# basic text cleaners for the ACE step model
# I didn't copy the ones from the reference code because I didn't want to deal with the dependencies
# TODO: more languages than english?

import re

def japanese_to_romaji(japanese_text):
    """
    Convert Japanese hiragana and katakana to romaji (Latin alphabet representation).

    Args:
        japanese_text (str): Text containing hiragana and/or katakana characters

    Returns:
        str: The romaji (Latin alphabet) equivalent
    """
    # Dictionary mapping kana characters to their romaji equivalents
    kana_map = {
        # Katakana characters
        'ア': 'a', 'イ': 'i', 'ウ': 'u', 'エ': 'e', 'オ': 'o',
        'カ': 'ka', 'キ': 'ki', 'ク': 'ku', 'ケ': 'ke', 'コ': 'ko',
        'サ': 'sa', 'シ': 'shi', 'ス': 'su', 'セ': 'se', 'ソ': 'so',
        'タ': 'ta', 'チ': 'chi', 'ツ': 'tsu', 'テ': 'te', 'ト': 'to',
        'ナ': 'na', 'ニ': 'ni', 'ヌ': 'nu', 'ネ': 'ne', 'ノ': 'no',
        'ハ': 'ha', 'ヒ': 'hi', 'フ': 'fu', 'ヘ': 'he', 'ホ': 'ho',
        'マ': 'ma', 'ミ': 'mi', 'ム': 'mu', 'メ': 'me', 'モ': 'mo',
        'ヤ': 'ya', 'ユ': 'yu', 'ヨ': 'yo',
        'ラ': 'ra', 'リ': 'ri', 'ル': 'ru', 'レ': 're', 'ロ': 'ro',
        'ワ': 'wa', 'ヲ': 'wo', 'ン': 'n',

        # Katakana voiced consonants
        'ガ': 'ga', 'ギ': 'gi', 'グ': 'gu', 'ゲ': 'ge', 'ゴ': 'go',
        'ザ': 'za', 'ジ': 'ji', 'ズ': 'zu', 'ゼ': 'ze', 'ゾ': 'zo',
        'ダ': 'da', 'ヂ': 'ji', 'ヅ': 'zu', 'デ': 'de', 'ド': 'do',
        'バ': 'ba', 'ビ': 'bi', 'ブ': 'bu', 'ベ': 'be', 'ボ': 'bo',
        'パ': 'pa', 'ピ': 'pi', 'プ': 'pu', 'ペ': 'pe', 'ポ': 'po',

        # Katakana combinations
        'キャ': 'kya', 'キュ': 'kyu', 'キョ': 'kyo',
        'シャ': 'sha', 'シュ': 'shu', 'ショ': 'sho',
        'チャ': 'cha', 'チュ': 'chu', 'チョ': 'cho',
        'ニャ': 'nya', 'ニュ': 'nyu', 'ニョ': 'nyo',
        'ヒャ': 'hya', 'ヒュ': 'hyu', 'ヒョ': 'hyo',
        'ミャ': 'mya', 'ミュ': 'myu', 'ミョ': 'myo',
        'リャ': 'rya', 'リュ': 'ryu', 'リョ': 'ryo',
        'ギャ': 'gya', 'ギュ': 'gyu', 'ギョ': 'gyo',
        'ジャ': 'ja', 'ジュ': 'ju', 'ジョ': 'jo',
        'ビャ': 'bya', 'ビュ': 'byu', 'ビョ': 'byo',
        'ピャ': 'pya', 'ピュ': 'pyu', 'ピョ': 'pyo',

        # Katakana small characters and special cases
        'ッ': '', # Small tsu (doubles the following consonant)
        'ャ': 'ya', 'ュ': 'yu', 'ョ': 'yo',

        # Katakana extras
        'ヴ': 'vu', 'ファ': 'fa', 'フィ': 'fi', 'フェ': 'fe', 'フォ': 'fo',
        'ウィ': 'wi', 'ウェ': 'we', 'ウォ': 'wo',

        # Hiragana characters
        'あ': 'a', 'い': 'i', 'う': 'u', 'え': 'e', 'お': 'o',
        'か': 'ka', 'き': 'ki', 'く': 'ku', 'け': 'ke', 'こ': 'ko',
        'さ': 'sa', 'し': 'shi', 'す': 'su', 'せ': 'se', 'そ': 'so',
        'た': 'ta', 'ち': 'chi', 'つ': 'tsu', 'て': 'te', 'と': 'to',
        'な': 'na', 'に': 'ni', 'ぬ': 'nu', 'ね': 'ne', 'の': 'no',
        'は': 'ha', 'ひ': 'hi', 'ふ': 'fu', 'へ': 'he', 'ほ': 'ho',
        'ま': 'ma', 'み': 'mi', 'む': 'mu', 'め': 'me', 'も': 'mo',
        'や': 'ya', 'ゆ': 'yu', 'よ': 'yo',
        'ら': 'ra', 'り': 'ri', 'る': 'ru', 'れ': 're', 'ろ': 'ro',
        'わ': 'wa', 'を': 'wo', 'ん': 'n',

        # Hiragana voiced consonants
        'が': 'ga', 'ぎ': 'gi', 'ぐ': 'gu', 'げ': 'ge', 'ご': 'go',
        'ざ': 'za', 'じ': 'ji', 'ず': 'zu', 'ぜ': 'ze', 'ぞ': 'zo',
        'だ': 'da', 'ぢ': 'ji', 'づ': 'zu', 'で': 'de', 'ど': 'do',
        'ば': 'ba', 'び': 'bi', 'ぶ': 'bu', 'べ': 'be', 'ぼ': 'bo',
        'ぱ': 'pa', 'ぴ': 'pi', 'ぷ': 'pu', 'ぺ': 'pe', 'ぽ': 'po',

        # Hiragana combinations
        'きゃ': 'kya', 'きゅ': 'kyu', 'きょ': 'kyo',
        'しゃ': 'sha', 'しゅ': 'shu', 'しょ': 'sho',
        'ちゃ': 'cha', 'ちゅ': 'chu', 'ちょ': 'cho',
        'にゃ': 'nya', 'にゅ': 'nyu', 'にょ': 'nyo',
        'ひゃ': 'hya', 'ひゅ': 'hyu', 'ひょ': 'hyo',
        'みゃ': 'mya', 'みゅ': 'myu', 'みょ': 'myo',
        'りゃ': 'rya', 'りゅ': 'ryu', 'りょ': 'ryo',
        'ぎゃ': 'gya', 'ぎゅ': 'gyu', 'ぎょ': 'gyo',
        'じゃ': 'ja', 'じゅ': 'ju', 'じょ': 'jo',
        'びゃ': 'bya', 'びゅ': 'byu', 'びょ': 'byo',
        'ぴゃ': 'pya', 'ぴゅ': 'pyu', 'ぴょ': 'pyo',

        # Hiragana small characters and special cases
        'っ': '', # Small tsu (doubles the following consonant)
        'ゃ': 'ya', 'ゅ': 'yu', 'ょ': 'yo',

        # Common punctuation and spaces
        '　': ' ', # Japanese space
        '、': ', ', '。': '. ',
    }

    result = []
    i = 0

    while i < len(japanese_text):
        # Check for small tsu (doubling the following consonant)
        if i < len(japanese_text) - 1 and (japanese_text[i] == 'っ' or japanese_text[i] == 'ッ'):
            if i < len(japanese_text) - 1 and japanese_text[i+1] in kana_map:
                next_romaji = kana_map[japanese_text[i+1]]
                if next_romaji and next_romaji[0] not in 'aiueon':
                    result.append(next_romaji[0])  # Double the consonant
            i += 1
            continue

        # Check for combinations with small ya, yu, yo
        if i < len(japanese_text) - 1 and japanese_text[i+1] in ('ゃ', 'ゅ', 'ょ', 'ャ', 'ュ', 'ョ'):
            combo = japanese_text[i:i+2]
            if combo in kana_map:
                result.append(kana_map[combo])
                i += 2
                continue

        # Regular character
        if japanese_text[i] in kana_map:
            result.append(kana_map[japanese_text[i]])
        else:
            # If it's not in our map, keep it as is (might be kanji, romaji, etc.)
            result.append(japanese_text[i])

        i += 1

    return ''.join(result)

def number_to_text(num, ordinal=False):
    """
    Convert a number (int or float) to its text representation.

    Args:
        num: The number to convert

    Returns:
        str: Text representation of the number
    """

    if not isinstance(num, (int, float)):
        return "Input must be a number"

    # Handle special case of zero
    if num == 0:
        return "zero"

    # Handle negative numbers
    negative = num < 0
    num = abs(num)

    # Handle floats
    if isinstance(num, float):
        # Split into integer and decimal parts
        int_part = int(num)

        # Convert both parts
        int_text = _int_to_text(int_part)

        # Handle decimal part (convert to string and remove '0.')
        decimal_str = str(num).split('.')[1]
        decimal_text = " point " + " ".join(_digit_to_text(int(digit)) for digit in decimal_str)

        result = int_text + decimal_text
    else:
        # Handle integers
        result = _int_to_text(num)

    # Add 'negative' prefix for negative numbers
    if negative:
        result = "negative " + result

    return result


def _int_to_text(num):
    """Helper function to convert an integer to text"""

    ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine",
            "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen",
            "seventeen", "eighteen", "nineteen"]

    tens = ["", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"]

    if num < 20:
        return ones[num]

    if num < 100:
        return tens[num // 10] + (" " + ones[num % 10] if num % 10 != 0 else "")

    if num < 1000:
        return ones[num // 100] + " hundred" + (" " + _int_to_text(num % 100) if num % 100 != 0 else "")

    if num < 1000000:
        return _int_to_text(num // 1000) + " thousand" + (" " + _int_to_text(num % 1000) if num % 1000 != 0 else "")

    if num < 1000000000:
        return _int_to_text(num // 1000000) + " million" + (" " + _int_to_text(num % 1000000) if num % 1000000 != 0 else "")

    return _int_to_text(num // 1000000000) + " billion" + (" " + _int_to_text(num % 1000000000) if num % 1000000000 != 0 else "")


def _digit_to_text(digit):
    """Convert a single digit to text"""
    digits = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
    return digits[digit]


_whitespace_re = re.compile(r"\s+")


# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = {
    "en": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("mrs", "misess"),
            ("mr", "mister"),
            ("dr", "doctor"),
            ("st", "saint"),
            ("co", "company"),
            ("jr", "junior"),
            ("maj", "major"),
            ("gen", "general"),
            ("drs", "doctors"),
            ("rev", "reverend"),
            ("lt", "lieutenant"),
            ("hon", "honorable"),
            ("sgt", "sergeant"),
            ("capt", "captain"),
            ("esq", "esquire"),
            ("ltd", "limited"),
            ("col", "colonel"),
            ("ft", "fort"),
        ]
    ],
}


def expand_abbreviations_multilingual(text, lang="en"):
    for regex, replacement in _abbreviations[lang]:
        text = re.sub(regex, replacement, text)
    return text


_symbols_multilingual = {
    "en": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " and "),
            ("@", " at "),
            ("%", " percent "),
            ("#", " hash "),
            ("$", " dollar "),
            ("£", " pound "),
            ("°", " degree "),
        ]
    ],
}


def expand_symbols_multilingual(text, lang="en"):
    for regex, replacement in _symbols_multilingual[lang]:
        text = re.sub(regex, replacement, text)
        text = text.replace("  ", " ")  # Ensure there are no double spaces
    return text.strip()


_ordinal_re = {
    "en": re.compile(r"([0-9]+)(st|nd|rd|th)"),
}
_number_re = re.compile(r"[0-9]+")
_currency_re = {
    "USD": re.compile(r"((\$[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+\$))"),
    "GBP": re.compile(r"((£[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+£))"),
    "EUR": re.compile(r"(([0-9\.\,]*[0-9]+€)|((€[0-9\.\,]*[0-9]+)))"),
}

_comma_number_re = re.compile(r"\b\d{1,3}(,\d{3})*(\.\d+)?\b")
_dot_number_re = re.compile(r"\b\d{1,3}(.\d{3})*(\,\d+)?\b")
_decimal_number_re = re.compile(r"([0-9]+[.,][0-9]+)")


def _remove_commas(m):
    text = m.group(0)
    if "," in text:
        text = text.replace(",", "")
    return text


def _remove_dots(m):
    text = m.group(0)
    if "." in text:
        text = text.replace(".", "")
    return text


def _expand_decimal_point(m, lang="en"):
    amount = m.group(1).replace(",", ".")
    return number_to_text(float(amount))


def _expand_currency(m, lang="en", currency="USD"):
    amount = float((re.sub(r"[^\d.]", "", m.group(0).replace(",", "."))))
    full_amount = number_to_text(amount)

    and_equivalents = {
        "en": ", ",
        "es": " con ",
        "fr": " et ",
        "de": " und ",
        "pt": " e ",
        "it": " e ",
        "pl": ", ",
        "cs": ", ",
        "ru": ", ",
        "nl": ", ",
        "ar": ", ",
        "tr": ", ",
        "hu": ", ",
        "ko": ", ",
    }

    if amount.is_integer():
        last_and = full_amount.rfind(and_equivalents[lang])
        if last_and != -1:
            full_amount = full_amount[:last_and]

    return full_amount


def _expand_ordinal(m, lang="en"):
    return number_to_text(int(m.group(1)), ordinal=True)


def _expand_number(m, lang="en"):
    return number_to_text(int(m.group(0)))


def expand_numbers_multilingual(text, lang="en"):
    if lang in ["en", "ru"]:
        text = re.sub(_comma_number_re, _remove_commas, text)
    else:
        text = re.sub(_dot_number_re, _remove_dots, text)
    try:
        text = re.sub(_currency_re["GBP"], lambda m: _expand_currency(m, lang, "GBP"), text)
        text = re.sub(_currency_re["USD"], lambda m: _expand_currency(m, lang, "USD"), text)
        text = re.sub(_currency_re["EUR"], lambda m: _expand_currency(m, lang, "EUR"), text)
    except:
        pass

    text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text)
    text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text)
    text = re.sub(_number_re, lambda m: _expand_number(m, lang), text)
    return text


def lowercase(text):
    return text.lower()


def collapse_whitespace(text):
    return re.sub(_whitespace_re, " ", text)


def multilingual_cleaners(text, lang):
    text = text.replace('"', "")
    if lang == "tr":
        text = text.replace("İ", "i")
        text = text.replace("Ö", "ö")
        text = text.replace("Ü", "ü")
    text = lowercase(text)
    try:
        text = expand_numbers_multilingual(text, lang)
    except:
        pass
    try:
        text = expand_abbreviations_multilingual(text, lang)
    except:
        pass
    try:
        text = expand_symbols_multilingual(text, lang=lang)
    except:
        pass
    text = collapse_whitespace(text)
    return text


def basic_cleaners(text):
    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text