#!/bin/bash
  
  # please see lmrescore_rnnlm_lat.sh which is a newer script using lattices.
  
  # Begin configuration section.
  N=10
  inv_acwt=12
  cmd=run.pl
  use_phi=false  # This is kind of an obscure option.  If true, we'll remove the old
    # LM weights (times 1-RNN_scale) using a phi (failure) matcher, which is
    # appropriate if the old LM weights were added in this way, e.g. by
    # lmrescore.sh.  Otherwise we'll use normal composition, which is appropriate
    # if the lattices came directly from decoding.  This won't actually make much
    # difference (if any) to WER, it's more so we know we are doing the right thing.
  test=false # Activate a testing option.
  stage=1 # Stage of this script, for partial reruns.
  rnnlm_ver=rnnlm-0.3e
  skip_scoring=false
  keep_ali=true
  # End configuration section.
  
  
  echo "$0 $@"  # Print the command line for logging
  
  [ -f ./path.sh ] && . ./path.sh
  . utils/parse_options.sh
  
  
  if [ $# != 6 ]; then
     echo "Do language model rescoring of lattices (partially remove old LM, add new LM)"
     echo "This version applies an RNNLM and mixes it with the LM scores"
     echo "previously in the lattices., controlled by the first parameter (rnnlm-weight)"
     echo ""
     echo "Usage: utils/rnnlmrescore.sh <rnn-weight> <old-lang-dir> <rnn-dir> <data-dir> <input-decode-dir> <output-decode-dir>"
     echo "Main options:"
     echo "  --inv-acwt <inv-acwt>          # default 12.  e.g. --inv-acwt 17.  Equivalent to LM scale to use."
     echo "                                 # for N-best list generation... note, we'll score at different acwt's"
     echo "  --cmd <run.pl|queue.pl [opts]> # how to run jobs."
     echo "  --phi (true|false)             # Should be set to true if the source lattices were created"
     echo "                                 # by lmrescore.sh, false if they came from decoding."
     echo "  --N <N>                        # Value of N in N-best rescoring (default: 10)"
     exit 1;
  fi
  
  
  
  rnnweight=$1
  oldlang=$2
  rnndir=$3
  data=$4
  indir=$5
  dir=$6
  
  
  acwt=`perl -e "print (1.0/$inv_acwt);"` # Note: we'll actually produce lattices
   # that will be scored at a range of acoustic weights.  This acwt should be close
   # to the final one we'll pick, though, for best performance (it controls the
   # N-best list generation).
  
  # Figures out if the old LM is G.fst or G.carpa
  oldlm=$oldlang/G.fst
  if [ -f $oldlang/G.carpa ]; then
    oldlm=$oldlang/G.carpa
  elif [ ! -f $oldlm ]; then
    echo "$0: expecting either $oldlang/G.fst or $oldlang/G.carpa to exist" &&\
      exit 1;
  fi
  
  for f in $rnndir/rnnlm $data/feats.scp $indir/lat.1.gz; do
    [ ! -f $f ] && echo "$0: expected file $f to exist." && exit 1;
  done
  
  nj=`cat $indir/num_jobs` || exit 1;
  mkdir -p $dir;
  cp $indir/num_jobs $dir/num_jobs
  
  adir=$dir/archives
  
  phi=`grep -w '#0' $oldlang/words.txt | awk '{print $2}'`
  
  rm $dir/.error 2>/dev/null
  mkdir -p $dir/log
  
  # First convert lattice to N-best.  Be careful because this
  # will be quite sensitive to the acoustic scale; this should be close
  # to the one we'll finally get the best WERs with.
  # Note: the lattice-rmali part here is just because we don't
  # need the alignments for what we're doing.
  if [ $stage -le 1 ]; then
    echo "$0: converting lattices to N-best."
    if $keep_ali; then
      $cmd JOB=1:$nj $dir/log/lat2nbest.JOB.log \
        lattice-to-nbest --acoustic-scale=$acwt --n=$N \
        "ark:gunzip -c $indir/lat.JOB.gz|" \
        "ark:|gzip -c >$dir/nbest1.JOB.gz" || exit 1;
    else
      $cmd JOB=1:$nj $dir/log/lat2nbest.JOB.log \
        lattice-to-nbest --acoustic-scale=$acwt --n=$N \
        "ark:gunzip -c $indir/lat.JOB.gz|" ark:- \|  \
        lattice-rmali ark:- "ark:|gzip -c >$dir/nbest1.JOB.gz" || exit 1;
    fi
  fi
  
  # next remove part of the old LM probs.
  if [ "$oldlm" == "$oldlang/G.fst" ]; then
    if $use_phi; then
      if [ $stage -le 2 ]; then
        echo "$0: removing old LM scores."
        # Use the phi-matcher style of composition.. this is appropriate
        # if the old LM scores were added e.g. by lmrescore.sh, using
        # phi-matcher composition.
        $cmd JOB=1:$nj $dir/log/remove_old.JOB.log \
          lattice-scale --acoustic-scale=-1 --lm-scale=-1 "ark:gunzip -c $dir/nbest1.JOB.gz|" ark:- \| \
          lattice-compose --phi-label=$phi ark:- $oldlm ark:- \| \
          lattice-scale --acoustic-scale=-1 --lm-scale=-1 ark:- "ark:|gzip -c >$dir/nbest2.JOB.gz" \
          || exit 1;
      fi
    else
      if [ $stage -le 2 ]; then
        echo "$0: removing old LM scores."
        # this approach chooses the best path through the old LM FST, while
        # subtracting the old scores.  If the lattices came straight from decoding,
        # this is what we want.  Note here: each FST in "nbest1.JOB.gz" is a linear FST,
        # it has no alternatives (the N-best format works by having multiple keys
        # for each utterance).  When we do "lattice-1best" we are selecting the best
        # path through the LM, there are no alternatives to consider within the
        # original lattice.
        $cmd JOB=1:$nj $dir/log/remove_old.JOB.log \
          lattice-scale --acoustic-scale=-1 --lm-scale=-1 "ark:gunzip -c $dir/nbest1.JOB.gz|" ark:- \| \
          lattice-compose ark:- "fstproject --project_output=true $oldlm |" ark:- \| \
          lattice-1best ark:- ark:- \| \
          lattice-scale --acoustic-scale=-1 --lm-scale=-1 ark:- "ark:|gzip -c >$dir/nbest2.JOB.gz" \
          || exit 1;
      fi
    fi
  else
    if [ $stage -le 2 ]; then
      echo "$0: removing old LM scores."
      $cmd JOB=1:$nj $dir/log/remove_old.JOB.log \
        lattice-lmrescore-const-arpa --lm-scale=-1.0 \
        "ark:gunzip -c $dir/nbest1.JOB.gz|" $oldlm \
        "ark:|gzip -c >$dir/nbest2.JOB.gz"  || exit 1;
    fi
  fi
  
  if [ $stage -le 3 ]; then
  # Decompose the n-best lists into 4 archives.
    echo "$0: creating separate-archive form of N-best lists."
    $cmd JOB=1:$nj $dir/log/make_new_archives.JOB.log \
      mkdir -p $adir.JOB '&&' \
      nbest-to-linear "ark:gunzip -c $dir/nbest2.JOB.gz|" \
      "ark,t:$adir.JOB/ali" "ark,t:$adir.JOB/words" \
      "ark,t:$adir.JOB/lmwt.nolm" "ark,t:$adir.JOB/acwt" || exit 1;
  fi
  
  if [ $stage -le 4 ]; then
    echo "$0: doing the same with old LM scores."
  # Create an archive with the LM scores before we
  # removed the LM probs (will help us do interpolation).
  $cmd JOB=1:$nj $dir/log/make_old_archives.JOB.log \
    nbest-to-linear "ark:gunzip -c $dir/nbest1.JOB.gz|" "ark:/dev/null" \
    "ark:/dev/null" "ark,t:$adir.JOB/lmwt.withlm" "ark:/dev/null" || exit 1;
  fi
  
  if $test; then # This branch is a sanity check that at the acwt where we generated
    # the N-best list, we get the same WER.
    echo "$0 [testing branch]: generating lattices without changing scores."
    $cmd JOB=1:$nj $dir/log/test.JOB.log \
      linear-to-nbest "ark:$adir.JOB/ali" "ark:$adir.JOB/words" "ark:$adir.JOB/lmwt.withlm" \
       "ark:$adir.JOB/acwt" ark:- \| \
      nbest-to-lattice ark:- "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
    exit 0;
  fi
  
  if [ $stage -le 5 ]; then
    echo "$0: Creating archives with text-form of words, and LM scores without graph scores."
      # Do some small tasks; for these we don't use the queue, it will only slow us down.
    for n in `seq $nj`; do
      utils/int2sym.pl -f 2- $oldlang/words.txt < $adir.$n/words > $adir.$n/words_text || exit 1;
      mkdir -p $adir.$n/temp
      paste $adir.$n/lmwt.nolm $adir.$n/lmwt.withlm | awk '{print $1, ($4-$2);}' > \
        $adir.$n/lmwt.lmonly || exit 1;
    done
  fi
  if [ $stage -le 6 ]; then
    echo "$0: invoking utils/rnnlm_compute_scores.sh which calls rnnlm, to get RNN LM scores."
    $cmd JOB=1:$nj $dir/log/rnnlm_compute_scores.JOB.log \
      utils/rnnlm_compute_scores.sh --rnnlm_ver $rnnlm_ver $rnndir $adir.JOB/temp $adir.JOB/words_text $adir.JOB/lmwt.rnn \
      || exit 1;
  fi
  if [ $stage -le 7 ]; then
    echo "$0: reconstructing total LM+graph scores including interpolation of RNNLM and old LM scores."
    for n in `seq $nj`; do
      paste $adir.$n/lmwt.nolm $adir.$n/lmwt.lmonly $adir.$n/lmwt.rnn | awk -v rnnweight=$rnnweight \
        '{ key=$1; graphscore=$2; lmscore=$4; rnnscore=$6;
       score = graphscore+(rnnweight*rnnscore)+((1-rnnweight)*lmscore);
       print $1,score; } ' > $adir.$n/lmwt.interp.$rnnweight || exit 1;
    done
  fi
  
  if [ $stage -le 8 ]; then
    echo "$0: reconstructing archives back into lattices."
    $cmd JOB=1:$nj $dir/log/reconstruct_lattice.JOB.log \
      linear-to-nbest "ark:$adir.JOB/ali" "ark:$adir.JOB/words" \
      "ark:$adir.JOB/lmwt.interp.$rnnweight" "ark:$adir.JOB/acwt" ark:- \| \
      nbest-to-lattice ark:- "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
  fi
  
  if ! $skip_scoring ; then
    [ ! -x local/score.sh ] && \
      echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
    local/score.sh --cmd "$cmd" $data $oldlang $dir ||
      { echo "$0: Scoring failed. (ignore by '--skip-scoring true')"; exit 1; }
  fi
  
  exit 0;