def METHOD_NAME():
    args = data_args()
    # load training data
    path_to_train = args.training_file
    ds = pq.read_table(path_to_train)
    train = ds.to_pandas()
    # load validation data if provided and append to training data
    if args.validation_file:
        path_to_val = args.validation_file
        ds = pq.read_table(path_to_val)
        val = ds.to_pandas()
        train = pd.concat([train, val])
    # randomize the order of the data
    data_full = train.sample(frac=1, random_state=0).reset_index(drop=True)
    # split data into training, validation and testing
    val_ratio = args.validation_ratio
    test_ratio = args.testing_ratio
    output_dir = args.output_dir
    split_to_jsonl(data_full, output_dir, val_ratio, test_ratio)