import os
import torch
import numpy as np
import matplotlib.pyplot as plt

# 读取 .pt 文件（与脚本同目录）
script_dir = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(script_dir, "LLM_P.pt")
try:
    data = torch.load(file_path, map_location=torch.device('cpu'))
except FileNotFoundError:
    raise FileNotFoundError(f"File {file_path} not found. Please check the path.")


def process_and_plot(data, xlabel, ylim, save_name):
    """处理概率数据并绘制柱状图"""
    if data.dim() == 1:
        probs = data.numpy()
    elif data.dim() == 2:
        probs = data.mean(dim=0).numpy()
    else:
        raise ValueError(f"Unexpected tensor shape: {data.shape}")

    if abs(probs.sum() - 1.0) > 1e-5:
        probs = probs / probs.sum()

    labels = np.arange(len(probs))

    plt.rcParams['font.weight'] = 'bold'
    plt.figure(figsize=(10, 6))
    plt.bar(labels, probs, color='skyblue', edgecolor='black')
    plt.xlabel(xlabel, fontsize=16, fontweight='bold')
    plt.ylabel('Probability', fontsize=16, fontweight='bold')
    plt.ylim(ylim)
    plt.grid(True, axis='y', linestyle='--', alpha=0.7)
    ax = plt.gca()
    for label in ax.get_xticklabels() + ax.get_yticklabels():
        label.set_fontweight('bold')
    plt.tight_layout()
    save_path = os.path.join(script_dir, save_name)
    plt.savefig(save_path, dpi=300, bbox_inches='tight')
    print(f"已保存: {save_path}")
    plt.show()


# 图1: Text Token Index
process_and_plot(data, 'Text Token Index', (0, 0.2), 'LLM_P.png')

# 图2: Image Token Index
process_and_plot(data, 'Image Token Index', (0, 0.1), 'p_index.png')