import numpy as np
import pytest

from starVLA.scoring.residual_cache import (
    interpolate_grid_scores,
    percentile_intensity_masses,
    quantile_anchor_positions,
)


def test_quantile_grid_is_unique_and_covers_endpoints():
    positions = quantile_anchor_positions(101, 8)
    assert len(positions) == 8
    assert positions[0] == 0
    assert positions[-1] == 100
    assert np.all(np.diff(positions) > 0)


def test_quantile_grid_uses_every_short_episode_anchor():
    np.testing.assert_array_equal(quantile_anchor_positions(3, 8), [0, 1, 2])


def test_interpolation_preserves_grid_values():
    positions = np.asarray([0, 2, 4])
    scores = np.asarray([0.0, 2.0, 0.0])
    interpolated = interpolate_grid_scores(5, positions, scores)
    np.testing.assert_allclose(interpolated, [0.0, 1.0, 2.0, 1.0, 0.0])


def test_interpolation_requires_endpoint_coverage():
    with pytest.raises(ValueError, match="endpoints"):
        interpolate_grid_scores(5, np.asarray([1, 4]), np.asarray([0.0, 1.0]))


def test_percentile_masses_have_floor_and_preserve_order():
    masses = percentile_intensity_masses(
        np.asarray([5.0, 1.0, 3.0]),
        epsilon=0.1,
    )
    np.testing.assert_allclose(masses, [1.1, 0.1, 0.6])