// gmmbin/gmm-fmpe-acc-stats.cc
  
  // Copyright 2012  Johns Hopkins University (Author: 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 "gmm/am-diag-gmm.h"
  #include "hmm/transition-model.h"
  #include "transform/fmpe.h"
  
  
  int main(int argc, char *argv[]) {
    using namespace kaldi;
    using kaldi::int32;
    try {
      const char *usage =
          "Accumulate stats for fMPE training, using GMM model.  Note: this could
  "
          "be done using gmm-get-feat-deriv and fmpe-acc-stats (but you'd be computing
  "
          "the features twice).  Features input should be pre-fMPE features.
  "
          "
  "
          "Usage:  gmm-fmpe-acc-stats [options] <model-in> <fmpe-in> <feature-rspecifier> "
          "<gselect-rspecifier> <posteriors-rspecifier> <fmpe-stats-out>
  "
          "e.g.: 
  "
          " gmm-fmpe-acc-stats --model-derivative 1.accs 1.mdl 1.fmpe \"$feats\" ark:1.gselect ark:1.post 1.fmpe_stats
  ";
          
      ParseOptions po(usage);
      bool binary = true;
      std::string model_derivative_rxfilename;
      po.Register("binary", &binary, "If true, write stats in binary mode.");
      po.Register("model-derivative", &model_derivative_rxfilename,
                  "GMM-accs file containing model derivative [note: contains no transition stats].  Used for indirect differential.  Warning: this will only work correctly in the case of MMI/BMMI objective function, with non-canceled stats.");
      po.Read(argc, argv);
  
      if (po.NumArgs() != 6) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_rxfilename = po.GetArg(1),
          fmpe_rxfilename = po.GetArg(2),
          feature_rspecifier = po.GetArg(3),
          gselect_rspecifier = po.GetArg(4),
          posteriors_rspecifier = po.GetArg(5),
          stats_wxfilename = po.GetArg(6);
      
      AmDiagGmm am_gmm;
      TransitionModel trans_model;
      {
        bool binary;
        Input ki(model_rxfilename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_gmm.Read(ki.Stream(), binary);
      }
  
      Fmpe fmpe;
      ReadKaldiObject(fmpe_rxfilename, &fmpe);
  
  
      bool have_indirect = (model_derivative_rxfilename != "");
      AccumAmDiagGmm model_derivative;
      if (have_indirect)
        ReadKaldiObject(model_derivative_rxfilename, &model_derivative);
      
      FmpeStats fmpe_stats(fmpe);
      
      SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
      RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
      RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
  
      BaseFloat tot_like = 0.0; // tot like weighted by posterior.
      int32 num_frames = 0;
      int32 num_done = 0, num_err = 0;
      
      for (; !feature_reader.Done(); feature_reader.Next()) {
        std::string key = feature_reader.Key();
        if (!posteriors_reader.HasKey(key)) {
          num_err++;
          KALDI_WARN << "No posteriors for utterance " << key;
          continue;
        } 
        const Matrix<BaseFloat> &feat_in = feature_reader.Value();
        const Posterior &posterior = posteriors_reader.Value(key);
  
        if (static_cast<int32>(posterior.size()) != feat_in.NumRows()) {
          KALDI_WARN << "Posterior vector has wrong size " <<
              (posterior.size()) << " vs. "<< (feat_in.NumRows());
          num_err++;
          continue;
        }
  
        if (!gselect_reader.HasKey(key)) {
          KALDI_WARN << "No gselect information for key " << key;
          num_err++;
          continue;
        }
        const std::vector<std::vector<int32> > &gselect =
            gselect_reader.Value(key);
        if (static_cast<int32>(gselect.size()) != feat_in.NumRows()) {
          KALDI_WARN << "gselect information has wrong size";
          num_err++;
          continue;
        }
        
        num_done++;
        Matrix<BaseFloat> fmpe_feat(feat_in.NumRows(), feat_in.NumCols());
        fmpe.ComputeFeatures(feat_in, gselect, &fmpe_feat);
        fmpe_feat.AddMat(1.0, feat_in);
        
        Matrix<BaseFloat> direct_deriv, indirect_deriv;
  
        tot_like += ComputeAmGmmFeatureDeriv(am_gmm, trans_model, posterior,
                                             fmpe_feat, &direct_deriv,
                                             (have_indirect ? &model_derivative : NULL),
                                             (have_indirect ? &indirect_deriv : NULL));
        num_frames += feat_in.NumRows();
  
        fmpe.AccStats(feat_in, gselect, direct_deriv,
                      (have_indirect ? &indirect_deriv : NULL), &fmpe_stats);
        
        if (num_done % 100 == 0)
          KALDI_LOG << "Processed " << num_done << " utterances.";
      }
  
      KALDI_LOG << "Done " << num_done << " files, " << num_err
                << " with errors.";
      KALDI_LOG << "Overall weighted acoustic likelihood per frame is "
                << (tot_like/num_frames) << " over " << num_frames << " frames.";
  
      Output ko(stats_wxfilename, binary);
      fmpe_stats.Write(ko.Stream(), binary);
      
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }