from lyra_worker.confidence import score_confidence
from lyra_worker.types import Candidate, MBTarget, Quality

Q = Quality(fmt="FLAC", lossless=True, bit_depth=16, sample_rate=44100)


def _cand(artist, album, tracks=10):
    return Candidate(source="qobuz", source_ref="x", matched_artist=artist,
                     matched_album=album, quality=Q, track_count=tracks)


def test_exact_match_scores_high():
    target = MBTarget(artist="Radiohead", album="In Rainbows", track_count=10)
    assert score_confidence(target, _cand("Radiohead", "In Rainbows", 10)) > 0.95


def test_case_and_whitespace_insensitive():
    target = MBTarget(artist="Radiohead", album="In Rainbows")
    assert score_confidence(target, _cand("  radiohead ", "IN RAINBOWS")) > 0.95


def test_wrong_album_scores_low():
    target = MBTarget(artist="Radiohead", album="In Rainbows", track_count=10)
    assert score_confidence(target, _cand("Radiohead", "OK Computer", 12)) < 0.7


def test_unknown_track_count_does_not_penalize():
    target = MBTarget(artist="Radiohead", album="In Rainbows", track_count=None)
    # track_count unknown -> track_factor neutral; name match still drives a high score
    assert score_confidence(target, _cand("Radiohead", "In Rainbows", 99)) > 0.95


def test_track_count_mismatch_lowers_score():
    target = MBTarget(artist="Radiohead", album="In Rainbows", track_count=10)
    good = score_confidence(target, _cand("Radiohead", "In Rainbows", 10))
    bad = score_confidence(target, _cand("Radiohead", "In Rainbows", 4))
    assert bad < good