"""
GTA: Gaze Target Agent - 无训练视线引导推理
论文: arXiv:2609.06208 (ECCV 2026)
核心方法: 预训练VLM + 视觉引导提示 + 记忆检索
"""

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

class VisualGuidedPromptGenerator:
    """
    视觉引导提示生成器
    
    将视线方向信息转换为VLM可理解的视觉提示
    
    论文Section 3.2: "visually guided prompts"
    """
    
    def __init__(self, grid_size: int = 7):
        self.grid_size = grid_size  # 视觉提示网格大小
        
    def generate_gaze_prompt(self, image: torch.Tensor,
                              gaze_direction: torch.Tensor,
                              person_bbox: torch.Tensor) -> torch.Tensor:
        """
        生成视线视觉提示
        
        在图像上叠加视线方向箭头/热力图
        
        Args:
            image: (B, 3, H, W) 输入图像
            gaze_direction: (B, 2) 视线2D方向
            person_bbox: (B, 4) 人脸框 [x1, y1, x2, y2]
            
        Returns:
            prompted_image: (B, 3, H, W) 带视觉提示的图像
        """
        B, C, H, W = image.shape
        prompted = image.clone()
        
        for i in range(B):
            # 人脸中心
            cx = (person_bbox[i, 0] + person_bbox[i, 2]) / 2 * W
            cy = (person_bbox[i, 1] + person_bbox[i, 3]) / 2 * H
            
            # 视线方向
            dx, dy = gaze_direction[i]
            dx, dy = dx * W, dy * H
            
            # 在图像上绘制视线箭头（红色叠加）
            self._draw_gaze_arrow(prompted[i], cx, cy, dx, dy, H, W)
            
            # 绘制视线热力图区域
            self._draw_gaze_heatmap(prompted[i], cx, cy, dx, dy, H, W)
        
        return prompted
    
    def _draw_gaze_arrow(self, img, x0, y0, dx, dy, H, W):
        """在图像tensor上绘制箭头"""
        length = min(abs(dx), abs(dy), W * 0.3, H * 0.3)
        if length < 1:
            return
        steps = int(length)
        for s in range(steps):
            x = int(x0 + dx * s / steps)
            y = int(y0 + dy * s / steps)
            if 0 <= x < W and 0 <= y < H:
                # 红色通道增强
                img[0, y, x] = torch.clamp(img[0, y, x] + 0.5, max=1.0)
    
    def _draw_gaze_heatmap(self, img, x0, y0, dx, dy, H, W):
        """绘制视线方向热力图"""
        for r in range(20, 0, -2):
            x = int(x0 + dx * r / 20)
            y = int(y0 + dy * r / 20)
            if 0 <= x < W and 0 <= y < H:
                intensity = 0.3 * (1 - r / 20)
                img[0, y, x] = torch.clamp(img[0, y, x] + intensity, max=1.0)


class MemoryRetrieval:
    """
    记忆检索策略
    
    对高不确定性样本，从记忆库中检索相似样本增强推理
    
    论文Section 3.3: "memory-based retrieval strategy"
    """
    
    def __init__(self, memory_size: int = 1000, embed_dim: int = 768):
        self.memory_size = memory_size
        self.embed_dim = embed_dim
        self.memory = torch.randn(memory_size, embed_dim)
        self.labels = [None] * memory_size
        self.ptr = 0
        
    def store(self, embedding: torch.Tensor, label: str):
        """存储样本到记忆库"""
        self.memory[self.ptr] = embedding.detach()
        self.labels[self.ptr] = label
        self.ptr = (self.ptr + 1) % self.memory_size
        
    def retrieve(self, query: torch.Tensor, top_k: int = 3) -> List[Tuple[str, float]]:
        """
        检索最相似的记忆样本
        
        Args:
            query: (embed_dim,) 查询嵌入
            top_k: 返回最相似的k个
            
        Returns:
            results: [(label, similarity), ...]
        """
        # 计算余弦相似度
        sim = F.cosine_similarity(
            query.unsqueeze(0), self.memory, dim=1
        )
        top_vals, top_idx = sim.topk(top_k)
        
        results = []
        for val, idx in zip(top_vals, top_idx):
            if self.labels[idx.item()] is not None:
                results.append((self.labels[idx.item()], val.item()))
        
        return results
    
    def is_uncertain(self, prediction: torch.Tensor, 
                     threshold: float = 0.3) -> bool:
        """判断预测是否高不确定性"""
        entropy = -(prediction * torch.log(prediction + 1e-8)).sum()
        return entropy > threshold


class GazeTargetAgent(nn.Module):
    """
    GTA: Gaze Target Agent
    
    论文: arXiv:2609.06208 (ECCV 2026)
    
    无训练Agent，通过视觉引导提示+记忆检索实现：
    1. 视线目标预测
    2. 注意力物体定位
    3. 语义解释
    
    依赖预训练VLM（如LLaVA、Qwen-VL）
    """
    
    def __init__(self, vlm_model: nn.Module = None):
        super().__init__()
        self.vlm = vlm  # 预训练VLM（冻结）
        self.prompt_gen = VisualGuidedPromptGenerator()
        self.memory = MemoryRetrieval()
        
    def predict_gaze_target(self, image: torch.Tensor,
                            person_bbox: torch.Tensor,
                            gaze_dir: torch.Tensor) -> Dict:
        """
        视线目标预测
        
        Args:
            image: (B, 3, H, W)
            person_bbox: (B, 4) [x1, y1, x2, y2]
            gaze_dir: (B, 2) 2D视线方向
            
        Returns:
            results: {
                'gaze_target': (B, 2),     # 目标坐标
                'attended_object': (B, 4),  # 物体框
                'explanation': List[str],    # 语义解释
                'confidence': (B,),          # 置信度
            }
        """
        # 1. 生成视觉引导提示
        prompted_image = self.prompt_gen.generate_gaze_prompt(
            image, gaze_dir, person_bbox
        )
        
        # 2. 构造VLM提示文本
        vlm_prompts = [
            f"Given the gaze direction overlay, predict where "
            f"the person is looking. Provide the target location "
            f"and identify the attended object."
            for _ in range(image.shape[0])
        ]
        
        # 3. VLM推理（模拟）
        B = image.shape[0]
        results = {
            'gaze_target': torch.zeros(B, 2),
            'attended_object': torch.zeros(B, 4),
            'explanation': [],
            'confidence': torch.zeros(B),
        }
        
        for i in range(B):
            # 模拟VLM输出
            gaze_point = torch.tensor([
                person_bbox[i, 0] + gaze_dir[i, 0],
                person_bbox[i, 1] + gaze_dir[i, 1]
            ])
            results['gaze_target'][i] = gaze_point
            results['attended_object'][i] = torch.tensor([
                gaze_point[0] - 0.05, gaze_point[1] - 0.05,
                gaze_point[0] + 0.05, gaze_point[1] + 0.05
            ])
            results['explanation'].append(
                f"The person appears to be looking at "
                f"({gaze_point[0]:.2f}, {gaze_point[1]:.2f}), "
                f"likely attending to a region of interest."
            )
            results['confidence'][i] = 0.85
            
            # 4. 高不确定性样本记忆增强
            pred_dist = torch.tensor([0.7, 0.3])  # 模拟预测分布
            if self.memory.is_uncertain(pred_dist):
                # 检索相似样本
                query = torch.randn(768)
                retrieved = self.memory.retrieve(query, top_k=3)
                if retrieved:
                    # 用检索结果增强推理
                    results['explanation'][i] += (
                        f" [Memory-enhanced: {len(retrieved)} similar cases]"
                    )
            
            # 存储到记忆
            self.memory.store(torch.randn(768), 
                            results['explanation'][i])
        
        return results
    
    def forward(self, image, person_bbox, gaze_dir):
        """统一推理入口"""
        return self.predict_gaze_target(image, person_bbox, gaze_dir)


# ========== 测试 ==========
if __name__ == "__main__":
    torch.manual_seed(42)
    
    agent = GazeTargetAgent()
    
    # 测试: 单人场景
    print("=" * 60)
    print("测试: 单人视线目标预测")
    image = torch.randn(2, 3, 224, 224)
    bbox = torch.tensor([[0.1, 0.1, 0.3, 0.3], [0.5, 0.2, 0.7, 0.4]])
    gaze = torch.tensor([[0.5, 0.2], [-0.3, 0.4]])
    
    results = agent(image, bbox, gaze)
    
    for i in range(2):
        print(f"\n  Person {i+1}:")
        print(f"    Gaze target: ({results['gaze_target'][i, 0]:.3f}, "
              f"{results['gaze_target'][i, 1]:.3f})")
        print(f"    Attended object: {results['attended_object'][i].tolist()}")
        print(f"    Explanation: {results['explanation'][i]}")
        print(f"    Confidence: {results['confidence'][i]:.3f}")
    
    # 测试: 记忆库状态
    print(f"\n记忆库大小: {agent.memory.memory_size}")
    print(f"已存储: {agent.memory.ptr} 条")
    
    print("\n✅ 测试通过")

# 座舱场景记忆库结构
cabin_memory = {
    "look_at_dashboard": [
        {"embedding": [0.12, ...], "label": "仪表盘查看", "count": 342},
        {"embedding": [0.15, ...], "label": "速度表查看", "count": 128},
    ],
    "look_at_window": [
        {"embedding": [0.08, ...], "label": "左侧窗外", "count": 256},
    ],
    "look_at_phone": [
        {"embedding": [0.21, ...], "label": "手机操作", "count": 89},
    ],
}