// bin/align-compiled-mapped.cc
  
  // Copyright 2009-2012  Microsoft Corporation, Karel Vesely
  //                2014 Johns Hopkins University (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 "hmm/hmm-utils.h"
  #include "fstext/fstext-lib.h"
  #include "decoder/decoder-wrappers.h"
  #include "decoder/training-graph-compiler.h"
  #include "decoder/decodable-matrix.h"
  #include "lat/kaldi-lattice.h" // for {Compact}LatticeArc
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      typedef kaldi::int32 int32;
      using fst::SymbolTable;
      using fst::VectorFst;
      using fst::StdArc;
  
      const char *usage =
          "Generate alignments, reading log-likelihoods as matrices.
  "
          " (model is needed only for the integer mappings in its transition-model)
  "
          "Usage:   align-compiled-mapped [options] trans-model-in graphs-rspecifier feature-rspecifier alignments-wspecifier
  "
          "e.g.: 
  "
          " nnet-align-compiled trans.mdl ark:graphs.fsts scp:train.scp ark:nnet.ali
  "
          "or:
  "
          " compile-train-graphs tree trans.mdl lex.fst ark:train.tra b, ark:- | \\
  "
          "   nnet-align-compiled trans.mdl ark:- scp:loglikes.scp t, ark:nnet.ali
  ";
  
      ParseOptions po(usage);
      AlignConfig align_config;
      bool binary = true;
      BaseFloat acoustic_scale = 1.0;
      BaseFloat transition_scale = 1.0;
      BaseFloat self_loop_scale = 1.0;
  
      align_config.Register(&po);
      po.Register("binary", &binary, "Write output in binary mode");
      po.Register("transition-scale", &transition_scale,
                  "Transition-probability scale [relative to acoustics]");
      po.Register("acoustic-scale", &acoustic_scale,
                  "Scaling factor for acoustic likelihoods");
      po.Register("self-loop-scale", &self_loop_scale,
                  "Scale of self-loop versus non-self-loop log probs [relative to acoustics]");
      po.Read(argc, argv);
  
      if (po.NumArgs() < 4 || po.NumArgs() > 5) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_in_filename = po.GetArg(1);
      std::string fst_rspecifier = po.GetArg(2);
      std::string feature_rspecifier = po.GetArg(3);
      std::string alignment_wspecifier = po.GetArg(4);
      std::string scores_wspecifier = po.GetOptArg(5);
  
      TransitionModel trans_model;
      ReadKaldiObject(model_in_filename, &trans_model);
  
      SequentialBaseFloatMatrixReader loglikes_reader(feature_rspecifier);
      RandomAccessTableReader<fst::VectorFstHolder> fst_reader(fst_rspecifier);
      Int32VectorWriter alignment_writer(alignment_wspecifier);
      BaseFloatWriter scores_writer(scores_wspecifier);
  
      int num_done = 0, num_err = 0, num_retry = 0;
      double tot_like = 0.0;
      kaldi::int64 frame_count = 0;
      
      for (; !loglikes_reader.Done(); loglikes_reader.Next()) {
        std::string utt = loglikes_reader.Key();
        if (!fst_reader.HasKey(utt)) {
          KALDI_WARN << "No fst for utterance " << utt;
          num_err++;
          continue;
        }
        const Matrix<BaseFloat> &loglikes = loglikes_reader.Value();
        VectorFst<StdArc> decode_fst(fst_reader.Value(utt));
        // fst_reader.FreeCurrent();  // this stops copy-on-write of the fst
        // by deleting the fst inside the reader, since we're about to mutate
        // the fst by adding transition probs.
  
        if (loglikes.NumRows() == 0) {
          KALDI_WARN << "Empty loglikes matrix utterance: " << utt;
          num_err++;
          continue;
        }
        if (decode_fst.Start() == fst::kNoStateId) {
          KALDI_WARN << "Empty decoding graph for " << utt;
          num_err++;
          continue;
        }
  
        {  // Add transition-probs to the FST.
          std::vector<int32> disambig_syms;  // empty.
          AddTransitionProbs(trans_model, disambig_syms,
                             transition_scale, self_loop_scale,
                             &decode_fst);
        }
  
        DecodableMatrixScaledMapped decodable(trans_model, loglikes, acoustic_scale);
  
        AlignUtteranceWrapper(align_config, utt,
                              acoustic_scale, &decode_fst, &decodable,
                              &alignment_writer, &scores_writer,
                              &num_done, &num_err, &num_retry,
                              &tot_like, &frame_count);
      }
      KALDI_LOG << "Overall log-likelihood per frame is " << (tot_like/frame_count)
                << " over " << frame_count<< " frames.";
      KALDI_LOG << "Retried " << num_retry << " out of "
                << (num_done + num_err) << " utterances.";
      KALDI_LOG << "Done " << num_done << ", errors on " << num_err;
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }