def __init__(self, dialect, script, numeral="Latin"):
    """
    Initialization of the Preprocess class

    Arguments:
        dialect (str): the name of the dialect or its ISO 639-3  code
        script (str): the name of the script
        numeral (str): the type of the numeral

    """
    with open(klpt.get_data("data/preprocess_map.json"), encoding = "utf-8") as preprocess_file:
        self.preprocess_map = json.load(preprocess_file)

    configuration = Configuration({"dialect": dialect, "script": script, "numeral": numeral})
    self.dialect = configuration.dialect
    self.script = configuration.script
    self.numeral = configuration.numeral
    # self.preprocess_map = config.preprocess_map

    with open(klpt.data_directory["stopwords"], "r", encoding = "utf-8") as f:
        self.stopwords = json.load(f)[dialect][script]

def normalize(self, text):
    """
    Text normalization

    This function deals with different encodings and unifies characters based on dialects and scripts as follows:

    - Sorani-Arabic:

        - replace frequent Arabic characters with their equivalent Kurdish ones, e.g. "ي" by "ی" and "ك" by "ک"
        - replace "ه" followed by zero-width non-joiner (ZWNJ, U+200C) with "ە" where ZWNJ is removed ("ره‌زبه‌ر" is converted to "رەزبەر"). ZWNJ in HTML is also taken into account.
        - replace "هـ" with "ھ" (U+06BE, ARABIC LETTER HEH DOACHASHMEE)
        - remove Kashida "ـ"
        - "ھ" in the middle of a word is replaced by ه (U+0647)
        - replace different types of y, such as 'ARABIC LETTER ALEF MAKSURA' (U+0649)

    It should be noted that the order of the replacements is important. Check out provided files for further details and test cases.

    Arguments:
        text (str): a string

    Returns:
        str: normalized text

     """
    temp_text = " " + self.unify_numerals(text) + " "

    for normalization_type in ["universal", self.dialect]:
        for rep in self.preprocess_map["normalizer"][normalization_type][self.script]:
            rep_tar = self.preprocess_map["normalizer"][normalization_type][self.script][rep]
            temp_text = re.sub(rf"{rep}", rf"{rep_tar}", temp_text, flags=re.I)

    return temp_text.strip()

def preprocess(self, text):
    """
    One single function for normalization, standardization and unification of numerals

    Arguments:
        text (str): a string

    Returns:
        str: preprocessed text
    """
    return self.unify_numerals(self.standardize(self.normalize(text)))

def standardize(self, text):
    """
    Method of standardization of Kurdish orthographies

    Given a normalized text, it returns standardized text based on the Kurdish orthographies.

    - Sorani-Arabic:
        - replace alveolar flap ر (/ɾ/) at the begging of the word by the alveolar trill ڕ (/r/)
        - replace double rr and ll with ř and ł respectively

    - Kurmanji-Latin:
        - replace "-an" or "'an" in dates and numerals ("di sala 2018'an" and "di sala 2018-an" -> "di sala 2018an")

    Open issues:
        - replace " وە " by  " و "? But this is not always possible, "min bo we" (ریزگـرتنا من بو وە  نە ئە وە ئــە ز)
        - "pirtükê": "pirtûkê"?
        - Should [ı (LATIN SMALL LETTER DOTLESS I](https://www.compart.com/en/unicode/U+0131) be replaced by i?

    Arguments:
        text (str): a string

    Returns:
        str: standardized text

    """
    temp_text = " " + self.unify_numerals(text) + " "

    for standardization_type in [self.dialect]:
        for rep in self.preprocess_map["standardizer"][standardization_type][self.script]:
            rep_tar = self.preprocess_map["standardizer"][standardization_type][self.script][rep]
            temp_text = re.sub(rf"{rep}", rf"{rep_tar}", temp_text, flags=re.I)

    return temp_text.strip()

def unify_numerals(self, text):
    """
    Convert numerals to the desired one

    There are three types of numerals:
    - Arabic [١٢٣٤٥٦٧٨٩٠]
    - Farsi [۱۲۳۴۵۶۷۸۹۰]
    - Latin [1234567890]

    Arguments:
        text (str): a string

    Returns:
        str: text with unified numerals

    """
    for i, j in self.preprocess_map["normalizer"]["universal"]["numerals"][self.numeral].items():
        text = text.replace(i, j)
    return text