maya-mac: Maya TTS inference on Apple Silicon (MPS)

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"""Targeted pre/post rules around the vendored normaliser (maya3.text.tn), derived from the eval rows that still
failed with text normalisation on.
The vendored copy stays byte-identical. Each rule rewrites one input shape that the strict normaliser either rejects
(it then falls back to reading the span character by character: "15-08-25" -> "one five minus zero eight minus two
five") or reads in a way listeners do not expect (a PIN code as "five lakh sixty thousand thirty eight"):
  R1 date_2y      DD-MM-YY, DD/MM/YY, DD.MM.YY            -> four-digit year (pivot: 00-50 -> 20xx, 51-99 -> 19xx),
                                                              then the normaliser's own date reading
  R2 expiry       MM/YY or MM/YYYY next to an expiry / validity / date word or a CVV -> "<month> <year>"
  R3 fy_range     2023-24 (second part = next year)       -> "<year> <two-digit number>"
  R4 digit_groups 4111 2233 4455 6677, 98765 43210, 1800 123 4567 (>= 10 digits in space-separated groups)
                                                           -> one digit run, which the normaliser reads digit by digit
  R5 pin_keyword  PIN / पिन / ... followed by 4-6 digits    -> digit by digit
  R6 pincode      a bare 6-digit number (no currency or unit next to it, no grouping commas) -> digit by digit
  R7 version      v4.5.2                                  -> "v four point five point two" (language's decimal word)
  R8 ta_date_year Tamil "ஆகஸ்ட் 15, 2025"                  -> "15 ஆகஸ்ட் 2025" (the normaliser keeps the year)
  R10 pct_range   3.5%-4%                                 -> 3.5-4% (the normaliser's own percent range)
  R11 per_price   ₹500/kg                                 -> "<amount> <per word> K G"
  R12 per_annum   8.5% p.a.                               -> "P A" (en: "per annum")
  R9 paise        rupee amounts with two decimals read "<n> rupees <m> paise" in every language (the tables of
                  bn en gu kn ml mr pa ta had no paise word, so they read "point five zero rupees")
  post:
  P1 tag_comma    a piece that ends in "," before an inline [tag] keeps its comma (the normaliser turns a trailing
                  comma into a full stop, and the model then ends the sentence at the tag)
  P2 uri_dash     "-" inside an e-mail / URL is spoken in the request language, not as the English word "dash"
  P3 uri_acronym  lowercase acronym labels in an e-mail / URL (sbi, kyc, hdfc in hdfcbank, sbi in onlinesbi) are
                  spelled letter by letter in the request language; other labels keep their English spelling
Language-table corrections (Assamese lexicon, "per" units, sub-domain hosts) live in maya3.text.lexicon.
"""
from __future__ import annotations

import re

__all__ = ["pre", "post", "install", "RULES"]

RULES = ("date_2y", "expiry", "fy_range", "digit_groups", "pin_keyword", "pincode", "version", "ta_date_year",
         "paise", "pct_range", "per_price", "per_annum", "tag_comma", "uri_dash", "uri_acronym")

PAISE = {"en": "paise", "bn": "পয়সা", "gu": "પૈસા", "kn": "ಪೈಸೆ", "ml": "പൈസ", "mr": "पैसे", "pa": "ਪੈਸੇ",
         "ta": "பைசா"}            # hi as or te already have one
DASH = {"hi": "डैश", "mr": "डॅश", "bn": "ড্যাশ", "as": "ডেশ্ব", "gu": "ડૅશ", "pa": "ਡੈਸ਼", "or": "ଡ୍ୟାସ୍",
        "ta": "டாஷ்", "te": "డాష్", "kn": "ಡ್ಯಾಶ್", "ml": "ഡാഷ്"}

# expiry / validity / date words (any language; matched case-insensitively in the 40 characters before the span)
_EXPIRY_WORDS = [
    "expiry", "expires", "expire", "exp", "valid", "validity", "thru", "expiration",
    "एक्सपायरी", "समाप्ति", "वैधता", "मुदत", "समाप्ती",
    "মেয়াদ", "মেয়াদ", "এক্সপায়ারি", "এক্সপায়ারি", "এক্সপায়াৰী", "ম্যাদ",
    "એક્સપાયરી", "સમાપ્તિ", "માન્યતા", "ਮਿਆਦ", "ਐਕਸਪਾਇਰੀ",
    "காலாவதி", "எக்ஸ்பைரி", "గడువు", "ఎక్స్‌పైరీ", "ఎక్స్పైరీ",
    "ಮುಕ್ತಾಯ", "ಅಂತಿಮ ದಿನಾಂಕ", "ಎಕ್ಸ್‌ಪೈರಿ", "കാലാവധി", "എക്സ്പയറി", "ମିଆଦ", "ଏକ୍ସପାୟାରୀ", "ସମାପ୍ତି",
]
_DATE_WORDS = ["date", "तारीख", "तिथि", "दिनांक", "डेट", "তারিখ", "তাৰিখ", "તારીખ", "ડેટ", "ਤਾਰੀਖ਼", "ਤਾਰੀਖ", "ਡੇਟ",
               "தேதி", "டேட்", "తేదీ", "డేట్", "ದಿನಾಂಕ", "ಡೇಟ್", "തീയതി", "ഡേറ്റ്", "ତାରିଖ", "ଡେଟ୍"]
_alt = lambda ws: "|".join(sorted(map(re.escape, ws), key=len, reverse=True))
_EXPIRY_RE = re.compile(_alt(_EXPIRY_WORDS), re.I)
_DATE_RE = re.compile(_alt(_DATE_WORDS), re.I)
_PIN_WORDS = ["pin", "पिन", "पिनकोड", "ಪಿನ್", "పిన్", "பின்", "പിൻ", "পিন", "ପିନ", "ਪਿੰਨ", "ਪਿਨ", "પિન"]
_PIN_RE = re.compile(r"(?<![\w])(?:" + "|".join(map(re.escape, _PIN_WORDS)) + r")(?:[\s-]*(?:code|कोड|কোড|કોડ|ਕੋਡ|କୋଡ|ಕೋಡ್|కోడ్|கோட்|കോഡ്))?"
                     r"(?:\s*(?:no\.?|number|नंबर|नम्बर))?\s*[:\-]?\s*(\d{4,6})(?![\d,]|\.\d)", re.I)
# words right after a number that make it an amount / quantity (R6 must not read those digit by digit)
_AMOUNT_AFTER = re.compile(
    r"\s*(?:rupees?|rs\.?|inr|dollars?|usd|paise|lakh|crore|thousand|people|users|customers|km|kg|kms|"
    r"रुपये|रुपए|रुपया|रु\.?|लाख|करोड़|हज़ार|हजार|लोग|লাখ|টাকা|টকা|কোটি|રૂપિયા|લાખ|ਰੁਪਏ|ਲੱਖ|ரூபாய்|ரூ\.?|லட்சம்|"
    r"రూపాయలు|రూ\.?|లక్షల|ರೂಪಾಯಿ|ರೂ\.?|ಲಕ್ಷ|രൂപ|ലക്ഷം|ଟଙ୍କା|ଲକ୍ଷ|ਕਰੋੜ|कोटी)(?![\w])", re.I)
_CURRENCY_BEFORE = re.compile(r"(?:₹|\$|€|£|\brs\.?|\binr|रु\.?|ரூ\.?|రూ\.?|ರೂ\.?)\s*$", re.I)
_NUMBER_WORD_BEFORE = re.compile(
    r"(?:\bno\.?|\bnumber|\bplot|\bflat|\bhouse|\bdoor|\bh\.\s?no\.?|नंबर|नम्बर|क्रमांक|नं\.?|নং|নম্বর|নম্বৰ|ਨੰਬਰ|નંબર|ନମ୍ବର|"
    r"எண்|నంబర్|ಸಂಖ್ಯೆ|ನಂಬರ್|നമ്പർ)\s*[:\-]?\s*$", re.I)
_TA_MONTHS = ["ஜனவரி", "பிப்ரவரி", "மார்ச்", "ஏப்ரல்", "மே", "ஜூன்", "ஜூலை", "ஆகஸ்ட்", "செப்டம்பர்", "அக்டோபர்",
              "நவம்பர்", "டிசம்பர்"]
_URI_RE = re.compile(r"https?://\S+|www\.\S+|\b[\w.%+\-]+@[\w.\-]+\.[A-Za-z]{2,}|\b[A-Za-z][\w.\-]*@[A-Za-z][A-Za-z0-9]{1,15}\b|\b[A-Za-z][\w\-]*(?:\.[A-Za-z0-9][\w\-]*)*\.(?:com|org|net|ai|io|co|dev|app|edu|gov|in)(?:\.(?:in|uk|us))?(?:[/?#][^\s<>]*)?")

# P3: lowercase acronym labels inside e-mail / URL hosts and paths are spelled letter by letter (sbi.co.in ->
# "S B I dot co dot in", hdfcbank.com -> "H D F C bank dot com"); other labels keep their English spelling.
_URI_ACRONYMS = {"sbi", "hdfc", "icici", "kyc", "irctc", "pnb", "rbi", "idbi", "npci", "uidai", "epfo", "mf", "lic",
                 "upi", "gst", "sms", "atm", "emi", "bob", "boi", "uco", "iob", "nsdl", "cdsl", "pnr", "otp", "ifsc"}
_URI_ACR_PREFIX = ("icici", "irctc", "uidai", "hdfc", "idbi", "npci", "epfo", "sbi", "pnb", "kyc", "rbi")
_URI_ACR_SUFFIX = ("sbi", "mf")

_SPELL_UNITS = {"kg", "km", "gm", "g", "ml", "ltr", "l"}   # R11: read letter by letter (as the normaliser reads "15 kg")

_installed = False


def install(on: bool = True):
    """R9: add the paise word to the language tables that lack it (bn en gu kn ml mr pa ta), so the normaliser's own
    main+sub-unit path reads ₹95.41 as "<n> rupees <m> paise" (with its native scale words and case suffixes).
    The tables are module-level; install(False) removes the added words again (used only by rules=False, the
    eval-parity mode; do not mix rules=False with concurrent rules=True calls)."""
    global _installed
    if on == _installed:
        return
    from maya_mac.text.tn.normalize import language_table
    for code, word in PAISE.items():
        sym = language_table(code).SYM
        if on:
            sym.setdefault("paisa", word)
        elif sym.get("paisa") == word:
            del sym["paisa"]
    _installed = on


def _table(lang):
    from maya_mac.text.tn.normalize import language_table
    return language_table(lang)


def _tn_id_context(L, before):
    """True when the normaliser itself will read the next number digit by digit (an ID keyword such as PIN / OTP /
    number in the three tokens before it); the rule then leaves the span alone so the normaliser's postposition
    sandhi ("...ಒಂದರಲ್ಲಿ") is kept."""
    from maya_mac.text.tn import normalize as _N   # the function; its module holds the keyword sets
    import sys
    mod = sys.modules["maya_mac.text.tn.normalize"]
    kws = mod._ID_KEYWORDS_EN | mod._OTP_INPUT_WORDS | {w.lower() for w in L.ID_KEYWORDS}
    toks = re.findall(r"[\w#\u0900-\u0DFF']+", before.replace("\u200c", "").lower())[-3:]
    return any(t in kws for t in toks)


def _digits(L, s):
    return ", ".join(L.digit(int(c)) for c in s)


def _year4(yy):
    y = int(yy)
    return 2000 + y if y <= 50 else 1900 + y


def _decimal_word(L):
    w = L.decimal(1, "2")
    a, b = L.cardinal(1), L.digit(2)
    return w[len(a):len(w) - len(b)].strip() if w.startswith(a) and w.endswith(b) else "point"


def pre(text: str, lang: str) -> str:
    """Apply R1-R8 outside e-mail / URL spans (those are identifiers, spelled by the normaliser)."""
    try:
        L = _table(lang)
    except Exception:
        return text
    out, pos = [], 0
    for m in _URI_RE.finditer(text):
        out.append(_pre(text[pos:m.start()], lang, L)); out.append(m.group(0)); pos = m.end()
    out.append(_pre(text[pos:], lang, L))
    return "".join(out)


def _pre(text, lang, L):
    t = text

    # R10 percent ranges written with the sign twice: 3.5%-4% (the normaliser read "...percentminus four percent")
    t = re.sub(r"(?<![\d.])(\d+(?:\.\d+)?)\s?%\s?[-–]\s?(\d+(?:\.\d+)?)\s?%", r"\1-\2%", t)

    # R11 price per unit: ₹500/kg (the "/" was dropped and "kg" left in Latin letters)
    def r11(m):
        unit, per = m.group(2), L.SYM.get("per") or "per"
        if lang != "en" and unit.lower() in _SPELL_UNITS:
            unit = " ".join(L.letter(c) for c in unit.upper())
        return f"{m.group(1)} {per} {unit}"
    t = re.sub(r"((?:[₹$€£]|\b(?:Rs\.?|INR)\s?)\s?\d[\d,]*(?:\.\d+)?)\s?/\s?(kg|km|gm|g|ml|ltr|l|day|month|night|year|person)\b", r11, t,
               flags=re.I)

    # R12 "p.a." (per annum) after a rate was left as the raw token "p.a."
    t = re.sub(r"(?<![\w.])[pP]\.\s?[aA]\.?(?![\w])", lambda m: "per annum" if lang == "en" else f"{L.letter('P')} {L.letter('A')}", t)

    # R7 version strings
    def r7(m):
        parts = m.group(1).split(".")
        dw = _decimal_word(L)
        return f"{L.letter('v')} " + f" {dw} ".join(L.cardinal(int(p)) for p in parts)
    t = re.sub(r"(?<![\w.])[vV](\d+(?:\.\d+){1,3})(?![\w]|\.\d)", r7, t)

    # R4 space-separated digit groups (card / phone numbers): join into one run (>= 10 digits in total)
    def r4(m):
        groups = m.group(0).split()
        return "".join(groups) if sum(len(g) for g in groups) >= 10 else m.group(0)
    t = re.sub(r"(?<![\d,.])(?:\d{4,5}(?: \d{3,5}){1,3}|\d{2,4} \d{3,4} \d{4})(?![\d,]|\.\d)", r4, t)

    # R1 dates with a two-digit year
    def r1(m):
        d, sep, mo, yy = m.group(1), m.group(2), m.group(3), m.group(4)
        if not (1 <= int(d) <= 31 and 1 <= int(mo) <= 12):
            return m.group(0)
        if sep == "-" and len(d) == 1 and len(mo) == 1:          # 8-2-45: a door number, not a date
            return m.group(0)
        if _NUMBER_WORD_BEFORE.search(t[max(0, m.start() - 16):m.start()]):   # "House No. 12-5-24", "ঘৰ নং ..."
            return m.group(0)
        return f"{d}{sep}{mo}{sep}{_year4(yy)}"
    t = re.sub(r"(?<![\d/.\-])(\d{1,2})([/.\-])(\d{1,2})\2(\d{2})(?![\d/\-]|\.\d)", r1, t)

    # R2 card expiry / month-year next to an expiry, date or CVV word
    def r2(m):
        mo, y = int(m.group(1)), m.group(2)
        before = t[max(0, m.start() - 40):m.start()]
        after = t[m.end():m.end() + 40]
        strong = _EXPIRY_RE.search(before) or re.search(r"\bcvv\b", before + " " + after, re.I)
        weak = _DATE_RE.search(before) and (len(y) == 4 or int(y) > 12)    # second part > 12: not DD/MM, so MM/YY
        if not (strong or weak):
            return m.group(0)
        year = int(y) if len(y) == 4 else 2000 + int(y)
        return f"{L.month(mo)} {L.year(year)}"
    t = re.sub(r"(?<![\d/.\-])(0?[1-9]|1[0-2])/(\d{2}|20\d{2})(?![\d/]|\.\d)", r2, t)

    # R3 financial-year ranges 2023-24
    def r3(m):
        y, yy = int(m.group(1)), int(m.group(2))
        if (y + 1) % 100 != yy:
            return m.group(0)
        return f"{L.year(y)} {L.cardinal(yy)}"
    t = re.sub(r"(?<![\d\-])((?:19|20)\d{2})[-–](\d{2})(?![\d\-])", r3, t)

    # R5 PIN keyword + digits
    def r5(m):
        if _tn_id_context(L, t[max(0, m.start(1) - 60):m.start(1)]):
            return m.group(0)
        return m.group(0)[:m.start(1) - m.start(0)] + _digits(L, m.group(1))
    t = _PIN_RE.sub(r5, t)

    # R6 bare six-digit numbers (Indian PIN codes): digit by digit unless an amount / quantity
    def r6(m):
        before = t[max(0, m.start() - 8):m.start()]
        if (_CURRENCY_BEFORE.search(before) or _AMOUNT_AFTER.match(t, m.end())
                or re.search(r"[A-Za-z0-9]\s?-\s?$|[#÷×*/+=<>]\s*$", before)       # ID suffix (CUST-567890), #ref, maths
                or re.match(r"\s*[÷×*/+=<>^]", t[m.end():])
                or _tn_id_context(L, re.split(r"[.!?;।॥\n]", t[max(0, m.start() - 60):m.start()])[-1])):
            return m.group(0)
        return _digits(L, m.group(0))
    t = re.sub(r"(?<![\d,.₹$#\w])[1-9]\d{5}(?![\d,]|\.\d|\s*%)", r6, t)

    # R8 Tamil "Month D, YYYY" -> "D Month YYYY"
    if lang == "ta":
        months = "|".join(_TA_MONTHS)
        t = re.sub(rf"(?<!\w)({months})\s+(\d{{1,2}}),?\s+(\d{{4}})(?!\d)", r"\2 \1 \3", t)
    return t


TAG_COMMA = False   # P1 off: no measured gain (6 vs 6 backstop cuts on the 14 affected emotion rows x2 seeds)


def post(out: str, seg: str, lang: str, before_tag: bool) -> str:
    # P1 keep a trailing comma that precedes an inline [tag] (disabled, see TAG_COMMA)
    if TAG_COMMA and before_tag and seg.rstrip().endswith(",") and out.rstrip().endswith("."):
        out = out.rstrip()[:-1] + ","
    # P2 spoken hyphen inside e-mail / URL in the request language
    uris = [m.group(0) for m in _URI_RE.finditer(seg)]
    if lang in DASH and uris:
        out = re.sub(r"(?<=\s)dash(?=\s)", DASH[lang], out)
    # P3 acronym labels inside e-mail / URL
    labels = {w for u in uris for w in re.findall(r"[a-z]+", re.sub(r"^https?://", "", u))
              if w in _URI_ACRONYMS or w.startswith(_URI_ACR_PREFIX) or w.endswith(_URI_ACR_SUFFIX)}
    if labels:
        try:
            L = _table(lang)
        except Exception:
            return out
        spell = lambda w: " ".join(L.letter(c.upper()) for c in w)

        def p3(m):
            w = m.group(0)
            if w in _URI_ACRONYMS:
                return spell(w)
            for a in _URI_ACR_PREFIX:
                if w.startswith(a) and len(w) - len(a) >= 3:
                    return spell(a) + " " + w[len(a):]
            for a in _URI_ACR_SUFFIX:
                if w.endswith(a) and len(w) - len(a) >= 4:
                    return w[:-len(a)] + " " + spell(a)
            return w
        out = re.sub(r"(?<![\w.@/])(?:" + "|".join(sorted(map(re.escape, labels), key=len, reverse=True)) + r")(?![\w]|[.@/\-]\w)",
                     p3, out)
    return out

	"""Targeted pre/post rules around the vendored normaliser (maya3.text.tn), derived from the eval rows that still
	failed with text normalisation on.

	The vendored copy stays byte-identical. Each rule rewrites one input shape that the strict normaliser either rejects
	(it then falls back to reading the span character by character: "15-08-25" -> "one five minus zero eight minus two
	five") or reads in a way listeners do not expect (a PIN code as "five lakh sixty thousand thirty eight"):

	R1 date_2y DD-MM-YY, DD/MM/YY, DD.MM.YY -> four-digit year (pivot: 00-50 -> 20xx, 51-99 -> 19xx),
	then the normaliser's own date reading
	R2 expiry MM/YY or MM/YYYY next to an expiry / validity / date word or a CVV -> "<month> <year>"
	R3 fy_range 2023-24 (second part = next year) -> "<year> <two-digit number>"
	R4 digit_groups 4111 2233 4455 6677, 98765 43210, 1800 123 4567 (>= 10 digits in space-separated groups)
	-> one digit run, which the normaliser reads digit by digit
	R5 pin_keyword PIN / पिन / ... followed by 4-6 digits -> digit by digit
	R6 pincode a bare 6-digit number (no currency or unit next to it, no grouping commas) -> digit by digit
	R7 version v4.5.2 -> "v four point five point two" (language's decimal word)
	R8 ta_date_year Tamil "ஆகஸ்ட் 15, 2025" -> "15 ஆகஸ்ட் 2025" (the normaliser keeps the year)
	R10 pct_range 3.5%-4% -> 3.5-4% (the normaliser's own percent range)
	R11 per_price ₹500/kg -> "<amount> <per word> K G"
	R12 per_annum 8.5% p.a. -> "P A" (en: "per annum")
	R9 paise rupee amounts with two decimals read "<n> rupees <m> paise" in every language (the tables of
	bn en gu kn ml mr pa ta had no paise word, so they read "point five zero rupees")
	post:
	P1 tag_comma a piece that ends in "," before an inline [tag] keeps its comma (the normaliser turns a trailing
	comma into a full stop, and the model then ends the sentence at the tag)
	P2 uri_dash "-" inside an e-mail / URL is spoken in the request language, not as the English word "dash"
	P3 uri_acronym lowercase acronym labels in an e-mail / URL (sbi, kyc, hdfc in hdfcbank, sbi in onlinesbi) are
	spelled letter by letter in the request language; other labels keep their English spelling
	Language-table corrections (Assamese lexicon, "per" units, sub-domain hosts) live in maya3.text.lexicon.
	"""
	from __future__ import annotations

	import re

	__all__ = ["pre", "post", "install", "RULES"]

	RULES = ("date_2y", "expiry", "fy_range", "digit_groups", "pin_keyword", "pincode", "version", "ta_date_year",
	"paise", "pct_range", "per_price", "per_annum", "tag_comma", "uri_dash", "uri_acronym")

	PAISE = {"en": "paise", "bn": "পয়সা", "gu": "પૈસા", "kn": "ಪೈಸೆ", "ml": "പൈസ", "mr": "पैसे", "pa": "ਪੈਸੇ",
	"ta": "பைசா"} # hi as or te already have one
	DASH = {"hi": "डैश", "mr": "डॅश", "bn": "ড্যাশ", "as": "ডেশ্ব", "gu": "ડૅશ", "pa": "ਡੈਸ਼", "or": "ଡ୍ୟାସ୍",
	"ta": "டாஷ்", "te": "డాష్", "kn": "ಡ್ಯಾಶ್", "ml": "ഡാഷ്"}

	# expiry / validity / date words (any language; matched case-insensitively in the 40 characters before the span)
	_EXPIRY_WORDS = [
	"expiry", "expires", "expire", "exp", "valid", "validity", "thru", "expiration",
	"एक्सपायरी", "समाप्ति", "वैधता", "मुदत", "समाप्ती",
	"মেয়াদ", "মেয়াদ", "এক্সপায়ারি", "এক্সপায়ারি", "এক্সপায়াৰী", "ম্যাদ",
	"એક્સપાયરી", "સમાપ્તિ", "માન્યતા", "ਮਿਆਦ", "ਐਕਸਪਾਇਰੀ",
	"காலாவதி", "எக்ஸ்பைரி", "గడువు", "ఎక్స్‌పైరీ", "ఎక్స్పైరీ",
	"ಮುಕ್ತಾಯ", "ಅಂತಿಮ ದಿನಾಂಕ", "ಎಕ್ಸ್‌ಪೈರಿ", "കാലാവധി", "എക്സ്പയറി", "ମିଆଦ", "ଏକ୍ସପାୟାରୀ", "ସମାପ୍ତି",
	]
	_DATE_WORDS = ["date", "तारीख", "तिथि", "दिनांक", "डेट", "তারিখ", "তাৰিখ", "તારીખ", "ડેટ", "ਤਾਰੀਖ਼", "ਤਾਰੀਖ", "ਡੇਟ",
	"தேதி", "டேட்", "తేదీ", "డేట్", "ದಿನಾಂಕ", "ಡೇಟ್", "തീയതി", "ഡേറ്റ്", "ତାରିଖ", "ଡେଟ୍"]
	_alt = lambda ws: "\|".join(sorted(map(re.escape, ws), key=len, reverse=True))
	_EXPIRY_RE = re.compile(_alt(_EXPIRY_WORDS), re.I)
	_DATE_RE = re.compile(_alt(_DATE_WORDS), re.I)
	_PIN_WORDS = ["pin", "पिन", "पिनकोड", "ಪಿನ್", "పిన్", "பின்", "പിൻ", "পিন", "ପିନ", "ਪਿੰਨ", "ਪਿਨ", "પિન"]
	_PIN_RE = re.compile(r"(?<![\w])(?:" + "\|".join(map(re.escape, _PIN_WORDS)) + r")(?:[\s-]*(?:code\|कोड\|কোড\|કોડ\|ਕੋਡ\|କୋଡ\|ಕೋಡ್\|కోడ్\|கோட்\|കോഡ്))?"
	r"(?:\s(?:no\.?\|number\|नंबर\|नम्बर))?\s[:\-]?\s*(\d{4,6})(?![\d,]\|\.\d)", re.I)
	# words right after a number that make it an amount / quantity (R6 must not read those digit by digit)
	_AMOUNT_AFTER = re.compile(
	r"\s*(?:rupees?\|rs\.?\|inr\|dollars?\|usd\|paise\|lakh\|crore\|thousand\|people\|users\|customers\|km\|kg\|kms\|"
	r"रुपये\|रुपए\|रुपया\|रु\.?\|लाख\|करोड़\|हज़ार\|हजार\|लोग\|লাখ\|টাকা\|টকা\|কোটি\|રૂપિયા\|લાખ\|ਰੁਪਏ\|ਲੱਖ\|ரூபாய்\|ரூ\.?\|லட்சம்\|"
	r"రూపాయలు\|రూ\.?\|లక్షల\|ರೂಪಾಯಿ\|ರೂ\.?\|ಲಕ್ಷ\|രൂപ\|ലക്ഷം\|ଟଙ୍କା\|ଲକ୍ଷ\|ਕਰੋੜ\|कोटी)(?![\w])", re.I)
	_CURRENCY_BEFORE = re.compile(r"(?:₹\|\$\|€\|£\|\brs\.?\|\binr\|रु\.?\|ரூ\.?\|రూ\.?\|ರೂ\.?)\s*$", re.I)
	_NUMBER_WORD_BEFORE = re.compile(
	r"(?:\bno\.?\|\bnumber\|\bplot\|\bflat\|\bhouse\|\bdoor\|\bh\.\s?no\.?\|नंबर\|नम्बर\|क्रमांक\|नं\.?\|নং\|নম্বর\|নম্বৰ\|ਨੰਬਰ\|નંબર\|ନମ୍ବର\|"
	r"எண்\|నంబర్\|ಸಂಖ್ಯೆ\|ನಂಬರ್\|നമ്പർ)\s[:\-]?\s$", re.I)
	_TA_MONTHS = ["ஜனவரி", "பிப்ரவரி", "மார்ச்", "ஏப்ரல்", "மே", "ஜூன்", "ஜூலை", "ஆகஸ்ட்", "செப்டம்பர்", "அக்டோபர்",
	"நவம்பர்", "டிசம்பர்"]
	_URI_RE = re.compile(r"https?://\S+\|www\.\S+\|\b[\w.%+\-]+@[\w.\-]+\.[A-Za-z]{2,}\|\b[A-Za-z][\w.\-]@[A-Za-z][A-Za-z0-9]{1,15}\b\|\b[A-Za-z][\w\-](?:\.[A-Za-z0-9][\w\-])\.(?:com\|org\|net\|ai\|io\|co\|dev\|app\|edu\|gov\|in)(?:\.(?:in\|uk\|us))?(?:[/?#][^\s<>]*)?")

	# P3: lowercase acronym labels inside e-mail / URL hosts and paths are spelled letter by letter (sbi.co.in ->
	# "S B I dot co dot in", hdfcbank.com -> "H D F C bank dot com"); other labels keep their English spelling.
	_URI_ACRONYMS = {"sbi", "hdfc", "icici", "kyc", "irctc", "pnb", "rbi", "idbi", "npci", "uidai", "epfo", "mf", "lic",
	"upi", "gst", "sms", "atm", "emi", "bob", "boi", "uco", "iob", "nsdl", "cdsl", "pnr", "otp", "ifsc"}
	_URI_ACR_PREFIX = ("icici", "irctc", "uidai", "hdfc", "idbi", "npci", "epfo", "sbi", "pnb", "kyc", "rbi")
	_URI_ACR_SUFFIX = ("sbi", "mf")

	_SPELL_UNITS = {"kg", "km", "gm", "g", "ml", "ltr", "l"} # R11: read letter by letter (as the normaliser reads "15 kg")

	_installed = False


	def install(on: bool = True):
	"""R9: add the paise word to the language tables that lack it (bn en gu kn ml mr pa ta), so the normaliser's own
	main+sub-unit path reads ₹95.41 as "<n> rupees <m> paise" (with its native scale words and case suffixes).
	The tables are module-level; install(False) removes the added words again (used only by rules=False, the
	eval-parity mode; do not mix rules=False with concurrent rules=True calls)."""
	global _installed
	if on == _installed:
	return
	from maya_mac.text.tn.normalize import language_table
	for code, word in PAISE.items():
	sym = language_table(code).SYM
	if on:
	sym.setdefault("paisa", word)
	elif sym.get("paisa") == word:
	del sym["paisa"]
	_installed = on


	def _table(lang):
	from maya_mac.text.tn.normalize import language_table
	return language_table(lang)


	def _tn_id_context(L, before):
	"""True when the normaliser itself will read the next number digit by digit (an ID keyword such as PIN / OTP /
	number in the three tokens before it); the rule then leaves the span alone so the normaliser's postposition
	sandhi ("...ಒಂದರಲ್ಲಿ") is kept."""
	from maya_mac.text.tn import normalize as _N # the function; its module holds the keyword sets
	import sys
	mod = sys.modules["maya_mac.text.tn.normalize"]
	kws = mod._ID_KEYWORDS_EN \| mod._OTP_INPUT_WORDS \| {w.lower() for w in L.ID_KEYWORDS}
	toks = re.findall(r"[\w#\u0900-\u0DFF']+", before.replace("\u200c", "").lower())[-3:]
	return any(t in kws for t in toks)


	def _digits(L, s):
	return ", ".join(L.digit(int(c)) for c in s)


	def _year4(yy):
	y = int(yy)
	return 2000 + y if y <= 50 else 1900 + y


	def _decimal_word(L):
	w = L.decimal(1, "2")
	a, b = L.cardinal(1), L.digit(2)
	return w[len(a):len(w) - len(b)].strip() if w.startswith(a) and w.endswith(b) else "point"


	def pre(text: str, lang: str) -> str:
	"""Apply R1-R8 outside e-mail / URL spans (those are identifiers, spelled by the normaliser)."""
	try:
	L = _table(lang)
	except Exception:
	return text
	out, pos = [], 0
	for m in _URI_RE.finditer(text):
	out.append(_pre(text[pos:m.start()], lang, L)); out.append(m.group(0)); pos = m.end()
	out.append(_pre(text[pos:], lang, L))
	return "".join(out)


	def _pre(text, lang, L):
	t = text

	# R10 percent ranges written with the sign twice: 3.5%-4% (the normaliser read "...percentminus four percent")
	t = re.sub(r"(?<![\d.])(\d+(?:\.\d+)?)\s?%\s?[-–]\s?(\d+(?:\.\d+)?)\s?%", r"\1-\2%", t)

	# R11 price per unit: ₹500/kg (the "/" was dropped and "kg" left in Latin letters)
	def r11(m):
	unit, per = m.group(2), L.SYM.get("per") or "per"
	if lang != "en" and unit.lower() in _SPELL_UNITS:
	unit = " ".join(L.letter(c) for c in unit.upper())
	return f"{m.group(1)} {per} {unit}"
	t = re.sub(r"((?:[₹$€£]\|\b(?:Rs\.?\|INR)\s?)\s?\d[\d,]*(?:\.\d+)?)\s?/\s?(kg\|km\|gm\|g\|ml\|ltr\|l\|day\|month\|night\|year\|person)\b", r11, t,
	flags=re.I)

	# R12 "p.a." (per annum) after a rate was left as the raw token "p.a."
	t = re.sub(r"(?<![\w.])[pP]\.\s?[aA]\.?(?![\w])", lambda m: "per annum" if lang == "en" else f"{L.letter('P')} {L.letter('A')}", t)

	# R7 version strings
	def r7(m):
	parts = m.group(1).split(".")
	dw = _decimal_word(L)
	return f"{L.letter('v')} " + f" {dw} ".join(L.cardinal(int(p)) for p in parts)
	t = re.sub(r"(?<![\w.])[vV](\d+(?:\.\d+){1,3})(?![\w]\|\.\d)", r7, t)

	# R4 space-separated digit groups (card / phone numbers): join into one run (>= 10 digits in total)
	def r4(m):
	groups = m.group(0).split()
	return "".join(groups) if sum(len(g) for g in groups) >= 10 else m.group(0)
	t = re.sub(r"(?<![\d,.])(?:\d{4,5}(?: \d{3,5}){1,3}\|\d{2,4} \d{3,4} \d{4})(?![\d,]\|\.\d)", r4, t)

	# R1 dates with a two-digit year
	def r1(m):
	d, sep, mo, yy = m.group(1), m.group(2), m.group(3), m.group(4)
	if not (1 <= int(d) <= 31 and 1 <= int(mo) <= 12):
	return m.group(0)
	if sep == "-" and len(d) == 1 and len(mo) == 1: # 8-2-45: a door number, not a date
	return m.group(0)
	if _NUMBER_WORD_BEFORE.search(t[max(0, m.start() - 16):m.start()]): # "House No. 12-5-24", "ঘৰ নং ..."
	return m.group(0)
	return f"{d}{sep}{mo}{sep}{_year4(yy)}"
	t = re.sub(r"(?<![\d/.\-])(\d{1,2})([/.\-])(\d{1,2})\2(\d{2})(?![\d/\-]\|\.\d)", r1, t)

	# R2 card expiry / month-year next to an expiry, date or CVV word
	def r2(m):
	mo, y = int(m.group(1)), m.group(2)
	before = t[max(0, m.start() - 40):m.start()]
	after = t[m.end():m.end() + 40]
	strong = _EXPIRY_RE.search(before) or re.search(r"\bcvv\b", before + " " + after, re.I)
	weak = _DATE_RE.search(before) and (len(y) == 4 or int(y) > 12) # second part > 12: not DD/MM, so MM/YY
	if not (strong or weak):
	return m.group(0)
	year = int(y) if len(y) == 4 else 2000 + int(y)
	return f"{L.month(mo)} {L.year(year)}"
	t = re.sub(r"(?<![\d/.\-])(0?[1-9]\|1[0-2])/(\d{2}\|20\d{2})(?![\d/]\|\.\d)", r2, t)

	# R3 financial-year ranges 2023-24
	def r3(m):
	y, yy = int(m.group(1)), int(m.group(2))
	if (y + 1) % 100 != yy:
	return m.group(0)
	return f"{L.year(y)} {L.cardinal(yy)}"
	t = re.sub(r"(?<![\d\-])((?:19\|20)\d{2})[-–](\d{2})(?![\d\-])", r3, t)

	# R5 PIN keyword + digits
	def r5(m):
	if _tn_id_context(L, t[max(0, m.start(1) - 60):m.start(1)]):
	return m.group(0)
	return m.group(0)[:m.start(1) - m.start(0)] + _digits(L, m.group(1))
	t = _PIN_RE.sub(r5, t)

	# R6 bare six-digit numbers (Indian PIN codes): digit by digit unless an amount / quantity
	def r6(m):
	before = t[max(0, m.start() - 8):m.start()]
	if (_CURRENCY_BEFORE.search(before) or _AMOUNT_AFTER.match(t, m.end())
	or re.search(r"[A-Za-z0-9]\s?-\s?$\|[#÷×/+=<>]\s$", before) # ID suffix (CUST-567890), #ref, maths
	or re.match(r"\s[÷×/+=<>^]", t[m.end():])
	or _tn_id_context(L, re.split(r"[.!?;।॥\n]", t[max(0, m.start() - 60):m.start()])[-1])):
	return m.group(0)
	return _digits(L, m.group(0))
	t = re.sub(r"(?<![\d,.₹$#\w])[1-9]\d{5}(?![\d,]\|\.\d\|\s*%)", r6, t)

	# R8 Tamil "Month D, YYYY" -> "D Month YYYY"
	if lang == "ta":
	months = "\|".join(_TA_MONTHS)
	t = re.sub(rf"(?<!\w)({months})\s+(\d{{1,2}}),?\s+(\d{{4}})(?!\d)", r"\2 \1 \3", t)
	return t


	TAG_COMMA = False # P1 off: no measured gain (6 vs 6 backstop cuts on the 14 affected emotion rows x2 seeds)


	def post(out: str, seg: str, lang: str, before_tag: bool) -> str:
	# P1 keep a trailing comma that precedes an inline [tag] (disabled, see TAG_COMMA)
	if TAG_COMMA and before_tag and seg.rstrip().endswith(",") and out.rstrip().endswith("."):
	out = out.rstrip()[:-1] + ","
	# P2 spoken hyphen inside e-mail / URL in the request language
	uris = [m.group(0) for m in _URI_RE.finditer(seg)]
	if lang in DASH and uris:
	out = re.sub(r"(?<=\s)dash(?=\s)", DASH[lang], out)
	# P3 acronym labels inside e-mail / URL
	labels = {w for u in uris for w in re.findall(r"[a-z]+", re.sub(r"^https?://", "", u))
	if w in _URI_ACRONYMS or w.startswith(_URI_ACR_PREFIX) or w.endswith(_URI_ACR_SUFFIX)}
	if labels:
	try:
	L = _table(lang)
	except Exception:
	return out
	spell = lambda w: " ".join(L.letter(c.upper()) for c in w)

	def p3(m):
	w = m.group(0)
	if w in _URI_ACRONYMS:
	return spell(w)
	for a in _URI_ACR_PREFIX:
	if w.startswith(a) and len(w) - len(a) >= 3:
	return spell(a) + " " + w[len(a):]
	for a in _URI_ACR_SUFFIX:
	if w.endswith(a) and len(w) - len(a) >= 4:
	return w[:-len(a)] + " " + spell(a)
	return w
	out = re.sub(r"(?<![\w.@/])(?:" + "\|".join(sorted(map(re.escape, labels), key=len, reverse=True)) + r")(?![\w]\|[.@/\-]\w)",
	p3, out)
	return out