"""Canonical Bangla text normalizer = the paper's Stage-2 pipeline (single source of truth for
TTS input, CER reference, and training text). Order follows the paper:
  1. digit conversion (Latin 0-9 -> Bengali)            [paper step 1]
  2. colon-numeral rewriting  ("৪:৪৫" -> "৪ এর ৪৫")      [paper step 2, from phonemizer.py]
  3. numeral-to-word expansion via bnnumerizer            [paper step 3]  -- applied PER NUMBER
     (bnnumerizer mangles the 2nd+ number in a string; per-number application is a bug-fix)
  4. per-word Unicode normalization (bnunicodenormalizer) [paper step 4]
  5. honorific expansion (paper attribution_dict, 12)     [paper step 5]
  6. whitespace collapse                                  [paper step 6 = sentence seg + collapse]
Extensions beyond the paper (flagged, small): ordinals (৩য়->তৃতীয়), title abbreviations (ড./ডা./মো.),
thousands-comma stripping. The paper lists domain abbreviations as a known gap.
Use `for_tts(text)` before synthesis and `for_cer(text)` for reference/hypothesis before CER."""
import re, unicodedata
from bnnumerizer import numerize
try:
    from bnunicodenormalizer import Normalizer
    _bnorm = Normalizer()
except Exception:
    _bnorm = None

# --- paper attribution_dict (phonemizer.py) ---
HONORIFICS = {
    "সাঃ": "সাল্লাল্লাহু আলাইহি ওয়া সাল্লাম", "রাঃ": "রাদিআল্লাহু আনহু", "রহঃ": "রাহিমাহুল্লাহ",
    "হাঃ": "হাফিজাহুল্লাহ", "মাঃ": "মাদ্দাজিল্লুহু", "দাঃ বাঃ": "দামাত বারাকাতুহুম",
    "আঃ": "আলাইহিস সালাম", "জিঃ": "জিল্লুল্লাহ", "কুঃ সিঃ": "কুদ্দিসা সিরুহু",
    "আলঃ": "আলাইহিমুস সালাম", "রাযিঃ": "রাযিআল্লাহু আনহু", "মুহঃ": "মুহতারাম",
}
# --- extensions (not in paper; keep explicit) ---
ORDINALS = {"১ম":"প্রথম","২য়":"দ্বিতীয়","৩য়":"তৃতীয়","৪র্থ":"চতুর্থ","৫ম":"পঞ্চম","৬ষ্ঠ":"ষষ্ঠ","৭ম":"সপ্তম","৮ম":"অষ্টম","৯ম":"নবম","১০ম":"দশম"}
TITLES = {"ড.":"ডক্টর","ডা.":"ডাক্তার","মো.":"মোহাম্মদ","মোঃ":"মোহাম্মদ","প্রফ.":"প্রফেসর","ইঞ্জি.":"ইঞ্জিনিয়ার","অ্যাড.":"অ্যাডভোকেট"}
EN2BN = {"laptop":"ল্যাপটপ","monday":"মানডে","email":"ইমেইল","password":"পাসওয়ার্ড","software":"সফটওয়্যার",
         "performance":"পারফরম্যান্স","team":"টিম","project":"প্রজেক্ট","online":"অনলাইন"}
_LAT2BN = str.maketrans("0123456789", "০১২৩৪৫৬৭৮৯")
_NUM = re.compile(r"[০-৯]+(?:[.][০-৯]+)*")
_COLON = re.compile(r"([০-৯]+):([০-৯]+)")

def _unicode_words(s):
    if _bnorm is None: return s
    out = []
    for w in s.split():
        try: r = _bnorm(w); out.append(r["normalized"] or w)
        except Exception: out.append(w)
    return " ".join(out)

def for_tts(text: str) -> str:
    """Paper-order normalization producing the string the model should speak."""
    t = str(text)
    for k, v in sorted(TITLES.items(), key=lambda kv: -len(kv[0])): t = t.replace(k, v)   # ext
    t = t.translate(_LAT2BN)                                                              # 1
    t = _COLON.sub(lambda m: f"{m.group(1)} এর {m.group(2)}", t)                          # 2
    for k, v in sorted(ORDINALS.items(), key=lambda kv: -len(kv[0])): t = t.replace(k, v) # ext
    t = t.replace(",", "")                                                                # ext (bnnumerizer breaks on ১,২৫০)
    t = _NUM.sub(lambda m: numerize(m.group(0)), t)                                       # 3 (per number)
    t = _unicode_words(t)                                                                 # 4
    for k, v in sorted(HONORIFICS.items(), key=lambda kv: -len(kv[0])): t = t.replace(k, _unicode_words(v))  # 5
    return re.sub(r"\s+", " ", t).strip()                                                 # 6

def for_cer(text: str) -> str:
    """Same expansions as for_tts, plus English->Bengali map, NFC, ZW strip, punctuation/space removal."""
    t = re.sub(r"[A-Za-z]+", lambda m: EN2BN.get(m.group(0).lower(), m.group(0)), str(text))
    t = for_tts(t)
    t = unicodedata.normalize("NFC", t).replace("‌", "").replace("‍", "")
    return re.sub(r"[।,\.!?;:\-—\s\"'‘’“”()]", "", t)

if __name__ == "__main__":
    for s in ["নবী (সাঃ) বলেছেন","হযরত আলী (রাঃ) ও ইমাম আবু হানিফা (রহঃ)","সভা ৪:৪৫ এ শুরু","ড. ইউনূস ৩য় তলায় ১,২৫০ টাকা দিলেন","1966 সালে 400 টি বাঘ"]:
        print(repr(s), "->", for_tts(s))