import numpy as np
import pytest

import behavision.gallery.index as index_mod
from behavision.gallery.index import VectorIndex


def _unit(v):
    v = np.asarray(v, dtype=np.float32)
    return v / np.linalg.norm(v)


@pytest.fixture(params=["numpy", "faiss"])
def make_index(request, monkeypatch):
    if request.param == "numpy":
        monkeypatch.setattr(index_mod, "_HAVE_FAISS", False)
    elif not index_mod._HAVE_FAISS:
        pytest.skip("faiss not installed")
    return lambda dim=8: VectorIndex(dim)


def test_empty_index_returns_no_matches(make_index):
    idx = make_index()
    assert idx.search(_unit(np.ones(8))) == []


def test_add_search_finds_nearest(make_index):
    idx = make_index()
    a = _unit([1, 0, 0, 0, 0, 0, 0, 0])
    b = _unit([0, 1, 0, 0, 0, 0, 0, 0])
    idx.add([10, 20], np.vstack([a, b]))
    results = idx.search(_unit([0.9, 0.1, 0, 0, 0, 0, 0, 0]), k=2)
    assert results[0][0] == 10
    assert results[0][1] > results[1][1]
    assert results[0][1] == pytest.approx(1.0, abs=0.05)


def test_remove(make_index):
    idx = make_index()
    a = _unit(np.arange(1, 9))
    idx.add([7], a.reshape(1, -1))
    idx.remove([7])
    assert len(idx) == 0
    assert idx.search(a) == []