def clean_text(
    text: str, lower: bool = True, stem: bool = False, stopwords=config.STOPWORDS
) -> str:
    """Clean raw text.
    Args:
        text (str): raw text to be cleaned.
        lower (bool): whether to lowercase the text.
        stem (bool): whether to stem the text.
    Returns:
        str: cleaned text.
    """

    # Lower
    if lower:
        text = text.lower()

    # Remove stopwords
    if len(stopwords):
        pattern = re.compile(r"\b(" + r"|".join(stopwords) + r")\b\s*")
        text = pattern.sub("", text)

    # Spacing and filters
    text = re.sub(
        r"([!\"'#$%&()*\+,-./:;<=>?@\\\[\]^_`{|}~])", r" \1 ", text
    )  # add spacing between objects to be filtered
    text = re.sub("[^A-Za-z0-9]+", " ", text)  # remove non alphanumeric chars
    text = re.sub(" +", " ", text)  # remove multiple spaces
    text = text.strip()  # strip white space at the ends

    # Remove links
    text = re.sub(r"http\S+", "", text)

    # Stemming
    if stem:
        stemmer = PorterStemmer()
        text = " ".join([stemmer.stem(word, to_lowercase=lower) for word in text.split(" ")])

    return text

def get_data_splits(X: pd.Series, y: np.ndarray, train_size: float = 0.7) -> Tuple:
    """Generate balanced data splits.
    Args:
        X (pd.Series): input features.
        y (np.ndarray): encoded labels.
        train_size (float, optional): proportion of data to use for training. Defaults to 0.7.
    Returns:
        Tuple: data splits as Numpy arrays.
    """
    X_train, X_, y_train, y_ = train_test_split(X, y, train_size=train_size, stratify=y)
    X_val, X_test, y_val, y_test = train_test_split(X_, y_, train_size=0.5, stratify=y_)
    return X_train, X_val, X_test, y_train, y_val, y_test

def preprocess(df: pd.DataFrame, lower: bool, stem: bool, min_freq: int) -> pd.DataFrame:
    """Preprocess the data.
    Args:
        df (pd.DataFrame): Pandas DataFrame with data.
        lower (bool): whether to lowercase the text.
        stem (bool): whether to stem the text.
        min_freq (int): minimum # of data points a label must have.
    Returns:
        pd.DataFrame: Dataframe with preprocessed data.
    """
    df["text"] = df.title + " " + df.description  # feature engineering
    df.text = df.text.apply(clean_text, lower=lower, stem=stem)  # clean text
    df = replace_oos_labels(
        df=df, labels=config.ACCEPTED_TAGS, label_col="tag", oos_label="other"
    )  # replace OOS labels
    df = replace_minority_labels(
        df=df, label_col="tag", min_freq=min_freq, new_label="other"
    )  # replace labels below min freq

    return df

def replace_minority_labels(
    df: pd.DataFrame, label_col: str, min_freq: int, new_label: str = "other"
) -> pd.DataFrame:
    """Replace minority labels with another label.
    Args:
        df (pd.DataFrame): Pandas DataFrame with data.
        label_col (str): name of the dataframe column that has the labels.
        min_freq (int): minimum # of data points a label must have.
        new_label (str, optional): name of the new label to replace minority labels. Defaults to "other".
    Returns:
        pd.DataFrame: Dataframe with replaced minority labels.
    """
    labels = Counter(df[label_col].values)
    labels_above_freq = Counter(label for label in labels.elements() if (labels[label] >= min_freq))
    df[label_col] = df[label_col].apply(lambda label: label if label in labels_above_freq else None)
    df[label_col] = df[label_col].fillna(new_label)
    return df

def replace_oos_labels(
    df: pd.DataFrame, labels: List, label_col: str, oos_label: str = "other"
) -> pd.DataFrame:
    """Replace out of scope (OOS) labels.
    Args:
        df (pd.DataFrame): Pandas DataFrame with data.
        labels (List): list of accepted labels.
        label_col (str): name of the dataframe column that has the labels.
        oos_label (str, optional): name of the new label for OOS labels. Defaults to "other".
    Returns:
        pd.DataFrame: Dataframe with replaced OOS labels.
    """
    oos_tags = [item for item in df[label_col].unique() if item not in labels]
    df[label_col] = df[label_col].apply(lambda x: oos_label if x in oos_tags else x)
    return df