// sgmm2bin/sgmm2-acc-stats.cc
  
  // Copyright 2009-2012   Saarland University (Author:  Arnab Ghoshal),
  //                       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 "sgmm2/am-sgmm2.h"
  #include "hmm/transition-model.h"
  #include "sgmm2/estimate-am-sgmm2.h"
  #include "hmm/posterior.h"
  
  int main(int argc, char *argv[]) {
    using namespace kaldi;
    try {
      const char *usage =
          "Accumulate stats for SGMM training.
  "
          "Usage: sgmm2-acc-stats [options] <model-in> <feature-rspecifier> "
          "<posteriors-rspecifier> <stats-out>
  "
          "e.g.: sgmm2-acc-stats --gselect=ark:gselect.ark 1.mdl 1.ali scp:train.scp 'ark:ali-to-post 1.ali ark:-|' 1.acc
  "
          "(note: gselect option is mandatory)
  ";
          
      ParseOptions po(usage);
      bool binary = true;
      std::string gselect_rspecifier, spkvecs_rspecifier, utt2spk_rspecifier;
      std::string update_flags_str = "vMNwcSt";
      BaseFloat rand_prune = 1.0e-05;
  
      po.Register("binary", &binary, "Write output in binary mode");
      po.Register("gselect", &gselect_rspecifier, "Precomputed Gaussian indices (rspecifier)");
      po.Register("spk-vecs", &spkvecs_rspecifier, "Speaker vectors (rspecifier)");
      po.Register("utt2spk", &utt2spk_rspecifier,
                  "rspecifier for utterance to speaker map");
      po.Register("rand-prune", &rand_prune, "Pruning threshold for posteriors");
      po.Register("update-flags", &update_flags_str, "Which SGMM parameters to accumulate "
                  "stats for: subset of vMNwcS.");
  
      po.Read(argc, argv);
  
      kaldi::SgmmUpdateFlagsType acc_flags = StringToSgmmUpdateFlags(update_flags_str);
  
      if (po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
      if (gselect_rspecifier == "")
        KALDI_ERR << "--gselect option is mandatory.";
      
      std::string model_filename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          posteriors_rspecifier = po.GetArg(3),
          accs_wxfilename = po.GetArg(4);
  
      using namespace kaldi;
      typedef kaldi::int32 int32;
  
      int32 num_done = 0, num_err = 0;
      Vector<double> transition_accs;
      MleAmSgmm2Accs sgmm_accs(rand_prune);
  
      { // this anonymous scope is to ensure deallocation of unnecessary stuff
        // while we're writing out the accs, which could be a long time for large
        // models.
        
        // Initialize the readers before the model, as the model can
        // be large, and we don't want to call fork() after reading it if
        // virtual memory may be low.
        SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
        RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
        RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
        RandomAccessBaseFloatVectorReaderMapped spkvecs_reader(spkvecs_rspecifier,
                                                               utt2spk_rspecifier);
        RandomAccessTokenReader utt2spk_map(utt2spk_rspecifier);
        
        AmSgmm2 am_sgmm;
        TransitionModel trans_model;
        {
          bool binary;
          Input ki(model_filename, &binary);
          trans_model.Read(ki.Stream(), binary);
          am_sgmm.Read(ki.Stream(), binary);
        }
  
  
        trans_model.InitStats(&transition_accs);
        sgmm_accs.ResizeAccumulators(am_sgmm, acc_flags, (spkvecs_rspecifier!=""));
  
        double tot_like = 0.0;
        double tot_t = 0;
  
        kaldi::Sgmm2PerFrameDerivedVars per_frame_vars;
        std::string cur_spk;
        Sgmm2PerSpkDerivedVars spk_vars;
                
        for (; !feature_reader.Done(); feature_reader.Next()) {
          std::string utt = feature_reader.Key();
          std::string spk = utt;
          if (!utt2spk_rspecifier.empty()) {
            if (!utt2spk_map.HasKey(utt)) {
              KALDI_WARN << "utt2spk map does not have value for " << utt
                         << ", ignoring this utterance.";
              continue;
            } else { spk = utt2spk_map.Value(utt); }
          }
  
          if (spk != cur_spk && cur_spk != "")
            sgmm_accs.CommitStatsForSpk(am_sgmm, spk_vars);        
          
          if (spk != cur_spk || spk_vars.Empty()) {
            spk_vars.Clear();
            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;
                num_err++;
                continue;
              }
            } // else spk_vars is "empty"
          }
          
          cur_spk = spk;
          
          const Matrix<BaseFloat> &features = feature_reader.Value();
          if (!posteriors_reader.HasKey(utt) ||
              posteriors_reader.Value(utt).size() != features.NumRows()) {
            KALDI_WARN << "No posterior info available for utterance "
                       << utt << " (or wrong size)";
            num_err++;
            continue;
          }
          const Posterior &posterior = posteriors_reader.Value(utt);
        
          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)";
            num_err++;
          }
          const std::vector<std::vector<int32> > &gselect =
              gselect_reader.Value(utt);
  
          num_done++;
        
          BaseFloat tot_like_this_file = 0.0, tot_weight = 0.0;
  
          Posterior pdf_posterior;
          ConvertPosteriorToPdfs(trans_model, posterior, &pdf_posterior);
          for (size_t i = 0; i < posterior.size(); i++) {
            am_sgmm.ComputePerFrameVars(features.Row(i), gselect[i], spk_vars,
                                        &per_frame_vars);
            // Accumulates for SGMM.
            for (size_t j = 0; j < pdf_posterior[i].size(); j++) {
              int32 pdf_id = pdf_posterior[i][j].first;
              BaseFloat weight = pdf_posterior[i][j].second;
              tot_like_this_file += sgmm_accs.Accumulate(am_sgmm, per_frame_vars,
                                                         pdf_id, weight, &spk_vars)
                  * weight;
              tot_weight += weight;
            }
  
            // Accumulates for transitions.
            for (size_t j = 0; j < posterior[i].size(); j++) {
              int32 tid = posterior[i][j].first;
              BaseFloat weight = posterior[i][j].second;
              trans_model.Accumulate(weight, tid, &transition_accs);
            }
          }
          
          KALDI_VLOG(2) << "Average like for this file is "
                        << (tot_like_this_file/tot_weight) << " over "
                        << tot_weight <<" frames.";
          tot_like += tot_like_this_file;
          tot_t += tot_weight;
          if (num_done % 50 == 0) {
            KALDI_LOG << "Processed " << num_done << " utterances; for utterance "
                      << utt << " avg. like is "
                      << (tot_like_this_file/tot_weight)
                      << " over " << tot_weight <<" frames.";
          }
        }
        sgmm_accs.CommitStatsForSpk(am_sgmm, spk_vars); // commit stats for
        // last speaker.
        
        KALDI_LOG << "Overall like per frame (Gaussian only) = "
                  << (tot_like/tot_t) << " over " << tot_t << " frames.";
  
        KALDI_LOG << "Done " << num_done << " files, " << num_err
                  << " with errors.";
      } 
  
      {
        Output ko(accs_wxfilename, binary);
        transition_accs.Write(ko.Stream(), binary);
        sgmm_accs.Write(ko.Stream(), binary);
      }
      KALDI_LOG << "Written accs.";
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }