# from joblib import load
#
# model = load('AgentPPO/reward_data.pkl')
#
# data = model['mean_episode_reward']
# print(data)


# import torch
#
# model_path = 'AgentDDPG/actor.pth'
#
# checkpoint = torch.load(model_path, map_location=torch.device('cuda'))
#
# # 如果.pth文件保存的是整个模型（包含模型结构和参数）
# model = checkpoint  # 这里假设checkpoint直接就是模型实例，有时候可能需要model.load_state_dict(checkpoint['state_dict'])

# 如果.pth文件仅保存了state_dict（模型参数）
# model = YourModelClass()  # 实例化你的模型
# model.load_state_dict(checkpoint)

# print(model)

# import pickle
#
# a = 'DDPG'
# b = 'PPO'
# c = 'SAC'
# d = 'TD3'
#
# a1 = '/reward_data.pkl'
# a2 = '/loss_data.pkl'
# a3 = '/test_data.pkl'
#
# filename = './Agent' + a + a3
#
# # 使用 'rb' 模式打开文件，读取二进制数据
# with open(filename, 'rb') as f:
#     data = pickle.load(f)
#
# print(data)
# import json
# import numpy as np
#
#
# def get_llm_action(index) -> np.ndarray:
#     with open('data/llm_action.json', 'r') as file:
#         data = json.load(file)
#     normalized_index = index % len(data)
#     action = np.array(data[normalized_index])
#     return action
#
# data = get_llm_action(2)
# print(data)
import torch


def get_available_gpus():
    if torch.cuda.is_available():
        num_gpus = torch.cuda.device_count()
        print(f"Number of available GPUs: {num_gpus}")
        for i in range(num_gpus):
            print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
    else:
        print("No GPUs are available.")


get_available_gpus()