// nnet2bin/nnet-align-compiled.cc
  
  // Copyright 2009-2012     Microsoft Corporation
  //                         Johns Hopkins University (author: Daniel Povey)
  //                2015     Vijayaditya Peddinti
  //                2015-16  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 "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "gmm/am-diag-gmm.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 "nnet3/nnet-am-decodable-simple.h"
  #include "nnet3/nnet-utils.h"
  #include "lat/kaldi-lattice.h"
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
      using fst::SymbolTable;
      using fst::VectorFst;
      using fst::StdArc;
  
      const char *usage =
          "Align features given nnet3 neural net model
  "
          "Usage:   nnet3-align-compiled [options] <nnet-in> <graphs-rspecifier> "
          "<features-rspecifier> <alignments-wspecifier>
  "
          "e.g.: 
  "
          " nnet3-align-compiled 1.mdl ark:graphs.fsts scp:train.scp ark:1.ali
  "
          "or:
  "
          " compile-train-graphs tree 1.mdl lex.fst 'ark:sym2int.pl -f 2- words.txt text|' \\
  "
          "   ark:- | nnet3-align-compiled 1.mdl ark:- scp:train.scp t, ark:1.ali
  ";
  
      ParseOptions po(usage);
      AlignConfig align_config;
      NnetSimpleComputationOptions decodable_opts;
      std::string use_gpu = "yes";
      BaseFloat transition_scale = 1.0;
      BaseFloat self_loop_scale = 1.0;
      std::string per_frame_acwt_wspecifier;
  
      std::string ivector_rspecifier,
          online_ivector_rspecifier,
          utt2spk_rspecifier;
      int32 online_ivector_period = 0;
      align_config.Register(&po);
      decodable_opts.Register(&po);
  
      po.Register("use-gpu", &use_gpu,
                  "yes|no|optional|wait, only has effect if compiled with CUDA");
      po.Register("transition-scale", &transition_scale,
                  "Transition-probability scale [relative to acoustics]");
      po.Register("self-loop-scale", &self_loop_scale,
                  "Scale of self-loop versus non-self-loop "
                  "log probs [relative to acoustics]");
      po.Register("write-per-frame-acoustic-loglikes", &per_frame_acwt_wspecifier,
                  "Wspecifier for table of vectors containing the acoustic log-likelihoods "
                  "per frame for each utterance. E.g. ark:foo/per_frame_logprobs.1.ark");
      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() > 5) {
        po.PrintUsage();
        exit(1);
      }
  
  #if HAVE_CUDA==1
      CuDevice::Instantiate().SelectGpuId(use_gpu);
  #endif
  
      std::string model_in_filename = po.GetArg(1),
          fst_rspecifier = po.GetArg(2),
          feature_rspecifier = po.GetArg(3),
          alignment_wspecifier = po.GetArg(4),
          scores_wspecifier = po.GetOptArg(5);
  
      int num_done = 0, num_err = 0, num_retry = 0;
      double tot_like = 0.0;
      kaldi::int64 frame_count = 0;
  
  
      {
        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()));
        // this compiler object allows caching of computations across
        // different utterances.
        CachingOptimizingCompiler compiler(am_nnet.GetNnet(),
                                           decodable_opts.optimize_config);
  
        RandomAccessBaseFloatMatrixReader online_ivector_reader(
            online_ivector_rspecifier);
        RandomAccessBaseFloatVectorReaderMapped ivector_reader(
            ivector_rspecifier, utt2spk_rspecifier);
  
  
        SequentialTableReader<fst::VectorFstHolder> fst_reader(fst_rspecifier);
        RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
        Int32VectorWriter alignment_writer(alignment_wspecifier);
        BaseFloatWriter scores_writer(scores_wspecifier);
        BaseFloatVectorWriter per_frame_acwt_writer(per_frame_acwt_wspecifier);
  
        for (; !fst_reader.Done(); fst_reader.Next()) {
          std::string utt = fst_reader.Key();
          if (!feature_reader.HasKey(utt)) {
            KALDI_WARN << "No features for utterance " << utt;
            num_err++;
            continue;
          }
          const Matrix<BaseFloat> &features = feature_reader.Value(utt);
          VectorFst<StdArc> decode_fst(fst_reader.Value());
          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 (features.NumRows() == 0) {
            KALDI_WARN << "Zero-length utterance: " << utt;
            num_err++;
            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_err++;
              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_err++;
              continue;
            } else {
              online_ivectors = &online_ivector_reader.Value(utt);
            }
          }
  
          {  // Add transition-probs to the FST.
            std::vector<int32> disambig_syms;  // empty.
            AddTransitionProbs(trans_model, disambig_syms,
                               transition_scale, self_loop_scale,
                               &decode_fst);
          }
  
          DecodableAmNnetSimple nnet_decodable(
              decodable_opts, trans_model, am_nnet,
              features, ivector, online_ivectors,
              online_ivector_period, &compiler);
  
          AlignUtteranceWrapper(align_config, utt,
                                decodable_opts.acoustic_scale,
                                &decode_fst, &nnet_decodable,
                                &alignment_writer, &scores_writer,
                                &num_done, &num_err, &num_retry,
                                &tot_like, &frame_count, &per_frame_acwt_writer);
        }
        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;
      }
  
  #if HAVE_CUDA==1
      CuDevice::Instantiate().PrintProfile();
  #endif
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }