// tfrnnlmbin/lattice-lmrescore-tf-rnnlm-pruned.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 "lat/compose-lattice-pruned.h"
  #include "lm/const-arpa-lm.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;
      using fst::SymbolTable;
      using fst::VectorFst;
      using fst::StdArc;
      using fst::ReadFstKaldi;
  
      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-pruned [options] [unk-file] \\
  "
          "             <old-lm> <fst-wordlist> <rnnlm-wordlist> \\
  "
          "             <rnnlm-rxfilename> <lattice-rspecifier> <lattice-wspecifier>
  "
          " e.g.: lattice-lmrescore-tf-rnnlm-pruned --lm-scale=0.5 data/tensorflow_lstm/unkcounts.txt \\
  "
          "              data/test/G.fst data/lang/words.txt data/tensorflow_lstm/rnnwords.txt \\
  "
          "              data/tensorflow_lstm/rnnlm ark:in.lats ark:out.lats
  
  "
          " e.g.: lattice-lmrescore-tf-rnnlm-pruned --lm-scale=0.5 data/tensorflow_lstm/unkcounts.txt \\
  "
          "              data/test_fg/G.carpa data/lang/words.txt data/tensorflow_lstm/rnnwords.txt \\
  "
          "              data/tensorflow_lstm/rnnlm ark:in.lats ark:out.lats
  ";
  
      ParseOptions po(usage);
      int32 max_ngram_order = 3;
      BaseFloat lm_scale = 0.5;
      BaseFloat acoustic_scale = 0.1;
      bool use_carpa = false;
  
      po.Register("lm-scale", &lm_scale, "Scaling factor for <lm-to-add>; its negative "
                  "will be applied to <lm-to-subtract>.");
      po.Register("acoustic-scale", &acoustic_scale, "Scaling factor for acoustic "
                  "probabilities (e.g. 0.1 for non-chain systems); important because "
                  "of its effect on pruning.");
      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).");
      po.Register("use-const-arpa", &use_carpa, "If true, read the old-LM file "
                  "as a const-arpa file as opposed to an FST file");
  
      KaldiTfRnnlmWrapperOpts opts;
      ComposeLatticePrunedOptions compose_opts;
      opts.Register(&po);
      compose_opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 7 && po.NumArgs() != 6) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string lm_to_subtract_rxfilename, lats_rspecifier, rnn_word_list,
        word_symbols_rxfilename, rnnlm_rxfilename, lats_wspecifier, unk_prob_file;
      if (po.NumArgs() == 6) {
        lm_to_subtract_rxfilename = po.GetArg(1),
        word_symbols_rxfilename = po.GetArg(2);
        rnn_word_list = po.GetArg(3);
        rnnlm_rxfilename = po.GetArg(4);
        lats_rspecifier = po.GetArg(5);
        lats_wspecifier = po.GetArg(6);
      } else {
        lm_to_subtract_rxfilename = po.GetArg(1),
        word_symbols_rxfilename = po.GetArg(2);
        unk_prob_file = po.GetArg(3);
        rnn_word_list = po.GetArg(4);
        rnnlm_rxfilename = po.GetArg(5);
        lats_rspecifier = po.GetArg(6);
        lats_wspecifier = po.GetArg(7);
      }
  
      // for G.fst
      fst::ScaleDeterministicOnDemandFst *lm_to_subtract_det_scale = NULL;
      fst::BackoffDeterministicOnDemandFst<StdArc> *lm_to_subtract_det_backoff = NULL;
      VectorFst<StdArc> *lm_to_subtract_fst = NULL;
  
      // for G.carpa
      ConstArpaLm* const_arpa = NULL;
      fst::DeterministicOnDemandFst<StdArc> *carpa_lm_to_subtract_fst = NULL;
  
      KALDI_LOG << "Reading old LMs...";
      if (use_carpa) {
        const_arpa = new ConstArpaLm();
        ReadKaldiObject(lm_to_subtract_rxfilename, const_arpa);
        carpa_lm_to_subtract_fst = new ConstArpaLmDeterministicFst(*const_arpa);
        lm_to_subtract_det_scale
          = new fst::ScaleDeterministicOnDemandFst(-lm_scale,
                                                   carpa_lm_to_subtract_fst);
      } else {
        lm_to_subtract_fst = fst::ReadAndPrepareLmFst(
            lm_to_subtract_rxfilename);
        lm_to_subtract_det_backoff =
          new fst::BackoffDeterministicOnDemandFst<StdArc>(*lm_to_subtract_fst);
        lm_to_subtract_det_scale =
             new fst::ScaleDeterministicOnDemandFst(-lm_scale,
                                                    lm_to_subtract_det_backoff);
      }
  
      // 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;
  
      TfRnnlmDeterministicFst* lm_to_add_orig =
        new TfRnnlmDeterministicFst(max_ngram_order, &rnnlm);
  
      for (; !compact_lattice_reader.Done(); compact_lattice_reader.Next()) {
        fst::DeterministicOnDemandFst<StdArc> *lm_to_add =
           new fst::ScaleDeterministicOnDemandFst(lm_scale, lm_to_add_orig);
  
        std::string key = compact_lattice_reader.Key();
        CompactLattice clat = compact_lattice_reader.Value();
        compact_lattice_reader.FreeCurrent();
  
        // 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.
        if (acoustic_scale != 1.0) {
          fst::ScaleLattice(fst::AcousticLatticeScale(acoustic_scale), &clat);
        }
        TopSortCompactLatticeIfNeeded(&clat);
  
        fst::ComposeDeterministicOnDemandFst<StdArc> combined_lms(
            lm_to_subtract_det_scale, lm_to_add);
  
        // Composes lattice with language model.
        CompactLattice composed_clat;
        ComposeCompactLatticePruned(compose_opts, clat,
                                    &combined_lms, &composed_clat);
        lm_to_add_orig->Clear();
  
        if (composed_clat.NumStates() == 0) {
          // Something went wrong.  A warning will already have been printed.
          n_fail++;
        } else {
          if (acoustic_scale != 1.0) {
            if (acoustic_scale == 0.0)
              KALDI_ERR << "Acoustic scale cannot be zero.";
            fst::ScaleLattice(fst::AcousticLatticeScale(1.0 / acoustic_scale),
                              &composed_clat);
          }
          compact_lattice_writer.Write(key, composed_clat);
          n_done++;
        }
        delete lm_to_add;
      }
      delete lm_to_subtract_fst;
      delete lm_to_add_orig;
      delete lm_to_subtract_det_backoff;
      delete lm_to_subtract_det_scale;
  
      delete const_arpa;
      delete carpa_lm_to_subtract_fst;
  
      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;
    }
  }