// gmmbin/gmm-basis-fmllr-accs.cc
  
  // Copyright 2012  Carnegie Mellon University (author: Yajie Miao)
  //           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 <string>
  using std::string;
  #include <vector>
  using std::vector;
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "gmm/am-diag-gmm.h"
  #include "hmm/transition-model.h"
  #include "transform/fmllr-diag-gmm.h"
  #include "transform/basis-fmllr-diag-gmm.h"
  #include "hmm/posterior.h"
  
  namespace kaldi {
  void AccumulateForUtterance(const Matrix<BaseFloat> &feats,
                              const Posterior &post,
                              const TransitionModel &trans_model,
                              const AmDiagGmm &am_gmm,
                              FmllrDiagGmmAccs *spk_stats) {
    Posterior pdf_post;
    ConvertPosteriorToPdfs(trans_model, post, &pdf_post);
    for (size_t i = 0; i < post.size(); i++) {
      for (size_t j = 0; j < pdf_post[i].size(); j++) {
        int32 pdf_id = pdf_post[i][j].first;
        spk_stats->AccumulateForGmm(am_gmm.GetPdf(pdf_id),
                                    feats.Row(i),
                                    pdf_post[i][j].second);
      }
    }
  }
  
  
  }
  
  int main(int argc, char *argv[]) {
    try {
      typedef kaldi::int32 int32;
      using namespace kaldi;
      const char *usage =
          "Accumulate gradient scatter from training set, either per utterance or 
  "
          "for the supplied set of speakers (spk2utt option). Reads posterior to accumulate 
  "
          "fMLLR stats for each speaker/utterance. Writes gradient scatter matrix.
  "
          "Usage: gmm-basis-fmllr-accs [options] <model-in> <feature-rspecifier>"
          "<post-rspecifier> <accs-wspecifier>
  ";
  
      bool binary_write = true;
      string spk2utt_rspecifier;
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      po.Register("spk2utt", &spk2utt_rspecifier, "rspecifier for speaker to "
                  "utterance-list map");
  
      po.Read(argc, argv);
      if (po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
  
      string
          model_rxfilename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          post_rspecifier = po.GetArg(3),
          accs_wspecifier = po.GetArg(4);
  
      TransitionModel trans_model;
      AmDiagGmm am_gmm;
      {
        bool binary;
        Input ki(model_rxfilename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_gmm.Read(ki.Stream(), binary);
      }
  
      RandomAccessPosteriorReader post_reader(post_rspecifier);
      BasisFmllrAccus basis_accs(am_gmm.Dim());
  
      int32 num_done = 0, num_no_post = 0, num_other_error = 0;
      if (spk2utt_rspecifier != "") {  // per-speaker mode
        SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
        RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
  
        int32 num_spk = 0;
        for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
          FmllrDiagGmmAccs spk_stats(am_gmm.Dim());
          string spk = spk2utt_reader.Key();
          const vector<string> &uttlist = spk2utt_reader.Value();
          for (size_t i = 0; i < uttlist.size(); i++) {
            std::string utt = uttlist[i];
            if (!feature_reader.HasKey(utt)) {
              KALDI_WARN << "Did not find features for utterance " << utt;
              num_other_error++;
              continue;
            }
            if (!post_reader.HasKey(utt)) {
              KALDI_WARN << "Did not find posteriors for utterance " << utt;
              num_no_post++;
              continue;
            }
            const Matrix<BaseFloat> &feats = feature_reader.Value(utt);
            const Posterior &post = post_reader.Value(utt);
            if (static_cast<int32>(post.size()) != feats.NumRows()) {
              KALDI_WARN << "Posterior vector has wrong size " << (post.size())
                         << " vs. " << (feats.NumRows());
              num_other_error++;
              continue;
            }
  
            AccumulateForUtterance(feats, post, trans_model, am_gmm, &spk_stats);
  
            num_done++;
          }  // end looping over all utterances of this speaker
          basis_accs.AccuGradientScatter(spk_stats);
          num_spk++;
        }  // end looping over speakers
        KALDI_LOG << "Accumulate statistics from " << num_spk << " speakers";
  
      } else {  // per-utterance mode
        SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
        for (; !feature_reader.Done(); feature_reader.Next()) {
          string utt = feature_reader.Key();
          if (!post_reader.HasKey(utt)) {
            KALDI_WARN << "Did not find posts for utterance "
                       << utt;
            num_no_post++;
            continue;
          }
          const Matrix<BaseFloat> &feats = feature_reader.Value();
          const Posterior &post = post_reader.Value(utt);
  
          if (static_cast<int32>(post.size()) != feats.NumRows()) {
            KALDI_WARN << "Posterior has wrong size " << (post.size())
                       << " vs. " << (feats.NumRows());
            num_other_error++;
            continue;
          }
          // Accumulate stats for this utterance
          FmllrDiagGmmAccs utt_stats(am_gmm.Dim());
          AccumulateForUtterance(feats, post, trans_model, am_gmm, &utt_stats);
          num_done++;
  
          basis_accs.AccuGradientScatter(utt_stats);
        } // end looping over utterances
      }
      // Write out accumulations
      {
        Output ko(accs_wspecifier, binary_write);
        basis_accs.Write(ko.Stream(), binary_write);
      }
      KALDI_LOG << "Done " << num_done << " files, " << num_no_post
                << " with no posts, " << num_other_error << " with other errors.";
      KALDI_LOG << "Written gradient scatter to " << accs_wspecifier;
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception& e) {
      std::cerr << e.what();
      return -1;
    }
  }