"""Binoculars: pure ratio maths + the never-raise/abstain contract."""

import torch

from text_detection.detectors.binoculars import BinocularsDetector, binoculars_score
from text_detection.schema import Verdict


def test_score_is_finite_float():
    torch.manual_seed(0)
    obs = torch.randn(1, 8, 50)
    perf = torch.randn(1, 8, 50)
    labels = torch.randint(0, 50, (1, 8))
    s = binoculars_score(obs, perf, labels)
    assert isinstance(s, float)
    assert s == s


def test_machine_like_text_scores_lower():
    # When the observer is very confident on the true tokens (low perplexity)
    # while cross-perplexity stays moderate, the ratio drops - the machine signal.
    T, V = 10, 40
    labels = torch.randint(0, V, (1, T))
    confident_obs = torch.full((1, T, V), -10.0)
    for t in range(T):
        confident_obs[0, t, labels[0, t]] = 10.0
    unsure_obs = torch.zeros(1, T, V)
    perf = torch.randn(1, T, V)
    assert binoculars_score(confident_obs, perf, labels) < binoculars_score(unsure_obs, perf, labels)


async def test_empty_text_returns_error_result():
    det = BinocularsDetector()
    res = await det.detect_text("")
    assert res.error is not None
    assert res.confidence == 0.0


async def test_short_text_abstains_without_loading_models():
    det = BinocularsDetector()
    res = await det.detect_text("only five words here now")
    assert res.detector == "text_binoculars"
    assert res.verdict == Verdict.UNCERTAIN
    assert res.confidence == 0.0
    assert res.error is None