import numpy as np

from paperpod.vision.motion import find_segments, motion_score, prepare_gray


def series(pattern: list[tuple[float, int]], dt: float = 0.125):
    """Expand [(score, count), ...] into (timestamps, scores)."""
    scores = [s for s, n in pattern for _ in range(n)]
    timestamps = [i * dt for i in range(len(scores))]
    return timestamps, scores


def test_basic_stable_and_moving_segments():
    # stable 2s, moving 1s, stable 2s
    ts, sc = series([(0.5, 16), (10.0, 8), (0.5, 16)])
    segs = find_segments(ts, sc, threshold=4.0, stable_min_duration_s=1.0)
    kinds = [s.kind for s in segs]
    assert kinds == ["stable", "moving", "stable"]
    assert segs[0].duration >= 1.0
    assert segs[2].duration >= 1.0


def test_short_stable_blip_is_demoted_to_moving():
    # moving, tiny stable pause (0.25s), moving -> should merge into one moving segment
    ts, sc = series([(10.0, 8), (0.5, 2), (10.0, 8)])
    segs = find_segments(ts, sc, threshold=4.0, stable_min_duration_s=1.0)
    assert [s.kind for s in segs] == ["moving"]


def test_empty_input():
    assert find_segments([], [], threshold=4.0, stable_min_duration_s=1.0) == []


def test_motion_score_zero_for_identical_frames():
    img = np.random.default_rng(0).integers(0, 255, (100, 100, 3), dtype=np.uint8)
    g = prepare_gray(img)
    assert motion_score(g, g) == 0.0


def test_motion_score_positive_for_changed_frames():
    rng = np.random.default_rng(0)
    a = prepare_gray(rng.integers(0, 255, (100, 100, 3), dtype=np.uint8))
    b = prepare_gray(np.full((100, 100, 3), 200, dtype=np.uint8))
    assert motion_score(a, b) > 0