// ivectorbin/agglomerative-cluster.cc
  
  // Copyright 2016-2018  David Snyder
  //           2017-2018  Matthew Maciejewski
  //                2019  Dogan Can
  
  // 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 "util/stl-utils.h"
  #include "ivector/agglomerative-clustering.h"
  
  int main(int argc, char *argv[]) {
    using namespace kaldi;
    typedef kaldi::int32 int32;
    try {
      const char *usage =
        "Cluster utterances by similarity score, used in diarization.
  "
        "Takes a table of score matrices indexed by recording, with the
  "
        "rows/columns corresponding to the utterances of that recording in
  "
        "sorted order and a reco2utt file that contains the mapping from
  "
        "recordings to utterances, and outputs a list of labels in the form
  "
        "<utt> <label>.  Clustering is done using agglomerative hierarchical
  "
        "clustering with a score threshold as stop criterion.  By default, the
  "
        "program reads in similarity scores, but with --read-costs=true
  "
        "the scores are interpreted as costs (i.e. a smaller value indicates
  "
        "utterance similarity).
  "
        "Usage: agglomerative-cluster [options] <scores-rspecifier> "
        "<reco2utt-rspecifier> <labels-wspecifier>
  "
        "e.g.: 
  "
        " agglomerative-cluster ark:scores.ark ark:reco2utt 
  "
        "   ark,t:labels.txt
  ";
  
      ParseOptions po(usage);
      std::string reco2num_spk_rspecifier;
      BaseFloat threshold = 0.0, max_spk_fraction = 1.0;
      bool read_costs = false;
      int32 first_pass_max_utterances = std::numeric_limits<int16>::max();
  
      po.Register("reco2num-spk-rspecifier", &reco2num_spk_rspecifier,
        "If supplied, clustering creates exactly this many clusters for each"
        " recording and the option --threshold is ignored.");
      po.Register("threshold", &threshold, "Merge clusters if their distance"
        " is less than this threshold.");
      po.Register("read-costs", &read_costs, "If true, the first"
        " argument is interpreted as a matrix of costs rather than a"
        " similarity matrix.");
      po.Register("first-pass-max-utterances", &first_pass_max_utterances,
        "If the number of utterances is larger than first-pass-max-utterances,"
        " then clustering is done in two passes. In the first pass, input points"
        " are divided into contiguous subsets of size first-pass-max-utterances"
        " and each subset is clustered separately. In the second pass, the first"
        " pass clusters are merged into the final set of clusters.");
      po.Register("max-spk-fraction", &max_spk_fraction, "Merge clusters if the"
        " total fraction of utterances in them is less than this threshold."
        " This is active only when reco2num-spk-rspecifier is supplied and"
        " 1.0 / num-spk <= max-spk-fraction <= 1.0.");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string scores_rspecifier = po.GetArg(1),
        reco2utt_rspecifier = po.GetArg(2),
        label_wspecifier = po.GetArg(3);
  
      SequentialBaseFloatMatrixReader scores_reader(scores_rspecifier);
      RandomAccessTokenVectorReader reco2utt_reader(reco2utt_rspecifier);
      RandomAccessInt32Reader reco2num_spk_reader(reco2num_spk_rspecifier);
      Int32Writer label_writer(label_wspecifier);
  
      if (!read_costs)
        threshold = -threshold;
      for (; !scores_reader.Done(); scores_reader.Next()) {
        std::string reco = scores_reader.Key();
        Matrix<BaseFloat> costs = scores_reader.Value();
        // By default, the scores give the similarity between pairs of
        // utterances.  We need to multiply the scores by -1 to reinterpet
        // them as costs (unless --read-costs=true) as the agglomerative
        // clustering code requires.
        if (!read_costs)
          costs.Scale(-1);
        std::vector<std::string> uttlist = reco2utt_reader.Value(reco);
        std::vector<int32> spk_ids;
        if (reco2num_spk_rspecifier.size()) {
          int32 num_speakers = reco2num_spk_reader.Value(reco);
          if (1.0 / num_speakers <= max_spk_fraction && max_spk_fraction <= 1.0)
            AgglomerativeCluster(costs, std::numeric_limits<BaseFloat>::max(),
                                 num_speakers, first_pass_max_utterances,
                                 max_spk_fraction, &spk_ids);
          else
            AgglomerativeCluster(costs, std::numeric_limits<BaseFloat>::max(),
                                 num_speakers, first_pass_max_utterances,
                                 1.0, &spk_ids);
        } else {
          AgglomerativeCluster(costs, threshold, 1, first_pass_max_utterances,
                               1.0, &spk_ids);
        }
        for (int32 i = 0; i < spk_ids.size(); i++)
          label_writer.Write(uttlist[i], spk_ids[i]);
      }
      return 0;
  
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }