/// bench_spatial.cpp — R-Tree / KD-Tree build + kNN benchmarks
///
/// Metrics:
///   RTree_Build/N{size}    — STR-bulk-build time for N random 3D points
///   RTree_kNN/N{size}      — kNN(n=10) queries on a pre-built R-Tree
///   KDTree_Build/N{size}   — build time for N random 3D points
///   KDTree_kNN/N{size}     — kNN(n=10) queries on a pre-built KD-Tree

#include <benchmark/benchmark.h>
#include <vde/spatial/r_tree.h>
#include <vde/spatial/kd_tree.h>
#include <vde/core/point.h>
#include <random>
#include <vector>

using namespace vde;

namespace {

std::vector<core::Point3D> random_points_3d(int n, double R = 100.0) {
    std::mt19937 rng(42);
    std::uniform_real_distribution<double> dist(-R, R);
    std::vector<core::Point3D> pts;
    pts.reserve(n);
    for (int i = 0; i < n; ++i)
        pts.emplace_back(dist(rng), dist(rng), dist(rng));
    return pts;
}

/// 20 random query points for kNN
std::vector<core::Point3D> query_points(int n = 20, double R = 100.0) {
    return random_points_3d(n, R);
}

} // namespace

// ── R-Tree ────────────────────────────────────────────────────────────

static void RTree_Build(benchmark::State& state) {
    auto pts = random_points_3d(state.range(0));
    for (auto _ : state) {
        spatial::RTree<core::Point3D> tree;
        tree.build(pts);
        benchmark::DoNotOptimize(tree.size());
    }
    state.SetItemsProcessed(state.iterations() * state.range(0));
}
BENCHMARK(RTree_Build)->Arg(500)->Arg(1000)->Arg(2000);

class RTreeFixture : public benchmark::Fixture {
public:
    void SetUp(const benchmark::State& state) override {
        points = random_points_3d(state.range(0));
        tree.build(points);
        queries = query_points();
    }

    spatial::RTree<core::Point3D> tree;
    std::vector<core::Point3D> points;
    std::vector<core::Point3D> queries;
};

BENCHMARK_DEFINE_F(RTreeFixture, RTree_kNN)(benchmark::State& state) {
    size_t total = 0;
    for (auto _ : state) {
        for (const auto& q : queries) {
            auto result = tree.query_knn(q, 10);
            total += result.size();
        }
    }
    benchmark::DoNotOptimize(total);
    state.SetItemsProcessed(state.iterations() * queries.size());
}
BENCHMARK_REGISTER_F(RTreeFixture, RTree_kNN)->Arg(500)->Arg(1000)->Arg(2000);

// ── KD-Tree ──────────────────────────────────────────────────────────

static void KDTree_Build(benchmark::State& state) {
    auto pts = random_points_3d(state.range(0));
    for (auto _ : state) {
        spatial::KDTree<core::Point3D> tree;
        tree.build(pts);
        benchmark::DoNotOptimize(tree.size());
    }
    state.SetItemsProcessed(state.iterations() * state.range(0));
}
BENCHMARK(KDTree_Build)->Arg(500)->Arg(1000)->Arg(2000);

class KDTreeFixture : public benchmark::Fixture {
public:
    void SetUp(const benchmark::State& state) override {
        points = random_points_3d(state.range(0));
        tree.build(points);
        queries = query_points();
    }

    spatial::KDTree<core::Point3D> tree;
    std::vector<core::Point3D> points;
    std::vector<core::Point3D> queries;
};

BENCHMARK_DEFINE_F(KDTreeFixture, KDTree_kNN)(benchmark::State& state) {
    size_t total = 0;
    for (auto _ : state) {
        for (const auto& q : queries) {
            auto result = tree.query_knn(q, 10);
            total += result.size();
        }
    }
    benchmark::DoNotOptimize(total);
    state.SetItemsProcessed(state.iterations() * queries.size());
}
BENCHMARK_REGISTER_F(KDTreeFixture, KDTree_kNN)->Arg(500)->Arg(1000)->Arg(2000);