// nnet3bin/nnet3-average.cc
  
  // Copyright 2015  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 "hmm/transition-model.h"
  #include "nnet3/nnet-utils.h"
  
  
  namespace kaldi {
  
  void GetWeights(const std::string &weights_str,
                  int32 num_inputs,
                  std::vector<BaseFloat> *weights) {
    KALDI_ASSERT(num_inputs >= 1);
    if (!weights_str.empty()) {
      SplitStringToFloats(weights_str, ":", true, weights);
      if (weights->size() != num_inputs) {
        KALDI_ERR << "--weights option must be a colon-separated list "
                  << "with " << num_inputs << " elements, got: "
                  << weights_str;
      }
    } else {
      for (int32 i = 0; i < num_inputs; i++)
        weights->push_back(1.0 / num_inputs);
    }
    // normalize the weights to sum to one.
    float weight_sum = 0.0;
    for (int32 i = 0; i < num_inputs; i++)
      weight_sum += (*weights)[i];
    for (int32 i = 0; i < num_inputs; i++)
      (*weights)[i] = (*weights)[i] / weight_sum;
    if (fabs(weight_sum - 1.0) > 0.01) {
      KALDI_WARN << "Normalizing weights to sum to one, sum was " << weight_sum;
    }
  }
  
  // This job is run in a spawned thread; it reads a subset of models with
  // specified weights.  Sets *success to 1 for success and 0 for failure.  (We
  // don't use bool because of the weird implementation of std::vector<bool>).
  void ReadModels(std::vector<std::pair<std::string, BaseFloat> > models_and_weights,
                  nnet3::Nnet *output_nnet,
                  int32 *success) {
    using namespace nnet3;
    try {
      int32 n = models_and_weights.size();
      ReadKaldiObject(models_and_weights[0].first, output_nnet);
      ScaleNnet(models_and_weights[0].second, output_nnet);
      for (int32 i = 1; i < n; i++) {
        Nnet nnet;
        ReadKaldiObject(models_and_weights[i].first, &nnet);
        AddNnet(nnet, models_and_weights[i].second, output_nnet);
      }
      *success = 1;
    } catch (...) {
      *success = 0;
    }
  }
  
  }  // namespace kaldi
  
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
      typedef kaldi::int64 int64;
  
      const char *usage =
          "This program averages the parameters over a number of 'raw' nnet3 neural nets.
  "
          "
  "
          "Usage:  nnet3-average [options] <model1> <model2> ... <modelN> <model-out>
  "
          "
  "
          "e.g.:
  "
          " nnet3-average 1.1.nnet 1.2.nnet 1.3.nnet 2.nnet
  ";
  
      bool binary_write = true;
      int32 num_threads = -1;
  
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      std::string weights_str;
      po.Register("weights", &weights_str, "Colon-separated list of weights, one "
                  "for each input model.  These will be normalized to sum to one.");
      po.Register("num-threads", &num_threads, "Number of threads to read the "
                  "models (will be set automatically if not set.");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() < 2) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string
          first_nnet_rxfilename = po.GetArg(1),
          nnet_wxfilename = po.GetArg(po.NumArgs());
  
      int32 num_inputs = po.NumArgs() - 1;
  
      if (num_threads <= 0) {
        // Default logic for selecting the number of threads.
        if (num_inputs > 10) num_threads = 3;
        else if (num_inputs > 5) num_threads = 2;
        else num_threads = 1;
      }
  
      if (num_threads > 1 && num_threads * 2 > num_inputs) {
        num_threads = num_inputs / 2;
      }
  
      std::vector<BaseFloat> model_weights;
      GetWeights(weights_str, num_inputs, &model_weights);
  
      std::vector<Nnet> nnets(num_threads);
      std::vector<int32> return_statuses(num_threads);
  
      std::vector<std::thread*> threads(num_threads);
  
      for (int32 thread_id = 0; thread_id < num_threads; thread_id++) {
        std::vector<std::pair<std::string, BaseFloat> > this_models_and_weights;
        for (int32 j = 1 + thread_id; j < po.NumArgs(); j += num_threads) {
          this_models_and_weights.push_back(std::pair<std::string, BaseFloat>(
              po.GetArg(j), model_weights[j - 1]));
        }
        threads[thread_id] = new std::thread(ReadModels, this_models_and_weights,
                                             &(nnets[thread_id]),
                                             &(return_statuses[thread_id]));
      }
  
      bool success = true;
      for (int32 thread_id = 0; thread_id < num_threads; thread_id++) {
        threads[thread_id]->join();
        delete threads[thread_id];
        if (!return_statuses[thread_id])
          success = false;
        if (success && thread_id > 0)
          AddNnet(nnets[thread_id], 1.0, &(nnets[0]));
      }
  
      if (!success) {
        KALDI_ERR << "Error detected in a model-reading thread.";
      }
  
      WriteKaldiObject(nnets[0], nnet_wxfilename, binary_write);
  
      KALDI_LOG << "Averaged parameters of " << num_inputs
                << " neural nets, and wrote to " << nnet_wxfilename;
      return 0; // it will throw an exception if there are any problems.
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }