#!/bin/bash
  
  # Copyright 2012-2015 Johns Hopkins University (Author: Daniel Povey)
  # Copyright   2017  Hossein Hadian
  # Apache 2.0.
  #
  
  
  # This is simlilar to chain/get_egs.sh except it
  # uses training FSTs (instead of lattices) to generate end2end egs.
  # It calls nnet3-chain-e2e-get-egs binary
  
  
  # Begin configuration section.
  cmd=run.pl
  normalize_egs=true
  feat_type=raw     # set it to 'lda' to use LDA features.
  frame_subsampling_factor=3 # frames-per-second of features we train on divided
                             # by frames-per-second at output of chain model
  left_context=4    # amount of left-context per eg (i.e. extra frames of input features
                    # not present in the output supervision).
  right_context=4   # amount of right-context per eg.
  left_context_initial=-1    # if >=0, left-context for first chunk of an utterance
  right_context_final=-1     # if >=0, right-context for last chunk of an utterance
  compress=true   # set this to false to disable compression (e.g. if you want to see whether
                  # results are affected).
  
  num_utts_subset=1400     # number of utterances in validation and training
                          # subsets used for shrinkage and diagnostics.
  num_valid_egs_combine=0  # #validation examples for combination weights at the very end.
  num_train_egs_combine=250 # number of train examples for the above.
  num_egs_diagnostic=300 # number of frames for "compute_prob" jobs
  frames_per_iter=400000 # each iteration of training, see this many frames per
                         # job, measured at the sampling rate of the features
                         # used.  This is just a guideline; it will pick a number
                         # that divides the number of samples in the entire data.
  
  stage=0
  nj=15         # This should be set to the maximum number of jobs you are
                # comfortable to run in parallel; you can increase it if your disk
                # speed is greater and you have more machines.
  max_shuffle_jobs_run=50  # the shuffle jobs now include the nnet3-chain-normalize-egs command,
                           # which is fairly CPU intensive, so we can run quite a few at once
                           # without overloading the disks.
  srand=0     # rand seed for nnet3-chain-get-egs, nnet3-chain-copy-egs and nnet3-chain-shuffle-egs
  online_ivector_dir=  # can be used if we are including speaker information as iVectors.
  cmvn_opts=  # can be used for specifying CMVN options, if feature type is not lda (if lda,
              # it doesn't make sense to use different options than were used as input to the
              # LDA transform).  This is used to turn off CMVN in the online-nnet experiments.
  
  echo "$0 $@"  # Print the command line for logging
  
  if [ -f path.sh ]; then . ./path.sh; fi
  . parse_options.sh || exit 1;
  
  
  if [ $# != 4 ]; then
    echo "Usage: $0 [opts] <data> <chain-dir> <fsts-dir> <egs-dir>"
    echo " e.g.: $0 data/train exp/chain/e2e exp/chain/e2e/egs"
    echo ""
    echo "From <chain-dir>, 0.trans_mdl (the transition-model), tree (the tree)"
    echo "and normalization.fst (the normalization FST, derived from the denominator FST)"
    echo "are read."
    echo ""
    echo "Main options (for others, see top of script file)"
    echo "  --config <config-file>                           # config file containing options"
    echo "  --nj <nj>                                        # The maximum number of jobs you want to run in"
    echo "                                                   # parallel (increase this only if you have good disk and"
    echo "                                                   # network speed).  default=6"
    echo "  --cmd (utils/run.pl;utils/queue.pl <queue opts>) # how to run jobs."
    echo "  --frames-per-iter <#samples;400000>              # Number of frames of data to process per iteration, per"
    echo "                                                   # process."
    echo "  --feat-type <lda|raw>                            # (raw is the default).  The feature type you want"
    echo "                                                   # to use as input to the neural net."
    echo "  --frame-subsampling-factor <factor;3>            # factor by which num-frames at nnet output is reduced "
    echo "  --left-context <int;4>                           # Number of frames on left side to append for feature input"
    echo "  --right-context <int;4>                          # Number of frames on right side to append for feature input"
    echo "  --left-context-initial <int;-1>                  # If >= 0, left-context for first chunk of an utterance"
    echo "  --right-context-final <int;-1>                   # If >= 0, right-context for last chunk of an utterance"
    echo "  --num-egs-diagnostic <#frames;4000>              # Number of egs used in computing (train,valid) diagnostics"
    echo "  --num-valid-egs-combine <#frames;10000>          # Number of egss used in getting combination weights at the"
    echo "                                                   # very end."
    echo "  --stage <stage|0>                                # Used to run a partially-completed training process from somewhere in"
    echo "                                                   # the middle."
  
    exit 1;
  fi
  
  data=$1
  chaindir=$2
  fstdir=$3
  dir=$4
  
  # Check some files.
  [ ! -z "$online_ivector_dir" ] && \
    extra_files="$online_ivector_dir/ivector_online.scp $online_ivector_dir/ivector_period"
  
  for f in $data/feats.scp $data/allowed_lengths.txt \
           $chaindir/{0.trans_mdl,tree,normalization.fst} $extra_files; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  sdata=$data/split$nj
  utils/split_data.sh $data $nj
  
  mkdir -p $dir/log $dir/info
  
  # Get list of validation utterances.
  
  frame_shift=$(utils/data/get_frame_shift.sh $data)
  utils/data/get_utt2dur.sh $data
  
  frames_per_eg=$(cat $data/allowed_lengths.txt | tr '
  ' , | sed 's/,$//')
  
  [ ! -f "$data/utt2len" ] && feat-to-len scp:$data/feats.scp ark,t:$data/utt2len
  
  cat $data/utt2len | \
    awk '{print $1}' | \
    utils/shuffle_list.pl 2>/dev/null | head -$num_utts_subset > $dir/valid_uttlist
  
  
  len_uttlist=`wc -l $dir/valid_uttlist | awk '{print $1}'`
  if [ $len_uttlist -lt $num_utts_subset ]; then
    echo "Number of utterances which have length at least $frames_per_eg is really low. Please check your data." && exit 1;
  fi
  
  if [ -f $data/utt2uniq ]; then  # this matters if you use data augmentation.
    # because of this stage we can again have utts with lengths less than
    # frames_per_eg
    echo "File $data/utt2uniq exists, so augmenting valid_uttlist to"
    echo "include all perturbed versions of the same 'real' utterances."
    mv $dir/valid_uttlist $dir/valid_uttlist.tmp
    utils/utt2spk_to_spk2utt.pl $data/utt2uniq > $dir/uniq2utt
    cat $dir/valid_uttlist.tmp | utils/apply_map.pl $data/utt2uniq | \
      sort | uniq | utils/apply_map.pl $dir/uniq2utt | \
      awk '{for(n=1;n<=NF;n++) print $n;}' | sort  > $dir/valid_uttlist
    rm $dir/uniq2utt $dir/valid_uttlist.tmp
  fi
  
  # awk -v mf_len=222 '{if ($2 == mf_len) print $1}' | \
  cat $data/utt2len | \
    awk '{print $1}' | \
     utils/filter_scp.pl --exclude $dir/valid_uttlist | \
     utils/shuffle_list.pl 2>/dev/null | head -$num_utts_subset > $dir/train_subset_uttlist
  len_uttlist=`wc -l $dir/train_subset_uttlist | awk '{print $1}'`
  if [ $len_uttlist -lt $num_utts_subset ]; then
    echo "Number of utterances which have length at least $frames_per_eg is really low. Please check your data." && exit 1;
  fi
  
  
  ## Set up features.
  echo "$0: feature type is $feat_type"
  
  case $feat_type in
    raw) feats="ark,s,cs:utils/filter_scp.pl --exclude $dir/valid_uttlist $sdata/JOB/feats.scp | apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:- ark:- |"
      valid_feats="ark,s,cs:utils/filter_scp.pl $dir/valid_uttlist $data/feats.scp | apply-cmvn $cmvn_opts --utt2spk=ark:$data/utt2spk scp:$data/cmvn.scp scp:- ark:- |"
      train_subset_feats="ark,s,cs:utils/filter_scp.pl $dir/train_subset_uttlist $data/feats.scp | apply-cmvn $cmvn_opts --utt2spk=ark:$data/utt2spk scp:$data/cmvn.scp scp:- ark:- |"
      echo $cmvn_opts >$dir/cmvn_opts # caution: the top-level nnet training script should copy this to its own dir now.
     ;;
    *) echo "$0: invalid feature type --feat-type '$feat_type'" && exit 1;
  esac
  
  if [ ! -z "$online_ivector_dir" ]; then
    ivector_dim=$(feat-to-dim scp:$online_ivector_dir/ivector_online.scp -) || exit 1;
    echo $ivector_dim > $dir/info/ivector_dim
    steps/nnet2/get_ivector_id.sh $online_ivector_dir > $dir/info/final.ie.id || exit 1
    ivector_period=$(cat $online_ivector_dir/ivector_period) || exit 1;
    ivector_opts="--online-ivectors=scp:$online_ivector_dir/ivector_online.scp --online-ivector-period=$ivector_period"
  else
    ivector_opts=""
    echo 0 >$dir/info/ivector_dim
  fi
  
  if [ $stage -le 1 ]; then
    echo "$0: working out number of frames of training data"
    num_frames=$(steps/nnet2/get_num_frames.sh $data)
    echo $num_frames > $dir/info/num_frames
    echo "$0: working out feature dim"
    feats_one="$(echo $feats | sed s/JOB/1/g)"
    if ! feat_dim=$(feat-to-dim "$feats_one" - 2>/dev/null); then
      echo "Command failed (getting feature dim): feat-to-dim \"$feats_one\""
      exit 1
    fi
    echo $feat_dim > $dir/info/feat_dim
  else
    num_frames=$(cat $dir/info/num_frames) || exit 1;
    feat_dim=$(cat $dir/info/feat_dim) || exit 1;
  fi
  
  # the + 1 is to round up, not down... we assume it doesn't divide exactly.
  num_archives=$[$num_frames/$frames_per_iter+1]
  
  # We may have to first create a smaller number of larger archives, with number
  # $num_archives_intermediate, if $num_archives is more than the maximum number
  # of open filehandles that the system allows per process (ulimit -n).
  max_open_filehandles=500 #$(ulimit -n) || exit 1
  num_archives_intermediate=$num_archives
  archives_multiple=1
  while [ $[$num_archives_intermediate+4] -gt $max_open_filehandles ]; do
    archives_multiple=$[$archives_multiple+1]
    num_archives_intermediate=$[$num_archives/$archives_multiple] || exit 1;
  done
  # now make sure num_archives is an exact multiple of archives_multiple.
  num_archives=$[$archives_multiple*$num_archives_intermediate] || exit 1;
  
  echo $num_archives >$dir/info/num_archives
  echo $frames_per_eg >$dir/info/frames_per_eg
  # Work out the number of egs per archive
  egs_per_archive=$[$num_frames/($frames_per_eg*$num_archives)] || exit 1;
  ! [ $egs_per_archive -le $frames_per_iter ] && \
    echo "$0: script error: egs_per_archive=$egs_per_archive not <= frames_per_iter=$frames_per_iter" \
    && exit 1;
  
  echo $egs_per_archive > $dir/info/egs_per_archive
  
  echo "$0: creating $num_archives archives, each with $egs_per_archive egs, with"
  echo "$0:   $frames_per_eg labels per example, and (left,right) context = ($left_context,$right_context)"
  if [ $left_context_initial -ge 0 ] || [ $right_context_final -ge 0 ]; then
    echo "$0:   ... and (left-context-initial,right-context-final) = ($left_context_initial,$right_context_final)"
  fi
  
  
  if [ -e $dir/storage ]; then
    # Make soft links to storage directories, if distributing this way..  See
    # utils/create_split_dir.pl.
    echo "$0: creating data links"
    utils/create_data_link.pl $(for x in $(seq $num_archives); do echo $dir/cegs.$x.ark; done)
    for x in $(seq $num_archives_intermediate); do
      utils/create_data_link.pl $(for y in $(seq $nj); do echo $dir/cegs_orig.$y.$x.ark; done)
    done
  fi
  
  
  egs_opts="--left-context=$left_context --right-context=$right_context --num-frames=$frames_per_eg --frame-subsampling-factor=$frame_subsampling_factor --compress=$compress"
  [ $left_context_initial -ge 0 ] && egs_opts="$egs_opts --left-context-initial=$left_context_initial"
  [ $right_context_final -ge 0 ] && egs_opts="$egs_opts --right-context-final=$right_context_final"
  
  
  echo $left_context > $dir/info/left_context
  echo $right_context > $dir/info/right_context
  echo $left_context_initial > $dir/info/left_context_initial
  echo $right_context_final > $dir/info/right_context_final
  
  num_fst_jobs=$(cat $fstdir/num_jobs) || exit 1;
  for id in $(seq $num_fst_jobs); do cat $fstdir/fst.$id.scp; done > $fstdir/fst.scp
  
  if [ $stage -le 3 ]; then
    echo "$0: Getting validation and training subset examples."
    rm $dir/.error 2>/dev/null
  
    # do the filtering just once, as fst.scp may be long.
    utils/filter_scp.pl <(cat $dir/valid_uttlist $dir/train_subset_uttlist) \
      <$fstdir/fst.scp >$fstdir/fst_special.scp
    if $normalize_egs; then
      norm_opt=$chaindir/normalization.fst
    else
      norm_opt=
    fi
    $cmd $dir/log/create_valid_subset.log \
      utils/filter_scp.pl $dir/valid_uttlist $fstdir/fst_special.scp \| \
      fstcopy scp:- ark:- \| \
      nnet3-chain-e2e-get-egs $ivector_opts --srand=$srand \
        $egs_opts $norm_opt \
        "$valid_feats" ark,s,cs:- $chaindir/0.trans_mdl "ark:$dir/valid_all.cegs" || touch $dir/.error &
    $cmd $dir/log/create_train_subset.log \
      utils/filter_scp.pl $dir/train_subset_uttlist $fstdir/fst_special.scp \| \
      fstcopy scp:- ark:- \| \
      nnet3-chain-e2e-get-egs $ivector_opts --srand=$srand \
        $egs_opts $norm_opt \
        "$train_subset_feats" ark,s,cs:- $chaindir/0.trans_mdl "ark:$dir/train_subset_all.cegs" || touch $dir/.error &
    wait;
    [ -f $dir/.error ] && echo "Error detected while creating train/valid egs" && exit 1
    echo "... Getting subsets of validation examples for diagnostics and combination."
    $cmd $dir/log/create_valid_subset_combine.log \
      nnet3-chain-subset-egs --n=$num_valid_egs_combine ark:$dir/valid_all.cegs \
      ark:$dir/valid_combine.cegs || touch $dir/.error &
    $cmd $dir/log/create_valid_subset_diagnostic.log \
      nnet3-chain-subset-egs --n=$num_egs_diagnostic ark:$dir/valid_all.cegs \
      ark:$dir/valid_diagnostic.cegs || touch $dir/.error &
  
    $cmd $dir/log/create_train_subset_combine.log \
      nnet3-chain-subset-egs --n=$num_train_egs_combine ark:$dir/train_subset_all.cegs \
      ark:$dir/train_combine.cegs || touch $dir/.error &
    $cmd $dir/log/create_train_subset_diagnostic.log \
      nnet3-chain-subset-egs --n=$num_egs_diagnostic ark:$dir/train_subset_all.cegs \
      ark:$dir/train_diagnostic.cegs || touch $dir/.error &
    wait
    sleep 5  # wait for file system to sync.
    cat $dir/valid_combine.cegs $dir/train_combine.cegs > $dir/combine.cegs
  
    for f in $dir/{combine,train_diagnostic,valid_diagnostic}.cegs; do
      [ ! -s $f ] && echo "No examples in file $f" && exit 1;
    done
  
    #rm $dir/valid_all.cegs $dir/train_subset_all.cegs $dir/{train,valid}_combine.cegs
    #exit 0
  fi
  
  echo "num_archives_intermediate:" $num_archives_intermediate
  echo "num_archives: $num_archives"
  echo "archives_multiple: $archives_multiple"
  
  if [ $stage -le 4 ]; then
    # create cegs_orig.*.*.ark; the first index goes to $nj,
    # the second to $num_archives_intermediate.
  
    egs_list=
    for n in $(seq $num_archives_intermediate); do
      egs_list="$egs_list ark:$dir/cegs_orig.JOB.$n.ark"
    done
    echo "$0: Generating training examples on disk"
  
    # The examples will go round-robin to egs_list.  Note: we omit the
    # 'normalization.fst' argument while creating temporary egs: the phase of egs
    # preparation that involves the normalization FST is quite CPU-intensive and
    # it's more convenient to do it later, in the 'shuffle' stage.  Otherwise to
    # make it efficient we need to use a large 'nj', like 40, and in that case
    # there can be too many small files to deal with, because the total number of
    # files is the product of 'nj' by 'num_archives_intermediate', which might be
    # quite large.
    $cmd JOB=1:$nj $dir/log/get_egs.JOB.log \
      utils/filter_scp.pl $sdata/JOB/utt2spk $fstdir/fst.scp \| \
      fstcopy scp:- ark:- \| \
      nnet3-chain-e2e-get-egs $ivector_opts --srand=\$[JOB+$srand] $egs_opts \
       "$feats" ark,s,cs:- $chaindir/0.trans_mdl ark:- \| \
      nnet3-chain-copy-egs --random=true --srand=\$[JOB+$srand] ark:- $egs_list || exit 1;
  fi
  
  if [ $stage -le 5 ]; then
    echo "$0: recombining and shuffling order of archives on disk"
    # combine all the "egs_orig.*.JOB.scp" (over the $nj splits of the data) and
    # shuffle the order, writing to the egs.JOB.ark
  
    # the input is a concatenation over the input jobs.
    egs_list=
    for n in $(seq $nj); do
      egs_list="$egs_list $dir/cegs_orig.$n.JOB.ark"
    done
  
    if [ $archives_multiple == 1 ]; then # normal case.
      if $normalize_egs; then
        $cmd --max-jobs-run $max_shuffle_jobs_run --mem 8G JOB=1:$num_archives_intermediate $dir/log/shuffle.JOB.log \
          nnet3-chain-normalize-egs $chaindir/normalization.fst "ark:cat $egs_list|" ark:- \| \
          nnet3-chain-shuffle-egs --srand=\$[JOB+$srand] ark:- ark:$dir/cegs.JOB.ark  || exit 1;
      else
        $cmd --max-jobs-run $max_shuffle_jobs_run --mem 8G JOB=1:$num_archives_intermediate $dir/log/shuffle.JOB.log \
          nnet3-chain-shuffle-egs --srand=\$[JOB+$srand] "ark:cat $egs_list|" ark:$dir/cegs.JOB.ark  || exit 1;
      fi
    else
      # we need to shuffle the 'intermediate archives' and then split into the
      # final archives.  we create soft links to manage this splitting, because
      # otherwise managing the output names is quite difficult (and we don't want
      # to submit separate queue jobs for each intermediate archive, because then
      # the --max-jobs-run option is hard to enforce).
      output_archives="$(for y in $(seq $archives_multiple); do echo ark:$dir/cegs.JOB.$y.ark; done)"
      for x in $(seq $num_archives_intermediate); do
        for y in $(seq $archives_multiple); do
          archive_index=$[($x-1)*$archives_multiple+$y]
          # egs.intermediate_archive.{1,2,...}.ark will point to egs.archive.ark
          ln -sf cegs.$archive_index.ark $dir/cegs.$x.$y.ark || exit 1
        done
      done
      $cmd --max-jobs-run $max_shuffle_jobs_run --mem 8G JOB=1:$num_archives_intermediate $dir/log/shuffle.JOB.log \
        nnet3-chain-normalize-egs $chaindir/normalization.fst "ark:cat $egs_list|" ark:- \| \
        nnet3-chain-shuffle-egs --srand=\$[JOB+$srand] ark:- ark:- \| \
        nnet3-chain-copy-egs ark:- $output_archives || exit 1;
    fi
  fi
  
  if [ $stage -le 6 ]; then
    echo "$0: removing temporary archives"
    (
      cd $dir
      for f in $(ls -l . | grep 'cegs_orig' | awk '{ X=NF-1; Y=NF-2; if ($X == "->")  print $Y, $NF; }'); do rm $f; done
      # the next statement removes them if we weren't using the soft links to a
      # 'storage' directory.
      rm cegs_orig.*.ark 2>/dev/null
    )
    if [ $archives_multiple -gt 1 ]; then
      # there are some extra soft links that we should delete.
      for f in $dir/cegs.*.*.ark; do rm $f; done
    fi
    echo "$0: removing temporary alignments"
    rm $dir/ali.{ark,scp} 2>/dev/null
  
  fi
  
  echo "$0: Finished preparing training examples"