"""Fast-DetectGPT: pure estimator maths + the never-raise/abstain contract.

No model is loaded anywhere in this module - the estimator is tested on
synthetic logits, and the detector paths exercised are the ones that return
before any model load.
"""

import torch

from text_detection.detectors.fastdetectgpt import (
    FastDetectGPTDetector,
    sampling_discrepancy_analytic,
)
from text_detection.schema import Verdict


def test_discrepancy_is_finite_float():
    torch.manual_seed(0)
    logits = torch.randn(1, 8, 50)
    labels = torch.randint(0, 50, (1, 8))
    d = sampling_discrepancy_analytic(logits, labels)
    assert isinstance(d, float)
    assert d == d  # not NaN


def test_confident_correct_tokens_score_higher_than_random():
    # A sequence the model is very confident about (peaked logits on the true
    # token) yields a higher analytic discrepancy than uniform-ish logits.
    T, V = 10, 40
    labels = torch.randint(0, V, (1, T))
    peaked = torch.full((1, T, V), -10.0)
    for t in range(T):
        peaked[0, t, labels[0, t]] = 10.0
    flat = torch.zeros(1, T, V)
    assert sampling_discrepancy_analytic(peaked, labels) > sampling_discrepancy_analytic(flat, labels)


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


async def test_short_text_abstains_without_loading_model():
    det = FastDetectGPTDetector()
    res = await det.detect_text("only five words here now")
    assert res.detector == "text_fastdetectgpt"
    assert res.verdict == Verdict.UNCERTAIN
    assert res.confidence == 0.0
    assert res.error is None      # abstain is NOT an error


async def test_scoring_failure_is_swallowed(monkeypatch):
    # The never-raise contract: an exploding model must degrade, not propagate.
    det = FastDetectGPTDetector()

    def _boom(self, text):
        raise RuntimeError("model exploded")

    monkeypatch.setattr(FastDetectGPTDetector, "_score", _boom)
    res = await det.detect_text(" ".join(["word"] * 40))
    assert res.error is not None
    assert "model exploded" in res.error
    assert res.confidence == 0.0