def METHOD_NAME(tokenizer_name, checkpoint_name, dump_path, force_download):
    if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
        raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.")
    if tokenizer_name is None:
        tokenizer_names = TOKENIZER_CLASSES
    else:
        tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")}
    logger.info(f"Loading tokenizer classes: {tokenizer_names}")
    for tokenizer_name in tokenizer_names:
        tokenizer_class = TOKENIZER_CLASSES[tokenizer_name]
        add_prefix = True
        if checkpoint_name is None:
            checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys())
        else:
            checkpoint_names = [checkpoint_name]
        logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}")
        for checkpoint in checkpoint_names:
            logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}")
            # Load tokenizer
            tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download)
            # Save fast tokenizer
            logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}")
            # For organization names we create sub-directories
            if "/" in checkpoint:
                checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/")
                dump_path_full = os.path.join(dump_path, checkpoint_directory)
            elif add_prefix:
                checkpoint_prefix_name = checkpoint
                dump_path_full = dump_path
            else:
                checkpoint_prefix_name = None
                dump_path_full = dump_path
            logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
            if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]:
                file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint]
                next_char = file_path.split(checkpoint)[-1][0]
                if next_char == "/":
                    dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name)
                    checkpoint_prefix_name = None
                logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
            file_names = tokenizer.save_pretrained(
                dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name
            )
            logger.info(f"=> File names {file_names}")
            for file_name in file_names:
                if not file_name.endswith("tokenizer.json"):
                    os.remove(file_name)
                    logger.info(f"=> removing {file_name}")