from typing import List, Dict
import numpy as np

class CosmosTransferPipeline:
    """
    Cosmos Transfer 2.5 管线
    
    将 Omniverse 3D 场景转换为真实感视频
    
    输入：
    - 分割图 / 深度图 / 边缘图
    - 人体关键点
    - LiDAR 扫描
    - HD 地图 / 轨迹
    
    输出：
    - 真实感视频序列
    - 多样化环境/光照条件
    """
    
    def __init__(self, config: dict = None):
        config = config or {}
        self.variation_modes = [
            'lighting',      # 光照变化
            'weather',       # 天气变化
            'environment',   # 环境纹理
            'demographics',  # 人群多样性
            'occlusion'      # 遮挡变化
        ]
    
    def generate_cabin_scenarios(
        self,
        base_scene: Dict,
        num_variations: int = 100
    ) -> List[Dict]:
        """
        从基础座舱场景生成多样化变体
        
        Args:
            base_scene: Omniverse 3D 场景描述
                - driver_model: 驾驶员3D模型
                - cabin_geometry: 座舱几何
                - camera_layout: 摄像头配置
                - lighting_setup: 光照设置
            num_variations: 生成变体数
            
        Returns:
            scenarios: 多样化场景列表
        """
        scenarios = []
        
        for i in range(num_variations):
            scenario = base_scene.copy()
            
            # 1. 随机化驾驶员属性
            scenario['driver'] = self._randomize_driver()
            
            # 2. 随机化光照条件
            scenario['lighting'] = self._randomize_lighting()
            
            # 3. 随机化座舱环境
            scenario['environment'] = self._randomize_environment()
            
            # 4. 设置姿态/行为
            scenario['behavior'] = self._randomize_behavior()
            
            scenarios.append(scenario)
        
        return scenarios
    
    def _randomize_driver(self) -> dict:
        """随机化驾驶员属性"""
        return {
            'gender': np.random.choice(['male', 'female']),
            'age': np.random.randint(18, 75),
            'height': np.random.uniform(1.5, 1.95),  # 米
            'weight': np.random.uniform(45, 120),     # kg
            'skin_tone': np.random.uniform(0.2, 0.9),
            'glasses': np.random.choice([True, False], p=[0.4, 0.6]),
            'facial_hair': np.random.choice([True, False], p=[0.3, 0.7]),
            'hair_style': np.random.choice(['short', 'medium', 'long', 'bald']),
            'clothing': np.random.choice(['casual', 'formal', 'sport']),
        }
    
    def _randomize_lighting(self) -> dict:
        """随机化光照"""
        conditions = [
            {'type': 'daylight', 'lux': 5000, 'direction': 'front'},
            {'type': 'daylight', 'lux': 8000, 'direction': 'side'},
            {'type': 'sunset', 'lux': 500, 'color_temp': 3000},
            {'type': 'tunnel', 'lux': 50, 'color_temp': 4000},
            {'type': 'night_ir', 'lux': 5, 'ir_only': True},
            {'type': 'overcast', 'lux': 2000, 'diffuse': 0.9},
        ]
        return np.random.choice(conditions)
    
    def _randomize_environment(self) -> dict:
        """随机化环境"""
        return {
            'cabin_color': np.random.choice(['black', 'grey', 'beige', 'brown']),
            'seat_material': np.random.choice(['leather', 'fabric', 'alcantara']),
            'window_light': np.random.choice(['clear', 'tinted', 'raindrop']),
            'dashboard_objects': np.random.choice([
                [], ['phone'], ['cup'], ['phone', 'cup']
            ]),
        }
    
    def _randomize_behavior(self) -> dict:
        """随机化驾驶行为"""
        behaviors = [
            {'state': 'normal_driving', 'gaze': 'forward', 'posture': 'upright'},
            {'state': 'drowsy', 'gaze': 'forward', 'eyelid': 'drooping', 'perclos': 0.25},
            {'state': 'distracted_phone', 'gaze': 'down_right', 'hand': 'phone'},
            {'state': 'cognitive_distracted', 'gaze': 'forward', 'fixation': 'static'},
            {'state': 'looking_away', 'gaze': 'left', 'head_yaw': -25},
            {'state': 'yawning', 'mouth': 'open', 'mar': 0.4},
            {'state': 'talking', 'mouth': 'moving', 'gaze': 'forward'},
            {'state': 'reaching', 'hand': 'reaching', 'posture': 'leaning'},
        ]
        return np.random.choice(behaviors)


# ============ Cosmos Predict 2.5 ============

class CosmosPredictPipeline:
    """
    Cosmos Predict 2.5 管线
    
    从当前视频帧预测未来世界状态
    
    IMS 应用：
    - 预测驾驶员未来行为
    - 生成未来座舱状态
    - 长尾场景预测
    """
    
    def __init__(self, config: dict = None):
        config = config or {}
        self.max_sequence_length = 30  # 秒
        self.multiview_support = True
    
    def predict_driver_state(
        self,
        current_frames: np.ndarray,
        current_gaze: np.ndarray,
        prediction_horizon: int = 5  # 秒
    ) -> dict:
        """
        预测驾驶员未来状态
        
        Args:
            current_frames: 当前座舱视频帧
            current_gaze: 当前眼动数据
            prediction_horizon: 预测时长（秒）
            
        Returns:
            prediction: 预测结果
        """
        return {
            'predicted_state': 'drowsy_onset',
            'confidence': 0.82,
            'time_to_onset': 3.5,  # 秒
            'predicted_gaze_pattern': 'fixation_increase',
            'predicted_perclos': 0.22,
            'recommended_action': 'level_1_alert',
        }


# ============ Cosmos Reason 2 ============

class CosmosReasonPipeline:
    """
    Cosmos Reason 2 管线
    
    物理AI推理模型
    
    IMS 应用：
    - 座舱场景理解
    - 驾驶员行为推理
    - 安全风险评估
    """
    
    def __init__(self, config: dict = None):
        config = config or {}
        self.max_context = 256000  # 256K tokens
    
    def reason_about_scene(
        self,
        video_observation: np.ndarray,
        text_query: str
    ) -> dict:
        """
        对座舱场景进行推理
        
        Example queries:
        - "Is the driver cognitively distracted?"
        - "What is the risk level of current driving state?"
        - "Should we trigger an alert?"
        """
        return {
            'reasoning': """
            观察到驾驶员注视前方但眨眼频率降低(12→8次/分)，
            PERCLOS从8%上升至15%。面部AU4(眉头下压)
            强度增加。头部微动减少30%。综合判断：
            驾驶员正处于轻度疲劳向中度过渡阶段。
            建议触发一级警告。
            """,
            'risk_level': 'medium',
            'recommended_action': 'level_1_alert',
            'key_observations': [
                'blink_rate_decreasing',
                'perclos_rising',
                'au4_increasing',
                'head_motion_decreasing'
            ],
            'confidence': 0.87
        }


if __name__ == "__main__":
    # Cosmos Transfer 管线
    transfer = CosmosTransferPipeline()
    
    base_scene = {
        'cabin_geometry': 'sedan_standard',
        'camera_layout': ['dms_front', 'oms_rear_left', 'oms_rear_right'],
    }
    
    scenarios = transfer.generate_cabin_scenarios(base_scene, num_variations=10)
    print(f"=== Cosmos Transfer 场景生成 ===")
    print(f"生成场景数: {len(scenarios)}")
    print(f"场景1 驾驶员: {scenarios[0]['driver']}")
    print(f"场景1 光照: {scenarios[0]['lighting']}")
    print(f"场景1 行为: {scenarios[0]['behavior']}")
    
    # Cosmos Predict
    predict = CosmosPredictPipeline()
    frames = np.random.randn(30, 480, 640, 3)
    gaze = np.random.randn(30, 4)
    prediction = predict.predict_driver_state(frames, gaze)
    print(f"\n=== Cosmos Predict 预测 ===")
    print(f"预测状态: {prediction['predicted_state']}")
    print(f"置信度: {prediction['confidence']:.2f}")
    print(f"推荐动作: {prediction['recommended_action']}")
    
    # Cosmos Reason
    reason = CosmosReasonPipeline()
    result = reason.reason_about_scene(
        frames, "Is the driver cognitively distracted?"
    )
    print(f"\n=== Cosmos Reason 推理 ===")
    print(f"推理: {result['reasoning'][:100]}...")
    print(f"风险等级: {result['risk_level']}")
    print(f"置信度: {result['confidence']:.2f}")
    
    print(f"\n=== Cosmos 3 规格总结 ===")
    print(f"Super: 64B参数 (32B Reasoner + 32B Generator)")
    print(f"Nano: ~7B参数")
    print(f"Edge: ~1B参数 (未发布)")
    print(f"License: OpenMDW-1.1")
    print(f"输入模态: 文本/图像/视频/动作")
    print(f"输出模态: 文本/图像/视频/声音/动作")