def METHOD_NAME(args):
    if args.output_dir is not None:
        os.makedirs(args.output_dir, exist_ok=True)
    # Get the datasets:
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
    # Load pretrained tokenizer
    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
    # First we tokenize all the texts.
    column_names = raw_datasets["train"].column_names
    text_column_name = "text" if "text" in column_names else column_names[0]
    if args.line_by_line:
        # When using line_by_line, we just tokenize each nonempty line.
        padding = False
        def tokenize_function(examples):
            # Remove empty lines
            examples[text_column_name] = [
                line for line in examples[text_column_name] if len(line) > 0 and not line.isspace()
            ]
            return tokenizer(
                examples[text_column_name],
                padding=padding,
                truncation=True,
                max_length=args.max_seq_length,
                # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
                # receives the `special_tokens_mask`.
                return_special_tokens_mask=True,
            )
        tokenized_datasets = raw_datasets.map(
            tokenize_function,
            batched=True,
            num_proc=args.preprocessing_num_workers,
            remove_columns=[text_column_name],
            load_from_cache_file=not args.overwrite_cache,
            desc="Running tokenizer on dataset line_by_line",
        )
    else:
        # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
        # efficient when it receives the `special_tokens_mask`.
        def tokenize_function(examples):
            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
        tokenized_datasets = raw_datasets.map(
            tokenize_function,
            batched=True,
            num_proc=args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not args.overwrite_cache,
            desc="Running tokenizer on every text in dataset",
        )
        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
        # max_seq_length.
        def group_texts(examples):
            # Concatenate all texts.
            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
            total_length = len(concatenated_examples[list(examples.keys())[0]])
            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
            # customize this part to your needs.
            if total_length >= args.max_seq_length:
                total_length = (total_length // args.max_seq_length) * args.max_seq_length
            # Split by chunks of max_len.
            result = {
                k: [t[i : i + args.max_seq_length] for i in range(0, total_length, args.max_seq_length)]
                for k, t in concatenated_examples.items()
            }
            return result
        # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
        # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
        # might be slower to preprocess.
        tokenized_datasets = tokenized_datasets.map(
            group_texts,
            batched=True,
            num_proc=args.preprocessing_num_workers,
            load_from_cache_file=not args.overwrite_cache,
            desc=f"Grouping texts in chunks of {args.max_seq_length}",
        )
    tokenized_datasets.save_to_disk(args.output_dir)