# 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 numpy as np
import json
from data_manager import DataManager

data_manager = DataManager()
llm_actions = json.load(open('data/results.json', 'r'))

for action in llm_actions:
    data_manager.add_llm_element(action)
month = np.random.randint(1, 13)  # choose 12 month
day = np.random.randint(1, 20)
llm_action = data_manager.get_llm_data(month, day, 1)
print(len(data_manager.LLM))

# 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()