// sgmm2bin/sgmm2-latgen-faster.cc
  
  // Copyright 2009-2012  Saarland University;  Microsoft Corporation;
  //                      Johns Hopkins University (author: Daniel Povey)
  //                2014  Guoguo Chen
  
  // 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 <string>
  using std::string;
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "sgmm2/am-sgmm2.h"
  #include "hmm/transition-model.h"
  #include "fstext/fstext-lib.h"
  #include "decoder/decoder-wrappers.h"
  #include "sgmm2/decodable-am-sgmm2.h"
  #include "base/timer.h"
  
  namespace kaldi {
  
  // the reference arguments at the beginning are not const as the style guide
  // requires, but are best viewed as inputs.
  bool ProcessUtterance(LatticeFasterDecoder &decoder,
                        const AmSgmm2 &am_sgmm,
                        const TransitionModel &trans_model,
                        double log_prune,
                        double acoustic_scale,
                        const Matrix<BaseFloat> &features,
                        RandomAccessInt32VectorVectorReader &gselect_reader,
                        RandomAccessBaseFloatVectorReaderMapped &spkvecs_reader,
                        const fst::SymbolTable *word_syms,
                        const std::string &utt,
                        bool determinize,
                        bool allow_partial,
                        Int32VectorWriter *alignments_writer,
                        Int32VectorWriter *words_writer,
                        CompactLatticeWriter *compact_lattice_writer,
                        LatticeWriter *lattice_writer,
                        double *like_ptr) { // puts utterance's like in like_ptr on success.
    using fst::Fst;
  
    Sgmm2PerSpkDerivedVars spk_vars;
    if (spkvecs_reader.IsOpen()) {
      if (spkvecs_reader.HasKey(utt)) {
        spk_vars.SetSpeakerVector(spkvecs_reader.Value(utt));
        am_sgmm.ComputePerSpkDerivedVars(&spk_vars);
      } else {
        KALDI_WARN << "Cannot find speaker vector for " << utt << ", not decoding this utterance";
        return false; // We could use zero, but probably the user would want to know about this
        // (this would normally be a script error or some kind of failure).
      }
    }
    if (!gselect_reader.HasKey(utt) ||
        gselect_reader.Value(utt).size() != features.NumRows()) {
      KALDI_WARN << "No Gaussian-selection info available for utterance "
                 << utt << " (or wrong size)";
    }
  
    const std::vector<std::vector<int32> > &gselect =
        gselect_reader.Value(utt);
    
    DecodableAmSgmm2Scaled sgmm_decodable(am_sgmm, trans_model, features, gselect,
                                          log_prune, acoustic_scale, &spk_vars);
  
    return DecodeUtteranceLatticeFaster(
        decoder, sgmm_decodable, trans_model, word_syms, utt, acoustic_scale,
        determinize, allow_partial, alignments_writer, words_writer,
        compact_lattice_writer, lattice_writer, like_ptr);
  }
  
  } // end namespace kaldi
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      typedef kaldi::int32 int32;
      using fst::SymbolTable;
      using fst::Fst;
      using fst::StdArc;
  
      const char *usage =
          "Decode features using SGMM-based model.
  "
          "Usage:  sgmm2-latgen-faster [options] <model-in> (<fst-in>|<fsts-rspecifier>) "
          "<features-rspecifier> <lattices-wspecifier> [<words-wspecifier> [<alignments-wspecifier>] ]
  ";
      ParseOptions po(usage);
      BaseFloat acoustic_scale = 0.1;
      bool allow_partial = false;
      BaseFloat log_prune = 5.0;
      string word_syms_filename, gselect_rspecifier, spkvecs_rspecifier,
          utt2spk_rspecifier;
  
      LatticeFasterDecoderConfig decoder_opts;
      decoder_opts.Register(&po);    
  
      po.Register("acoustic-scale", &acoustic_scale,
          "Scaling factor for acoustic likelihoods");
      po.Register("log-prune", &log_prune,
                  "Pruning beam used to reduce number of exp() evaluations.");
      po.Register("word-symbol-table", &word_syms_filename,
          "Symbol table for words [for debug output]");
      po.Register("allow-partial", &allow_partial,
                  "Produce output even when final state was not reached");
      po.Register("gselect", &gselect_rspecifier,
                  "rspecifier for precomputed per-frame Gaussian indices.");
      po.Register("spk-vecs", &spkvecs_rspecifier,
                  "rspecifier for speaker vectors");
      po.Register("utt2spk", &utt2spk_rspecifier,
                  "rspecifier for utterance to speaker map");
      po.Read(argc, argv);
  
      if (po.NumArgs() < 4 || po.NumArgs() > 6) {
        po.PrintUsage();
        exit(1);
      }
  
      if (gselect_rspecifier == "")
        KALDI_ERR << "--gselect option is required.";
  
      std::string model_in_filename = po.GetArg(1),
          fst_in_str = po.GetArg(2),
          feature_rspecifier = po.GetArg(3),
          lattice_wspecifier = po.GetArg(4),
          words_wspecifier = po.GetOptArg(5),
          alignment_wspecifier = po.GetOptArg(6);
  
      TransitionModel trans_model;
      kaldi::AmSgmm2 am_sgmm;
      {
        bool binary;
        Input ki(model_in_filename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_sgmm.Read(ki.Stream(), binary);
      }
  
      CompactLatticeWriter compact_lattice_writer;
      LatticeWriter lattice_writer;
      bool determinize = decoder_opts.determinize_lattice;    
      if (! (determinize ? compact_lattice_writer.Open(lattice_wspecifier)
             : lattice_writer.Open(lattice_wspecifier)))
        KALDI_ERR << "Could not open table for writing lattices: "
                   << lattice_wspecifier;
      
      Int32VectorWriter words_writer(words_wspecifier);
  
      Int32VectorWriter alignment_writer(alignment_wspecifier);
  
      fst::SymbolTable *word_syms = NULL;
      if (word_syms_filename != "") 
        if (!(word_syms = fst::SymbolTable::ReadText(word_syms_filename)))
          KALDI_ERR << "Could not read symbol table from file "
                     << word_syms_filename;
  
      RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
      RandomAccessBaseFloatVectorReaderMapped spkvecs_reader(spkvecs_rspecifier,
                                                             utt2spk_rspecifier);
  
      BaseFloat tot_like = 0.0;
      kaldi::int64 frame_count = 0;
      int num_success = 0, num_err = 0;
  
      Timer timer;
          
      if (ClassifyRspecifier(fst_in_str, NULL, NULL) == kNoRspecifier) { // a single FST.
        SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
        // It's important that we initialize decode_fst after feature_reader, as it
        // can prevent crashes on systems installed without enough virtual memory.
        // It has to do with what happens on UNIX systems if you call fork() on a
        // large process: the page-table entries are duplicated, which requires a
        // lot of virtual memory.
        Fst<StdArc> *decode_fst = fst::ReadFstKaldiGeneric(fst_in_str);
        timer.Reset(); // exclude graph loading time.
        
        {
          LatticeFasterDecoder decoder(*decode_fst, decoder_opts);
      
          const std::vector<std::vector<int32> > empty_gselect;
  
          for (; !feature_reader.Done(); feature_reader.Next()) {
            string utt = feature_reader.Key();
            const Matrix<BaseFloat> &features(feature_reader.Value());
            if (features.NumRows() == 0) {
              KALDI_WARN << "Zero-length utterance: " << utt;
              num_err++;
              continue;
            }
            double like;
            if (ProcessUtterance(decoder, am_sgmm, trans_model, log_prune, acoustic_scale,
                                 features, gselect_reader, spkvecs_reader, word_syms,
                                 utt, determinize, allow_partial,
                                 &alignment_writer, &words_writer, &compact_lattice_writer,
                                 &lattice_writer, &like)) {
              tot_like += like;
              frame_count += features.NumRows();
              KALDI_LOG << "Log-like per frame for utterance " << utt << " is "
                        << (like / features.NumRows()) << " over "
                        << features.NumRows() << " frames.";
              num_success++;
            } else { num_err++; }
          }
        }
        delete decode_fst; // only safe to do this after decoder goes out of scope.
      } else { // We have different FSTs for different utterances.
        SequentialTableReader<fst::VectorFstHolder> fst_reader(fst_in_str);
        RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);          
        for (; !fst_reader.Done(); fst_reader.Next()) {
          std::string utt = fst_reader.Key();
          if (!feature_reader.HasKey(utt)) {
            KALDI_WARN << "Not decoding utterance " << utt
                       << " because no features available.";
            num_err++;
            continue;
          }
          const Matrix<BaseFloat> &features = feature_reader.Value(utt);
          if (features.NumRows() == 0) {
            KALDI_WARN << "Zero-length utterance: " << utt;
            num_err++;
            continue;
          }
          LatticeFasterDecoder decoder(fst_reader.Value(), decoder_opts);
          double like;
  
          if (ProcessUtterance(decoder, am_sgmm, trans_model, log_prune, acoustic_scale,
                               features, gselect_reader, spkvecs_reader, word_syms,
                               utt, determinize, allow_partial,
                               &alignment_writer, &words_writer, &compact_lattice_writer,
                               &lattice_writer, &like)) {
            tot_like += like;
            frame_count += features.NumRows();
            KALDI_LOG << "Log-like per frame for utterance " << utt << " is "
                      << (like / features.NumRows()) << " over "
                      << features.NumRows() << " frames.";
            num_success++;
          } else { num_err++; }
        }
      }
      double elapsed = timer.Elapsed();
      KALDI_LOG << "Time taken [excluding initialization] "<< elapsed
                << "s: real-time factor assuming 100 frames/sec is "
                << (elapsed*100.0/frame_count);
      KALDI_LOG << "Done " << num_success << " utterances, failed for "
                << num_err;
      KALDI_LOG << "Overall log-likelihood per frame = " << (tot_like/frame_count)
                << " over " << frame_count << " frames.";
  
      delete word_syms;
      return (num_success != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }