// nnet2bin/nnet-am-init.cc
  
  // Copyright 2012  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 "nnet2/am-nnet.h"
  #include "hmm/transition-model.h"
  #include "tree/context-dep.h"
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet2;
      typedef kaldi::int32 int32;
  
      // TODO: specify in the usage message where the example scripts are.
      const char *usage =
          "Initialize the neural network acoustic model and its associated
  "
          "transition-model, from a tree, a topology file, and a neural-net
  "
          "without an associated acoustic model.
  "
          "See example scripts to see how this works in practice.
  "
          "
  "
          "Usage:  nnet-am-init [options] <tree-in> <topology-in> <raw-nnet-in> <nnet-am-out>
  "
          "or:  nnet-am-init [options] <transition-model-in> <raw-nnet-in> <nnet-am-out>
  "
          "e.g.:
  "
          " nnet-am-init tree topo \"nnet-init nnet.config - |\" 1.mdl
  ";
          
      bool binary_write = true;
      
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      
      po.Read(argc, argv);
      
      if (po.NumArgs() != 3 && po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string raw_nnet_rxfilename, nnet_wxfilename;
      
      TransitionModel *trans_model = NULL;
  
      if (po.NumArgs() == 4) {
        std::string tree_rxfilename = po.GetArg(1),
            topo_rxfilename = po.GetArg(2);
        raw_nnet_rxfilename = po.GetArg(3);
        nnet_wxfilename = po.GetArg(4);
      
        ContextDependency ctx_dep;
        ReadKaldiObject(tree_rxfilename, &ctx_dep);
      
        HmmTopology topo;
        ReadKaldiObject(topo_rxfilename, &topo);
  
        // Construct the transition model from the tree and the topology file.
        trans_model = new TransitionModel(ctx_dep, topo);
      } else {
        std::string trans_model_rxfilename = po.GetArg(1);
        raw_nnet_rxfilename = po.GetArg(2);
        nnet_wxfilename = po.GetArg(3);
        trans_model = new TransitionModel();
        ReadKaldiObject(trans_model_rxfilename, trans_model);
      }
      
      AmNnet am_nnet;    
      {
        Nnet nnet;
        bool binary;
        Input ki(raw_nnet_rxfilename, &binary);
        nnet.Read(ki.Stream(), binary);
        am_nnet.Init(nnet);
      }
      
      if (am_nnet.NumPdfs() != trans_model->NumPdfs())
        KALDI_ERR << "Mismatch in number of pdfs, neural net has "
                  << am_nnet.NumPdfs() << ", transition model has "
                  << trans_model->NumPdfs();
  
      {
        Output ko(nnet_wxfilename, binary_write);
        trans_model->Write(ko.Stream(), binary_write);
        am_nnet.Write(ko.Stream(), binary_write);
      }
      delete trans_model;
      KALDI_LOG << "Initialized neural net and wrote it to " << nnet_wxfilename;
      return 0;
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }