#ifndef NNLAYER3_MT_INC_CPP

#define NNLAYER3_MT_INC_CPP

#include "nnlayer3.inc.cpp"

#include <atomic></atomic>

#include <thread></thread>

#include <vector></vector>

class Barrier {

private:

  std::atomic<unsigned int=""> &counter;</unsigned>

  const unsigned int threads;

  unsigned int next;

public:

  inline Barrier(std::atomic<unsigned int=""> &counter, unsigned int threads): counter(counter), threads(threads), next() { }</unsigned>

  inline void wait() { next += threads; ++counter; while(counter < next); }

  inline void subwait(int tid) { while(counter < next + tid); }

};

class TrainMT {

private:

  struct LDesc {

    int y0, y1;

    double sumQ;

    LDesc(): y0(), y1(), sumQ() { }

  };

public:

  Layer *layer;

  const unsigned char *dataX;

  const unsigned char *dataY;

  int strideX;

  int strideY;

  int *shuffle;

  int count;

  Real trainRatio;

  TrainMT():

    layer(),

    dataX(),

    dataY(),

    strideX(),

    strideY(),

    shuffle(),

    count(),

    trainRatio() { }

private:

  void trainFunc(int tid, int threads, std::atomic<unsigned int=""> &barrierCounter, LDesc *ldescs) {</unsigned>

    Barrier barrier(barrierCounter, threads);

    Layer &fl = *layer;

    Layer &bl = layer->back();

    int layersCount = fl.totalLayers();

    LDesc *fld = ldescs, *bld = fld + layersCount - 1;

    Real trainRatio = this->trainRatio;

    int fsxz = fl.sx*fl.sz;

    int bsxz = bl.sx*bl.sz;

    //barrier.subwait(tid);

    //for(LDesc *ld = fld; ld <= bld; ++ld)

    //  printf("t%d %d %d %d\n", tid, (int)(ld-fld), ld->y0, ld->y1);

    //barrier.wait();

    const unsigned char *dataX = this->dataX + fsxz*fld->y0;

    const unsigned char *dataY = this->dataY + bsxz*bld->y0;

    double sumQ = 0;

    for(int i = 0; i < count; ++i) {

      int ii = shuffle[i];

      const unsigned char *curX = dataX + strideX*ii;

      const unsigned char *curY = dataY + strideY*ii;

      barrier.wait();

      const unsigned char *px = curX;

      for(Neuron *in = fl.neurons + fsxz*fld->y0, *e = fl.neurons + fsxz*fld->y1; in < e; ++in, ++px)

        in->v = Real(*px)*Real(1/255.0);

      LDesc *ld = fld + 1;

      for(Layer *l = fl.next; l; l = l->next, ++ld) {

        barrier.wait();

        l->pass(ld->y0, ld->y1);

      }

      double q = 0;

      const unsigned char *py = curY;

      for(Neuron *in = bl.neurons + bsxz*bld->y0, *e = bl.neurons + bsxz*bld->y1; in < e; ++in, ++py) {

        Real v = (in->v - 0.25)*2;

        Real d = Real(*py)*Real(1/255.0) - v;

        in->d *= d * trainRatio;

        d *= d;

        q += d*d;

      }

      sumQ += q;

      if (trainRatio > 0) {

        ld = bld;

        for(Layer *l = &bl; l->prev; l = l->prev, --ld) {

          if (!l->prev->prev) {

            barrier.wait();

            l->backpassWeights(ld->y0, ld->y1);

            break;

          } else

          if (l->next) {

            barrier.wait();

            l->backpassTpl<true>(ld->y0, ld->y1);</true>

            //l->backpassTpl<false>(ld->y0, ld->y1);</false>

            //barrier.wait();

            //l->next->backpassWeights(ld[1].y0, ld[1].y1);

          }

        }

      }

      //if (!tid) printf(" - %d, %f, %f\n", i, q, sumQ);

    }

    ldescs->sumQ = sumQ;

  }

public:

  double train(int threads) {

    assert(threads > 0);

    assert(layer && !layer->prev);

    assert(dataX && dataY && shuffle);

    assert(count > 0);

    assert(trainRatio >= 0);

    int layersCount = layer->totalLayers();

    assert(layersCount > 0);

    std::vector<ldesc> ldescs( threads*layersCount );</ldesc>

    int layerId = 0;

    for(Layer *l = layer; l; l = l->next, ++layerId) {

      assert(layerId < layersCount);

      int tsy = l->sy/threads;

      for(int tid = 0; tid < threads; ++tid) {

        LDesc &desc = ldescs[tid*layersCount + layerId];

        desc.y0 = tid*tsy;

        desc.y1 = desc.y0 + tsy;

        if (tid == threads-1) desc.y1 = l->sy;

      }

    }

    assert(layerId == layersCount);

    std::atomic<unsigned int=""> barrierCounter(0);</unsigned>

    std::vector<std::thread*> t(threads - 1);</std::thread*>

    for(int i = 1; i < threads; ++i)

      t[i-1] = new std::thread(&TrainMT::trainFunc, this, i, threads, std::ref(barrierCounter), &ldescs[i*layersCount]);

    trainFunc(0, threads, barrierCounter, &ldescs[0]);

    double result = ldescs[0].sumQ;

    for(int i = 1; i < threads; ++i)

      { t[i-1]->join(); delete t[i-1]; result += ldescs[i*layersCount].sumQ; }

    return sqrt(sqrt( result/(count * layer->back().countNeurons()) ));

  }

};

#endif