self.config.driver_states)}种")
        print(f"  乘客配置: {len(self.config.passenger_configs)}种")
        print(f"  光照: {len(self.config.lighting_conditions)}种")
        
        return {
            'total_samples': total_samples,
            'total_combinations': total_combos,
            'configs': configs,
        }


# ========== 测试 ==========
if __name__ == "__main__":
    config = CabinSyntheticConfig()
    generator = Cosmos3CabinGenerator(config)
    
    print("=" * 60)
    print("NVIDIA Cosmos 3 座舱合成数据管道")
    print("=" * 60)
    
    # 单场景生成
    result = generator.generate_cabin_scene(
        environment="sedan_night_ir",
        driver_state="fatigue_severe",
        passenger="empty",
        lighting="night_ir_940nm",
        n_samples=100
    )
    
    print(f"\n单场景生成:")
    print(f"  环境: {result['environment']}")
    print(f"  驾驶员状态: {result['driver_state']}")
    print(f"  乘客: {result['passenger']}")
    print(f"  光照: {result['lighting']}")
    print(f"  样本数: {result['n_samples']}")
    print(f"  Prompt: {result['prompt'][:100]}...")
    
    # 完整数据集
    dataset = generator.generate_dataset(total_samples=10000)
    
    # 与真实数据对比
    print(f"\n{'='*60}")
    print(f"合成 vs 真实数据对比:")
    print(f"{'指标':<20} {'真实数据':<15} {'Cosmos 3合成':<15} {'说明'}")
    print(f"{'-'*60}")
    print(f"{'获取成本':<20} {'$50K+':<15} {'$500(GPU)':<15} {'100x cheaper'}")
    print(f"{'获取时间':<20} {'3-6个月':<15} {'<24小时':<15} {'100x faster'}")
    print(f"{'多样性':<20} '受限':<15} {'无限':<15} {'组合自由'}")
    print(f"{'标注':<20} '人工':<15} {'自动':<15} {'Cosmos3推理'}")
    print(f"{'隐私':<20} '需授权':<15} {'无隐私风险':<15} {'合成人脸'}")
    print(f"{'边缘case覆盖':<20} '稀少':<15} {'可控生成':<15} {'按需生成'}")

"""
Mixture-of-Transformers (MoT) 架构概念

与传统Mixture-of-Experts (MoE)的区别:
- MoE: 不同专家处理不同token，选择性地激活
- MoT: 不同transformer处理不同模态，联合优化

在座舱场景中:
- Autoregressive Transformer: 理解物理约束（人体、光照、遮挡）
- Diffusion Transformer: 生成高质量图像/视频
- 两者共享表示，推理指导生成
"""

class MoTConcept:
    """
    MoT架构在座舱合成中的工作方式:
    
    1. 输入: "疲劳驾驶员，闭眼，夜间IR"
    2. Autoregressive推理:
       - 人体骨骼约束 → 头部前倾角度≤30°
       - 眼睑闭合 → EAR<0.15
       - IR光照 → 940nm反射模型
       - 方向盘遮挡 → 面部右侧30%被遮挡
    3. Diffusion生成:
       - 基于推理约束生成物理合理的图像
       - 而非随机噪声去噪
    """
    pass