"""Guard against char-level title false positives in track matching.

Issue #769: playlist sync matched tracks that aren't in the library to a
DIFFERENT song by the SAME artist, with high confidence — e.g. "Dani
California" -> "Californication" (Red Hot Chili Peppers), "Under The Bridge"
-> "Around the World". The confidence formula is ``0.5*title + 0.5*artist``,
and a same-artist comparison always yields ``artist = 1.0``, so the title score
is the only thing that can tell two of an artist's songs apart. But the title
score is a ``difflib.SequenceMatcher`` character ratio, which over-credits
unrelated titles that happen to share a long substring ("californi…") or only a
stopword ("the"): 0.67 and 0.62 respectively. With the flat 0.5 artist term
that lands at 0.83 / 0.81 — well over the 0.7 sync threshold.

``titles_plausibly_same`` adds a cheap word-level sanity check on top of the
char ratio: accept a pair only when it's near-identical char-wise (so typos and
punctuation/casing variants — "Beleive"/"Believe", "HUMBLE."/"Humble" — still
match) OR the two titles share at least one significant (non-stopword) token.
Two genuinely different songs by the same artist share no content word, so they
get rejected; the real track is then correctly reported missing.
"""

from __future__ import annotations

import re

# Articles / prepositions / conjunctions only. Deliberately NOT pronouns
# ("you", "me", "i") — those carry meaning in song titles and dropping them
# could strip the only shared word from a real match. "the" MUST stay here:
# without it "Under The Bridge" and "Around the World" would falsely share it.
_TITLE_STOPWORDS = frozenset({
    "the", "a", "an", "of", "and", "or", "to", "in", "on",
    "for", "with", "at", "by", "from",
})

_TOKEN_RE = re.compile(r"[a-z0-9]+")

# Char ratio at/above which two titles are treated as the same regardless of
# shared words — covers typos, punctuation, casing, accents. Tuned so single-
# word typos ("Beleive"/"Believe" = 0.857) pass while the #769 false positives
# ("Dani California"/"Californication" = 0.667) do not.
_NEAR_IDENTICAL = 0.85


def _content_tokens(text: str) -> set[str]:
    return {t for t in _TOKEN_RE.findall((text or "").lower()) if t not in _TITLE_STOPWORDS}


def titles_plausibly_same(
    title_a: str,
    title_b: str,
    char_similarity: float,
    *,
    near_identical: float = _NEAR_IDENTICAL,
) -> bool:
    """Whether two titles could be the same track, given their char similarity.

    ``title_a`` / ``title_b`` should already be normalised/cleaned (lowercased,
    brackets stripped) the same way the caller computed ``char_similarity``.

    Returns ``True`` when the pair is near-identical char-wise OR shares at
    least one significant (non-stopword) token. Returns ``False`` for two
    titles that are only moderately char-similar and share no content word —
    i.e. different songs the char ratio over-credited (#769)."""
    if char_similarity >= near_identical:
        return True
    ta = _content_tokens(title_a)
    tb = _content_tokens(title_b)
    # Word-overlap is only a reliable "different song" signal when at least one
    # side has 2+ content words — that's the #769 case where the char ratio
    # over-credits a shared substring ("Dani California"/"Californication") or
    # a stopword ("Under The Bridge"/"Around the World"). For single-word
    # titles there's no other word to share, so applying it would wrongly fail
    # legitimate stylized spellings ("Grey"/"Gray", "Tonite"/"Tonight",
    # "Thru"/"Through") that the char ratio rightly accepts. In that case defer
    # to the caller's existing char-similarity floor instead of force-failing.
    if max(len(ta), len(tb)) < 2 or not ta or not tb:
        return True
    return not ta.isdisjoint(tb)


__all__ = ["titles_plausibly_same"]