import numpy as np

from evaluate_depth import depth_metrics, evaluate_depth_pair


def test_depth_metrics_reports_perfect_prediction():
    depth = np.array([[1.0, 2.0], [3.0, 4.0]])
    metrics = depth_metrics(depth, depth)
    assert metrics["valid_pixel_count"] == 4
    assert metrics["abs_rel"] == 0.0
    assert metrics["rmse"] == 0.0
    assert metrics["delta1"] == 1.0


def test_depth_pair_separates_relative_scale_from_raw_error():
    ground_truth = np.array([[1.0, 2.0], [3.0, 4.0]])
    prediction = ground_truth * 7.0
    report = evaluate_depth_pair(prediction, ground_truth)
    assert report["raw"]["abs_rel"] > 5.0
    assert report["median_scaled"]["rmse"] < 1e-12
    assert abs(report["median_scale"] - 1.0 / 7.0) < 1e-12


def test_inverse_depth_option_converts_before_scoring():
    ground_truth = np.array([[1.0, 2.0], [4.0, 8.0]])
    report = evaluate_depth_pair(1.0 / ground_truth, ground_truth, prediction_is_inverse_depth=True)
    assert report["raw"]["rmse"] < 1e-12


def test_depth_metrics_respects_mask_and_rejects_shape_mismatch():
    prediction = np.array([[1.0, 2.0], [30.0, 40.0]])
    ground_truth = np.array([[1.0, 2.0], [3.0, 4.0]])
    mask = np.array([[True, True], [False, False]])
    assert depth_metrics(prediction, ground_truth, mask)["valid_pixel_count"] == 2
    try:
        depth_metrics(prediction, ground_truth, np.ones((1, 1), dtype=bool))
    except ValueError as exc:
        assert "valid_mask" in str(exc)
    else:
        raise AssertionError("shape mismatch should fail")