def METHOD_NAME():
    set_seed(args.seed)
    tic_time = time.time()
    if not os.path.exists(args.doccano_file):
        raise ValueError("Please input the correct path of doccano file.")
    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)
    if len(args.splits) != 0 and len(args.splits) != 3:
        raise ValueError("Only []/ len(splits)==3 accepted for splits.")
    def _check_sum(splits):
        return Decimal(str(splits[0])) + Decimal(str(splits[1])) + Decimal(str(splits[2])) == Decimal("1")
    if len(args.splits) == 3 and not _check_sum(args.splits):
        raise ValueError("Please set correct splits, sum of elements in splits should be equal to 1.")
    with open(args.doccano_file, "r", encoding="utf-8") as f:
        raw_examples = f.readlines()
    def _create_ext_examples(
        examples,
        negative_ratio,
        prompt_prefix="情感倾向",
        options=["正向", "负向"],
        separator="##",
        shuffle=False,
        is_train=True,
        schema_lang="ch",
    ):
        entities, relations, aspects = convert_ext_examples(
            examples, negative_ratio, prompt_prefix, options, separator, is_train, schema_lang
        )
        examples = entities + relations + aspects
        if shuffle:
            indexes = np.random.permutation(len(examples))
            examples = [examples[i] for i in indexes]
        return examples
    def _create_cls_examples(examples, prompt_prefix, options, shuffle=False):
        examples = convert_cls_examples(examples, prompt_prefix, options)
        if shuffle:
            indexes = np.random.permutation(len(examples))
            examples = [examples[i] for i in indexes]
        return examples
    def _save_examples(save_dir, file_name, examples):
        count = 0
        save_path = os.path.join(save_dir, file_name)
        with open(save_path, "w", encoding="utf-8") as f:
            for example in examples:
                f.write(json.dumps(example, ensure_ascii=False) + "\n")
                count += 1
        logger.info("Save %d examples to %s." % (count, save_path))
    if len(args.splits) == 0:
        if args.task_type == "ext":
            examples = _create_ext_examples(
                raw_examples,
                args.negative_ratio,
                args.prompt_prefix,
                args.options,
                args.separator,
                args.is_shuffle,
                schema_lang=args.schema_lang,
            )
        else:
            examples = _create_cls_examples(raw_examples, args.prompt_prefix, args.options, args.is_shuffle)
        _save_examples(args.save_dir, "train.txt", examples)
    else:
        if args.is_shuffle:
            indexes = np.random.permutation(len(raw_examples))
            index_list = indexes.tolist()
            raw_examples = [raw_examples[i] for i in indexes]
        else:
            index_list = list(range(len(raw_examples)))
        i1, i2, _ = args.splits
        p1 = int(len(raw_examples) * i1)
        p2 = int(len(raw_examples) * (i1 + i2))
        train_ids = index_list[:p1]
        dev_ids = index_list[p1:p2]
        test_ids = index_list[p2:]
        with open(os.path.join(args.save_dir, "sample_index.json"), "w") as fp:
            maps = {"train_ids": train_ids, "dev_ids": dev_ids, "test_ids": test_ids}
            fp.write(json.dumps(maps))
        if args.task_type == "ext":
            train_examples = _create_ext_examples(
                raw_examples[:p1],
                args.negative_ratio,
                args.prompt_prefix,
                args.options,
                args.separator,
                args.is_shuffle,
                schema_lang=args.schema_lang,
            )
            dev_examples = _create_ext_examples(
                raw_examples[p1:p2],
                -1,
                args.prompt_prefix,
                args.options,
                args.separator,
                is_train=False,
                schema_lang=args.schema_lang,
            )
            test_examples = _create_ext_examples(
                raw_examples[p2:],
                -1,
                args.prompt_prefix,
                args.options,
                args.separator,
                is_train=False,
                schema_lang=args.schema_lang,
            )
        else:
            train_examples = _create_cls_examples(raw_examples[:p1], args.prompt_prefix, args.options)
            dev_examples = _create_cls_examples(raw_examples[p1:p2], args.prompt_prefix, args.options)
            test_examples = _create_cls_examples(raw_examples[p2:], args.prompt_prefix, args.options)
        _save_examples(args.save_dir, "train.txt", train_examples)
        _save_examples(args.save_dir, "dev.txt", dev_examples)
        _save_examples(args.save_dir, "test.txt", test_examples)
    logger.info("Finished! It takes %.2f seconds" % (time.time() - tic_time))