// nnet2bin/nnet-copy-egs-discriminative.cc
  
  // Copyright 2012-2013  Johns Hopkins University (author:  Daniel Povey)
  
  // See ../../COPYING for clarification regarding multiple authors
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //  http://www.apache.org/licenses/LICENSE-2.0
  //
  // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
  // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
  // MERCHANTABLITY OR NON-INFRINGEMENT.
  // See the Apache 2 License for the specific language governing permissions and
  // limitations under the License.
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "hmm/transition-model.h"
  #include "nnet2/nnet-example-functions.h"
  
  namespace kaldi {
  namespace nnet2 {
  // returns an integer randomly drawn with expected value "expected_count"
  // (will be either floor(expected_count) or ceil(expected_count)).
  // this will go into an infinite loop if expected_count is very huge, but
  // it should never be that huge.
  int32 GetCount(double expected_count) {
    KALDI_ASSERT(expected_count >= 0.0);
    int32 ans = 0;
    while (expected_count > 1.0) {
      ans++;
      expected_count--;
    }
    if (WithProb(expected_count))
      ans++;
    return ans;
  }
  void AverageConstPart(int32 const_feat_dim,
                        DiscriminativeNnetExample *eg) {
    if (eg->spk_info.Dim() != 0) {  // already has const part.
      KALDI_ASSERT(eg->spk_info.Dim() == const_feat_dim);
      // and nothing to do.
    } else {
      int32 dim = eg->input_frames.NumCols(),
          basic_dim = dim - const_feat_dim;
      KALDI_ASSERT(const_feat_dim < eg->input_frames.NumCols());
      Matrix<BaseFloat> mat(eg->input_frames);  // copy to non-compressed matrix.
      eg->input_frames = mat.Range(0, mat.NumRows(), 0, basic_dim);
      eg->spk_info.Resize(const_feat_dim);
      eg->spk_info.AddRowSumMat(1.0 / mat.NumRows(),
                                mat.Range(0, mat.NumRows(),
                                          basic_dim, const_feat_dim),
                                0.0);
    }
  }
                        
  
  } // namespace nnet2
  } // namespace kaldi
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet2;
      typedef kaldi::int32 int32;
      typedef kaldi::int64 int64;
  
      const char *usage =
          "Copy examples for discriminative neural
  "
          "network training.  Supports multiple wspecifiers, in
  "
          "which case it will write the examples round-robin to the outputs.
  "
          "
  "
          "Usage:  nnet-copy-egs-discriminative [options] <egs-rspecifier> <egs-wspecifier1> [<egs-wspecifier2> ...]
  "
          "
  "
          "e.g.
  "
          "nnet-copy-egs-discriminative ark:train.degs ark,t:text.degs
  "
          "or:
  "
          "nnet-copy-egs-discriminative ark:train.degs ark:1.degs ark:2.degs
  ";
          
      bool random = false;
      int32 srand_seed = 0;
      BaseFloat keep_proportion = 1.0;
      int32 const_feat_dim = 0;
  
      ParseOptions po(usage);
      po.Register("random", &random, "If true, will write frames to output "
                  "archives randomly, not round-robin.");
      po.Register("keep-proportion", &keep_proportion, "If <1.0, this program will "
                  "randomly keep this proportion of the input samples.  If >1.0, it will "
                  "in expectation copy a sample this many times.  It will copy it a number "
                  "of times equal to floor(keep-proportion) or ceil(keep-proportion).");
      po.Register("srand", &srand_seed, "Seed for random number generator "
                  "(only relevant if --random=true or --keep-proportion != 1.0)");
      po.Register("const-feat-dim", &const_feat_dim,
                  "Dimension of part of features (last dims) which varies little "
                  "or not at all with time, and which should be stored as a single "
                  "vector for each example rather than in the feature matrix."
                  "Useful in systems that use iVectors.  Helpful to save space.");
      
      po.Read(argc, argv);
  
      srand(srand_seed);
      
      if (po.NumArgs() < 2) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string examples_rspecifier = po.GetArg(1);
  
      SequentialDiscriminativeNnetExampleReader example_reader(
          examples_rspecifier);
  
      int32 num_outputs = po.NumArgs() - 1;
      std::vector<DiscriminativeNnetExampleWriter*> example_writers(num_outputs);
      for (int32 i = 0; i < num_outputs; i++)
        example_writers[i] = new DiscriminativeNnetExampleWriter(
            po.GetArg(i+2));
  
      
      int64 num_read = 0, num_written = 0, num_frames_written = 0;
      for (; !example_reader.Done(); example_reader.Next(), num_read++) {
        int32 count = GetCount(keep_proportion);
        for (int32 c = 0; c < count; c++) {
          int32 index = (random ? Rand() : num_written) % num_outputs;
          std::ostringstream ostr;
          ostr << num_written;
          if (const_feat_dim == 0) {
            example_writers[index]->Write(ostr.str(),
                                          example_reader.Value());
          } else {
            DiscriminativeNnetExample eg = example_reader.Value();
            AverageConstPart(const_feat_dim, &eg);
            example_writers[index]->Write(ostr.str(), eg);
          }
          num_written++;
          num_frames_written +=
              static_cast<int64>(example_reader.Value().num_ali.size());
        }
      }
      
      for (int32 i = 0; i < num_outputs; i++)
        delete example_writers[i];
      KALDI_LOG << "Read " << num_read << " discriminative neural-network training"
                << " examples, wrote " << num_written << ", consisting of "
                << num_frames_written << " frames.";
      return (num_written == 0 ? 1 : 0);
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }