#ifndef SEGMENT_CX4_INC_CPP
#define SEGMENT_CX4_INC_CPP


#include "segment.inc.cpp"
#include "func.inc.cpp"
#include "layer.conv.inc.cpp"


class SegmentCx4: public Segment {
public:
  enum {
    KSX = 4,
    KSY = 4,
    SX = 12,
    SY = 12,
    MSX = 5,
    MSY = 5,
  };
  
  const int msx, msy, msz;
  
  Neuron *m_neurons;
  Neuron *b_neurons;
  
  SegmentCx4(int sz, int msz, Weight *weights = nullptr):
    Segment(SX, SY, sz, msz*KSY*KSX*sz, weights), msx(MSX), msy(MSY), msz(msz)
  {
      m_neurons = new Neuron[msx*msy*msz + sx*sy*sz];
      b_neurons = m_neurons + msx*msy*msz;
      clear();
  }
  ~SegmentCx4()
    { delete[] m_neurons; }  
  
  
  void clear() override
    { memset(m_neurons, 0, sizeof(*m_neurons)*(msx*msy*msz + sx*sy*sz)); }

    
  inline void check(int x, int y, int z) {
    Segment::check(x, y, z);
    assert(layout.getD() == sz);
  }


  
  Quality pass(Barrier &barrier, int x, int y, int z, NeuronReal trainRatio) override {
    check(x, y, z);
    
    Layout l = layout;
    const int ksx = 4, ksy = 4;
    int tid = barrier.tid;
    int threads = barrier.threads;
    
    int sx = this->sx;
    int sy = this->sy;
    int sz = this->sz;
    int msx = this->msx;
    int msy = this->msy;
    int msz = this->msz;
    
    int ksxyz = ksx*ksy*sz;
    int fv_dkx = l.sz - sz;
    int fv_dky = (l.sx - ksx)*l.sz;
    
    NeuronReal *f_values = this->f_values + (y*l.sx + x)*l.sz + z;
    
    // stage 1: pass from front to mid
    
    Weight *iw = weights + tid*ksxyz;
    Neuron *imn = m_neurons + tid;
    NeuronReal *ifv = f_values;
    
    for(int mz = tid; mz < msz; mz += threads, iw += threads*ksxyz, imn += threads - msx*msy*msz, ifv = f_values)
    for(int my = 0; my < MSY; ++my, ifv += 2*(l.sx - MSX)*l.sz)
    for(int mx = 0; mx < MSX; ++mx, imn += msz, ifv += 2*l.sz) {
      AccumReal a = 0;
      
      Weight *iiw = iw;
      NeuronReal *iifv = ifv;
      
      for(int ky = 0; ky < KSY; ++ky, iifv += fv_dky)
      for(int kx = 0; kx < KSX; ++kx, iifv += fv_dkx)
      for(Weight *e = iiw + sz; iiw < e; ++iiw, ++iifv)
        a += *iifv * iiw->w;
      
      if (a > 0) imn->v = a, imn->d = 1; else imn->v = imn->d = 0;
    }
    
    barrier.wait();
    
    // stage 2: pass from mid to back and verify
    
    AccumReal qa = 0;
    for(int by = 2 + tid; by < 10; by += threads)
    for(int bx = 2; bx < 10; ++bx)
    for(int bz = 0; bz < sz; ++bz) {
      AccumReal a = 0;
      Neuron &bn = b_neurons[ (by*sx + bx)*sz + bz ];
      
      for(int ky = by%2; ky < ksy; ky += 2)
      for(int kx = bx%2; kx < ksx; kx += 2) {
        int mx = (bx - kx)/2;
        int my = (by - ky)/2;
        assert(mx >= 0 && mx < msx && (bx - kx)%2 == 0);
        assert(my >= 0 && my < msy && (by - ky)%2 == 0);
        for(int mz = 0; mz < msz; ++mz) {
          Neuron &mn = m_neurons[ (my*msx + mx)*msz + mz ];
          Weight &w = weights[ ((mz*ksy + ky)*ksx + kx)*sz + bz ];
          a += mn.v * w.w;
        }
      }
      
      if (a > 0) bn.v = a, bn.d = 1; else bn.v = bn.d = 0;
      
      NeuronReal fn = f_values[ (by*l.sx + bx)*l.sz + bz ];
      NeuronReal d = fn - bn.v;
      bn.d *= d*trainRatio;
      qa += d*d;
    }
    Quality q(qa/(64*sz));
    
    if (trainRatio <= 0) return q;
    
    barrier.wait();
    
    // stage 3: backpass deltas
    
    for(int mz = tid; mz < msz; mz += threads)
    for(int my = 1; my < 4; ++my)
    for(int mx = 1; mx < 4; ++mx) {
      AccumReal a = 0;
      Neuron &mn = m_neurons[ (my*msx + mx)*msz + mz ];
      
      for(int ky = 0; ky < ksy; ++ky)
      for(int kx = 0; kx < ksx; ++kx)
      for(int kz = 0; kz < sz;  ++kz) {
        int bx = mx*2 + kx;
        int by = my*2 + ky;
        Neuron &bn = b_neurons[ (by*sx + bx)*sz + kz ];
        Weight &w = weights[ ((mz*ksy + ky)*ksx + kx)*sz + kz ];
        a += bn.d * w.w;
      }
      mn.d *= a;
    }
    
    barrier.wait();
    
    // stage 4: update weights

    for(int mz = tid; mz < msz; mz += threads)
    for(int by = 4; by <  8; ++by)
    for(int bx = 4; bx <  8; ++bx)
    for(int bz = 0; bz < sz; ++bz) {
      Neuron &bn = b_neurons[ (by*sx + bx)*sz + bz ];
      NeuronReal fv = f_values[ (by*l.sx + bx)*l.sz + bz ];
      
      for(int ky = by%2; ky < ksy; ky += 2)
      for(int kx = bx%2; kx < ksx; kx += 2) {
        int mx = (bx - kx)/2;
        int my = (by - ky)/2;
        assert(mx >= 1 && mx < 4 && (bx - kx)%2 == 0);
        assert(my >= 1 && my < 4 && (by - ky)%2 == 0);
        Neuron &mn = m_neurons[ (my*msx + mx)*msz + mz ];
        Weight &w = weights[ ((mz*ksy + ky)*ksx + kx)*sz + bz ];
        w.w += bn.d*mn.v + mn.d*fv;
      }
    }
    
    return q;
  }
  
  
  
  Quality testPass(int x, int y, int z, NeuronReal trainRatio) override {
    check(x, y, z);
    
    Layout l = layout;
    const int ksx = 4, ksy = 4;
    
    // stage 1: pass
    
    clear();
    
    for(int my = 0; my < msy; ++my)
    for(int mx = 0; mx < msx; ++mx)
    for(int mz = 0; mz < msz; ++mz) {
      AccumReal a = 0;
      Neuron &mn = m_neurons[ (my*msx + mx)*msz + mz ];
      
      for(int ky = 0; ky < ksy; ++ky)
      for(int kx = 0; kx < ksx; ++kx)
      for(int kz = 0; kz < sz;  ++kz) {
        int fx = x + mx*2 + kx;
        int fy = y + my*2 + ky;
        int fz = z + kz;
        NeuronReal fv = f_values[ (fy*l.sx + fx)*l.sz + fz ];
        Weight &w = weights[ ((mz*ksy + ky)*ksx + kx)*sz + kz ];
        a += fv * w.w;
      }
      
      if (a < 0) { mn.v = mn.d = 0; continue; }
      mn.v = a; mn.d = 1;
      
      for(int ky = 0; ky < ksy; ++ky)
      for(int kx = 0; kx < ksx; ++kx)
      for(int kz = 0; kz < sz;  ++kz) {
        int bx = mx*2 + kx;
        int by = my*2 + ky;
        int bz = kz;
        Neuron &bn = b_neurons[ (by*sx + bx)*sz + bz ];
        Weight &w = weights[ ((mz*ksy + ky)*ksx + kx)*sz + kz ];
        bn.a.v += a * w.w;
      }
    }
    
    // stage 2: finalize values and verify
    
    AccumReal qa = 0;
    for(int by = 2; by < 10; ++by)
    for(int bx = 2; bx < 10; ++bx)
    for(int bz = 0; bz < sz; ++bz) {
        Neuron &bn = b_neurons[ (by*sx + bx)*sz + bz ];
        if (bn.a.v > 0) bn.v = bn.a.v, bn.d = 1; else bn.v = bn.d = 0;
        bn.a.v = 0;
        
        NeuronReal fn = f_values[ ((y + by)*l.sx + x + bx)*l.sz + z + bz ];
        NeuronReal d = fn - bn.v;
        bn.d *= d*trainRatio;
        qa += d*d;
    }
    Quality q(qa/(64*sz));
    
    if (trainRatio <= 0) return q;
    
    // stage 3: backpass deltas
    
    for(int my = 0; my < msy; ++my)
    for(int mx = 0; mx < msx; ++mx)
    for(int mz = 0; mz < msz; ++mz) {
      AccumReal a = 0;
      Neuron &mn = m_neurons[ (my*msx + mx)*msz + mz ];
      
      for(int ky = 0; ky < ksy; ++ky)
      for(int kx = 0; kx < ksx; ++kx)
      for(int kz = 0; kz < sz;  ++kz) {
        int bx = mx*2 + kx;
        int by = my*2 + ky;
        int bz = kz;
        Neuron &bn = b_neurons[ (by*sx + bx)*sz + bz ];
        Weight &w = weights[ ((mz*ksy + ky)*ksx + kx)*sz + kz ];
        a += bn.d * w.w;
      }
      mn.d *= a;
    }
      
    // stage 4: update weights

    for(int by = 4; by <  8; ++by)
    for(int bx = 4; bx <  8; ++bx)
    for(int bz = 0; bz < sz; ++bz) {
      Neuron &bn = b_neurons[ (by*sx + bx)*sz + bz ];
      NeuronReal fv = f_values[ ((y + by)*l.sx + x + bx)*l.sz + z + bz ];
      
      for(int ky = by%2; ky < ksy; ky += 2)
      for(int kx = bx%2; kx < ksx; kx += 2)
      for(int mz = 0; mz < msz; ++mz) {
        int mx = (bx - kx)/2;
        int my = (by - ky)/2;
        assert(mx >= 1 && mx < 4 && (bx - kx)%2 == 0);
        assert(my >= 1 && my < 4 && (by - ky)%2 == 0);
        Neuron &mn = m_neurons[ (my*msx + mx)*msz + mz ];
        Weight &w = weights[ ((mz*ksy + ky)*ksx + kx)*sz + bz ];
        w.w += bn.d*mn.v + mn.d*fv;
      }
    }
    
    return q;
  }

  
  bool saveDemo() override
    { return !filename || saveConvDemoImage(filename, msz, 4, 4, sz, weights); }
};




#endif