def METHOD_NAME(self, data_path: str, url: str = None,
         format: str = "csv", class_sep: str = None,
         *args, **kwargs) -> dict:
    """
    Read dataset from data_path directory.
    Reading files are all data_types + extension
    (i.e for data_types=["train", "valid"] files "train.csv" and "valid.csv" form
    data_path will be read)
    Args:
        data_path: directory with files
        url: download data files if data_path not exists or empty
        format: extension of files. Set of Values: ``"csv", "json"``
        class_sep: string separator of labels in column with labels
        sep (str): delimeter for ``"csv"`` files. Default: None -> only one class per sample
        header (int): row number to use as the column names
        names (array): list of column names to use
        orient (str): indication of expected JSON string format
        lines (boolean): read the file as a json object per line. Default: ``False``
    Returns:
        dictionary with types from data_types.
        Each field of dictionary is a list of tuples (x_i, y_i)
    """
    data_types = ["train", "valid", "test"]
    train_file = kwargs.get('train', 'train.csv')
    if not Path(data_path, train_file).exists():
        if url is None:
            raise Exception(
                "data path {} does not exist or is empty, and download url parameter not specified!".format(
                    data_path))
        log.info("Loading train data from {} to {}".format(url, data_path))
        download(source_url=url, dest_file_path=Path(data_path, train_file))
    data = {"train": [],
            "valid": [],
            "test": []}
    for data_type in data_types:
        file_name = kwargs.get(data_type, '{}.{}'.format(data_type, format))
        if file_name is None:
            continue
        file = Path(data_path).joinpath(file_name)
        if file.exists():
            if format == 'csv':
                keys = ('sep', 'header', 'names')
                options = {k: kwargs[k] for k in keys if k in kwargs}
                df = pd.read_csv(file, **options)
            elif format == 'json':
                keys = ('orient', 'lines')
                options = {k: kwargs[k] for k in keys if k in kwargs}
                df = pd.read_json(file, **options)
            else:
                raise Exception('Unsupported file format: {}'.format(format))
            x = kwargs.get("x", "text")
            y = kwargs.get('y', 'labels')
            if isinstance(x, list):
                if class_sep is None:
                    # each sample is a tuple ("text", "label")
                    data[data_type] = [([row[x_] for x_ in x], str(row[y]))
                                       for _, row in df.iterrows()]
                else:
                    # each sample is a tuple ("text", ["label", "label", ...])
                    data[data_type] = [([row[x_] for x_ in x], str(row[y]).split(class_sep))
                                       for _, row in df.iterrows()]
            else:
                if class_sep is None:
                    # each sample is a tuple ("text", "label")
                    data[data_type] = [(row[x], str(row[y])) for _, row in df.iterrows()]
                else:
                    # each sample is a tuple ("text", ["label", "label", ...])
                    data[data_type] = [(row[x], str(row[y]).split(class_sep)) for _, row in df.iterrows()]
        else:
            log.warning("Cannot find {} file".format(file))
    return data