// tfrnnlmbin/lattice-lmrescore-tf-rnnlm.cc
  
  // Copyright (C) 2017 Intellisist, Inc. (Author: Hainan Xu)
  
  // 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 "fstext/fstext-lib.h"
  #include "lat/kaldi-lattice.h"
  #include "lat/lattice-functions.h"
  #include "util/common-utils.h"
  
  // This should come after any OpenFst includes to avoid using the wrong macros.
  #include "tfrnnlm/tensorflow-rnnlm.h"
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::tf_rnnlm;
      typedef kaldi::int32 int32;
      typedef kaldi::int64 int64;
  
      const char *usage =
          "Rescores lattice with rnnlm that is trained with TensorFlow.
  "
          "An example script for training and rescoring with the TensorFlow
  "
          "RNNLM is at egs/ami/s5/local/tfrnnlm/run_lstm_fast.sh
  "
          "
  "
          "Usage: lattice-lmrescore-tf-rnnlm [options] [unk-file] <rnnlm-wordlist> \\
  "
          "             <word-symbol-table-rxfilename> <lattice-rspecifier> \\
  "
          "             <rnnlm-rxfilename> <lattice-wspecifier>
  "
          " e.g.: lattice-lmrescore-tf-rnnlm --lm-scale=0.5 "
          "    data/tensorflow_lstm/unkcounts.txt data/tensorflow_lstm/rnnwords.txt \\
  "
          "    data/lang/words.txt ark:in.lats data/tensorflow_lstm/rnnlm ark:out.lats
  ";
  
      ParseOptions po(usage);
      int32 max_ngram_order = 3;
      BaseFloat lm_scale = 0.5;
  
      po.Register("lm-scale", &lm_scale, "Scaling factor for language model "
                  "costs");
      po.Register("max-ngram-order", &max_ngram_order,
          "If positive, allow RNNLM histories longer than this to be identified "
          "with each other for rescoring purposes (an approximation that "
          "saves time and reduces output lattice size).");
      KaldiTfRnnlmWrapperOpts opts;
      opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 5 && po.NumArgs() != 6) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string lats_rspecifier, rnn_word_list,
        word_symbols_rxfilename, rnnlm_rxfilename, lats_wspecifier, unk_prob_file;
      if (po.NumArgs() == 5) {
        rnn_word_list = po.GetArg(1);
        word_symbols_rxfilename = po.GetArg(2);
        lats_rspecifier = po.GetArg(3);
        rnnlm_rxfilename = po.GetArg(4);
        lats_wspecifier = po.GetArg(5);
      } else {
        unk_prob_file = po.GetArg(1);
        rnn_word_list = po.GetArg(2);
        word_symbols_rxfilename = po.GetArg(3);
        lats_rspecifier = po.GetArg(4);
        rnnlm_rxfilename = po.GetArg(5);
        lats_wspecifier = po.GetArg(6);
      }
  
      // Reads the TF language model.
      KaldiTfRnnlmWrapper rnnlm(opts, rnn_word_list, word_symbols_rxfilename,
                                  unk_prob_file, rnnlm_rxfilename);
  
      // Reads and writes as compact lattice.
      SequentialCompactLatticeReader compact_lattice_reader(lats_rspecifier);
      CompactLatticeWriter compact_lattice_writer(lats_wspecifier);
  
      int32 n_done = 0, n_fail = 0;
      for (; !compact_lattice_reader.Done(); compact_lattice_reader.Next()) {
        std::string key = compact_lattice_reader.Key();
        CompactLattice &clat = compact_lattice_reader.Value();
  
        if (lm_scale != 0.0) {
          // Before composing with the LM FST, we scale the lattice weights
          // by the inverse of "lm_scale".  We'll later scale by "lm_scale".
          // We do it this way so we can determinize and it will give the
          // right effect (taking the "best path" through the LM) regardless
          // of the sign of lm_scale.
          fst::ScaleLattice(fst::GraphLatticeScale(1.0 / lm_scale), &clat);
          ArcSort(&clat, fst::OLabelCompare<CompactLatticeArc>());
  
          // Wraps the rnnlm into FST. We re-create it for each lattice to prevent
          // memory usage increasing with time.
          TfRnnlmDeterministicFst rnnlm_fst(max_ngram_order, &rnnlm);
  
          // Composes lattice with language model.
          CompactLattice composed_clat;
          ComposeCompactLatticeDeterministic(clat, &rnnlm_fst, &composed_clat);
  
          // Determinizes the composed lattice.
          Lattice composed_lat;
          ConvertLattice(composed_clat, &composed_lat);
          Invert(&composed_lat);
          CompactLattice determinized_clat;
          DeterminizeLattice(composed_lat, &determinized_clat);
          fst::ScaleLattice(fst::GraphLatticeScale(lm_scale), &determinized_clat);
          if (determinized_clat.Start() == fst::kNoStateId) {
            KALDI_WARN << "Empty lattice for utterance " << key
                << " (incompatible LM?)";
            n_fail++;
          } else {
            compact_lattice_writer.Write(key, determinized_clat);
            n_done++;
          }
        } else {
          // Zero scale so nothing to do.
          n_done++;
          compact_lattice_writer.Write(key, clat);
        }
      }
  
      KALDI_LOG << "Done " << n_done << " lattices, failed for " << n_fail;
      return (n_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }