import random

from multi_swarm.ga.loop import GAConfig, next_generation
from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier


def make(idx: int) -> HypothesisAgentGenome:
    return HypothesisAgentGenome(
        system_prompt=f"p-{idx}",
        feature_access=["close"],
        temperature=0.9,
        top_p=0.95,
        model_tier=ModelTier.C,
        lookback_window=100,
        cognitive_style="x",
    )


def test_next_generation_size_preserved() -> None:
    population = [make(i) for i in range(20)]
    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
    cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
    new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
    assert len(new_pop) == 20


def test_next_generation_includes_elites() -> None:
    population = [make(i) for i in range(20)]
    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
    cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
    new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
    elite_ids = {
        g.id for g in sorted(population, key=lambda g: fitnesses[g.id], reverse=True)[:2]
    }
    new_ids = {g.id for g in new_pop}
    assert elite_ids.issubset(new_ids)


def test_next_generation_increments_generation_for_offspring() -> None:
    population = [make(i) for i in range(20)]
    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
    cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
    new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
    new_offspring = [g for g in new_pop if g.id not in {p.id for p in population}]
    assert all(g.generation > 0 for g in new_offspring)