// gmmbin/gmm-latgen-biglm-faster.cc
  
  // Copyright 2009-2011  Microsoft Corporation
  //                2013  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 "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "gmm/am-diag-gmm.h"
  #include "tree/context-dep.h"
  #include "hmm/transition-model.h"
  #include "fstext/fstext-lib.h"
  #include "decoder/lattice-biglm-faster-decoder.h"
  #include "gmm/decodable-am-diag-gmm.h"
  #include "base/timer.h"
  
  
  namespace kaldi {
  // Takes care of output.  Returns true on success.
  bool DecodeUtterance(LatticeBiglmFasterDecoder &decoder, // not const but is really an input.
                       DecodableInterface &decodable, // not const but is really an input.
                       const TransitionModel &trans_model,
                       const fst::SymbolTable *word_syms,
                       std::string utt,
                       double acoustic_scale,
                       bool determinize,
                       bool allow_partial,
                       Int32VectorWriter *alignment_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::VectorFst;
  
    if (!decoder.Decode(&decodable)) {
      KALDI_WARN << "Failed to decode file " << utt;
      return false;
    }
    if (!decoder.ReachedFinal()) {
      if (allow_partial) {
        KALDI_WARN << "Outputting partial output for utterance " << utt
                   << " since no final-state reached
  ";
      } else {
        KALDI_WARN << "Not producing output for utterance " << utt
                   << " since no final-state reached and "
                   << "--allow-partial=false.
  ";
        return false;
      }
    }
  
    double likelihood;
    LatticeWeight weight;
    int32 num_frames;
    { // First do some stuff with word-level traceback...
      VectorFst<LatticeArc> decoded;
      decoder.GetBestPath(&decoded);
      if (decoded.NumStates() == 0)
        // Shouldn't really reach this point as already checked success.
        KALDI_ERR << "Failed to get traceback for utterance " << utt;
  
      std::vector<int32> alignment;
      std::vector<int32> words;
      GetLinearSymbolSequence(decoded, &alignment, &words, &weight);
      num_frames = alignment.size();
      if (words_writer->IsOpen())
        words_writer->Write(utt, words);
      if (alignment_writer->IsOpen())
        alignment_writer->Write(utt, alignment);
      if (word_syms != NULL) {
        std::cerr << utt << ' ';
        for (size_t i = 0; i < words.size(); i++) {
          std::string s = word_syms->Find(words[i]);
          if (s == "")
            KALDI_ERR << "Word-id " << words[i] <<" not in symbol table.";
          std::cerr << s << ' ';
        }
        std::cerr << '
  ';
      }
      likelihood = -(weight.Value1() + weight.Value2());
    }
  
    // Get lattice, and do determinization if requested.
    Lattice lat;
    decoder.GetRawLattice(&lat);
    if (lat.NumStates() == 0)
      KALDI_ERR << "Unexpected problem getting lattice for utterance " << utt;
    fst::Connect(&lat);
    if (determinize) {
      CompactLattice clat;
      if (!DeterminizeLatticePhonePrunedWrapper(
              trans_model,
              &lat,
              decoder.GetOptions().lattice_beam,
              &clat,
              decoder.GetOptions().det_opts))
        KALDI_WARN << "Determinization finished earlier than the beam for "
                   << "utterance " << utt;
      // We'll write the lattice without acoustic scaling.
      if (acoustic_scale != 0.0)
        fst::ScaleLattice(fst::AcousticLatticeScale(1.0 / acoustic_scale), &clat);
      compact_lattice_writer->Write(utt, clat);
    } else {
      Lattice fst;
      decoder.GetRawLattice(&fst);
      if (fst.NumStates() == 0)
        KALDI_ERR << "Unexpected problem getting lattice for utterance "
                  << utt;
      fst::Connect(&fst); // Will get rid of this later... shouldn't have any
      // disconnected states there, but we seem to.
      if (acoustic_scale != 0.0) // We'll write the lattice without acoustic scaling
        fst::ScaleLattice(fst::AcousticLatticeScale(1.0 / acoustic_scale), &fst); 
      lattice_writer->Write(utt, fst);
    }
    KALDI_LOG << "Log-like per frame for utterance " << utt << " is "
              << (likelihood / num_frames) << " over "
              << num_frames << " frames.";
    KALDI_VLOG(2) << "Cost for utterance " << utt << " is "
                  << weight.Value1() << " + " << weight.Value2();
    *like_ptr = likelihood;
    return true;
  }
  
  }
  
  
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      typedef kaldi::int32 int32;
      using fst::SymbolTable;
      using fst::VectorFst;
      using fst::Fst;
      using fst::StdArc;
      using fst::ReadFstKaldi;
  
      const char *usage =
          "Generate lattices using GMM-based model.
  "
          "User supplies LM used to generate decoding graph, and desired LM;
  "
          "this decoder applies the difference during decoding
  "
          "Usage: gmm-latgen-biglm-faster [options] model-in (fst-in|fsts-rspecifier) "
          "oldlm-fst-in newlm-fst-in features-rspecifier"
          " lattice-wspecifier [ words-wspecifier [alignments-wspecifier] ]
  ";
      ParseOptions po(usage);
      Timer timer;
      bool allow_partial = false;
      BaseFloat acoustic_scale = 0.1;
      LatticeBiglmFasterDecoderConfig config;
      
      std::string word_syms_filename;
      config.Register(&po);
      po.Register("acoustic-scale", &acoustic_scale, "Scaling factor for acoustic likelihoods");
  
      po.Register("word-symbol-table", &word_syms_filename, "Symbol table for words [for debug output]");
      po.Register("allow-partial", &allow_partial, "If true, produce output even if end state was not reached.");
      
      po.Read(argc, argv);
  
      if (po.NumArgs() < 6 || po.NumArgs() > 8) {
        po.PrintUsage();
        exit(1);
      }
      
      std::string model_in_filename = po.GetArg(1),
          fst_in_str = po.GetArg(2),
          old_lm_fst_rxfilename = po.GetArg(3),
          new_lm_fst_rxfilename = po.GetArg(4),
          feature_rspecifier = po.GetArg(5),
          lattice_wspecifier = po.GetArg(6),
          words_wspecifier = po.GetOptArg(7),
          alignment_wspecifier = po.GetOptArg(8);
      
      TransitionModel trans_model;
      AmDiagGmm am_gmm;
      {
        bool binary;
        Input ki(model_in_filename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_gmm.Read(ki.Stream(), binary);
      }
      
      VectorFst<StdArc> *old_lm_fst = fst::CastOrConvertToVectorFst(
          fst::ReadFstKaldiGeneric(old_lm_fst_rxfilename));
      ApplyProbabilityScale(-1.0, old_lm_fst); // Negate old LM probs...
      
      VectorFst<StdArc> *new_lm_fst = fst::CastOrConvertToVectorFst(
          fst::ReadFstKaldiGeneric(new_lm_fst_rxfilename));
  
      fst::BackoffDeterministicOnDemandFst<StdArc> old_lm_dfst(*old_lm_fst);
      fst::BackoffDeterministicOnDemandFst<StdArc> new_lm_dfst(*new_lm_fst);
      fst::ComposeDeterministicOnDemandFst<StdArc> compose_dfst(&old_lm_dfst,
                                                                &new_lm_dfst);
      fst::CacheDeterministicOnDemandFst<StdArc> cache_dfst(&compose_dfst);
  
      bool determinize = config.determinize_lattice;
      CompactLatticeWriter compact_lattice_writer;
      LatticeWriter lattice_writer;
      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;
  
      double tot_like = 0.0;
      kaldi::int64 frame_count = 0;
      int num_success = 0, num_fail = 0;
  
  
      if (ClassifyRspecifier(fst_in_str, NULL, NULL) == kNoRspecifier) {
        SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
        // Input FST is just one FST, not a table of FSTs.
        Fst<StdArc> *decode_fst = fst::ReadFstKaldiGeneric(fst_in_str);
  
        {
          LatticeBiglmFasterDecoder decoder(*decode_fst, config, &cache_dfst);
      
          for (; !feature_reader.Done(); feature_reader.Next()) {
            std::string utt = feature_reader.Key();
            Matrix<BaseFloat> features (feature_reader.Value());
            feature_reader.FreeCurrent();
            if (features.NumRows() == 0) {
              KALDI_WARN << "Zero-length utterance: " << utt;
              num_fail++;
              continue;
            }
        
            DecodableAmDiagGmmScaled gmm_decodable(am_gmm, trans_model, features,
                                                   acoustic_scale);
  
  
            double like;
            if (DecodeUtterance(decoder, gmm_decodable, trans_model, word_syms,
                                utt, acoustic_scale, determinize, allow_partial,
                                &alignment_writer, &words_writer,
                                &compact_lattice_writer, &lattice_writer,
                                &like)) {
              tot_like += like;
              frame_count += features.NumRows();
              num_success++;
            } else num_fail++;
          }
        }
        delete decode_fst; // delete this only 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_fail++;
            continue;
          }
          const Matrix<BaseFloat> &features = feature_reader.Value(utt);
          if (features.NumRows() == 0) {
            KALDI_WARN << "Zero-length utterance: " << utt;
            num_fail++;
            continue;
          }
          LatticeBiglmFasterDecoder decoder(fst_reader.Value(), config,
                                            &cache_dfst);
          DecodableAmDiagGmmScaled gmm_decodable(am_gmm, trans_model, features,
                                                 acoustic_scale);
          double like;
          if (DecodeUtterance(decoder, gmm_decodable, trans_model, word_syms, utt,
                              acoustic_scale, determinize, allow_partial,
                              &alignment_writer, &words_writer,
                              &compact_lattice_writer, &lattice_writer,
                              &like)) {
            tot_like += like;
            frame_count += features.NumRows();
            num_success++;
          } else num_fail++;
        }
      }
        
      double elapsed = timer.Elapsed();
      KALDI_LOG << "Time taken "<< elapsed
                << "s: real-time factor assuming 100 frames/sec is "
                << (elapsed*100.0/frame_count);
      KALDI_LOG << "Done " << num_success << " utterances, failed for "
                << num_fail;
      KALDI_LOG << "Overall log-likelihood per frame is " << (tot_like/frame_count) << " over "
                << frame_count<<" frames.";
  
      delete word_syms;
      if (num_success != 0) return 0;
      else return 1;
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }