"""Video title normalization — clean the noisy YouTube titles for display + storage.

Applied where a video's title is written (enrichment) and where a download's title comes from
yt-dlp, so the feed, search, channel pages, and the download center all show tidy titles. The
raw title is preserved in `Video.original_title` so this is reversible / re-derivable.

Rules (user-approved "option b"):
  0. Strip a leading social engagement prefix ("1.6M views · 66K reactions | …") some sources
     (Facebook) prepend, up to and including the pipe.
  1. Drop emoji / pictographs / symbols / control chars (keep accents — HU/DE/…).
  2. Strip trailing SEO hashtag clusters (word hashtags at the end); a numeric "#3" episode
     marker survives.
  3. De-shout ALL-CAPS words, context-aware:
       - If the title is *mostly shouting* (≥ half the multi-letter words are all-caps), every
         all-caps word is Title-cased (short shouted words like CAR/WAR/ÉN get fixed too), and
         the first letter is capitalized. Known acronyms (PS, AI, USA, PC…) and words with
         digits (PS5, 3D) are kept; function words (to/the/és/az…) are lowercased.
       - Otherwise only long all-caps words (≥4 letters) are Title-cased, so a lone acronym in
         an otherwise normal title is left alone.
  4. Collapse repeated punctuation (!!! → !) and whitespace.
"""
import re
import unicodedata

_DROP_CATEGORIES = {"So", "Sk", "Cc", "Cf", "Cs", "Co", "Cn"}
# Social engagement prefix some sources (Facebook) prepend, e.g. "1.6M views · 66K reactions | ".
# Match one-or-more "<count> <label>" tokens (· / , separated) terminated by a pipe, then drop it.
# Deliberately does NOT consume the space after `|`: leaving it lets a hashtag-only remainder
# ("… | #foo #bar") keep the leading whitespace `_TRAILING_HASHTAGS` needs to strip it; the normal
# case's leading space is removed later by the whitespace collapse.
_ENGAGEMENT_PREFIX = re.compile(
    r"^\s*(?:\d[\d.,]*\s*[KMB]?\s*"
    r"(?:views?|reactions?|likes?|comments?|shares?|plays?|followers?)\s*[·,;]?\s*)+\|",
    re.IGNORECASE,
)
_LETTER_RUN = re.compile(r"[^\W\d_]+", re.UNICODE)
# Trailing SEO hashtag cluster: a run of whitespace-separated `#tags` at the very end, where each
# tag contains at least one letter (a bare numeric "#3" episode marker is NOT a hashtag and
# survives). The "has a letter" test is a zero-width lookahead and the tag body is matched
# possessively (`*+`), so there is exactly ONE way to split each tag and nothing to backtrack over.
# The previous form `[^\s#]*[letter][^\s#]*` had TWO overlapping unbounded quantifiers around the
# required letter: on a long hashtag run that ends with trailing non-tag text (a Facebook
# "… #a #b #c | Page" title), the `$` anchor fails and the engine backtracks catastrophically
# (ReDoS) — it pinned a worker thread at 100% CPU forever inside the download progress hook.
_TRAILING_HASHTAGS = re.compile(r"(?:\s+#(?=[^\s#]*[^\W\d\s_])[^\s#]*+)+\s*$", re.UNICODE)
_REPEAT_PUNCT = re.compile(r"([!?.,\-])\1{1,}")

# Short function words → lowercased when the title is de-shouted (proper title-case style).
_SHORT_LOWER = {
    "to", "the", "and", "of", "a", "an", "in", "on", "at", "for", "or", "vs", "the",
    "és", "az", "egy", "meg", "nem", "hogy", "de", "ha", "der", "die", "das", "und", "von",
}
# Kept as-is even when everything else is de-shouted (common acronyms; compared lowercased).
_ACRONYMS = {
    "ps", "ai", "pc", "tv", "hd", "4k", "8k", "uk", "eu", "us", "usa", "gta", "rpg", "fps",
    "diy", "vr", "ar", "id", "ok", "faq", "nasa", "fbi", "cia", "ceo", "amd", "pdf", "usb",
    "gps", "api", "dj", "mc", "suv", "gpu", "cpu", "ssd", "hdmi", "led", "ufo", "dna", "nba",
    "nfl", "asmr", "pov", "diy", "wtf", "lol", "rtx", "gtx", "ios", "mmo", "vip", "hp",
}


def _titlecase(w: str) -> str:
    return w[0].upper() + w[1:].lower()


def normalize_title(raw: str | None) -> str | None:
    """Return the cleaned title, or the input unchanged for empty/whitespace."""
    if not raw or not raw.strip():
        return raw
    t = unicodedata.normalize("NFKC", raw)
    t = "".join(ch for ch in t if unicodedata.category(ch) not in _DROP_CATEGORIES)
    stripped = _ENGAGEMENT_PREFIX.sub("", t)
    prefix_removed = stripped != t
    t = _TRAILING_HASHTAGS.sub("", stripped)
    base = t  # after prefix + trailing-hashtag removal — the fallback if de-shouting empties it

    multi = [r for r in _LETTER_RUN.findall(t) if len(r) >= 2]
    caps = [r for r in multi if r.isupper()]
    shouting = len(caps) >= 2 and len(caps) >= 0.5 * len(multi)

    def repl(m: re.Match) -> str:
        w = m.group(0)
        if len(w) < 2 or not w.isupper():
            return w
        low = w.lower()
        if low in _ACRONYMS:
            return w
        if low in _SHORT_LOWER:
            return low
        if shouting or len(w) >= 4:
            return _titlecase(w)
        return w  # short all-caps in a non-shouting title → likely an acronym, keep

    t = _LETTER_RUN.sub(repl, t)
    t = _REPEAT_PUNCT.sub(r"\1", t)
    t = re.sub(r"\s+", " ", t).strip()

    if shouting:  # ensure the first letter is capitalized (a leading function word got lowered)
        for i, ch in enumerate(t):
            if ch.isalpha():
                t = t[:i] + ch.upper() + t[i + 1:]
                break
    # Nothing survived cleaning: fall back to the prefix+hashtag-stripped text (so neither the
    # stats prefix nor the SEO hashtags are resurrected). Only use the untouched raw when no prefix
    # was there to strip — e.g. an all-emoji title whose glyphs were dropped, where returning the
    # original is the lesser evil.
    return t or base.strip() or ("" if prefix_removed else raw)