"""Bayesian log-odds fusion across text detectors."""

from text_detection.fusion import TextEnsembleFusion
from text_detection.schema import DetectionResult, Verdict


def _det(name, p_fake, verdict, confidence=0.8, error=None):
    return DetectionResult(
        detector=name, p_fake=p_fake, verdict=verdict,
        confidence=confidence, error=error, evidence=[f"{name} says {p_fake}"],
    )


def test_no_usable_results_returns_uncertain_at_zero_confidence():
    fused = TextEnsembleFusion().fuse(
        [_det("text_fastdetectgpt", 0.9, Verdict.AI_GENERATED, error="boom")]
    )
    assert fused.verdict == Verdict.UNCERTAIN
    assert fused.confidence == 0.0
    assert fused.p_fake == 0.5


def test_empty_input_returns_uncertain():
    fused = TextEnsembleFusion().fuse([])
    assert fused.verdict == Verdict.UNCERTAIN
    assert fused.confidence == 0.0


def test_abstaining_detector_is_ignored():
    # A zero-confidence abstain must not drag a confident AI verdict to 0.5.
    results = [
        _det("text_fastdetectgpt", 0.95, Verdict.AI_GENERATED, confidence=0.9),
        _det("text_provenance", 0.5, Verdict.UNCERTAIN, confidence=0.0),
    ]
    fused = TextEnsembleFusion().fuse(results)
    assert fused.verdict == Verdict.AI_GENERATED
    assert fused.p_fake > 0.65


def test_high_weight_watermark_outvotes_zeroshot():
    # watermark weight 4.0 vs fastdetectgpt 1.5: the watermark should dominate.
    results = [
        _det("text_watermark", 0.98, Verdict.AI_GENERATED, confidence=0.95),
        _det("text_fastdetectgpt", 0.30, Verdict.REAL, confidence=0.40),
    ]
    fused = TextEnsembleFusion().fuse(results)
    assert fused.verdict == Verdict.AI_GENERATED


def test_conflict_is_flagged_and_penalises_confidence():
    results = [
        _det("text_watermark", 0.95, Verdict.AI_GENERATED, confidence=0.9),
        _det("text_fastdetectgpt", 0.05, Verdict.REAL, confidence=0.9),
    ]
    fused = TextEnsembleFusion().fuse(results)
    assert any("disagreement" in u.lower() for u in fused.uncertainty_factors)


def test_generator_is_propagated():
    r = _det("text_watermark", 0.98, Verdict.AI_GENERATED, confidence=0.95)
    r.generator = "watermarked-LLM"
    fused = TextEnsembleFusion().fuse([r])
    assert fused.generator == "watermarked-LLM"


def test_processing_time_is_carried_through():
    fused = TextEnsembleFusion().fuse(
        [_det("text_fastdetectgpt", 0.9, Verdict.AI_GENERATED)], processing_time_ms=42.0
    )
    assert fused.processing_time_ms == 42.0