"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import re
class EnglishNumberNormalizer:
    def __init__(self):
        self.ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
        self.teens = ["", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "nineteen"]
        self.tens = ["", "ten", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"]
        self.thousands = ["", "thousand", "million", "billion", "trillion", "quadrillion", "quintillion", "sextillion", "septillion", "octillion", "nonillion", "decillion"]
        self.currency_symbols = {'$': ' dollars', '€': ' euros', '£': ' pounds', '¢': ' cents', '¥': ' japanese yen', '﷼': ' saudi riyal', '₹': ' indian rupees', '₽': ' russian rubles',
        '฿': ' thai baht', '₺': ' turkish liras', '₴': ' ukrainian hryvnia', '₣': ' swiss francs', '₡': ' costa rican colon', '₱': ' philippine peso', '₪': ' israeli shekels',
        '₮': ' mongolian tögrög', '₩': ' south korean won', '₦': ' nigerian naira', '₫': ' vietnamese Đồng'}
    def spell_number(self, num):
        if num == 0: return "zero"
        parts = []
        for i in range(0, len(self.thousands)):
            if num % 1000 != 0:
                part = ""
                hundreds = num % 1000 // 100
                tens_units = num % 100
                if hundreds > 0:
                    part += self.ones[hundreds] + " hundred"
                    if tens_units > 0: part += " and "
                if tens_units > 10 and tens_units < 20: part += self.teens[tens_units - 10]
                else:
                    tens_digit = self.tens[tens_units // 10]
                    ones_digit = self.ones[tens_units % 10]
                    if tens_digit: part += tens_digit
                    if ones_digit:
                        if tens_digit: part += " "
                        part += ones_digit
                parts.append(part)
            num //= 1000
        return " ".join(reversed(parts))
    def convert(self, number):
        if "." in number: integer_part, decimal_part = number.split(".")
        else: integer_part, decimal_part = number, "00"
        currency_symbol = ""
        for symbol, name in self.currency_symbols.items():
            if integer_part.startswith(symbol):
                currency_symbol = name
                integer_part = integer_part[len(symbol) :]
                break
            if integer_part.startswith("-"):
                if integer_part[1:].startswith(symbol):
                    currency_symbol = name
                    integer_part = "-" + integer_part[len(symbol) + 1 :]
                    break
        minus_prefix = ""
        if integer_part.startswith("-"):
            minus_prefix = "minus "
            integer_part = integer_part[1:]
        elif integer_part.startswith("minus"):
            minus_prefix = "minus "
            integer_part = integer_part[len("minus") :]
        percent_suffix = ""
        if "%" in integer_part or "%" in decimal_part:
            percent_suffix = " percent"
            integer_part = integer_part.replace("%", "")
            decimal_part = decimal_part.replace("%", "")
        integer_part = integer_part.zfill(3 * ((len(integer_part) - 1) // 3 + 1))
        parts = []
        for i in range(0, len(integer_part), 3):
            chunk = int(integer_part[i : i + 3])
            if chunk > 0:
                part = self.spell_number(chunk)
                unit = self.thousands[len(integer_part[i:]) // 3 - 1]
                if unit: part += " " + unit
                parts.append(part)
        spelled_integer = " ".join(parts)
        if decimal_part == "00": return (f"{minus_prefix}{spelled_integer}{percent_suffix}{currency_symbol}" if minus_prefix or currency_symbol else f"{spelled_integer}{percent_suffix}")
        else:
            spelled_decimal = " ".join([self.spell_number(int(digit)) for digit in decimal_part])
            return (f"{minus_prefix}{spelled_integer} point {spelled_decimal}{percent_suffix}{currency_symbol}" if minus_prefix or currency_symbol else f"{minus_prefix}{spelled_integer} point {spelled_decimal}{percent_suffix}")
    def __call__(self, text):
        pattern = r"(?<!\w)(-?\$?\€?\£?\¢?\¥?\₹?\₽?\฿?\₺?\₴?\₣?\₡?\₱?\₪?\₮?\₩?\₦?\₫?\﷼?\d+(?:\.\d{1,2})?%?)(?!\w)"
        text = re.sub(r"(\d+,\d+)", lambda match: match.group(1).replace(",", ""), text)
        converted_text = re.sub(pattern, lambda match: self.convert(match.group(1)), text)
        converted_text = re.sub(" +", " ", converted_text)
        return converted_text
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
