import random

from gausse.components.database import ComponentDatabase
from gausse.optim.evolutionary import polish_best, run_evolution
from gausse.optim.search_space import SearchBounds, sample_genome
from gausse.storage.database import count_runs, fetch_runs, open_connection

DB = ComponentDatabase.load()


def test_run_evolution_writes_all_evaluations_and_returns_summary(tmp_path):
    db_path = tmp_path / "runs.sqlite3"
    bounds = SearchBounds(max_stages=2)

    summary = run_evolution(
        db_path,
        n_generations=2,
        population_size=6,
        bounds=bounds,
        n_workers=2,
        seed=7,
        polish=False,
    )

    assert summary["n_evaluated"] == 12  # 2 поколения x 6 особей
    assert "best_fitness" in summary

    conn = open_connection(db_path)
    assert count_runs(conn) == 12
    rows = fetch_runs(conn)
    assert all(r.search_mode == "evolve" for r in rows)
    conn.close()


def test_evolution_is_reproducible_given_same_seed(tmp_path):
    bounds = SearchBounds(max_stages=2)
    summary_a = run_evolution(
        tmp_path / "a.sqlite3", n_generations=2, population_size=6, bounds=bounds,
        n_workers=1, seed=99, polish=False,
    )
    summary_b = run_evolution(
        tmp_path / "b.sqlite3", n_generations=2, population_size=6, bounds=bounds,
        n_workers=1, seed=99, polish=False,
    )
    assert summary_a["best_fitness"] == summary_b["best_fitness"]
    assert summary_a["best_efficiency"] == summary_b["best_efficiency"]


def test_polish_does_not_make_the_best_genome_worse():
    rng = random.Random(15)
    bounds = SearchBounds(max_stages=2)
    # ищем реализуемый геном как стартовую точку доводки
    genome = None
    for _ in range(30):
        candidate = sample_genome(DB, bounds, rng)
        from gausse.optim.objective import evaluate
        result = evaluate(candidate, DB, bounds)
        if result.feasible:
            genome = candidate
            baseline_fitness = result.fitness
            break
    assert genome is not None, "не нашли реализуемый геном за 30 попыток"

    _, polished_result = polish_best(genome, DB, bounds)
    assert polished_result.fitness >= baseline_fitness - 1e-9