// nnet3bin/nnet3-discriminative-copy-egs.cc
  
  // Copyright 2012-2015  Johns Hopkins University (author:  Daniel Povey)
  //           2014-2015  Vimal Manohar
  
  // 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 "nnet3/nnet-discriminative-example.h"
  
  namespace kaldi {
  // returns an integer randomly drawn with expected value "expected_count"
  // (will be either floor(expected_count) or ceil(expected_count)).
  int32 GetCount(double expected_count) {
    KALDI_ASSERT(expected_count >= 0.0);
    int32 ans = floor(expected_count);
    expected_count -= ans;
    if (WithProb(expected_count))
      ans++;
    return ans;
  }
  }
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
      typedef kaldi::int64 int64;
  
      const char *usage =
          "Copy examples for nnet3 discriminative training, possibly changing the binary mode.
  "
          "Supports multiple wspecifiers, in which case it will write the examples
  "
          "round-robin to the outputs.
  "
          "
  "
          "Usage:  nnet3-discriminative-copy-egs [options] <egs-rspecifier> <egs-wspecifier1> [<egs-wspecifier2> ...]
  "
          "
  "
          "e.g.
  "
          "nnet3-discriminative-copy-egs ark:train.degs ark,t:text.degs
  "
          "or:
  "
          "nnet3-discriminative-copy-egs ark:train.degs ark:1.degs ark:2.degs
  ";
  
      bool random = false;
      int32 srand_seed = 0;
      int32 frame_shift = 0;
      BaseFloat keep_proportion = 1.0;
  
      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("frame-shift", &frame_shift, "Allows you to shift time values "
                  "in the supervision data (excluding iVector data) - useful in "
                  "augmenting data.  Note, the outputs will remain at the closest "
                  "exact multiples of the frame subsampling factor");
  
      po.Read(argc, argv);
  
      srand(srand_seed);
  
      if (po.NumArgs() < 2) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string examples_rspecifier = po.GetArg(1);
  
      SequentialNnetDiscriminativeExampleReader example_reader(examples_rspecifier);
  
      int32 num_outputs = po.NumArgs() - 1;
      std::vector<NnetDiscriminativeExampleWriter*> example_writers(num_outputs);
      for (int32 i = 0; i < num_outputs; i++)
        example_writers[i] = new NnetDiscriminativeExampleWriter(po.GetArg(i+2));
  
      std::vector<std::string> exclude_names; // names we never shift times of;
                                              // not configurable for now.
      exclude_names.push_back(std::string("ivector"));
  
  
      int64 num_read = 0, num_written = 0;
      for (; !example_reader.Done(); example_reader.Next(), num_read++) {
        // count is normally 1; could be 0, or possibly >1.
        int32 count = GetCount(keep_proportion);
        std::string key = example_reader.Key();
        if (frame_shift == 0) {
          const NnetDiscriminativeExample &eg = example_reader.Value();
          for (int32 c = 0; c < count; c++) {
            int32 index = (random ? Rand() : num_written) % num_outputs;
            example_writers[index]->Write(key, eg);
            num_written++;
          }
        } else if (count > 0) {
          NnetDiscriminativeExample eg = example_reader.Value();
          if (frame_shift != 0)
            ShiftDiscriminativeExampleTimes(frame_shift, exclude_names, &eg);
          for (int32 c = 0; c < count; c++) {
            int32 index = (random ? Rand() : num_written) % num_outputs;
            example_writers[index]->Write(key, eg);
            num_written++;
          }
        }
      }
      for (int32 i = 0; i < num_outputs; i++)
        delete example_writers[i];
      KALDI_LOG << "Read " << num_read
                << " neural-network training examples, wrote " << num_written;
      return (num_written == 0 ? 1 : 0);
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }