//! Per-algorithm property tests.
//!
//! For every `Optimizer` impl in heuropt we check the same three properties:
//!   1. **Deterministic-with-seed**: two runs with the same seed produce
//!      the same `best.evaluation.objectives`.
//!   2. **No panic on random valid inputs**: random seeds, random tiny
//!      problems, random bounds — the algorithm runs to completion.
//!   3. **Population-size invariant** (where the algorithm documents one):
//!      the final population has the configured size.

use proptest::prelude::*;

use heuropt::core::evaluation::Evaluation;
use heuropt::core::objective::{Objective, ObjectiveSpace};
use heuropt::core::problem::Problem;
use heuropt::prelude::*;

// -----------------------------------------------------------------------------
// Tiny problems
// -----------------------------------------------------------------------------

struct Sphere1D;
impl Problem for Sphere1D {
    type Decision = Vec<f64>;
    fn objectives(&self) -> ObjectiveSpace {
        ObjectiveSpace::new(vec![Objective::minimize("f")])
    }
    fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
        Evaluation::new(vec![x[0] * x[0]])
    }
}

struct SchafferN1;
impl Problem for SchafferN1 {
    type Decision = Vec<f64>;
    fn objectives(&self) -> ObjectiveSpace {
        ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
    }
    fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
        let v = x[0];
        Evaluation::new(vec![v * v, (v - 2.0).powi(2)])
    }
}

struct OneMax {
    #[allow(dead_code)]
    bits: usize,
}
impl Problem for OneMax {
    type Decision = Vec<bool>;
    fn objectives(&self) -> ObjectiveSpace {
        ObjectiveSpace::new(vec![Objective::maximize("count")])
    }
    fn evaluate(&self, x: &Vec<bool>) -> Evaluation {
        Evaluation::new(vec![x.iter().filter(|b| **b).count() as f64])
    }
}

// -----------------------------------------------------------------------------
// Helpers
// -----------------------------------------------------------------------------

fn so_bounds() -> RealBounds {
    RealBounds::new(vec![(-3.0, 3.0)])
}
fn so_bounds_2d() -> RealBounds {
    RealBounds::new(vec![(-3.0, 3.0); 2])
}
fn mo_bounds() -> Vec<(f64, f64)> {
    vec![(-3.0, 3.0)]
}

fn mo_variation() -> CompositeVariation<SimulatedBinaryCrossover, PolynomialMutation> {
    let bounds = mo_bounds();
    CompositeVariation {
        crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
        mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
    }
}

// -----------------------------------------------------------------------------
// Single-objective continuous
// -----------------------------------------------------------------------------

proptest! {
    #[test]
    fn random_search_deterministic(seed in any::<u64>()) {
        let make = || RandomSearch::new(
            RandomSearchConfig { iterations: 20, batch_size: 1, seed },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn hill_climber_deterministic(seed in any::<u64>()) {
        let make = || HillClimber::new(
            HillClimberConfig { iterations: 20, seed },
            so_bounds(),
            GaussianMutation { sigma: 0.1 },
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn one_plus_one_es_deterministic(seed in any::<u64>()) {
        let make = || OnePlusOneEs::new(
            OnePlusOneEsConfig {
                iterations: 50,
                initial_sigma: 0.5,
                adaptation_period: 10,
                step_increase: 1.22,
                seed,
            },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn simulated_annealing_deterministic(seed in any::<u64>()) {
        let make = || SimulatedAnnealing::new(
            SimulatedAnnealingConfig {
                iterations: 50,
                initial_temperature: 1.0,
                final_temperature: 1e-3,
                seed,
            },
            so_bounds(),
            GaussianMutation { sigma: 0.1 },
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn ga_deterministic(seed in any::<u64>()) {
        let bounds = mo_bounds();
        let make = || GeneticAlgorithm::new(
            GeneticAlgorithmConfig {
                population_size: 10,
                generations: 5,
                tournament_size: 2,
                elitism: 1,
                seed,
            },
            RealBounds::new(bounds.clone()),
            CompositeVariation {
                crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
                mutation: PolynomialMutation::new(bounds.clone(), 20.0, 1.0),
            },
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn pso_deterministic(seed in any::<u64>()) {
        let make = || ParticleSwarm::new(
            ParticleSwarmConfig {
                swarm_size: 10,
                generations: 5,
                inertia: 0.7,
                cognitive: 1.5,
                social: 1.5,
                seed,
            },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn de_deterministic(seed in any::<u64>()) {
        let make = || DifferentialEvolution::new(
            DifferentialEvolutionConfig {
                population_size: 10,
                generations: 5,
                differential_weight: 0.5,
                crossover_probability: 0.9,
                seed,
            },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn cmaes_deterministic(seed in any::<u64>()) {
        let cfg = CmaEsConfig {
            population_size: 8,
            generations: 5,
            initial_sigma: 0.5,
            eigen_decomposition_period: 1,
            initial_mean: None,
            seed,
        };
        let mut a = CmaEs::new(cfg.clone(), so_bounds());
        let mut b = CmaEs::new(cfg, so_bounds());
        let r1 = a.run(&Sphere1D);
        let r2 = b.run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn ipop_cmaes_deterministic(seed in any::<u64>()) {
        let cfg = IpopCmaEsConfig {
            initial_population_size: 8,
            total_generations: 30,
            initial_sigma: 0.5,
            eigen_decomposition_period: 1,
            stall_generations: None,
            seed,
        };
        let mut a = IpopCmaEs::new(cfg.clone(), so_bounds());
        let mut b = IpopCmaEs::new(cfg, so_bounds());
        let r1 = a.run(&Sphere1D);
        let r2 = b.run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn snes_deterministic(seed in any::<u64>()) {
        let make = || SeparableNes::new(
            SeparableNesConfig {
                population_size: 8,
                generations: 5,
                initial_sigma: 0.5,
                mean_learning_rate: 1.0,
                sigma_learning_rate: None,
                seed,
            },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn tlbo_deterministic(seed in any::<u64>()) {
        let make = || Tlbo::new(
            TlboConfig { population_size: 10, generations: 5, seed },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn nelder_mead_deterministic(_dummy in any::<bool>()) {
        // Nelder-Mead is purely deterministic; no seed.
        let make = || NelderMead::new(
            NelderMeadConfig { iterations: 50, ..NelderMeadConfig::default() },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn bayesian_opt_deterministic(seed in any::<u64>()) {
        let make = || BayesianOpt::new(
            BayesianOptConfig {
                initial_samples: 5,
                iterations: 10,
                length_scales: None,
                signal_variance: 1.0,
                noise_variance: 1e-6,
                acquisition_samples: 100,
                seed,
            },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn tpe_deterministic(seed in any::<u64>()) {
        let make = || Tpe::new(
            TpeConfig {
                initial_samples: 5,
                iterations: 10,
                good_fraction: 0.25,
                candidate_samples: 12,
                bandwidth_factor: 1.0,
                seed,
            },
            so_bounds(),
        );
        let r1 = make().run(&Sphere1D);
        let r2 = make().run(&Sphere1D);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }
}

// -----------------------------------------------------------------------------
// Multi-objective
// -----------------------------------------------------------------------------

proptest! {
    #[test]
    fn nsga2_deterministic_and_pop_size(seed in any::<u64>()) {
        let make = || Nsga2::new(
            Nsga2Config { population_size: 10, generations: 3, seed },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
        prop_assert_eq!(r1.population.len(), 10);
    }

    #[test]
    fn nsga3_deterministic_and_pop_size(seed in any::<u64>()) {
        let make = || Nsga3::new(
            Nsga3Config {
                population_size: 12,
                generations: 3,
                reference_divisions: 11,
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
        prop_assert_eq!(r1.population.len(), 12);
    }

    #[test]
    fn spea2_deterministic(seed in any::<u64>()) {
        let make = || Spea2::new(
            Spea2Config {
                population_size: 10,
                archive_size: 10,
                generations: 3,
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn moead_deterministic(seed in any::<u64>()) {
        let make = || Moead::new(
            MoeadConfig {
                generations: 3,
                reference_divisions: 9,
                neighborhood_size: 4,
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.population.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.population.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn mopso_deterministic(seed in any::<u64>()) {
        let make = || Mopso::new(
            MopsoConfig {
                swarm_size: 10,
                generations: 3,
                archive_size: 10,
                inertia: 0.7,
                cognitive: 1.5,
                social: 1.5,
                seed,
            },
            RealBounds::new(mo_bounds()),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn ibea_deterministic(seed in any::<u64>()) {
        let make = || Ibea::new(
            IbeaConfig {
                population_size: 10,
                generations: 3,
                kappa: 0.05,
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn sms_emoa_deterministic(seed in any::<u64>()) {
        let make = || SmsEmoa::new(
            SmsEmoaConfig {
                population_size: 8,
                generations: 5,
                reference_point: vec![10.0, 10.0],
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn hype_deterministic(seed in any::<u64>()) {
        let make = || Hype::new(
            HypeConfig {
                population_size: 10,
                generations: 3,
                reference_point: vec![10.0, 10.0],
                mc_samples: 100,
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn pesa2_deterministic(seed in any::<u64>()) {
        let make = || PesaII::new(
            PesaIIConfig {
                population_size: 10,
                archive_size: 10,
                generations: 3,
                grid_divisions: 4,
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn epsilon_moea_deterministic(seed in any::<u64>()) {
        let make = || EpsilonMoea::new(
            EpsilonMoeaConfig {
                population_size: 10,
                evaluations: 30,
                epsilon: vec![0.05, 0.05],
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn age_moea_deterministic(seed in any::<u64>()) {
        let make = || AgeMoea::new(
            AgeMoeaConfig { population_size: 10, generations: 3, seed },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn grea_deterministic(seed in any::<u64>()) {
        let make = || Grea::new(
            GreaConfig {
                population_size: 10,
                generations: 3,
                grid_divisions: 4,
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn knea_deterministic(seed in any::<u64>()) {
        let make = || Knea::new(
            KneaConfig { population_size: 10, generations: 3, seed },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn rvea_deterministic(seed in any::<u64>()) {
        let make = || Rvea::new(
            RveaConfig {
                population_size: 10,
                generations: 3,
                reference_divisions: 9,
                alpha: 2.0,
                seed,
            },
            RealBounds::new(mo_bounds()),
            mo_variation(),
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }

    #[test]
    fn paes_deterministic(seed in any::<u64>()) {
        let make = || Paes::new(
            PaesConfig { iterations: 30, archive_size: 10, seed },
            RealBounds::new(mo_bounds()),
            GaussianMutation { sigma: 0.1 },
        );
        let r1 = make().run(&SchafferN1);
        let r2 = make().run(&SchafferN1);
        let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
            .map(|c| c.evaluation.objectives.clone()).collect();
        prop_assert_eq!(oa, ob);
    }
}

// -----------------------------------------------------------------------------
// Other decision types
// -----------------------------------------------------------------------------

proptest! {
    #[test]
    fn umda_deterministic(seed in any::<u64>(), bits in 4usize..16) {
        let problem = OneMax { bits };
        let make = || Umda::new(UmdaConfig {
            population_size: 10,
            selected_size: 5,
            generations: 3,
            bits,
            seed,
        });
        let r1 = make().run(&problem);
        let r2 = make().run(&problem);
        prop_assert_eq!(
            r1.best.unwrap().evaluation.objectives,
            r2.best.unwrap().evaluation.objectives,
        );
    }

    #[test]
    fn random_search_evaluation_count_invariant(
        iterations in 1usize..30,
        batch_size in 1usize..5,
        seed in any::<u64>(),
    ) {
        let mut opt = RandomSearch::new(
            RandomSearchConfig { iterations, batch_size, seed },
            so_bounds(),
        );
        let r = opt.run(&Sphere1D);
        prop_assert_eq!(r.evaluations, iterations * batch_size);
        prop_assert_eq!(r.population.len(), iterations * batch_size);
        prop_assert_eq!(r.generations, iterations);
    }
}

// -----------------------------------------------------------------------------
// Cross-cutting: best is at least as good as any front member
// -----------------------------------------------------------------------------

proptest! {
    #[test]
    fn so_optimizer_best_beats_initial(seed in any::<u64>()) {
        // After running an SO optimizer, the result's best.evaluation
        // should be at least as good as the worst point sampled — i.e.
        // the optimizer doesn't return None or some random non-best.
        let mut opt = DifferentialEvolution::new(
            DifferentialEvolutionConfig {
                population_size: 10,
                generations: 5,
                differential_weight: 0.5,
                crossover_probability: 0.9,
                seed,
            },
            so_bounds_2d(),
        );
        let r = opt.run(&Sphere1D);
        let best_f = r.best.unwrap().evaluation.objectives[0];
        let pop_min = r.population.iter()
            .map(|c| c.evaluation.objectives[0])
            .fold(f64::INFINITY, f64::min);
        prop_assert!(
            best_f <= pop_min + 1e-12,
            "best f = {best_f}, pop min = {pop_min}",
        );
    }
}