import json
from pathlib import Path

from database import MotionDatabase


def test_similarity_cache_roundtrip(tmp_path: Path):
    db_file = tmp_path / "motions.db"
    # Create MotionDatabase which should initialize schema
    db = MotionDatabase(db_path=str(db_file))

    # If MotionDatabase fell back to file mode, check JSON files
    if getattr(db, "_file_mode", False):
        emb_file = Path(str(db_file) + ".embeddings.json")
        sim_file = Path(str(db_file) + ".similarity_cache.json")
        assert emb_file.exists()
        assert sim_file.exists()
        assert json.loads(emb_file.read_text(encoding="utf-8")) == []
        assert json.loads(sim_file.read_text(encoding="utf-8")) == []
    else:
        # Try to import duckdb only when needed
        import duckdb

        conn = duckdb.connect(str(db_file))
        embeddings_count = conn.execute("SELECT COUNT(*) FROM embeddings").fetchone()[0]
        similarity_count = conn.execute(
            "SELECT COUNT(*) FROM similarity_cache"
        ).fetchone()[0]
        conn.close()
        assert embeddings_count == 0
        assert similarity_count == 0

    # Insert two similarity rows via helper
    rows = [
        {
            "source_motion_id": 1,
            "target_motion_id": 2,
            "score": 0.5,
            "vector_type": "text",
            "window_id": None,
        },
        {
            "source_motion_id": 1,
            "target_motion_id": 3,
            "score": 0.9,
            "vector_type": "text",
            "window_id": None,
        },
    ]

    db.store_similarity_batch(rows)

    # Read back cached similarities and verify ordering (highest score first)
    results = db.get_cached_similarities(source_motion_id=1, vector_type="text")
    assert len(results) == 2
    # results may be dicts from DB or file-backed dicts
    assert results[0]["target_motion_id"] == 3
    assert abs(float(results[0]["score"]) - 0.9) < 1e-6
    assert results[1]["target_motion_id"] == 2
    assert abs(float(results[1]["score"]) - 0.5) < 1e-6

    # Clear cache and verify it's empty
    db.clear_similarity_cache(vector_type="text")
    results_after_clear = db.get_cached_similarities(
        source_motion_id=1, vector_type="text"
    )
    assert results_after_clear == []