"""
CKA 分析 - Centered Kernel Alignment

用于分析特征相似性：
1. DeCLIP 的 Q 特征 vs V 特征
2. Integrated 输出特征 vs DeCLIP Q 特征
3. Integrated 输出特征 vs DeCLIP V 特征

CKA 是一种测量神经网络表示相似性的方法，值在 [0, 1] 之间，1 表示完全相似
"""

import torch
import torch.nn.functional as F
import numpy as np
from typing import Dict, List, Tuple, Optional
import matplotlib.pyplot as plt
import seaborn as sns
import os


def centering_matrix(n: int, device: str = "cpu") -> torch.Tensor:
    """创建中心化矩阵 H = I - 1/n * ones"""
    I = torch.eye(n, device=device)
    ones = torch.ones(n, n, device=device) / n
    return I - ones


def linear_kernel(X: torch.Tensor) -> torch.Tensor:
    """线性核 K = X @ X.T"""
    return X @ X.T


def rbf_kernel(X: torch.Tensor, sigma: float = 1.0) -> torch.Tensor:
    """RBF 核"""
    sq_dists = torch.cdist(X, X, p=2) ** 2
    return torch.exp(-sq_dists / (2 * sigma ** 2))


def hsic(K: torch.Tensor, L: torch.Tensor, H: torch.Tensor) -> torch.Tensor:
    """
    Hilbert-Schmidt Independence Criterion
    HSIC(K, L) = trace(KHLH) / (n-1)^2
    """
    n = K.shape[0]
    KHLH = K @ H @ L @ H
    return torch.trace(KHLH) / ((n - 1) ** 2)


def cka(X: torch.Tensor, Y: torch.Tensor, kernel: str = "linear") -> float:
    """
    Centered Kernel Alignment
    
    CKA(X, Y) = HSIC(K_X, K_Y) / sqrt(HSIC(K_X, K_X) * HSIC(K_Y, K_Y))
    
    Args:
        X: 特征矩阵 (n_samples, n_features_x)
        Y: 特征矩阵 (n_samples, n_features_y)
        kernel: 核函数类型 ("linear" or "rbf")
        
    Returns:
        CKA 值 [0, 1]
    """
    assert X.shape[0] == Y.shape[0], "X and Y must have same number of samples"
    
    n = X.shape[0]
    device = X.device
    
    # 中心化矩阵
    H = centering_matrix(n, device)
    
    # 核矩阵
    if kernel == "linear":
        K_X = linear_kernel(X)
        K_Y = linear_kernel(Y)
    elif kernel == "rbf":
        K_X = rbf_kernel(X)
        K_Y = rbf_kernel(Y)
    else:
        raise ValueError(f"Unknown kernel: {kernel}")
    
    # HSIC
    hsic_xy = hsic(K_X, K_Y, H)
    hsic_xx = hsic(K_X, K_X, H)
    hsic_yy = hsic(K_Y, K_Y, H)
    
    # CKA
    cka_value = hsic_xy / (torch.sqrt(hsic_xx * hsic_yy) + 1e-8)
    
    return cka_value.item()


class CKAAnalyzer:
    """
    CKA 特征相似性分析器
    """
    
    def __init__(self, save_dir: str = "cka_analysis_results"):
        self.save_dir = save_dir
        os.makedirs(save_dir, exist_ok=True)
        self.results = {}
        
    def extract_features(
        self,
        model: torch.nn.Module,
        image: torch.Tensor,
        mode: str = "vanilla"
    ) -> Dict[str, torch.Tensor]:
        """
        提取特征
        
        Args:
            model: CLIP 模型
            image: 输入图像 (1, 3, H, W)
            mode: 提取模式
            
        Returns:
            特征字典
        """
        model.eval()
        features = {}
        
        with torch.no_grad():
            if mode in ["csa_vfm_distill", "qq_vfm_distill"]:
                output, context = model.encode_dense(image, normalize=False, keep_shape=True, mode=mode)
                features["output"] = output
                
                if isinstance(context, tuple):
                    if len(context) >= 2:
                        features["Q"] = context[0]
                        features["V"] = context[1]
                    elif len(context) == 1:
                        features["context"] = context[0]
                else:
                    features["context"] = context
            else:
                output = model.encode_dense(image, normalize=False, keep_shape=True, mode=mode)
                features["output"] = output
                
        return features
    
    def analyze_qv_similarity(
        self,
        model: torch.nn.Module,
        images: List[torch.Tensor],
        mode: str = "csa_vfm_distill"
    ) -> float:
        """
        分析 Q 和 V 特征的相似性
        
        Args:
            model: DeCLIP 模型
            images: 图像列表
            mode: 解耦模式
            
        Returns:
            Q 和 V 的 CKA 相似度
        """
        all_q = []
        all_v = []
        
        for image in images:
            features = self.extract_features(model, image, mode)
            
            if "Q" in features and "V" in features:
                # 展平特征
                q = features["Q"].flatten(start_dim=1)  # (B, N*dim)
                v = features["V"].flatten(start_dim=1)
                all_q.append(q)
                all_v.append(v)
        
        if not all_q:
            print("No Q/V features extracted")
            return 0.0
        
        # 合并所有样本
        Q_all = torch.cat(all_q, dim=0)  # (total_samples, features)
        V_all = torch.cat(all_v, dim=0)
        
        # 计算 CKA
        cka_qv = cka(Q_all, V_all, kernel="linear")
        
        self.results["Q_vs_V"] = cka_qv
        return cka_qv
    
    def analyze_integrated_vs_decoupled(
        self,
        decoupled_model: torch.nn.Module,
        integrated_model: torch.nn.Module,
        images: List[torch.Tensor]
    ) -> Dict[str, float]:
        """
        分析 Integrated 输出与 DeCLIP Q/V 的相似性
        
        Args:
            decoupled_model: 解耦蒸馏模型
            integrated_model: 集成蒸馏模型
            images: 图像列表
            
        Returns:
            CKA 相似度字典
        """
        all_integrated = []
        all_q = []
        all_v = []
        
        for image in images:
            # 提取 integrated 特征
            int_features = self.extract_features(integrated_model, image, mode="vanilla")
            int_output = int_features["output"].flatten(start_dim=1)
            all_integrated.append(int_output)
            
            # 提取 decoupled 特征
            dec_features = self.extract_features(decoupled_model, image, mode="csa_vfm_distill")
            if "Q" in dec_features and "V" in dec_features:
                q = dec_features["Q"].flatten(start_dim=1)
                v = dec_features["V"].flatten(start_dim=1)
                all_q.append(q)
                all_v.append(v)
        
        if not all_q:
            print("No Q/V features extracted from decoupled model")
            return {}
        
        # 合并
        Int_all = torch.cat(all_integrated, dim=0)
        Q_all = torch.cat(all_q, dim=0)
        V_all = torch.cat(all_v, dim=0)
        
        # 确保特征维度一致
        min_dim = min(Int_all.shape[1], Q_all.shape[1], V_all.shape[1])
        Int_all = Int_all[:, :min_dim]
        Q_all = Q_all[:, :min_dim]
        V_all = V_all[:, :min_dim]
        
        # 计算 CKA
        results = {
            "Integrated_vs_Q": cka(Int_all, Q_all, kernel="linear"),
            "Integrated_vs_V": cka(Int_all, V_all, kernel="linear"),
            "Q_vs_V": cka(Q_all, V_all, kernel="linear")
        }
        
        self.results.update(results)
        return results
    
    def visualize_cka_matrix(
        self,
        cka_matrix: np.ndarray,
        labels: List[str],
        save_name: str = "cka_matrix.png",
        title: str = "CKA Similarity Matrix"
    ):
        """
        可视化 CKA 相似度矩阵
        """
        plt.figure(figsize=(8, 6))
        sns.heatmap(
            cka_matrix,
            xticklabels=labels,
            yticklabels=labels,
            annot=True,
            fmt=".3f",
            cmap="YlOrRd",
            vmin=0,
            vmax=1,
            square=True
        )
        plt.title(title, fontsize=14)
        plt.tight_layout()
        
        save_path = os.path.join(self.save_dir, save_name)
        plt.savefig(save_path, dpi=150, bbox_inches="tight")
        plt.close()
        
        print(f"CKA matrix saved to {save_path}")
    
    def create_comparison_matrix(self) -> Tuple[np.ndarray, List[str]]:
        """
        创建对比矩阵
        """
        labels = ["DeCLIP Q", "DeCLIP V", "Integrated Output"]
        n = len(labels)
        matrix = np.eye(n)
        
        if "Q_vs_V" in self.results:
            matrix[0, 1] = matrix[1, 0] = self.results["Q_vs_V"]
        if "Integrated_vs_Q" in self.results:
            matrix[0, 2] = matrix[2, 0] = self.results["Integrated_vs_Q"]
        if "Integrated_vs_V" in self.results:
            matrix[1, 2] = matrix[2, 1] = self.results["Integrated_vs_V"]
            
        return matrix, labels
    
    def print_summary(self):
        """打印分析摘要"""
        print("\n" + "="*50)
        print("CKA Analysis Summary")
        print("="*50)
        
        for key, value in self.results.items():
            print(f"  {key}: {value:.4f}")
            
        print("\nInterpretation:")
        if "Q_vs_V" in self.results:
            qv = self.results["Q_vs_V"]
            if qv < 0.5:
                print(f"  Q and V are fairly different (CKA={qv:.3f}), indicating successful decoupling")
            else:
                print(f"  Q and V are similar (CKA={qv:.3f}), suggesting features are mixed")
                
        print("="*50 + "\n")
    
    def save_results(self, filename: str = "cka_results.json"):
        """保存分析结果"""
        import json
        
        filepath = os.path.join(self.save_dir, filename)
        with open(filepath, "w") as f:
            json.dump(self.results, f, indent=2)
        print(f"CKA results saved to {filepath}")


def run_cka_analysis(
    decoupled_checkpoint: str,
    integrated_checkpoint: str,
    image_paths: List[str],
    model_name: str = "EVA02-CLIP-B-16",
    save_dir: str = "cka_analysis_results"
):
    """
    运行完整的 CKA 分析
    
    Args:
        decoupled_checkpoint: 解耦蒸馏模型的 checkpoint 路径
        integrated_checkpoint: 集成蒸馏模型的 checkpoint 路径
        image_paths: 测试图像路径列表
        model_name: 模型名称
        save_dir: 保存目录
    """
    from open_clip import create_model
    from torchvision import transforms
    from PIL import Image
    
    print("Loading models...")
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    # 加载模型
    decoupled_model = create_model(model_name, pretrained="eva", device=device)
    decoupled_model.load_state_dict(torch.load(decoupled_checkpoint, map_location=device)["state_dict"])
    decoupled_model.eval()
    
    integrated_model = create_model(model_name, pretrained="eva", device=device)
    integrated_model.load_state_dict(torch.load(integrated_checkpoint, map_location=device)["state_dict"])
    integrated_model.eval()
    
    # 预处理
    transform = transforms.Compose([
        transforms.Resize((560, 560)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    
    # 加载图像
    print(f"Loading {len(image_paths)} images...")
    images = []
    for path in image_paths:
        image = Image.open(path).convert("RGB")
        image_tensor = transform(image).unsqueeze(0).to(device)
        images.append(image_tensor)
    
    # CKA 分析
    analyzer = CKAAnalyzer(save_dir)
    
    print("Analyzing Q vs V similarity...")
    analyzer.analyze_qv_similarity(decoupled_model, images)
    
    print("Analyzing Integrated vs Decoupled similarity...")
    analyzer.analyze_integrated_vs_decoupled(decoupled_model, integrated_model, images)
    
    # 可视化
    matrix, labels = analyzer.create_comparison_matrix()
    analyzer.visualize_cka_matrix(matrix, labels, save_name="cka_comparison.png")
    
    # 保存结果
    analyzer.print_summary()
    analyzer.save_results()
    
    print("Done!")


if __name__ == "__main__":
    print("CKA Analysis Tool")
    print("Usage:")
    print("  from decoupling_analysis.cka_analysis import CKAAnalyzer, cka")
    print("  analyzer = CKAAnalyzer('save_dir')")
    print("  analyzer.analyze_qv_similarity(model, images)")
    print("  analyzer.analyze_integrated_vs_decoupled(dec_model, int_model, images)")