import os
import argparse
import pickle
import torch
import numpy as np
import random
import networkx as nx
import matplotlib.pyplot as plt
import math
from tqdm import tqdm
from torch.nn.utils.rnn import pad_sequence
from sklearn.decomposition import PCA
from sklearn.metrics.pairwise import cosine_similarity

# Import project modules
from model.transformer import GPTConfig, GPT
from cli_utils import parse_count, format_count


def parse_args():
    parser = argparse.ArgumentParser(
        description='Visualize hidden states for multiple tasks clustered by current node using PCA.')

    # --- Model & Data Configuration ---
    parser.add_argument('--ckpt_iter', type=int, default=10000)
    parser.add_argument('--config', type=str, default='6_6_384')
    parser.add_argument('--device', type=str, default='cuda:0')
    parser.add_argument('--num_nodes', type=int, default=100)
    parser.add_argument('--num_of_paths', type=int, default=20)
    parser.add_argument('--multitasks', action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument('--num_train_dataset', type=parse_count, default="5M")
    parser.add_argument('--tasks', type=str, default='A1C1', help='Tasks to visualize (e.g., A1E1, A1C1, A1B1, etc.). Format: Task1[1-9]Task2[1-9]...')
    parser.add_argument('--CL', action=argparse.BooleanOptionalAction, default=False)
    parser.add_argument('--path_type', type=str, default='RWs', choices=['RWc', 'RWa', 'RWs'])
    parser.add_argument('--no_task_tag', action='store_true', default=False)
    parser.add_argument('--local', action='store_true', default=False)

    # --- Visualization Specific ---
    parser.add_argument('--vis_samples', type=int, default=50000,
                        help='Number of prefix sequences to extract and process.')
    parser.add_argument('--batch_size', type=int, default=100)
    parser.add_argument('--num_plot_nodes', type=int, default=10,
                        help='Number of distinct nodes to color and plot (to avoid visual clutter).')

    return parser.parse_args()


def load_lines(path):
    if not os.path.exists(path): return []
    try:
        with open(path, 'r', encoding='gbk') as f:
            return [line.strip() for line in f if line.strip()]
    except:
        with open(path, 'r', encoding='utf-8') as f:
            return [line.strip() for line in f if line.strip()]


def extract_taskA_prefixes(lines, stoi, max_samples, no_task_tag, grid_n, num_nodes):
    """
    专门提取 Task A 的数据，并穷举路径上的每一步。
    返回列表，每个元素为: {'ids': tensor, 'node': current_node, 'task': 'A'}
    """
    labels_chars = {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'}
    data = []

    # 随机打乱以保证采样均匀
    lines = list(lines)
    random.shuffle(lines)

    for line in lines:
        if len(data) >= max_samples:
            break

        parts = line.split()
        if ':' not in parts: continue
        colon_idx = parts.index(':')

        # 识别并过滤只留 Task A
        if no_task_tag:
            # 如果没有 task tag，Task E 会包含字母，Task C 会包含 L/R/F/T
            is_task_A = not any(c in labels_chars or c in {'L', 'R', 'F', 'T'} for c in parts)
            if not is_task_A: continue
            try:
                source = int(parts[0])
            except:
                continue
        else:
            if parts[0] != 'A': continue
            try:
                source = int(parts[1])
            except:
                continue

        actions = parts[colon_idx + 1:]
        if not actions: continue

        try:
            token_ids = [stoi[t] for t in parts]
        except KeyError:
            continue

        curr = source

        # 沿着路径走，每走一步截断一次，作为一条独立的样本
        for i, move in enumerate(actions):
            if move == 'N':
                curr -= grid_n
            elif move == 'S':
                curr += grid_n
            elif move == 'E':
                curr += 1
            elif move == 'W':
                curr -= 1

            if not (0 <= curr < num_nodes): break

            prefix_ids = token_ids[:colon_idx + 2 + i]
            data.append({'ids': torch.tensor(prefix_ids, dtype=torch.long), 'node': curr, 'task': 'A'})

            if len(data) >= max_samples:
                break

    return data


def extract_taskE_prefixes(lines, stoi, max_samples, no_task_tag, grid_n, num_nodes):
    """
    专门提取 Task E 的数据，并穷举路径上的每一对 (dir, label)。
    返回列表，每个元素为: {'ids': tensor, 'node': current_node, 'task': 'E'}
    """
    labels_chars = {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'}
    data = []

    lines = list(lines)
    random.shuffle(lines)

    for line in lines:
        if len(data) >= max_samples:
            break

        parts = line.split()
        if ':' not in parts: continue
        colon_idx = parts.index(':')

        # 识别并过滤只留 Task E
        if no_task_tag:
            # 如果没有 task tag，Task E 会包含字母（label）
            is_task_E = any(c in labels_chars for c in parts[colon_idx + 1:])
            if not is_task_E: continue
            try:
                source = int(parts[0])
                target = int(parts[1])
            except:
                continue
        else:
            if parts[0] != 'E': continue
            try:
                source = int(parts[1])
                target = int(parts[2])
            except:
                continue

        actions = parts[colon_idx + 1:]  # (dir, label, dir, label, ...)
        if len(actions) < 2: continue  # 至少需要一个 (dir, label) 对

        try:
            token_ids = [stoi[t] for t in parts]
        except KeyError:
            continue

        # Task E 的 actions 是 (dir, label) 交替，每步走一对
        curr = source
        dirs_map = {'N': -grid_n, 'S': grid_n, 'E': 1, 'W': -1}

        for step in range(0, len(actions), 2):
            if step >= len(actions) - 1:
                break  # 需要完整的 (dir, label) 对
            
            direction = actions[step]
            label = actions[step + 1]
            
            if direction not in dirs_map or label not in labels_chars:
                break

            curr += dirs_map[direction]
            if not (0 <= curr < num_nodes):
                break

            # 截断到这一对（包括 dir 和 label）
            prefix_ids = token_ids[:colon_idx + 2 + step + 2]  # +2 for dir and label
            data.append({'ids': torch.tensor(prefix_ids, dtype=torch.long), 'node': curr, 'task': 'E'})

            if len(data) >= max_samples:
                break

    return data


def extract_taskC_prefixes(lines, stoi, max_samples, no_task_tag, grid_n, num_nodes):
    """
    专门提取 Task C 的数据，并穷举路径上的每一个相对转向操作。
    Task C：起始方向东方(E)，每个操作(L/R/F/T)既转向又移动一步。
    返回列表，每个元素为: {'ids': tensor, 'node': current_node, 'task': 'C'}
    """
    data = []
    
    lines = list(lines)
    random.shuffle(lines)
    
    # 方向映射
    left_of = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'}
    right_of = {v: k for k, v in left_of.items()}
    opposite_of = {'N': 'S', 'S': 'N', 'E': 'W', 'W': 'E'}
    delta = {'N': -grid_n, 'S': grid_n, 'E': 1, 'W': -1}
    
    for line in lines:
        if len(data) >= max_samples:
            break
        
        parts = line.split()
        if ':' not in parts: continue
        colon_idx = parts.index(':')
        
        # 识别并过滤只留 Task C
        if no_task_tag:
            # 如果没有 task tag，Task C 会包含 L/R/F/T
            actions_part = parts[colon_idx + 1:]
            is_task_C = any(c in {'L', 'R', 'F', 'T'} for c in actions_part)
            if not is_task_C: continue
            try:
                source = int(parts[0])
                target = int(parts[1])
            except:
                continue
        else:
            if parts[0] != 'C': continue
            try:
                source = int(parts[1])
                target = int(parts[2])
            except:
                continue
        
        actions = parts[colon_idx + 1:]
        if not actions: continue
        
        try:
            token_ids = [stoi[t] for t in parts]
        except KeyError:
            continue
        
        # Task C：起始在source，方向东方
        curr = source
        orientation = 'E'
        
        # 沿着路径走，每走一步截断一次
        for i, action in enumerate(actions):
            if action not in {'L', 'R', 'F', 'T'}:
                break
            
            # 计算下一个方向
            if action == 'F':
                next_orientation = orientation
            elif action == 'L':
                next_orientation = left_of[orientation]
            elif action == 'R':
                next_orientation = right_of[orientation]
            else:  # 'T'
                next_orientation = opposite_of[orientation]
            
            # 沿着下一个方向移动
            next_node = curr + delta[next_orientation]
            if not (0 <= next_node < num_nodes):
                break
            
            orientation = next_orientation
            curr = next_node
            
            # 截断到这一步操作
            prefix_ids = token_ids[:colon_idx + 2 + i]
            data.append({'ids': torch.tensor(prefix_ids, dtype=torch.long), 'node': curr, 'task': 'C'})
            
            if len(data) >= max_samples:
                break
    
    return data


activations = {}


def get_layer_hook(layer_idx):
    def hook(model, input, output):
        activations[layer_idx] = output.detach()

    return hook


def main():
    args = parse_args()
    seed = 42
    torch.manual_seed(seed);
    np.random.seed(seed);
    random.seed(seed)
    grid_n = int(args.num_nodes ** 0.5)

    tasks_tag = f"{args.tasks}_CL" if args.CL else args.tasks
    tasks_tag = f"{tasks_tag}_{args.path_type}"
    if args.no_task_tag: tasks_tag += "_NT"

    data_dir = f'data/maze/{args.num_nodes}'
    nt_suffix = '_NT' if args.no_task_tag else ''
    out_dir = f'out/transformer/maze_{args.config}_{args.num_nodes}{nt_suffix}/'
    os.makedirs(out_dir, exist_ok=True)

    def pick_file(candidates):
        for path in candidates:
            if os.path.exists(path): return path
        return candidates[0]

    # --- 读取 Meta ---
    meta = pickle.load(open(pick_file([f'{data_dir}/meta_{tasks_tag}.pkl', f'{data_dir}/meta.pkl']), 'rb'))
    stoi, itos = meta['stoi'], meta['itos']

    # --- 读取 Model ---
    train_label = format_count(args.num_train_dataset)
    ckpt_path = pick_file([os.path.join(out_dir, f'{args.ckpt_iter}_ckpt_maze_{tasks_tag}_{train_label}.pt'),
                           os.path.join(out_dir, f'{args.ckpt_iter}_ckpt_maze_{args.num_of_paths}.pt')])

    print(f"Loading Model from: {ckpt_path}")
    checkpoint = torch.load(ckpt_path, map_location=args.device)
    conf = GPTConfig(**checkpoint['model_args'])

    if args.local: conf.use_flash = False

    model = GPT(conf)
    model.load_state_dict({k.replace('_orig_mod.', ''): v for k, v in checkpoint['model'].items()})
    model.to(args.device).eval()
    for p in model.parameters(): p.requires_grad = False

    # 注册 Hook 获取每一层输出
    for i in range(conf.n_layer):
        model.transformer.h[i].register_forward_hook(get_layer_hook(i))
    
    # 注册 Hook 获取最终层归一化的输出
    model.transformer.ln_f.register_forward_hook(get_layer_hook('ln_f'))

    # --- 准备数据 ---
    train_txt = pick_file([os.path.join(data_dir, f"train_{tasks_tag}_{train_label}.txt"),
                           os.path.join(data_dir, f'train_{args.num_of_paths}.txt')])

    # 解析任务参数，确定要提取哪些任务
    # 支持格式：A1E1、A1C1、A1、E1、C1等
    tasks_to_extract = []
    task_str = args.tasks
    i = 0
    while i < len(task_str):
        if task_str[i].isalpha():
            task_type = task_str[i]
            if i + 1 < len(task_str) and task_str[i+1].isdigit():
                task_num = int(task_str[i+1])
                tasks_to_extract.append((task_type, task_num))
                i += 2
            else:
                i += 1
        else:
            i += 1
    
    print(f"Extracting tasks from: {train_txt}")
    print(f"Tasks to extract: {tasks_to_extract}")
    
    dataset = []
    dataset_counts = {}
    lines = load_lines(train_txt)
    samples_per_task = args.vis_samples // len(tasks_to_extract) if tasks_to_extract else 0
    
    for task_type, task_num in tasks_to_extract:
        if task_type == 'A':
            dataset_A = extract_taskA_prefixes(lines, stoi, samples_per_task,
                                              args.no_task_tag, grid_n, args.num_nodes)
            dataset.extend(dataset_A)
            dataset_counts['A'] = len(dataset_A)
            print(f"Successfully extracted {len(dataset_A)} Task A samples.")
        elif task_type == 'E':
            dataset_E = extract_taskE_prefixes(lines, stoi, samples_per_task,
                                              args.no_task_tag, grid_n, args.num_nodes)
            dataset.extend(dataset_E)
            dataset_counts['E'] = len(dataset_E)
            print(f"Successfully extracted {len(dataset_E)} Task E samples.")
        elif task_type == 'C':
            dataset_C = extract_taskC_prefixes(lines, stoi, samples_per_task,
                                              args.no_task_tag, grid_n, args.num_nodes)
            dataset.extend(dataset_C)
            dataset_counts['C'] = len(dataset_C)
            print(f"Successfully extracted {len(dataset_C)} Task C samples.")
    
    random.shuffle(dataset)
    print(f"Total: {len(dataset)} prefix samples.\n")

    # --- 执行推理，收集隐藏状态 ---
    all_feats = {i: [] for i in range(conf.n_layer)}
    all_feats['ln_f'] = []  # For final LayerNorm output
    node_labels = []
    task_labels = []  # Track which task (A or E) each sample belongs to

    for i in tqdm(range(0, len(dataset), args.batch_size), desc="Running Inference"):
        batch = dataset[i: i + args.batch_size]
        # Pad sequence
        x_padded = pad_sequence([item['ids'] for item in batch], batch_first=True, padding_value=0).to(args.device)

        with torch.no_grad():
            model(x_padded)

        for lk in all_feats.keys():
            if lk not in activations:
                continue
            out = activations[lk]
            for j, item in enumerate(batch):
                # 提取序列最后一个 token (即刚才走的最后一步) 的隐状态
                all_feats[lk].append(out[j, len(item['ids']) - 1, :].cpu().numpy())

        for item in batch:
            node_labels.append(item['node'])
            task_labels.append(item['task'])

    node_labels = np.array(node_labels)
    task_labels = np.array(task_labels)

    # --- 筛选着色节点 (Target Nodes) ---
    # 找出出现频率最高的 N 个节点，这样散点图的点比较密集，聚类效果明显
    unique_nodes, counts = np.unique(node_labels, return_counts=True)
    top_indices = np.argsort(-counts)[:args.num_plot_nodes]
    target_nodes = unique_nodes[top_indices]

    print(f"\nSelected top {args.num_plot_nodes} nodes for visualization: {target_nodes}")

    # 过滤数据，只保留属于 target_nodes 的样本
    mask = np.isin(node_labels, target_nodes)
    filtered_labels = node_labels[mask]
    filtered_tasks = task_labels[mask]

    # --- 准备颜色和 marker 映射 ---
    cmap = plt.get_cmap('tab10') if args.num_plot_nodes <= 10 else plt.get_cmap('tab20')
    color_map = {node: cmap(i / len(target_nodes)) for i, node in enumerate(target_nodes)}
    
    # Task 到 marker 的映射
    task_markers = {'A': 'o', 'E': '^', 'C': 's', 'D': 'P', 'F': 'X', 'G': 'D'}
    unique_tasks = np.unique(task_labels)

    # --- 绘图准备：所有层 + 1 个归一化后的子图 ---
    print(f"\nApplying PCA and Plotting...")
    cols = min(3, conf.n_layer + 1)  # +1 for normalization subplot
    rows = math.ceil((conf.n_layer + 1) / cols)
    fig, axes = plt.subplots(rows, cols, figsize=(5 * cols + 2, 4 * rows))

    if conf.n_layer + 1 == 1:
        axes = [axes]
    else:
        axes = axes.flatten()

    first_ax_handles = []

    # 绘制每一层的原始特征
    for l in range(conf.n_layer):
        feats = np.array(all_feats[l])[mask]  # (Filtered_N, Hidden_Dim)

        # PCA 降维
        pca = PCA(n_components=2, random_state=42)
        feats_2d = pca.fit_transform(feats)

        ax = axes[l]
        # 按节点和任务类型绘制
        for node in target_nodes:
            for task in unique_tasks:
                idx = (filtered_labels == node) & (filtered_tasks == task)
                if np.sum(idx) > 0:
                    marker = task_markers.get(task, 'o')
                    ax.scatter(feats_2d[idx, 0], feats_2d[idx, 1],
                             marker=marker, color=color_map[node], alpha=0.7, s=20)
                    if l == 0:
                        first_ax_handles.append(plt.Line2D([0], [0], marker=marker, color='w',
                                                           markerfacecolor=color_map[node], markersize=8))

        ax.set_title(f'Layer {l}')
        ax.set_xticks([])
        ax.set_yticks([])

    # 绘制最终层归一化后的特征（在最后一个 subplot）
    ax_norm = axes[conf.n_layer]
    
    # 使用模型的最终 LayerNorm 输出
    feats_ln_f = np.array(all_feats['ln_f'])[mask]

    pca_norm = PCA(n_components=2, random_state=42)
    feats_2d_norm = pca_norm.fit_transform(feats_ln_f)

    for node in target_nodes:
        for task in unique_tasks:
            idx = (filtered_labels == node) & (filtered_tasks == task)
            if np.sum(idx) > 0:
                marker = task_markers.get(task, 'o')
                ax_norm.scatter(feats_2d_norm[idx, 0], feats_2d_norm[idx, 1],
                               marker=marker, color=color_map[node], alpha=0.7, s=20)

    ax_norm.set_title(f'Layer Final (Norm)')
    ax_norm.set_xticks([])
    ax_norm.set_yticks([])

    # 统一图例放在图形右侧
    legend_handles = []
    for node in target_nodes:
        for task in sorted(unique_tasks):
            marker = task_markers.get(task, 'o')
            legend_handles.append(plt.Line2D([0], [0], marker=marker, color='w',
                                             markerfacecolor=color_map[node], markersize=8, label=f'Node {node} (Task {task})'))

    fig.legend(handles=legend_handles, loc='center right', bbox_to_anchor=(1.02, 0.5), title="Current Node & Task")
    plt.subplots_adjust(right=0.85)

    # 隐藏多余的子图
    for l in range(conf.n_layer + 1, len(axes)):
        fig.delaxes(axes[l])

    # 生成输出文件名，包含任务信息
    task_suffix = ''.join([t for t, _ in tasks_to_extract])
    output_png = os.path.join(out_dir, f"vis_{task_suffix}_nodes_iter{args.ckpt_iter}.png")
    fig.savefig(output_png, dpi=300, bbox_inches='tight')
    plt.close(fig)

    print(f"Visualization successfully saved to: {output_png}")

    # --- 计算相似度统计 ---
    print("\n" + "="*100)
    print("Similarity Statistics (Cosine Similarity)")
    print("="*100)

    # 采样 1/10 的数据以加快统计计算
    n_total_samples = len(task_labels)
    sample_rate = 0.1
    n_samples_to_use = max(1, int(n_total_samples * sample_rate))
    sample_indices = np.random.choice(n_total_samples, size=n_samples_to_use, replace=False)
    print(f"Using {n_samples_to_use} out of {n_total_samples} samples ({sample_rate*100:.0f}%)")
    
    # 遍历所有层（包括 ln_f）
    layer_keys = list(range(conf.n_layer)) + ['ln_f']
    
    for layer_key in layer_keys:
        if layer_key not in all_feats:
            continue
        
        feats_all = np.array(all_feats[layer_key])  # 全部数据
        feats = feats_all[sample_indices]  # 采样 10%
        sampled_task_labels = [task_labels[i] for i in sample_indices]
        sampled_node_labels = [node_labels[i] for i in sample_indices]
        
        if layer_key == 'ln_f':
            layer_name = "Layer Final (Norm)"
        else:
            layer_name = f"Layer {layer_key}"
        
        # 初始化统计量：用 (task_pair, same/diff) 为 key 分类收集相似度
        # task_pair 格式：单任务 'A'，跨任务 'A_vs_C'（按字母序）
        stats = {}

        # 用向量化的方式计算所有配对的余弦相似度（快很多）
        print(f"  Computing similarity matrix for {layer_name}...")
        sim_matrix = cosine_similarity(feats)  # (n_samples_to_use, n_samples_to_use)

        # 遍历上三角部分（i < j）
        n_samples = len(feats)
        for i in range(n_samples):
            for j in range(i + 1, n_samples):
                sim = sim_matrix[i, j]

                task_i = sampled_task_labels[i]
                task_j = sampled_task_labels[j]
                node_i = sampled_node_labels[i]
                node_j = sampled_node_labels[j]
                same_node = bool(node_i == node_j)

                # 归一化 task_pair 键
                if task_i == task_j:
                    task_pair = task_i
                else:
                    task_pair = f"{task_i}_vs_{task_j}" if task_i < task_j else f"{task_j}_vs_{task_i}"

                kind = 'same' if same_node else 'diff'
                key = (task_pair, kind)
                if key not in stats:
                    stats[key] = []
                stats[key].append(sim)

        # 计算平均值并以表格形式打印（每个 task_pair 一行，Same 和 Diff 并列）
        print(f"\n{layer_name}:")

        # 收集所有出现的 task_pair：先列出单任务（按字母），再列出跨任务配对（按字母）
        single_pairs = sorted({tp for tp, _ in stats.keys() if '_vs_' not in tp})
        cross_pairs = sorted({tp for tp, _ in stats.keys() if '_vs_' in tp})
        task_pairs = single_pairs + cross_pairs

        for tp in task_pairs:
            same_vals = stats.get((tp, 'same'), [])
            diff_vals = stats.get((tp, 'diff'), [])
            mean_same = np.mean(same_vals) if same_vals else 0.0
            mean_diff = np.mean(diff_vals) if diff_vals else 0.0
            n_same = len(same_vals)
            n_diff = len(diff_vals)

            # 显示名：单任务显示为 "Task X"，跨任务显示为 "Task X vs Y"
            if '_vs_' in tp:
                t1, t2 = tp.split('_vs_')
                label = f"Task {t1} vs {t2}"
            else:
                label = f"Task {tp}    "

            print(f"  {label} - Same: {mean_same:7.4f} (n={n_same:7d}) | Diff: {mean_diff:7.4f} (n={n_diff:7d})")

    print("="*100)


if __name__ == "__main__":
    main()