#!/bin/bash
  
  # Copyright 2016  Vincent Nguyen
  #           2016  Johns Hopkins University (author: Daniel Povey)
  #           2017  Ashish Arora
  #           2017  Hossein Hadian
  # Apache 2.0
  #
  # This script trains a LM on the training transcriptions and corpus text.
  # It is based on the example scripts distributed with PocoLM
  
  # It will check if pocolm is installed and if not will proceed with installation
  
  set -e
  stage=0
  dir=data/local/local_lm
  order=6
  echo "$0 $@"  # Print the command line for logging
  . ./utils/parse_options.sh || exit 1;
  
  lm_dir=${dir}/data
  
  
  mkdir -p $dir
  . ./path.sh || exit 1; # for KALDI_ROOT
  export PATH=$KALDI_ROOT/tools/pocolm/scripts:$PATH
  ( # First make sure the pocolm toolkit is installed.
   cd $KALDI_ROOT/tools || exit 1;
   if [ -d pocolm ]; then
     echo Not installing the pocolm toolkit since it is already there.
   else
     echo "$0: Please install the PocoLM toolkit with: "
     echo " cd ../../../tools; extras/install_pocolm.sh; cd -"
     exit 1;
   fi
  ) || exit 1;
  
  bypass_metaparam_optim_opt=
  # If you want to bypass the metaparameter optimization steps with specific metaparameters
  # un-comment the following line, and change the numbers to some appropriate values.
  # You can find the values from output log of train_lm.py.
  # These example numbers of metaparameters is for 4-gram model (with min-counts)
  # running with train_lm.py.
  # The dev perplexity should be close to the non-bypassed model.
  #bypass_metaparam_optim_opt="--bypass-metaparameter-optimization=0.031,0.860,0.678,0.194,0.037,0.006,0.928,0.712,0.454,0.220,0.926,0.844,0.749,0.358,0.966,0.879,0.783,0.544,0.966,0.826,0.674,0.450"
  # Note: to use these example parameters, you may need to remove the .done files
  # to make sure the make_lm_dir.py be called and tain only 3-gram model
  #for order in 3; do
  #rm -f ${lm_dir}/${num_word}_${order}.pocolm/.done
  
  if [ $stage -le 0 ]; then
    mkdir -p ${dir}/data
    mkdir -p ${dir}/data/text
  
    echo "$0: Getting the Data sources"
  
    rm ${dir}/data/text/* 2>/dev/null || true
  
    # use the validation data as the dev set.
    # Note: the name 'dev' is treated specially by pocolm, it automatically
    # becomes the dev set.
  
    cat data/local/text/cleaned/bpe_val.txt  > ${dir}/data/text/dev.txt
    # use the training data as an additional data source.
    # we can later fold the dev data into this.
    cat data/train/text | cut -d " " -f 2- >  ${dir}/data/text/train.txt
    cat data/local/text/cleaned/bpe_corpus.txt > ${dir}/data/text/corpus_text.txt
    # for reporting perplexities, we'll use the "real" dev set.
    # (the validation data is used as ${dir}/data/text/dev.txt to work
    # out interpolation weights.)
    # note, we can't put it in ${dir}/data/text/, because then pocolm would use
    # it as one of the data sources.
    cut -d " " -f 2-  < data/test/text  > ${dir}/data/real_dev_set.txt
  
    # get the wordlist from train and corpus text
    cat ${dir}/data/text/{train,corpus_text}.txt | tr '[:space:]' '[
  *]' | grep -v "^\s*$" | sort | uniq -c | sort -bnr > ${dir}/data/word_count
    cat ${dir}/data/word_count | awk '{print $2}' > ${dir}/data/wordlist
  fi
  
  if [ $stage -le 1 ]; then
    # decide on the vocabulary.
    # Note: you'd use --wordlist if you had a previously determined word-list
    # that you wanted to use.
    # Note: if you have more than one order, use a certain amount of words as the
    # vocab and want to restrict max memory for 'sort',
    echo "$0: training the unpruned LM"
    min_counts='train=1'
    wordlist=${dir}/data/wordlist
  
    lm_name="`basename ${wordlist}`_${order}"
    if [ -n "${min_counts}" ]; then
      lm_name+="_`echo ${min_counts} | tr -s "[:blank:]" "_" | tr "=" "-"`"
    fi
    unpruned_lm_dir=${lm_dir}/${lm_name}.pocolm
    train_lm.py  --wordlist=${wordlist} --num-splits=20 --warm-start-ratio=20 \
                 --limit-unk-history=true \
                 ${bypass_metaparam_optim_opt} \
                 ${dir}/data/text ${order} ${lm_dir}/work ${unpruned_lm_dir}
  
    get_data_prob.py ${dir}/data/real_dev_set.txt ${unpruned_lm_dir} 2>&1 | grep -F '[perplexity'
    #3grm: [perplexity = 27.7734168008] over 151116.0 words
    #6grm: [perplexity = 18.6681627154] over 151116.0 words
    mkdir -p ${dir}/data/arpa
    format_arpa_lm.py ${unpruned_lm_dir} | gzip -c > ${dir}/data/arpa/${order}gram_unpruned.arpa.gz
  fi
  
  if [ $stage -le 2 ]; then
    echo "$0: pruning the LM (to larger size)"
    # Using 10 million n-grams for a big LM for rescoring purposes.
    size=10000000
    prune_lm_dir.py --target-num-ngrams=$size --initial-threshold=0.02 ${unpruned_lm_dir} ${dir}/data/lm_${order}_prune_big
  
    get_data_prob.py ${dir}/data/real_dev_set.txt ${dir}/data/lm_${order}_prune_big 2>&1 | grep -F '[perplexity'
    #[perplexity = 22.0613098868] over 151116.0 words
    mkdir -p ${dir}/data/arpa
    format_arpa_lm.py ${dir}/data/lm_${order}_prune_big | gzip -c > ${dir}/data/arpa/${order}gram_big.arpa.gz
  fi
  
  if [ $stage -le 3 ]; then
    echo "$0: pruning the LM (to smaller size)"
    # Using 2 million n-grams for a smaller LM for graph building.  Prune from the
    # bigger-pruned LM, it'll be faster.
    size=2000000
    prune_lm_dir.py --target-num-ngrams=$size ${dir}/data/lm_${order}_prune_big ${dir}/data/lm_${order}_prune_small
  
    get_data_prob.py ${dir}/data/real_dev_set.txt ${dir}/data/lm_${order}_prune_small 2>&1 | grep -F '[perplexity'
    #[perplexity = 23.4801171202] over 151116.0 words
    format_arpa_lm.py ${dir}/data/lm_${order}_prune_small | gzip -c > ${dir}/data/arpa/${order}gram_small.arpa.gz
  fi