def METHOD_NAME():
    parser = argparse.ArgumentParser()
    parser.add_argument('--data', type=str, help='prefix to data files')
    parser.add_argument('--dataset-impl', type=str, default='infer',
                        choices=['lazy', 'cached', 'mmap', 'infer'])
    parser.add_argument('--count', type=int, default=10,
                        help='Number of samples/documents to print')
    group = parser.add_argument_group(title='tokenizer')
    group.add_argument('--tokenizer-type', type=str, required=True,
                       choices=['BertWordPieceLowerCase',
                                'GPT2BPETokenizer'],
                       help='What type of tokenizer to use.')
    group.add_argument('--vocab-file', type=str, default=None,
                       help='Path to the vocab file')
    group.add_argument('--merge-file', type=str, default=None,
                       help='Path to the BPE merge file (if necessary).')
    parser.add_argument('--epochs', type=int, default=5,
                        help='Number of epochs to plan for')
    parser.add_argument('--max-num-samples', type=int, default=None,
                        help='Maximum number of samples to plan for')
    parser.add_argument('--masked-lm-prob', type=float, default=0.15,
                        help='probability of masking tokens')
    parser.add_argument('--seq-length', type=int, default=512,
                        help='maximum sequence length')
    parser.add_argument('--short-seq-prob', type=float, default=0.1,
                        help='probability of creating a short sequence')
    parser.add_argument('--seed', type=int, default=1234,
                        help='random seed')
    args = parser.parse_args()
    args.rank = 0
    args.make_vocab_size_divisible_by = 128
    args.tensor_model_parallel_size = 1
    if args.dataset_impl == "infer":
        args.dataset_impl = indexed_dataset.infer_dataset_impl(args.data)