import os

import numpy as np
import torch


# def transform_list_to_tensor(model_params_list):
#     for k in model_params_list.keys():
#         model_params_list[k] = torch.from_numpy(
#             np.asarray(model_params_list[k])
#         ).float()
#     return model_params_list


# def transform_tensor_to_list(model_params):
#     for k in model_params.keys():
#         model_params[k] = model_params[k].detach().numpy().tolist()
#     return model_params


def post_complete_message_to_sweep_process(args):
    pipe_path = "./tmp/fedml"
    os.system("mkdir ./tmp/; touch ./tmp/fedml")
    if not os.path.exists(pipe_path):
        os.mkfifo(pipe_path)
    pipe_fd = os.open(pipe_path, os.O_WRONLY)

    with os.fdopen(pipe_fd, "w") as pipe:
        pipe.write("training is finished! \n%s\n" % (str(args)))
