// nnet3bin/nnet3-discriminative-get-egs.cc
  
  // Copyright      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 <sstream>
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "hmm/transition-model.h"
  #include "hmm/posterior.h"
  #include "nnet3/nnet-discriminative-example.h"
  #include "nnet3/discriminative-supervision.h"
  #include "nnet3/nnet-example-utils.h"
  #include "chain/chain-supervision.h"
  
  namespace kaldi {
  namespace nnet3 {
  
  // This function does all the processing for one utterance, and outputs the
  // examples to 'example_writer'.
  // returns true if we got as far as calling GetChunksForUtterance()
  // [in which case stats will be accumulated by class UtteranceSplitter]
  static bool ProcessFile(const discriminative::SplitDiscriminativeSupervisionOptions &config,
                          const TransitionModel &tmodel,
                          const MatrixBase<BaseFloat> &feats,
                          const MatrixBase<BaseFloat> *ivector_feats,
                          int32 ivector_period,
                          const discriminative::DiscriminativeSupervision &supervision,
                          const std::string &utt_id,
                          bool compress,
                          UtteranceSplitter *utt_splitter,
                          NnetDiscriminativeExampleWriter *example_writer) {
    KALDI_ASSERT(supervision.num_sequences == 1);
    int32 num_input_frames = feats.NumRows(),
        num_output_frames = supervision.frames_per_sequence;
  
    if (!utt_splitter->LengthsMatch(utt_id, num_input_frames, num_output_frames))
      return false;  // LengthsMatch() will have printed a warning.
  
    std::vector<ChunkTimeInfo> chunks;
  
    utt_splitter->GetChunksForUtterance(num_input_frames, &chunks);
  
    if (chunks.empty()) {
      KALDI_WARN << "Not producing egs for utterance " << utt_id
                 << " because it is too short: "
                 << num_input_frames << " frames.";
    }
  
    int32 frame_subsampling_factor = utt_splitter->Config().frame_subsampling_factor;
  
    discriminative::DiscriminativeSupervisionSplitter splitter(config, tmodel,
                                                               supervision);
  
    for (size_t c = 0; c < chunks.size(); c++) {
      ChunkTimeInfo &chunk = chunks[c];
  
      NnetDiscriminativeExample nnet_discriminative_eg;
      nnet_discriminative_eg.outputs.resize(1);
  
      int32 start_frame_subsampled = chunk.first_frame / frame_subsampling_factor,
          num_frames_subsampled = chunk.num_frames / frame_subsampling_factor;
  
      discriminative::DiscriminativeSupervision supervision_part;
  
      splitter.GetFrameRange(start_frame_subsampled,
                             num_frames_subsampled,
                             (c == 0 ? false : true),
                             &supervision_part);
  
      SubVector<BaseFloat> output_weights(
          &(chunk.output_weights[0]),
          static_cast<int32>(chunk.output_weights.size()));
  
      int32 first_frame = 0;  // we shift the time-indexes of all these parts so
                              // that the supervised part starts from frame 0.
      NnetDiscriminativeSupervision nnet_supervision("output", supervision_part,
                                                     output_weights,
                                                     first_frame,
                                                     frame_subsampling_factor);
      nnet_discriminative_eg.outputs[0].Swap(&nnet_supervision);
  
      nnet_discriminative_eg.inputs.resize(ivector_feats != NULL ? 2 : 1);
  
  
      int32 tot_input_frames = chunk.left_context + chunk.num_frames +
          chunk.right_context;
  
      Matrix<BaseFloat> input_frames(tot_input_frames, feats.NumCols(),
                                     kUndefined);
  
      int32 start_frame = chunk.first_frame - chunk.left_context;
      for (int32 t = start_frame; t < start_frame + tot_input_frames; t++) {
        int32 t2 = t;
        if (t2 < 0) t2 = 0;
        if (t2 >= num_input_frames) t2 = num_input_frames - 1;
        int32 j = t - start_frame;
        SubVector<BaseFloat> src(feats, t2),
            dest(input_frames, j);
        dest.CopyFromVec(src);
      }
  
      NnetIo input_io("input", -chunk.left_context, input_frames);
      nnet_discriminative_eg.inputs[0].Swap(&input_io);
  
      if (ivector_feats != NULL) {
        // if applicable, add the iVector feature.
        // choose iVector from a random frame in the chunk
        int32 ivector_frame = RandInt(start_frame,
                                      start_frame + num_input_frames - 1),
            ivector_frame_subsampled = ivector_frame / ivector_period;
        if (ivector_frame_subsampled < 0)
          ivector_frame_subsampled = 0;
        if (ivector_frame_subsampled >= ivector_feats->NumRows())
          ivector_frame_subsampled = ivector_feats->NumRows() - 1;
        Matrix<BaseFloat> ivector(1, ivector_feats->NumCols());
        ivector.Row(0).CopyFromVec(ivector_feats->Row(ivector_frame_subsampled));
        NnetIo ivector_io("ivector", 0, ivector);
        nnet_discriminative_eg.inputs[1].Swap(&ivector_io);
      }
  
      if (compress)
        nnet_discriminative_eg.Compress();
  
      std::ostringstream os;
      os << utt_id << "-" << chunk.first_frame;
  
      std::string key = os.str(); // key is <utt_id>-<frame_id>
  
      example_writer->Write(key, nnet_discriminative_eg);
    }
    return true;
  }
  
  
  } // namespace nnet3
  } // namespace kaldi
  
  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 =
          "Get frame-by-frame examples of data for nnet3+sequence neural network
  "
          "training.  This involves breaking up utterances into pieces of sizes
  "
          "determined by the --num-frames option.
  "
          "
  "
          "Usage:  nnet3-discriminative-get-egs [options] <model> <features-rspecifier> "
          "<denominator-lattice-rspecifier> <numerator-alignment-rspecifier> <egs-wspecifier>
  "
          "
  "
          "An example [where $feats expands to the actual features]:
  "
          "  nnet3-discriminative-get-egs --left-context=25 --right-context=9 --num-frames=150,100,90 \\
  "
          "  \"$feats\" \"ark,s,cs:gunzip -c lat.1.gz\" scp:ali.scp ark:degs.1.ark
  ";
  
      bool compress = true;
      int32 length_tolerance = 100, online_ivector_period = 1;
  
      std::string online_ivector_rspecifier;
  
      ExampleGenerationConfig eg_config;  // controls num-frames,
                                          // left/right-context, etc.
      discriminative::SplitDiscriminativeSupervisionOptions splitter_config;
  
      ParseOptions po(usage);
  
      eg_config.Register(&po);
      po.Register("compress", &compress, "If true, write egs in "
                  "compressed format (recommended)");
      po.Register("ivectors", &online_ivector_rspecifier, "Alias for --online-ivectors "
                  "option, for back compatibility");
      po.Register("online-ivectors", &online_ivector_rspecifier, "Rspecifier of ivector "
                  "features, as a matrix.");
      po.Register("online-ivector-period", &online_ivector_period, "Number of frames "
                  "between iVectors in matrices supplied to the --online-ivectors "
                  "option");
      po.Register("length-tolerance", &length_tolerance, "Tolerance for "
                  "difference in num-frames between feat and ivector matrices");
  
      splitter_config.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 5) {
        po.PrintUsage();
        exit(1);
      }
  
      eg_config.ComputeDerived();
      UtteranceSplitter utt_splitter(eg_config);
  
      std::string model_wxfilename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          den_lat_rspecifier = po.GetArg(3),
          num_ali_rspecifier = po.GetArg(4),
          examples_wspecifier = po.GetArg(5);
  
  
      TransitionModel tmodel;
      {
        bool binary;
        Input ki(model_wxfilename, &binary);
        tmodel.Read(ki.Stream(), binary);
      }
  
      SequentialBaseFloatMatrixReader feat_reader(feature_rspecifier);
      RandomAccessLatticeReader den_lat_reader(den_lat_rspecifier);
      RandomAccessInt32VectorReader ali_reader(num_ali_rspecifier);
      NnetDiscriminativeExampleWriter example_writer(examples_wspecifier);
      RandomAccessBaseFloatMatrixReader online_ivector_reader(
          online_ivector_rspecifier);
  
      int32 num_err = 0;
  
      for (; !feat_reader.Done(); feat_reader.Next()) {
        std::string key = feat_reader.Key();
        const Matrix<BaseFloat> &feats = feat_reader.Value();
        if (!den_lat_reader.HasKey(key)) {
          KALDI_WARN << "No denominator lattice for key " << key;
          num_err++;
        } else if (!ali_reader.HasKey(key)) {
          KALDI_WARN << "No numerator alignment for key " << key;
          num_err++;
        } else {
          discriminative::DiscriminativeSupervision supervision;
          if (!supervision.Initialize(ali_reader.Value(key),
                                      den_lat_reader.Value(key),
                                      1.0)) {
            KALDI_WARN << "Failed to convert lattice to supervision "
                       << "for utterance " << key;
            num_err++;
            continue;
          }
  
          const Matrix<BaseFloat> *online_ivector_feats = NULL;
          if (!online_ivector_rspecifier.empty()) {
            if (!online_ivector_reader.HasKey(key)) {
              KALDI_WARN << "No iVectors for utterance " << key;
              num_err++;
              continue;
            } else {
              // this address will be valid until we call HasKey() or Value()
              // again.
              online_ivector_feats = &(online_ivector_reader.Value(key));
            }
          }
          if (online_ivector_feats != NULL &&
              (abs(feats.NumRows() - (online_ivector_feats->NumRows() *
                                      online_ivector_period)) > length_tolerance
               || online_ivector_feats->NumRows() == 0)) {
            KALDI_WARN << "Length difference between feats " << feats.NumRows()
                       << " and iVectors " << online_ivector_feats->NumRows()
                       << "exceeds tolerance " << length_tolerance;
            num_err++;
            continue;
          }
          if (!ProcessFile(splitter_config, tmodel,
                           feats, online_ivector_feats, online_ivector_period,
                           supervision, key, compress,
                           &utt_splitter, &example_writer))
            num_err++;
        }
      }
      if (num_err > 0)
        KALDI_WARN << num_err << " utterances had errors and could "
            "not be processed.";
      // utt_splitter prints diagnostics.
      return utt_splitter.ExitStatus();
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }