// 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.\n"
        "An example script for training and rescoring with the TensorFlow\n"
        "RNNLM is at egs/ami/s5/local/tfrnnlm/run_lstm_fast.sh\n"
        "\n"
        "Usage: lattice-lmrescore-tf-rnnlm-pruned [options] [unk-file] \\\n"
        "             <old-lm> <fst-wordlist> <rnnlm-wordlist> \\\n"
        "             <rnnlm-rxfilename> <lattice-rspecifier> <lattice-wspecifier>\n"
        " e.g.: lattice-lmrescore-tf-rnnlm-pruned --lm-scale=0.5 data/tensorflow_lstm/unkcounts.txt \\\n"
        "              data/test/G.fst data/lang/words.txt data/tensorflow_lstm/rnnwords.txt \\\n"
        "              data/tensorflow_lstm/rnnlm ark:in.lats ark:out.lats\n\n"
        " e.g.: lattice-lmrescore-tf-rnnlm-pruned --lm-scale=0.5 data/tensorflow_lstm/unkcounts.txt \\\n"
        "              data/test_fg/G.carpa data/lang/words.txt data/tensorflow_lstm/rnnwords.txt \\\n"
        "              data/tensorflow_lstm/rnnlm ark:in.lats ark:out.lats\n";

    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;
  }
}