// nnet3bin/nnet3-latgen-faster-parallel.cc
  
  // Copyright 2012-2016   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/timer.h"
  #include "base/kaldi-common.h"
  #include "decoder/decoder-wrappers.h"
  #include "fstext/fstext-lib.h"
  #include "hmm/transition-model.h"
  #include "nnet3/nnet-am-decodable-simple.h"
  #include "nnet3/nnet-utils.h"
  #include "util/kaldi-thread.h"
  #include "tree/context-dep.h"
  #include "util/common-utils.h"
  
  
  
  int main(int argc, char *argv[]) {
    // note: making this program work with GPUs is as simple as initializing the
    // device, but it probably won't make a huge difference in speed for typical
    // setups.
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
      using fst::SymbolTable;
      using fst::Fst;
      using fst::StdArc;
  
      const char *usage =
          "Generate lattices using nnet3 neural net model.  This version supports
  "
          "multiple decoding threads (using a shared decoding graph.)
  "
          "Usage: nnet3-latgen-faster-parallel [options] <nnet-in> <fst-in|fsts-rspecifier> <features-rspecifier>"
          " <lattice-wspecifier> [ <words-wspecifier> [<alignments-wspecifier>] ]
  "
          "See also: nnet3-latgen-faster-batch (which supports GPUs)
  ";
      ParseOptions po(usage);
  
      Timer timer;
      bool allow_partial = false;
      TaskSequencerConfig sequencer_config; // has --num-threads option
      LatticeFasterDecoderConfig config;
      NnetSimpleComputationOptions decodable_opts;
  
      std::string word_syms_filename;
      std::string ivector_rspecifier,
          online_ivector_rspecifier,
          utt2spk_rspecifier;
      int32 online_ivector_period = 0;
      sequencer_config.Register(&po);
      config.Register(&po);
      decodable_opts.Register(&po);
      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.Register("ivectors", &ivector_rspecifier, "Rspecifier for "
                  "iVectors as vectors (i.e. not estimated online); per utterance "
                  "by default, or per speaker if you provide the --utt2spk option.");
      po.Register("online-ivectors", &online_ivector_rspecifier, "Rspecifier for "
                  "iVectors estimated online, as matrices.  If you supply this,"
                  " you must set the --online-ivector-period option.");
      po.Register("online-ivector-period", &online_ivector_period, "Number of frames "
                  "between iVectors in matrices supplied to the --online-ivectors "
                  "option");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() < 4 || po.NumArgs() > 6) {
        po.PrintUsage();
        exit(1);
      }
  
      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);
  
      TaskSequencer<DecodeUtteranceLatticeFasterClass> sequencer(sequencer_config);
      TransitionModel trans_model;
      AmNnetSimple am_nnet;
      {
        bool binary;
        Input ki(model_in_filename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_nnet.Read(ki.Stream(), binary);
        SetBatchnormTestMode(true, &(am_nnet.GetNnet()));
        SetDropoutTestMode(true, &(am_nnet.GetNnet()));
        CollapseModel(CollapseModelConfig(), &(am_nnet.GetNnet()));
      }
  
      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;
  
      RandomAccessBaseFloatMatrixReader online_ivector_reader(
          online_ivector_rspecifier);
      RandomAccessBaseFloatVectorReaderMapped ivector_reader(
          ivector_rspecifier, utt2spk_rspecifier);
  
      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);
        timer.Reset();
  
        {
          for (; !feature_reader.Done(); feature_reader.Next()) {
            std::string utt = feature_reader.Key();
            const Matrix<BaseFloat> &features (feature_reader.Value());
            if (features.NumRows() == 0) {
              KALDI_WARN << "Zero-length utterance: " << utt;
              num_fail++;
              continue;
            }
            const Matrix<BaseFloat> *online_ivectors = NULL;
            const Vector<BaseFloat> *ivector = NULL;
            if (!ivector_rspecifier.empty()) {
              if (!ivector_reader.HasKey(utt)) {
                KALDI_WARN << "No iVector available for utterance " << utt;
                num_fail++;
                continue;
              } else {
                ivector = &ivector_reader.Value(utt);
              }
            }
            if (!online_ivector_rspecifier.empty()) {
              if (!online_ivector_reader.HasKey(utt)) {
                KALDI_WARN << "No online iVector available for utterance " << utt;
                num_fail++;
                continue;
              } else {
                online_ivectors = &online_ivector_reader.Value(utt);
              }
            }
  
            LatticeFasterDecoder *decoder =
                new LatticeFasterDecoder(*decode_fst, config);
  
            DecodableInterface *nnet_decodable = new
                DecodableAmNnetSimpleParallel(
                    decodable_opts, trans_model, am_nnet,
                    features, ivector, online_ivectors,
                    online_ivector_period);
  
            DecodeUtteranceLatticeFasterClass *task =
                new DecodeUtteranceLatticeFasterClass(
                    decoder, nnet_decodable, // takes ownership of these two.
                    trans_model, word_syms, utt, decodable_opts.acoustic_scale,
                    determinize, allow_partial, &alignment_writer, &words_writer,
                     &compact_lattice_writer, &lattice_writer,
                     &tot_like, &frame_count, &num_success, &num_fail, NULL);
  
            sequencer.Run(task); // takes ownership of "task",
                                 // and will delete it when done.
          }
        }
        sequencer.Wait(); // Waits for all tasks to be done.
        delete decode_fst;
      } 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;
          }
  
          const Matrix<BaseFloat> *online_ivectors = NULL;
          const Vector<BaseFloat> *ivector = NULL;
          if (!ivector_rspecifier.empty()) {
            if (!ivector_reader.HasKey(utt)) {
              KALDI_WARN << "No iVector available for utterance " << utt;
              num_fail++;
              continue;
            } else {
              ivector = &ivector_reader.Value(utt);
            }
          }
          if (!online_ivector_rspecifier.empty()) {
            if (!online_ivector_reader.HasKey(utt)) {
              KALDI_WARN << "No online iVector available for utterance " << utt;
              num_fail++;
              continue;
            } else {
              online_ivectors = &online_ivector_reader.Value(utt);
            }
          }
  
          // the following constructor takes ownership of the FST pointer so that
          // it is deleted when 'decoder' is deleted.
          LatticeFasterDecoder *decoder =
              new LatticeFasterDecoder(config, fst_reader.Value().Copy());
  
          DecodableInterface *nnet_decodable = new
              DecodableAmNnetSimpleParallel(
                  decodable_opts, trans_model, am_nnet,
                  features, ivector, online_ivectors,
                  online_ivector_period);
  
          DecodeUtteranceLatticeFasterClass *task =
              new DecodeUtteranceLatticeFasterClass(
                  decoder, nnet_decodable, // takes ownership of these two.
                  trans_model, word_syms, utt, decodable_opts.acoustic_scale,
                  determinize, allow_partial, &alignment_writer, &words_writer,
                  &compact_lattice_writer, &lattice_writer,
                  &tot_like, &frame_count, &num_success, &num_fail, NULL);
  
          sequencer.Run(task); // takes ownership of "task",
          // and will delete it when done.
        }
        sequencer.Wait(); // Waits for all tasks to be done.
      }
  
      kaldi::int64 input_frame_count =
          frame_count * decodable_opts.frame_subsampling_factor;
  
      double elapsed = timer.Elapsed();
      KALDI_LOG << "Time taken " << elapsed
                << "s: real-time factor assuming 100 feature frames/sec is "
                << (sequencer_config.num_threads * elapsed * 100.0 /
                    input_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;
    }
  }