#!/bin/bash
  
  # Copyright 2018    Hossein Hadian
  #                   Ashish Arora
  #                   Jonathan Chang
  # Apache 2.0
  
  set -e
  stage=0
  nj=30
  
  language_main=Korean
  slam_dir=/export/corpora5/slam/SLAM/
  yomdle_dir=/export/corpora5/slam/YOMDLE/
  corpus_dir=/export/corpora5/handwriting_ocr/corpus_data/ko/
  . ./cmd.sh
  . ./path.sh
  . ./utils/parse_options.sh
  
  ./local/check_tools.sh
  # Start from stage=-2 for data preparation. This stage stores line images,
  # csv files and splits{train,test,train_unsup} data/download/truth_line_image,
  # data/download/truth_csv and data/local/splits respectively.
  if [ $stage -le -2 ]; then
    echo "$(date): preparing data, obtaining line images and csv files..."
    local/yomdle/create_download_dir.sh --language_main $language_main \
      --slam_dir $slam_dir --yomdle_dir $yomdle_dir
  fi
  
  if [ $stage -le -1 ]; then
    echo "$(date): getting corpus text for language modelling..."
    mkdir -p data/local/text/cleaned
    cat $corpus_dir/* > data/local/text/ko.txt
    head -20000 data/local/text/ko.txt > data/local/text/cleaned/val.txt
    tail -n +20000 data/local/text/ko.txt > data/local/text/cleaned/corpus.txt
  fi
  
  mkdir -p data/{train,test}/data
  if [ $stage -le 0 ]; then
    echo "$0 stage 0: Processing train and test data.$(date)"
    echo " creating text, images.scp, utt2spk and spk2utt"
    #local/prepare_data.sh data/download/
    for set in train test; do
      local/process_data.py data/download/ \
        data/local/splits/${set}.txt data/${set}
      image/fix_data_dir.sh data/${set}
    done
  fi
  
  if [ $stage -le 1 ]; then
    echo "$(date) stage 1: getting allowed image widths for e2e training..."
    image/get_image2num_frames.py --feat-dim 40 data/train
    image/get_allowed_lengths.py --frame-subsampling-factor 4 10 data/train
    for set in train test; do
      echo "$(date) Extracting features, creating feats.scp file"
      local/extract_features.sh --nj $nj --cmd "$cmd" --feat-dim 40 data/${set}
      steps/compute_cmvn_stats.sh data/${set} || exit 1;
    done
    image/fix_data_dir.sh data/train
  fi
  
  if [ $stage -le 3 ]; then
    echo "$(date) stage 3: BPE preparation"
    # getting non-silence phones.
    cut -d' ' -f2- data/train/text | \
  python3 <(
  cat << "END"
  import os, sys, io;
  infile = io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8');
  output = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8');
  phone_dict = dict();
  for line in infile:
      line_vect = line.strip().split();
      for word in line_vect:
          for phone in word:
              phone_dict[phone] = phone;
  
  for phone in phone_dict.keys():
        output.write(phone+ '
  ');
  END
     ) > data/local/text/cleaned/phones.txt
  
    cut -d' ' -f2- data/train/text > data/local/text/cleaned/train.txt
  
    echo "learning BPE..."
    # it is currently learned with only training text but we can also use all corpus text
    # to learn BPE. phones are added so that one isolated occurance of every phone exists.
    cat data/local/text/cleaned/phones.txt data/local/text/cleaned/train.txt | \
      utils/lang/bpe/prepend_words.py | utils/lang/bpe/learn_bpe.py -s 700 > data/local/bpe.txt || exit 1;
  fi
  
  if [ $stage -le 4 ]; then
    echo "$(date) stage 4: applying BPE..."
    echo "applying BPE on train, test text..."
    for set in test train; do
      cut -d' ' -f1 data/$set/text > data/$set/ids
      cut -d' ' -f2- data/$set/text | utils/lang/bpe/prepend_words.py | \
        utils/lang/bpe/apply_bpe.py -c data/local/bpe.txt | \
        sed 's/@@//g' > data/$set/bpe_text
      mv data/$set/text data/$set/text.old
      paste -d' ' data/$set/ids data/$set/bpe_text > data/$set/text
      rm -f data/$set/bpe_text data/$set/ids
    done
  
    echo "applying BPE to corpus text..."
    cat data/local/text/cleaned/corpus.txt | utils/lang/bpe/prepend_words.py | \
      utils/lang/bpe/apply_bpe.py -c data/local/bpe.txt | \
      sed 's/@@//g' > data/local/text/cleaned/bpe_corpus.txt
    cat data/local/text/cleaned/val.txt | utils/lang/bpe/prepend_words.py | \
      utils/lang/bpe/apply_bpe.py -c data/local/bpe.txt | \
      sed 's/@@//g' > data/local/text/cleaned/bpe_val.txt
  fi
  
  if [ $stage -le 5 ]; then
    echo "$(date) stage 5: Preparing dictionary and lang..."
    local/prepare_dict.sh --dir data/local/dict
    utils/prepare_lang.sh --num-sil-states 4 --num-nonsil-states 4 --sil-prob 0.0 --position-dependent-phones false \
      data/local/dict "<sil>" data/lang/temp data/lang
    utils/lang/bpe/add_final_optional_silence.sh --final-sil-prob 0.5 data/lang
  fi
  
  if [ $stage -le 6 ]; then
    echo "$(date) stage 6: Calling the flat-start chain recipe..."
    local/chain/run_e2e_cnn.sh
  fi
  
  if [ $stage -le 7 ]; then
    echo "$(date) stage 7: Aligning the training data using the e2e chain model..."
    steps/nnet3/align.sh --nj $nj --cmd "$cmd" \
      --scale-opts '--transition-scale=1.0 --acoustic-scale=1.0 --self-loop-scale=1.0' \
      data/train data/lang exp/chain/e2e_cnn_1a exp/chain/e2e_ali_train
  fi
  
  chunk_width='340,300,200,100'
  lang_decode=data/lang
  lang_rescore=data/lang_rescore_6g
  if [ $stage -le 8 ]; then
    echo "$(date) stage 8: Building a tree and training a regular chain model using the e2e alignments..."
    local/chain/run_cnn_e2eali.sh --chunk_width $chunk_width
  fi
  
  if [ $stage -le 9 ]; then
    echo "$(date) stage 9: Estimating a language model for decoding..."
    local/train_lm.sh
    utils/format_lm.sh data/lang data/local/local_lm/data/arpa/6gram_small.arpa.gz \
                       data/local/dict/lexicon.txt data/lang
    utils/build_const_arpa_lm.sh data/local/local_lm/data/arpa/6gram_unpruned.arpa.gz \
                                 data/lang data/lang_rescore_6g
  fi
  
  if [ $stage -le 10 ] && $decode_e2e; then
    echo "$(date) stage 10: decoding end2end setup..."
  
    utils/mkgraph.sh \
      --self-loop-scale 1.0 $lang_decode \
      exp/chain/e2e_cnn_1a/ exp/chain/e2e_cnn_1a/graph || exit 1;
  
    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
      --nj 30 --cmd "$cmd" --beam 12 \
      exp/chain/e2e_cnn_1a/graph data/test exp/chain/e2e_cnn_1a/decode_test || exit 1;
  
    steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
                                  data/test exp/chain/e2e_cnn_1a/decode_test{,_rescored} || exit 1
  
    echo "Done. Date: $(date). Results:"
    local/chain/compare_wer.sh exp/chain/e2e_cnn_1a/
  fi
  
  if [ $stage -le 11 ] && $decode_chain; then
    echo "$(date) stage 11: decoding chain alignment setup..."
  
    utils/mkgraph.sh \
      --self-loop-scale 1.0 $lang_decode \
      exp/chain/cnn_e2eali_1a/ exp/chain/cnn_e2eali_1a/graph || exit 1;
  
    frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
      --nj 30 --cmd "$cmd" --beam 12 \
      exp/chain/cnn_e2eali_1a/graph data/test exp/chain/cnn_e2eali_1a/decode_test || exit 1;
  
    steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
                                  data/test exp/chain/cnn_e2eali_1a/decode_test{,_rescored} || exit 1
  
    echo "Done. Date: $(date). Results:"
    local/chain/compare_wer.sh exp/chain/cnn_e2eali_1a
  fi