// nnet3bin/nnet3-copy.cc
  
  // Copyright 2012  Johns Hopkins University (author:  Daniel Povey)
  //           2015  Xingyu Na
  
  // 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 <typeinfo>
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "hmm/transition-model.h"
  #include "nnet3/am-nnet-simple.h"
  #include "nnet3/nnet-utils.h"
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
  
      const char *usage =
          "Copy 'raw' nnet3 neural network to standard output
  "
          "Also supports setting all the learning rates to a value
  "
          "(the --learning-rate option)
  "
          "
  "
          "Usage:  nnet3-copy [options] <nnet-in> <nnet-out>
  "
          "e.g.:
  "
          " nnet3-copy --binary=false 0.raw text.raw
  ";
  
      bool binary_write = true;
      BaseFloat learning_rate = -1;
      std::string nnet_config, edits_config, edits_str;
      BaseFloat scale = 1.0;
      bool prepare_for_test = false;
  
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      po.Register("learning-rate", &learning_rate,
                  "If supplied, all the learning rates of updatable components"
                  "are set to this value.");
      po.Register("nnet-config", &nnet_config,
                  "Name of nnet3 config file that can be used to add or replace "
                  "components or nodes of the neural network (the same as you "
                  "would give to nnet3-init).");
      po.Register("edits-config", &edits_config,
                  "Name of edits-config file that can be used to modify the network "
                  "(applied after nnet-config).  See comments for ReadEditConfig()"
                  "in nnet3/nnet-utils.h to see currently supported commands.");
      po.Register("edits", &edits_str,
                  "Can be used as an inline alternative to edits-config; semicolons "
                  "will be converted to newlines before parsing.  E.g. "
                  "'--edits=remove-orphans'.");
      po.Register("scale", &scale, "The parameter matrices are scaled"
                  " by the specified value.");
      po.Register("prepare-for-test", &prepare_for_test,
                  "If true, prepares the model for test time (may reduce model size "
                  "slightly.  Involves setting test mode in dropout and batch-norm "
                  "components, and calling CollapseModel() which may remove some "
                  "components.");
      po.Read(argc, argv);
  
      if (po.NumArgs() != 2) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string raw_nnet_rxfilename = po.GetArg(1),
                  raw_nnet_wxfilename = po.GetArg(2);
  
      Nnet nnet;
      ReadKaldiObject(raw_nnet_rxfilename, &nnet);
  
      if (!nnet_config.empty()) {
        Input ki(nnet_config);
        nnet.ReadConfig(ki.Stream());
      }
  
      if (learning_rate >= 0)
        SetLearningRate(learning_rate, &nnet);
  
      if (scale != 1.0)
        ScaleNnet(scale, &nnet);
  
      if (!edits_config.empty()) {
        Input ki(edits_config);
        ReadEditConfig(ki.Stream(), &nnet);
      }
      if (!edits_str.empty()) {
        for (size_t i = 0; i < edits_str.size(); i++)
          if (edits_str[i] == ';')
            edits_str[i] = '
  ';
        std::istringstream is(edits_str);
        ReadEditConfig(is, &nnet);
      }
      if (prepare_for_test) {
        SetBatchnormTestMode(true, &nnet);
        SetDropoutTestMode(true, &nnet);
        CollapseModel(CollapseModelConfig(), &nnet);
      }
      WriteKaldiObject(nnet, raw_nnet_wxfilename, binary_write);
      KALDI_LOG << "Copied raw neural net from " << raw_nnet_rxfilename
                << " to " << raw_nnet_wxfilename;
  
      return 0;
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }