#include "Render_Executor.h"
#include "../architecture/Render_Time.h"
#include <algorithm>
#include <array>
#include <atomic>
#include <memory>
#include <mutex>
#include <unordered_set>
#include <taskflow/taskflow.hpp>
#include <thread>

namespace renderive {
namespace {

struct Render_Executor_Metrics {
    std::size_t worker_count{};
    std::atomic<std::size_t> active_workers{0};
    std::atomic<std::size_t> active_frame_jobs{0};
    std::atomic<std::size_t> queued_frame_jobs{0};
    std::atomic<std::uint64_t> submitted_tasks{0};
    std::atomic<std::uint64_t> started_tasks{0};
    std::atomic<std::uint64_t> completed_tasks{0};
    std::atomic<std::uint64_t> rejected_frame_jobs{0};
    std::atomic<std::uint64_t> dropped_frame_attempts{0};
    std::atomic<std::uint64_t> peak_concurrency{0};
    std::atomic<std::uint64_t> queue_wait_total_ns{0};
    std::atomic<std::uint64_t> queue_wait_max_ns{0};
    std::atomic<std::uint64_t> task_duration_total_ns{0};
    std::atomic<std::uint64_t> task_duration_max_ns{0};
    std::atomic<std::uint64_t> curve_tasks{0};
    std::atomic<std::uint64_t> waterfall_tasks{0};
    std::atomic<std::uint64_t> primitive_tasks{0};
    std::atomic<std::uint64_t> compose_tasks{0};
    std::atomic<std::uint64_t> performance_tasks{0};
};

void update_peak(std::atomic<std::uint64_t>& target, std::uint64_t value) {
    std::uint64_t current = target.load(std::memory_order_acquire);
    while (current < value && !target.compare_exchange_weak(current, value, std::memory_order_acq_rel, std::memory_order_acquire)) {}
}

void add_kind_count(Render_Executor_Metrics& metrics, Render_Task_Kind kind) {
    switch (kind) {
    case Render_Task_Kind::Frame:
    case Render_Task_Kind::Primitive:
        metrics.primitive_tasks.fetch_add(1, std::memory_order_relaxed);
        break;
    case Render_Task_Kind::Curve:
        metrics.curve_tasks.fetch_add(1, std::memory_order_relaxed);
        break;
    case Render_Task_Kind::Waterfall:
        metrics.waterfall_tasks.fetch_add(1, std::memory_order_relaxed);
        break;
    case Render_Task_Kind::Compose:
        metrics.compose_tasks.fetch_add(1, std::memory_order_relaxed);
        break;
    case Render_Task_Kind::Performance:
        metrics.performance_tasks.fetch_add(1, std::memory_order_relaxed);
        break;
    }
}

} // namespace

class Render_Executor_Observer final : public tf::ObserverInterface {
public:
    explicit Render_Executor_Observer(std::shared_ptr<Render_Executor_Metrics> metrics)
        : metrics(std::move(metrics)) {}

    void set_up(std::size_t worker_count) override {
        if (metrics)
            metrics->worker_count = worker_count;
        worker_entry_ns.clear();
        worker_entry_ns.resize(worker_count);
    }

    void on_entry(tf::WorkerView worker, tf::TaskView) override {
        if (!metrics)
            return;
        std::size_t worker_id = worker.id();
        if (worker_id < worker_entry_ns.size())
            worker_entry_ns[worker_id] = steady_now_ns();
        std::size_t active = metrics->active_workers.fetch_add(1, std::memory_order_acq_rel) + 1;
        metrics->started_tasks.fetch_add(1, std::memory_order_relaxed);
        update_peak(metrics->peak_concurrency, static_cast<std::uint64_t>(active));
    }

    void on_exit(tf::WorkerView worker, tf::TaskView) override {
        if (!metrics)
            return;
        std::size_t worker_id = worker.id();
        if (worker_id < worker_entry_ns.size()) {
            std::uint64_t begin_ns = worker_entry_ns[worker_id];
            std::uint64_t end_ns = steady_now_ns();
            std::uint64_t duration_ns = end_ns > begin_ns ? end_ns - begin_ns : 0;
            metrics->task_duration_total_ns.fetch_add(duration_ns, std::memory_order_relaxed);
            update_peak(metrics->task_duration_max_ns, duration_ns);
        }
        metrics->completed_tasks.fetch_add(1, std::memory_order_relaxed);
        metrics->active_workers.fetch_sub(1, std::memory_order_acq_rel);
    }

private:
    std::shared_ptr<Render_Executor_Metrics> metrics;
    std::vector<std::uint64_t> worker_entry_ns;
};

class Render_Executor_Private {
public:
    explicit Render_Executor_Private(std::size_t worker_count)
        : metrics(std::make_shared<Render_Executor_Metrics>()),
          executor(worker_count) {
        metrics->worker_count = worker_count;
        observer = executor.make_observer<Render_Executor_Observer>(metrics);
    }

    ~Render_Executor_Private() {
        shutdown();
    }

    bool try_submit(Render_Executor_Task task) {
        if (shutting_down.load(std::memory_order_acquire) || !task.work) {
            reject(task);
            return false;
        }
        if (!admit(task)) {
            reject(task);
            return false;
        }
        std::uint64_t enqueue_ns = steady_now_ns();
        metrics->submitted_tasks.fetch_add(1, std::memory_order_relaxed);
        add_kind_count(*metrics, task.kind);
        if (task.frame_job)
            metrics->queued_frame_jobs.fetch_add(1, std::memory_order_relaxed);
        if (task.frame_stat)
            task.frame_stat->executor_at_submit = snapshot();

        auto topology = std::make_shared<tf::Taskflow>();
        auto task_ptr = std::make_shared<Render_Executor_Task>(std::move(task));
        auto completion = std::make_shared<Task>(std::move(task_ptr->completion));
        topology->emplace([this, enqueue_ns, task_ptr]() mutable {
            Render_Executor_Task& task = *task_ptr;
            std::uint64_t begin_ns = steady_now_ns();
            std::uint64_t queue_wait_ns = begin_ns > enqueue_ns ? begin_ns - enqueue_ns : 0;
            metrics->queue_wait_total_ns.fetch_add(queue_wait_ns, std::memory_order_relaxed);
            update_peak(metrics->queue_wait_max_ns, queue_wait_ns);
            if (task.frame_job) {
                metrics->queued_frame_jobs.fetch_sub(1, std::memory_order_relaxed);
                metrics->active_frame_jobs.fetch_add(1, std::memory_order_relaxed);
            }
            task.work();
            std::uint64_t end_ns = steady_now_ns();
            std::uint64_t run_ns = end_ns > begin_ns ? end_ns - begin_ns : 0;
            if (task.frame_stat) {
                Frame_Worker_Stat& stat = *task.frame_stat;
                stat.task_count++;
                stat.peak_parallelism = static_cast<std::uint32_t>(std::max<std::uint64_t>(stat.peak_parallelism, metrics->peak_concurrency.load(std::memory_order_acquire)));
                stat.queue_wait_total_ns += queue_wait_ns;
                stat.queue_wait_max_ns = std::max(stat.queue_wait_max_ns, queue_wait_ns);
                stat.worker_run_total_ns += run_ns;
                stat.parallel_stage_wall_ns += run_ns;
                stat.executor_at_finish = snapshot();
            }
            if (task.frame_job)
                metrics->active_frame_jobs.fetch_sub(1, std::memory_order_relaxed);
        });
        executor.run(*topology, [this, topology, completion, task_ptr]() mutable {
            release(*task_ptr);
            if (completion && *completion)
                (*completion)();
        });
        return true;
    }

    void shutdown() {
        if (shutting_down.exchange(true, std::memory_order_acq_rel))
            return;
        executor.wait_for_all();
    }

    Render_Executor_Snapshot snapshot() const {
        std::uint64_t submitted = metrics->submitted_tasks.load(std::memory_order_acquire);
        std::uint64_t completed = metrics->completed_tasks.load(std::memory_order_acquire);
        std::uint64_t started = metrics->started_tasks.load(std::memory_order_acquire);
        std::uint64_t queue_total = metrics->queue_wait_total_ns.load(std::memory_order_acquire);
        std::uint64_t duration_total = metrics->task_duration_total_ns.load(std::memory_order_acquire);
        std::size_t active = metrics->active_workers.load(std::memory_order_acquire);
        std::size_t workers = metrics->worker_count;
        return {
            workers,
            active,
            workers > active ? workers - active : 0,
            metrics->active_frame_jobs.load(std::memory_order_acquire),
            metrics->queued_frame_jobs.load(std::memory_order_acquire),
            submitted,
            started,
            completed,
            metrics->rejected_frame_jobs.load(std::memory_order_acquire),
            metrics->dropped_frame_attempts.load(std::memory_order_acquire),
            metrics->peak_concurrency.load(std::memory_order_acquire),
            submitted ? queue_total / submitted : 0,
            metrics->queue_wait_max_ns.load(std::memory_order_acquire),
            completed ? duration_total / completed : 0,
            metrics->task_duration_max_ns.load(std::memory_order_acquire),
            metrics->curve_tasks.load(std::memory_order_acquire),
            metrics->waterfall_tasks.load(std::memory_order_acquire),
            metrics->primitive_tasks.load(std::memory_order_acquire),
            metrics->compose_tasks.load(std::memory_order_acquire),
            metrics->performance_tasks.load(std::memory_order_acquire)
        };
    }

    std::size_t worker_count() const {
        return metrics->worker_count;
    }

private:
    bool admit(const Render_Executor_Task& task) {
        if (!task.frame_job)
            return true;
        std::lock_guard<std::mutex> lock(admission_mutex);
        std::size_t active = metrics->active_frame_jobs.load(std::memory_order_acquire);
        std::size_t queued = metrics->queued_frame_jobs.load(std::memory_order_acquire);
        std::size_t limit = std::max<std::size_t>(1, metrics->worker_count);
        if (active + queued >= limit)
            return false;
        if (task.plot_id && !admitted_frame_plots.insert(task.plot_id).second)
            return false;
        return true;
    }

    void release(const Render_Executor_Task& task) {
        if (!task.frame_job || !task.plot_id)
            return;
        std::lock_guard<std::mutex> lock(admission_mutex);
        admitted_frame_plots.erase(task.plot_id);
    }

    void reject(const Render_Executor_Task& task) {
        if (task.frame_job)
            metrics->rejected_frame_jobs.fetch_add(1, std::memory_order_relaxed);
        else
            metrics->dropped_frame_attempts.fetch_add(1, std::memory_order_relaxed);
        if (task.frame_stat)
            task.frame_stat->rejected_task_count++;
    }

    std::shared_ptr<Render_Executor_Metrics> metrics;
    tf::Executor executor;
    std::shared_ptr<tf::ObserverInterface> observer;
    std::mutex admission_mutex;
    std::unordered_set<Plot_Execution_Id> admitted_frame_plots;
    std::atomic_bool shutting_down{false};
};

std::size_t resolve_render_worker_count(std::size_t configured_count) {
    if (configured_count)
        return configured_count;
    auto cpu_count = std::thread::hardware_concurrency();
    return cpu_count > 1 ? cpu_count - 1 : 1;
}

Render_Executor::Render_Executor(Render_Runtime_Config config)
    : d(std::make_unique<Render_Executor_Private>(resolve_render_worker_count(config.worker_count))) {}

Render_Executor::~Render_Executor() = default;

bool Render_Executor::try_submit(Render_Executor_Task task) {
    return d->try_submit(std::move(task));
}

void Render_Executor::shutdown() {
    d->shutdown();
}

Render_Executor_Snapshot Render_Executor::snapshot() const {
    return d->snapshot();
}

std::size_t Render_Executor::worker_count() const {
    return d->worker_count();
}

} // namespace renderive