#ifndef TRAIN_CX4_INC_CPP

#define TRAIN_CX4_INC_CPP

#include "train.segment.inc.cpp"

#include "segment.cx4.inc.cpp"

#include "layer.inc.cpp"

class TrainerCx4: public TrainerSegment {

protected:

  FILE *f;

  std::vector<unsigned char=""> data;</unsigned>

  std::vector<neuronreal> values;</neuronreal>

  std::vector<neuronreal> valuesMeasure;</neuronreal>

  std::vector<unsigned char=""> tmpdata;</unsigned>

  std::vector<int> shuffle;</int>

  Layout trainLayout;

  Layout measureLayout;

  size_t imageSize;

  size_t preparedImageSize;

  int imagesInFile;

  int imagesInMemory;

  volatile unsigned int seed;

public:

  Layer *layerFull;

  Layer *layerPre;

  int loadImagesCount;

  int blocksPerLoading;

  const char *infile;

  const char *cachefile;

  const char *outfile;

  TrainerCx4():

    f(),

    imageSize(),

    preparedImageSize(),

    imagesInFile(),

    imagesInMemory(),

    seed(),

    layerFull(),

    layerPre(),

    loadImagesCount(),

    blocksPerLoading(1),

    infile(),

    cachefile(),

    outfile() { }

protected:

  void preprocess(unsigned char *src, NeuronReal *dst) {

    struct IL: public Iter {

      typedef const unsigned char* DataType;

      static inline void iter4(Neuron &n, DataType d, DataAccumType&) { n.v = *d/(NeuronReal)255; }

    };

    struct IS: public Iter {

      typedef NeuronReal* DataType;

      static inline void iter4(Neuron &n, DataType d, DataAccumType&) { *d = n.v; }

    };

    Layer &fl = *layerFull;

    Layer &bl = *layerPre;

    iterateNeurons2<il>(fl.layout, fl.layout, fl.neurons, src);</il>

    fl.passFull(&bl, threadsCount);

    iterateNeurons2<is>(bl.layout, bl.layout, bl.neurons, dst);</is>

  }

  bool loadImage(int fromIndex, int toIndex) {

    unsigned char *src = data.data();

    if (!layerPre) src += toIndex*imageSize;

    fseeko64(f, fromIndex*imageSize, SEEK_SET);

    if (!fread(src, imageSize, 1, f))

      return fclose(f), f = nullptr, false;

    if (layerPre) preprocess(src, values.data() + toIndex*preparedImageSize);

    return true;

  }

  bool loadImages() {

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

      int j = rand()%imagesInFile;

      if (i != j) std::swap(shuffle[i], shuffle[j]);

    }

    typedef std::pair<int, int=""> Pair;</int,>

    typedef std::set<pair> Set;</pair>

    Set set;

    for(int i = 0; i < imagesInMemory; ++i)

      set.insert(Pair(shuffle[i], i));

    for(Set::iterator i = set.begin(); i != set.end(); ++i)

      loadImage(i->first, i->second);

    return true;

  }

  void prepareMeasure() {

    if (measuresPerBlock <= 0) return;

    int sy = segment->sy;

    int sx = segment->sx;

    int sz = segment->sz;

    int sxz = sx*sz;

    int w = (layerPre ? layerPre : layerFull)->layout.getW();

    int h = (layerPre ? layerPre : layerFull)->layout.getH();

    int rowstride = w*sz;

    NeuronReal *dst = valuesMeasure.data();

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

      int index = rand()%imagesInMemory;

      int x = rand()%(w - sx + 1);

      int y = rand()%(h - sy + 1);

      if (layerPre) {

        const NeuronReal *src = values.data() + index*preparedImageSize + y*rowstride + x*sz;

        for(int j = 0; j < sy; ++j, src += rowstride, dst += sxz)

          memcpy(dst, src, sxz*sizeof(*dst));

      } else {

        const unsigned char *src = data.data() + index*preparedImageSize + y*rowstride + x*sz;

        for(int j = 0; j < sy; ++j, src += rowstride - sxz)

        for(int k = 0; k < sxz; ++k, ++src, ++dst)

          *dst = *src/(NeuronReal)255;

      }

    }

  }

  bool prepare() override {

    assert(infile);

    assert(layerFull);

    assert(loadImagesCount > 0);

    Layer &fl = layerFull->front();

    Layer &bl = layerFull->back();

    imageSize = fl.layout.getActiveCount();

    f = fopen(infile, "rb");

    if (!f) return false;

    fseeko64(f, 0, SEEK_END);

    imagesInFile = ftello64(f)/imageSize;

    if (imagesInFile < 1) return fclose(f), f = nullptr, false;

    imagesInMemory = loadImagesCount > imagesInFile ? imagesInFile : loadImagesCount;

    Layout l = layerPre ? layerPre->layout : layerFull->layout;

    assert(l.getW() >= segment->sx);

    assert(l.getH() >= segment->sy);

    assert(l.getD() == segment->sz);

    measureLayout = Layout(segment->sx, segment->sy, segment->sz);

    valuesMeasure.resize(measuresPerBlock * measureLayout.getActiveCount());

    if (layerPre) {

      assert(l);

      preparedImageSize = layerPre->layout.getActiveCount();

      trainLayout = Layout(l.getW(), l.getH(), l.getD());

      data.resize(imageSize);

      values.resize(imagesInMemory * preparedImageSize);

    } else {

      trainLayout = measureLayout;

      data.resize(imagesInMemory * imageSize);

      values.resize(segment->sx * segment->sy * segment->sz);

    }

    segment->f_values = values.data();

    segment->layout = trainLayout;

    tmpdata.resize(bl.layout.getActiveCount());

    if (tmpdata.size() < imageSize) tmpdata.resize(imageSize);

    size_t memsize = data.size()*sizeof(data.front())

                   + values.size()*sizeof(values.front())

                   + valuesMeasure.size()*sizeof(valuesMeasure.front())

                   + tmpdata.size()*sizeof(tmpdata.front());

    printf("allocated size: %lld\n", (long long)(memsize));

    shuffle.resize(imagesInFile);

    for(int i = 0; i < imagesInFile; ++i)

      shuffle[i] = i;

    if (!loadImages()) return false;

    prepareMeasure();

    return true;

  }

  void finish() override

    { if (f) fclose(f), f = nullptr; }

  bool prepareBlock(int block, bool measureOnly) override {

    if (block > 0 && blocksPerLoading > 0 && (block % blocksPerLoading) == 0 && !loadImages())

      return false;

    seed = rand();

    return true;

  }

  void finishBlock(int block) override {

    if (outfile) {

      struct IL: public Iter {

        typedef const unsigned char* DataType;

        static inline void iter4(Neuron &n, DataType d, DataAccumType&) { n.v = *d/(NeuronReal)255; }

      };

      struct IS: public Iter {

        typedef unsigned char* DataType;

        static inline void iter4(Neuron &n, DataType d, DataAccumType&) { *d = n.v < 0 ? 0 : n.v > 1 ? 255 : (unsigned char)(n.v*255.999); }

      };

      Layer &fl = *layerFull;

      Layer &bl = fl.back();

      std::string outfile0(outfile);

      std::string outfile1 = outfile0 + ".1.tga";

      outfile0 += ".0.tga";

      int index = rand()%imagesInFile;

      fseeko64(f, index*imageSize, SEEK_SET);

      fread(tmpdata.data(), imageSize, 1, f);

      tgaSave(outfile0.c_str(), tmpdata.data(), fl.layout.getW(), fl.layout.getH(), fl.layout.getD());

      iterateNeurons2<il>(fl.layout, fl.layout, fl.neurons, tmpdata.data());</il>

      fl.passFull(&bl, threadsCount);

      iterateNeurons2<is>(bl.layout, bl.layout, bl.neurons, tmpdata.data());</is>

      tgaSave(outfile1.c_str(), tmpdata.data(), bl.layout.getW(), bl.layout.getH(), bl.layout.getD());

      segment->saveDemo();

    }

  }

  void loadData(Barrier &barrier, int block, int iter, bool measureOnly) override {

    int tid = barrier.tid;

    int threads = barrier.threads;

    int sx = segment->sx;

    int sy = segment->sy;

    int sz = segment->sz;

    int sxz = sx*sz;

    if (measureOnly) {

      if (!tid) {

        segment->layout = measureLayout;

        segment->f_values = valuesMeasure.data() + iter*sy*sxz;

        x = y = z = 0;

      }

    } else

    if (layerPre) {

      if (!tid) {

        unsigned int s = randomNext(seed & iter);

        int index = (s = randomNext(s))%imagesInMemory;

        x = (s = randomNext(s)) % (layerPre->layout.getW() - sx + 1);

        y = (s = randomNext(s)) % (layerPre->layout.getH() - sy + 1);

        z = 0;

        segment->layout = trainLayout;

        segment->f_values = values.data() + index*preparedImageSize;

      }

    } else {

      int w = layerFull->layout.getW();

      int h = layerFull->layout.getH();

      unsigned int s = randomNext(seed & iter);

      int index = (s = randomNext(s))%imagesInMemory;

      int x0    = (s = randomNext(s))%(w - sx + 1);

      int y0    = (s = randomNext(s))%(h - sy + 1);

      int rowstride = w*sz;

      int dr = rowstride*threads - sxz;

      int vdr = sxz*(threads - 1);

      const unsigned char *id0 = data.data() + index*imageSize + y0*rowstride + x0*sz;

      const unsigned char *id = id0 + tid*rowstride;

      NeuronReal *iv = values.data() + tid*sxz;

      for(const unsigned char *e = id0 + sy*rowstride; id < e; id += dr, iv += vdr)

      for(const unsigned char *e = id + sxz;        id < e; ++id, ++iv)

        *iv = *id/(NeuronReal)255;

      if (!tid) {

        segment->layout = trainLayout;

        segment->f_values = values.data();

        x = 0, y = 0, z = 0;

      }

    }

  }

};

#endif