#ifndef ALSK_ALSK_EXECUTOR_IMPL_FIRSTLEVEL_GREEDY_H
#define ALSK_ALSK_EXECUTOR_IMPL_FIRSTLEVEL_GREEDY_H

#include <cmath>
#include <thread>
#include <set>
#include <vector>

#include "../../executorbase.h"
#include "../../executorstate.h"
#include "../../../skeleton/traits.h"

namespace alsk {
namespace exec {

template<typename S>
struct FirstLevelGreedy: ExecutorBase {
	using Tag = alsk::tag::Parallel;

public:
	struct Info {
		unsigned int parDepth;
	};

private:
	template<typename Impl>
	void buildSplit(Impl& impl) {
		typename Impl::State& state = impl.state;
		auto& split = state.executor.split;

		split.clear();

		auto traverser = [](std::size_t, auto&& skl, auto&&... values) {
			using Skl = decltype(skl);
			using Traits = alsk::SkeletonTraitsT<Skl>;
			if(Traits::serial) return max(values...);
			return Traits::parallelizability(std::forward<Skl>(skl));
		};

		auto firstLevelPar = SkeletonTraversal<S>::execute(impl.skeleton, traverser, 1ul);

		split.insert(0);
		for(auto const& k: repeatability.coresList) {
			std::size_t start{};
			std::size_t const step = (firstLevelPar + k-1)/k;
			std::size_t const rk = (firstLevelPar + step-1)/step;

			for(unsigned int i = 0; i < rk; ++i, start += step)
				split.insert(start * (state.executor.parTasksCount/firstLevelPar));
		}
	}

	unsigned int threadLimit(unsigned int level) const { return level? 1 : cores; }

public:
	template<typename Impl>
	void config(Impl& impl) {
		typename Impl::State& state = impl.state;

		state.executor.parTasksCount = impl.parallelTasksCount();;
		buildSplit(impl);
	}

	template<typename Impl>
	std::size_t contextIdCount(Impl& impl, std::size_t) {
		typename Impl::State& state = impl.state;
		return state.executor.split.size();
	}

	template<typename Impl>
	std::size_t contextId(Impl& impl, std::size_t id) { // O(log(n))
		typename Impl::State& state = impl.state;
		auto& split = state.executor.split;
		return std::distance(std::begin(split), split.upper_bound(id)) - 1;
	}

	template<typename Task, typename Impl, typename BTask, typename Parameters>
	void executeParallel(Impl& impl, BTask& task, Parameters const& parameters, std::size_t n) {
		auto const& parDepth = impl.executorInfo.parDepth;
		std::size_t const maxThreads = threadLimit(parDepth);

		std::size_t const nThreads = std::min(n, maxThreads);
		if(nThreads > 1) {
			Info info{parDepth+1};
			std::vector<std::thread> threads(nThreads-1);
			std::size_t const step = std::round(static_cast<double>(n)/nThreads);

			auto run = [&](std::size_t b, std::size_t k) {
				for(std::size_t i = 0; i < k; ++i)
					Task::execute(impl, task, b+i, info, parameters, std::tuple<>{});
			};

			for(std::size_t i = 0; i < nThreads-1; ++i)
				threads[i] = std::thread{run, i*step, step};

			run((nThreads-1)*step, n-(nThreads-1)*step);

			for(std::thread& thread: threads) thread.join();
		} else {
			Info info{parDepth};
			for(std::size_t i = 0; i < n; ++i)
				Task::execute(impl, task, i, info, parameters, std::tuple<>{});
		}
	}

	template<typename Value, typename Task, typename Select, typename Impl, typename BTask, typename BSelect, typename Parameters>
	Value executeParallelAccumulate(Impl& impl, BTask& task, BSelect& select, Parameters const& parameters, std::size_t n) {
		auto const& parDepth = impl.executorInfo.parDepth;
		std::size_t const maxThreads = threadLimit(parDepth); // TODO fix neighbours

		Value best{};

		std::size_t const nThreadsBase = std::min(n, maxThreads);
		if(nThreadsBase > 1) {
			Info info{parDepth+1};
			std::size_t const step = (n+nThreadsBase-1)/nThreadsBase;
			std::size_t const nThreads = (n+step-1)/step;
			std::vector<std::thread> threads(nThreads-1);

			auto run = [&](Value& out, std::size_t b, std::size_t k) {
				Value best{};

				if(k)
					best = Task::execute(impl, task, b+0, info, parameters, std::tuple<>{});
				for(std::size_t i = 1; i < k; ++i) {
					Value current = Task::execute(impl, task, b+i, info, parameters, std::tuple<>{});
					best = Select::execute(impl, select, b+i, info, parameters, std::tuple<>{}, std::move(current), std::move(best));
				}

				out = std::move(best);
			};

			std::size_t start{};
			std::vector<Value> bests(nThreads);
			for(std::size_t i = 0; i < nThreads-1; ++i, start += step)
				threads[i] = std::thread{run, std::ref(bests[i]), start, step};

			run(bests[nThreads-1], start, n - step*(nThreads-1));

			for(std::thread& thread: threads) thread.join();

			if(nThreads)  best = std::move(bests[0]);
			for(std::size_t i = 1; i < nThreads; ++i)
				best = Select::execute(impl, select, i, info, parameters, std::tuple<>{}, std::move(bests[i]), std::move(best));
		} else {
			Info info{parDepth};
			if(n)
				best = Task::execute(impl, task, 0, info, parameters, std::tuple<>{});
			for(std::size_t i = 1; i < n; ++i) {
				Value current = Task::execute(impl, task, i, info, parameters, std::tuple<>{});
				best = Select::execute(impl, select, i, info, parameters, std::tuple<>{}, std::move(current), std::move(best));
			}
		}

		return best;
	}
};

template<typename S>
struct ExecutorState<FirstLevelGreedy<S>> {
	std::size_t parTasksCount;
	std::set<std::size_t> split;
};

}
}

#endif