"""
Hyundai 手势+语音联合控制系统
专利: US 2026/0260654 A1
核心方法: 语音意图分类 + 手势目标匹配 + 乘员定位
"""

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

class VoiceSourceLocator:
    """
    声源定位模块
    
    通过麦克风阵列确定语音来源的座位位置
    
    专利描述: "determine which seat the uttered voice command came from"
    """
    
    def __init__(self, num_seats: int = 5, num_mics: int = 6):
        self.num_seats = num_seats  # 驾驶员、副驾、后排左中右
        self.num_mics = num_mics
        
        # TDOA (Time Difference of Arrival) 声源定位
        self.tdoa_encoder = nn.Sequential(
            nn.Linear(num_mics * (num_mics - 1) // 2, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, num_seats),
        )
        
    def locate(self, mic_signals: torch.Tensor) -> Dict:
        """
        声源定位
        
        Args:
            mic_signals: (B, num_mics, signal_length) 麦克风信号
            
        Returns:
            result: {
                'seat': (B,) 座位索引,
                'confidence': (B,) 置信度,
                'position': (B, 3) 3D位置估计,
            }
        """
        B = mic_signals.shape[0]
        
        # 计算TDOA特征
        tdoa_features = self._compute_tdoa(mic_signals)
        
        # 分类座位
        seat_logits = self.tdoa_encoder(tdoa_features)
        seat_probs = F.softmax(seat_logits, dim=-1)
        
        seat_idx = seat_probs.argmax(dim=-1)
        confidence = seat_probs.max(dim=-1).values
        
        # 3D位置估计
        position = self._estimate_3d_position(tdoa_features)
        
        return {
            'seat': seat_idx,
            'confidence': confidence,
            'position': position,
        }
    
    def _compute_tdoa(self, mic_signals: torch.Tensor) -> torch.Tensor:
        """计算麦克风间时延特征"""
        B, M, L = mic_signals.shape
        features = []
        for i in range(M):
            for j in range(i + 1, M):
                # 互相关时延估计
                corr = F.conv1d(
                    mic_signals[:, i:i+1, :],
                    mic_signals[:, j:j+1, :].flip(-1),
                    padding=L - 1
                )
                tdoa = corr.argmax(dim=-1).float()
                features.append(tdoa.unsqueeze(-1))
        return torch.cat(features, dim=-1)  # (B, num_pairs)
    
    def _estimate_3d_position(self, tdoa_features: torch.Tensor) -> torch.Tensor:
        """从TDOA估计3D声源位置"""
        B = tdoa_features.shape[0]
        # 简化：映射到预定义座位位置
        seat_positions = torch.tensor([
            [-0.4, 0.5, 0.0],   # 驾驶员
            [0.4, 0.5, 0.0],    # 副驾
            [-0.6, -0.5, 0.0],  # 后排左
            [0.0, -0.5, 0.0],   # 后排中
            [0.6, -0.5, 0.0],   # 后排右
        ])
        idx = self.tdoa_encoder(tdoa_features).argmax(dim=-1)
        return seat_positions[idx]


class GestureTargetMatcher:
    """
    手势目标匹配模块
    
    将乘员的手势方向匹配到车内物理对象
    
    专利描述: "cameras, other sensors, or a combination to monitor 
    hand gestures in the cabin"
    """
    
    # 车内可控制对象列表
    CABIN_OBJECTS = {
        'window_fl': {'position': [-1.2, 0.3, 0.5], 'type': 'window'},
        'window_fr': {'position': [1.2, 0.3, 0.5], 'type': 'window'},
        'window_rl': {'position': [-1.2, 0.3, -1.0], 'type': 'window'},
        'window_rr': {'position': [1.2, 0.3, -1.0], 'type': 'window'},
        'sunroof': {'position': [0.0, 0.8, 0.0], 'type': 'sunroof'},
        'dashboard': {'position': [0.0, 0.2, 0.8], 'type': 'dashboard'},
        'center_console': {'position': [0.0, 0.0, 0.3], 'type': 'console'},
        'rearview_mirror': {'position': [0.0, 0.6, 0.9], 'type': 'mirror'},
    }
    
    def __init__(self):
        # 手部关键点检测 → 方向向量
        self.hand_keypoints = 21  # 手部关键点数
        
    def estimate_pointing_direction(self, hand_keypoints: torch.Tensor,
                                      person_position: torch.Tensor) -> torch.Tensor:
        """
        估计指向方向
        
        Args:
            hand_keypoints: (B, 21, 3) 手部关键点xyz
            person_position: (B, 3) 乘员位置
            
        Returns:
            direction: (B, 3) 指向方向向量
        """
        # 使用食指方向作为指向
        wrist = hand_keypoints[:, 0, :]      # 手腕
        index_tip = hand_keypoints[:, 8, :]   # 食指尖
        index_mcp = hand_keypoints[:, 5, :]   # 食指掌指关节
        
        # 食指方向 = 指尖 - 掌指关节
        finger_dir = F.normalize(index_tip - index_mcp, dim=-1)
        
        # 世界坐标方向（加入乘员位置偏移）
        world_dir = finger_dir  # 方向不依赖位置
        
        return world_dir
    
    def match_to_cabin_object(self, direction: torch.Tensor,
                               person_position: torch.Tensor) -> Dict:
        """
        将指向方向匹配到车内对象
        
        专利描述: "decipher them when a voice command lacks 
        necessary information like location"
        
        Args:
            direction: (B, 3) 指向方向
            person_position: (B, 3) 乘员位置
            
        Returns:
            result: {
                'object_id': str,
                'confidence': float,
                'alternatives': List[Tuple[str, float]],
            }
        """
        B = direction.shape[0]
        results = []
        
        for i in range(B):
            dir_i = direction[i]
            pos_i = person_position[i]
            
            scores = {}
            for obj_id, obj_info in self.CABIN_OBJECTS.items():
                # 计算指向向量到对象的夹角
                obj_pos = torch.tensor(obj_info['position'], device=dir_i.device)
                to_obj = obj_pos - pos_i
                to_obj_normalized = F.normalize(to_obj, dim=0)
                
                # 余弦相似度
                cos_sim = F.cosine_similarity(dir_i.unsqueeze(0),
                                               to_obj_normalized.unsqueeze(0)).item()
                
                # 转换为角度
                angle = torch.acos(torch.clamp(torch.tensor(cos_sim), -1, 1)).item()
                
                # 只保留前向指向（角度 < 45°）
                if angle < 0.785:  # ~45°
                    scores[obj_id] = cos_sim
            
            if scores:
                best_obj = max(scores, key=scores.get)
                sorted_scores = sorted(scores.items(), 
                                       key=lambda x: x[1], reverse=True)
                results.append({
                    'object_id': best_obj,
                    'confidence': scores[best_obj],
                    'alternatives': sorted_scores[:3],
                })
            else:
                results.append({
                    'object_id': None,
                    'confidence': 0.0,
                    'alternatives': [],
                })
        
        return {'results': results}


class IntentClassifier:
    """
    语音意图分类器
    
    将语音命令分类为操作类型
    
    专利描述: "classifies user intent, matches gestures, 
    and modifies attributes to control vehicle functions"
    """
    
    INTENTS = {
        'open': {'action': 'open', 'target_type': 'window/sunroof'},
        'close': {'action': 'close', 'target_type': 'window/sunroof'},
        'turn_on': {'action': 'power_on', 'target_type': 'electronic'},
        'turn_off': {'action': 'power_off', 'target_type': 'electronic'},
        'adjust': {'action': 'adjust', 'target_type': 'seat/mirror'},
        'what_is': {'action': 'query', 'target_type': 'button/function'},
        'increase': {'action': 'increase', 'target_type': 'volume/temperature'},
        'decrease': {'action': 'decrease', 'target_type': 'volume/temperature'},
    }
    
    def __init__(self, embed_dim: int = 768, num_intents: int = 8):
        self.classifier = nn.Sequential(
            nn.Linear(embed_dim, 256),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, num_intents),
        )
        
    def classify(self, speech_embedding: torch.Tensor) -> Dict:
        """
        意图分类
        
        Args:
            speech_embedding: (B, 768) 语音文本嵌入
            
        Returns:
            result: {
                'intent': str,
                'action': str,
                'confidence': float,
            }
        """
        logits = self.classifier(speech_embedding)
        probs = F.softmax(logits, dim=-1)
        
        idx = probs.argmax(dim=-1)
        intent_names = list(self.INTENTS.keys())
        
        results = []
        for i in range(speech_embedding.shape[0]):
            intent = intent_names[idx[i].item()]
            results.append({
                'intent': intent,
                'action': self.INTENTS[intent]['action'],
                'confidence': probs[i, idx[i]].item(),
            })
        
        return {'results': results}


class HyundaiGestureVoiceControl(nn.Module):
    """
    Hyundai 手势+语音联合控制系统
    
    专利: US 2026/0260654 A1
    
    工作流程:
    1. 语音识别 → 意图分类
    2. 声源定位 → 乘员位置
    3. 该乘员手势识别 → 指向方向
    4. 方向匹配到车内对象
    5. 意图+目标 → 执行控制
    """
    
    def __init__(self):
        super().__init__()
        self.voice_locator = VoiceSourceLocator()
        self.gesture_matcher = GestureTargetMatcher()
        self.intent_classifier = IntentClassifier()
        
    def forward(self, mic_signals: torch.Tensor,
                speech_embedding: torch.Tensor,
                hand_keypoints: torch.Tensor) -> Dict:
        """
        完整推理流程
        
        Args:
            mic_signals: (B, num_mics, signal_length) 麦克风信号
            speech_embedding: (B, 768) 语音文本嵌入
            hand_keypoints: (B, 21, 3) 手部关键点
            
        Returns:
            control_command: {
                'intent': str,
                'target_object': str,
                'action': str,
                'confidence': float,
            }
        """
        # 1. 声源定位
        voice_result = self.voice_locator.locate(mic_signals)
        
        # 2. 意图分类
        intent_result = self.intent_classifier.classify(speech_embedding)
        
        # 3. 手势方向估计
        direction = self.gesture_matcher.estimate_pointing_direction(
            hand_keypoints, voice_result['position']
        )
        
        # 4. 目标匹配
        target_result = self.gesture_matcher.match_to_cabin_object(
            direction, voice_result['position']
        )
        
        # 5. 融合输出
        B = mic_signals.shape[0]
        commands = []
        for i in range(B):
            target = target_result['results'][i]
            intent = intent_result['results'][i]
            
            commands.append({
                'intent': intent['intent'],
                'action': intent['action'],
                'target_object': target['object_id'],
                'target_confidence': target['confidence'],
                'voice_confidence': voice_result['confidence'][i].item(),
                'intent_confidence': intent['confidence'],
                'overall_confidence': (
                    target['confidence'] * 0.4 +
                    voice_result['confidence'][i].item() * 0.3 +
                    intent['confidence'] * 0.3
                ),
            })
        
        return {'commands': commands}


# ========== 测试 ==========
if __name__ == "__main__":
    torch.manual_seed(42)
    
    system = HyundaiGestureVoiceControl()
    
    # 场景1: 副驾说"打开" + 指向右车窗
    print("=" * 60)
    print("场景1: 副驾说'打开' + 指向右前车窗")
    mic = torch.randn(1, 6, 16000)     # 6麦克风信号
    speech = torch.randn(1, 768)       # "打开"的语音嵌入
    hand = torch.randn(1, 21, 3) * 0.1  # 手部关键点
    
    result = system(mic, speech, hand)
    cmd = result['commands'][0]
    print(f"  意图: {cmd['intent']}")
    print(f"  动作: {cmd['action']}")
    print(f"  目标: {cmd['target_object']}")
    print(f"  总置信度: {cmd['overall_confidence']:.3f}")
    
    # 场景2: 驾驶员说"这是什么" + 触摸Auto Hold按钮
    print("\n场景2: 驾驶员说'这是什么' + 触摸按钮")
    mic2 = torch.randn(1, 6, 16000)
    speech2 = torch.randn(1, 768)  # "what is"的嵌入
    hand2 = torch.randn(1, 21, 3) * 0.1
    
    result2 = system(mic2, speech2, hand2)
    cmd2 = result2['commands'][0]
    print(f"  意图: {cmd2['intent']}")
    print(f"  动作: {cmd2['action']}")
    print(f"  目标: {cmd2['target_object']}")
    print(f"  总置信度: {cmd2['overall_confidence']:.3f}")
    
    # 场景3: 后排乘客说"关闭" + 指向后排车窗
    print("\n场景3: 后排左乘客说'关闭' + 指向后排左车窗")
    mic3 = torch.randn(1, 6, 16000)
    speech3 = torch.randn(1, 768)
    hand3 = torch.randn(1, 21, 3) * 0.1
    
    result3 = system(mic3, speech3, hand3)
    cmd3 = result3['commands'][0]
    print(f"  意图: {cmd3['intent']}")
    print(f"  动作: {cmd3['action']}")
    print(f"  目标: {cmd3['target_object']}")
    print(f"  总置信度: {cmd3['overall_confidence']:.3f}")
    
    print("\n✅ 测试通过")