#!/usr/bin/env bash

# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail

log() {
    local fname=${BASH_SOURCE[1]##*/}
    echo -e "$(date '+%Y-%m-%dT%H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
min() {
  local a b
  a=$1
  for b in "$@"; do
      if [ "${b}" -le "${a}" ]; then
          a="${b}"
      fi
  done
  echo "${a}"
}
SECONDS=0

# General configuration
stage=1                 # Processes starts from the specified stage.
stop_stage=10000        # Processes is stopped at the specified stage.
skip_data_prep=false    # Skip data preparation stages.
skip_train=false        # Skip training stages.
skip_eval=false         # Skip decoding and evaluation stages.
skip_packing=true       # Skip the packing stage.
skip_upload_hf=true     # Skip uploading to huggingface stage.
ngpu=1                  # The number of gpus ("0" uses cpu, otherwise use gpu).
num_nodes=1             # The number of nodes.
nj=32                   # The number of parallel jobs.
inference_nj=32         # The number of parallel jobs in decoding.
gpu_inference=false     # Whether to perform gpu decoding.
dumpdir=dump            # Directory to dump features.
expdir=exp              # Directory to save experiments.
python=python3          # Specify python to execute espnet commands.

# Data preparation related
local_data_opts= # The options given to local/data.sh.

# Feature extraction related
feats_type=raw       # Feature type (raw or fbank_pitch).

# Tokenization related
oov="<unk>"         # Out of vocabulary symbol.
blank="<blank>"     # CTC blank symbol
sos_eos="<sos/eos>" # sos and eos symbole
token_joint=false       # whether to use a single bpe system for both source and target languages
src_case=lc.rm
src_token_type=bpe      # Tokenization type (char or bpe) for source languages.
src_nbpe=30             # The number of BPE vocabulary for source language.
src_bpemode=unigram     # Mode of BPE for source language (unigram or bpe).
src_bpe_input_sentence_size=100000000 # Size of input sentence for BPE for source language.
src_bpe_nlsyms=         # non-linguistic symbols list, separated by a comma, for BPE of source language
src_bpe_char_cover=1.0  # character coverage when modeling BPE for source language
tgt_case=tc
tgt_token_type=bpe      # Tokenization type (char or bpe) for target language.
tgt_nbpe=30             # The number of BPE vocabulary for target language.
tgt_bpemode=unigram     # Mode of BPE (unigram or bpe) for target language.
tgt_bpe_input_sentence_size=100000000 # Size of input sentence for BPE for target language.
tgt_bpe_nlsyms=         # non-linguistic symbols list, separated by a comma, for BPE for target language.
tgt_bpe_char_cover=1.0  # character coverage when modeling BPE for target language.

# Ngram model related
use_ngram=false
ngram_exp=
ngram_num=3

# Language model related
use_lm=true       # Use language model for MT decoding.
lm_tag=           # Suffix to the result dir for language model training.
lm_exp=           # Specify the directory path for LM experiment.
                  # If this option is specified, lm_tag is ignored.
lm_stats_dir=     # Specify the directory path for LM statistics.
lm_config=        # Config for language model training.
lm_args=          # Arguments for language model training, e.g., "--max_epoch 10".
                  # Note that it will overwrite args in lm config.
use_word_lm=false # Whether to use word language model.
num_splits_lm=1   # Number of splitting for lm corpus.
# shellcheck disable=SC2034
word_vocab_size=10000 # Size of word vocabulary.

# MT model related
mt_tag=        # Suffix to the result dir for mt model training.
mt_exp=        # Specify the directory path for MT experiment.
               # If this option is specified, mt_tag is ignored.
mt_stats_dir=  # Specify the directory path for MT statistics.
mt_config=     # Config for mt model training.
mt_args=       # Arguments for mt model training, e.g., "--max_epoch 10".
               # Note that it will overwrite args in mt config.
ignore_init_mismatch=false      # Ignore initial mismatch
num_splits_mt=1            # Number of splitting for lm corpus.
src_lang=es                # source language abbrev. id (e.g., es)
tgt_lang=en                # target language abbrev. id (e.g., en)

# Upload model related
hf_repo=

# Decoding related
use_k2=false      # Whether to use k2 based decoder
batch_size=1
inference_tag=    # Suffix to the result dir for decoding.
inference_config= # Config for decoding.
inference_args=   # Arguments for decoding, e.g., "--lm_weight 0.1".
                  # Note that it will overwrite args in inference config.
inference_lm=valid.loss.ave.pth       # Language model path for decoding.
inference_ngram=${ngram_num}gram.bin
inference_mt_model=valid.acc.ave.pth # MT model path for decoding.
                                      # e.g.
                                      # inference_mt_model=train.loss.best.pth
                                      # inference_mt_model=3epoch.pth
                                      # inference_mt_model=valid.acc.best.pth
                                      # inference_mt_model=valid.loss.ave.pth
download_model= # Download a model from Model Zoo and use it for decoding.

# [Task dependent] Set the datadir name created by local/data.sh
train_set=       # Name of training set.
valid_set=       # Name of validation set used for monitoring/tuning network training.
test_sets=       # Names of test sets. Multiple items (e.g., both dev and eval sets) can be specified.
src_bpe_train_text=  # Text file path of bpe training set for source language.
tgt_bpe_train_text=  # Text file path of bpe training set for target language.
lm_train_text=   # Text file path of language model training set.
lm_dev_text=     # Text file path of language model development set.
lm_test_text=    # Text file path of language model evaluation set.
nlsyms_txt=none  # Non-linguistic symbol list if existing.
cleaner=none     # Text cleaner.
g2p=none         # g2p method (needed if token_type=phn).
lang=noinfo      # The language type of corpus.
score_opts=                # The options given to sclite scoring
local_score_opts=          # The options given to local/score.sh.
mt_text_fold_length=150   # fold_length for text data during MT training.
lm_fold_length=150         # fold_length for LM training.

help_message=$(cat << EOF
Usage: $0 --train-set "<train_set_name>" --valid-set "<valid_set_name>" --test_sets "<test_set_names>"

Options:
    # General configuration
    --stage              # Processes starts from the specified stage (default="${stage}").
    --stop_stage         # Processes is stopped at the specified stage (default="${stop_stage}").
    --skip_data_prep     # Skip data preparation stages (default="${skip_data_prep}").
    --skip_train         # Skip training stages (default="${skip_train}").
    --skip_eval          # Skip decoding and evaluation stages (default="${skip_eval}").
    --skip_packing       # Skip the packing stage (default="${skip_packing}").
    --skip_upload_hf     # Skip uploading to huggingface stage (default="${skip_upload_hf}").
    --ngpu               # The number of gpus ("0" uses cpu, otherwise use gpu, default="${ngpu}").
    --num_nodes          # The number of nodes (default="${num_nodes}").
    --nj                 # The number of parallel jobs (default="${nj}").
    --inference_nj       # The number of parallel jobs in decoding (default="${inference_nj}").
    --gpu_inference      # Whether to perform gpu decoding (default="${gpu_inference}").
    --dumpdir            # Directory to dump features (default="${dumpdir}").
    --expdir             # Directory to save experiments (default="${expdir}").
    --python             # Specify python to execute espnet commands (default="${python}").

    # Data preparation related
    --local_data_opts # The options given to local/data.sh (default="${local_data_opts}").

    # Feature extraction related
    --feats_type       # Feature type (raw, fbank_pitch or extracted, default="${feats_type}").

    # Tokenization related
    --oov                     # Out of vocabulary symbol (default="${oov}").
    --blank                   # CTC blank symbol (default="${blank}").
    --sos_eos                 # sos and eos symbole (default="${sos_eos}").
    --token_joint=false       # Whether to use a single bpe system for both source and target languages.
                              # if set as true, will use tgt_* for processing (default="${token_joint}").
    --src_token_type=bpe      # Tokenization type (char or bpe) for source languages. (default="${src_token_type}").
    --src_nbpe=30             # The number of BPE vocabulary for source language. (default="${src_nbpe}").
    --src_bpemode=unigram     # Mode of BPE for source language (unigram or bpe). (default="${src_bpemode}").
    --src_bpe_input_sentence_size=100000000 # Size of input sentence for BPE for source language. (default="${src_bpe_input_sentence_size}").
    --src_bpe_nlsyms=         # Non-linguistic symbols list, separated by a comma, for BPE of source language. (default="${src_bpe_nlsyms}").
    --src_bpe_char_cover=1.0  # Character coverage when modeling BPE for source language. (default="${src_bpe_char_cover}").
    --tgt_token_type=bpe      # Tokenization type (char or bpe) for target language. (default="${tgt_token_type}").
    --tgt_nbpe=30             # The number of BPE vocabulary for target language. (default="${tgt_nbpe}").
    --tgt_bpemode=unigram     # Mode of BPE (unigram or bpe) for target language. (default="${tgt_bpemode}").
    --tgt_bpe_input_sentence_size=100000000 # Size of input sentence for BPE for target language. (default="${tgt_bpe_input_sentence_size}").
    --tgt_bpe_nlsyms=         # Non-linguistic symbols list, separated by a comma, for BPE for target language. (default="${tgt_bpe_nlsyms}").
    --tgt_bpe_char_cover=1.0  # Character coverage when modeling BPE for target language. (default="${tgt_bpe_char_cover}").

    # Language model related
    --lm_tag          # Suffix to the result dir for language model training (default="${lm_tag}").
    --lm_exp          # Specify the directory path for LM experiment.
                      # If this option is specified, lm_tag is ignored (default="${lm_exp}").
    --lm_stats_dir    # Specify the directory path for LM statistics (default="${lm_stats_dir}").
    --lm_config       # Config for language model training (default="${lm_config}").
    --lm_args         # Arguments for language model training (default="${lm_args}").
                      # e.g., --lm_args "--max_epoch 10"
                      # Note that it will overwrite args in lm config.
    --use_word_lm     # Whether to use word language model (default="${use_word_lm}").
    --word_vocab_size # Size of word vocabulary (default="${word_vocab_size}").
    --num_splits_lm   # Number of splitting for lm corpus (default="${num_splits_lm}").

    # MT model related
    --mt_tag           # Suffix to the result dir for mt model training (default="${mt_tag}").
    --mt_exp           # Specify the directory path for MT experiment.
                       # If this option is specified, mt_tag is ignored (default="${mt_exp}").
    --mt_stats_dir     # Specify the directory path for MT statistics (default="${mt_stats_dir}").
    --mt_config        # Config for mt model training (default="${mt_config}").
    --mt_args          # Arguments for mt model training (default="${mt_args}").
                       # e.g., --mt_args "--max_epoch 10"
                       # Note that it will overwrite args in mt config.
    --ignore_init_mismatch=      # Ignore mismatch parameter init with pretrained model (default="${ignore_init_mismatch}").
    --num_splits_mt    # Number of splitting for lm corpus.  (default="${num_splits_mt}").
    --src_lang=        # source language abbrev. id (e.g., es). (default="${src_lang}")
    --tgt_lang=        # target language abbrev. id (e.g., en). (default="${tgt_lang}")

    # Decoding related
    --inference_tag       # Suffix to the result dir for decoding (default="${inference_tag}").
    --inference_config    # Config for decoding (default="${inference_config}").
    --inference_args      # Arguments for decoding (default="${inference_args}").
                          # e.g., --inference_args "--lm_weight 0.1"
                          # Note that it will overwrite args in inference config.
    --inference_lm        # Language model path for decoding (default="${inference_lm}").
    --inference_mt_model # MT model path for decoding (default="${inference_mt_model}").
    --download_model      # Download a model from Model Zoo and use it for decoding (default="${download_model}").

    # [Task dependent] Set the datadir name created by local/data.sh
    --train_set     # Name of training set (required).
    --valid_set     # Name of validation set used for monitoring/tuning network training (required).
    --test_sets     # Names of test sets.
                    # Multiple items (e.g., both dev and eval sets) can be specified (required).
    --src_bpe_train_text # Text file path of bpe training set for source language.
    --tgt_bpe_train_text # Text file path of bpe training set for target language
    --lm_train_text  # Text file path of language model training set.
    --lm_dev_text   # Text file path of language model development set (default="${lm_dev_text}").
    --lm_test_text  # Text file path of language model evaluation set (default="${lm_test_text}").
    --nlsyms_txt    # Non-linguistic symbol list if existing (default="${nlsyms_txt}").
    --cleaner       # Text cleaner (default="${cleaner}").
    --g2p           # g2p method (default="${g2p}").
    --lang          # The language type of corpus (default=${lang}).
    --score_opts             # The options given to sclite scoring (default="{score_opts}").
    --local_score_opts       # The options given to local/score.sh (default="{local_score_opts}").
    --mt_text_fold_length   # fold_length for text data during MT training (default="${mt_text_fold_length}").
    --lm_fold_length         # fold_length for LM training (default="${lm_fold_length}").
EOF
)

log "$0 $*"
# Save command line args for logging (they will be lost after utils/parse_options.sh)
run_args=$(scripts/utils/print_args.sh $0 "$@")
. utils/parse_options.sh

if [ $# -ne 0 ]; then
    log "${help_message}"
    log "Error: No positional arguments are required."
    exit 2
fi

. ./path.sh
. ./cmd.sh


# Check required arguments
[ -z "${train_set}" ] && { log "${help_message}"; log "Error: --train_set is required"; exit 2; };
[ -z "${valid_set}" ] && { log "${help_message}"; log "Error: --valid_set is required"; exit 2; };
[ -z "${test_sets}" ] && { log "${help_message}"; log "Error: --test_sets is required"; exit 2; };

# Check feature type
if [ "${feats_type}" = raw ]; then
    data_feats=${dumpdir}/raw
else
    log "${help_message}"
    log "Error: not supported: --feats_type ${feats_type}"
    exit 2
fi

# Extra files for translation process
utt_extra_files="text.${src_case}.${src_lang} text.${tgt_case}.${tgt_lang}"
# Use the same text as MT for bpe training if not specified.
[ -z "${src_bpe_train_text}" ] && src_bpe_train_text="${data_feats}/${train_set}/text.${src_case}.${src_lang}"
[ -z "${tgt_bpe_train_text}" ] && tgt_bpe_train_text="${data_feats}/${train_set}/text.${tgt_case}.${tgt_lang}"
# Use the same text as MT for lm training if not specified.
[ -z "${lm_train_text}" ] && lm_train_text="${data_feats}/${train_set}/text.${tgt_case}.${tgt_lang}"
# Use the same text as MT for lm training if not specified.
[ -z "${lm_dev_text}" ] && lm_dev_text="${data_feats}/${valid_set}/text.${tgt_case}.${tgt_lang}"
# Use the text of the 1st evaldir if lm_test is not specified
[ -z "${lm_test_text}" ] && lm_test_text="${data_feats}/${test_sets%% *}/text.${tgt_case}.${tgt_lang}"

# Check tokenization type
if [ "${lang}" != noinfo ]; then
    token_listdir=data/${lang}_token_list
else
    token_listdir=data/token_list
fi
# The tgt bpedir is set for all cases when using bpe
tgt_bpedir="${token_listdir}/tgt_bpe_${tgt_bpemode}${tgt_nbpe}"
tgt_bpeprefix="${tgt_bpedir}"/bpe
tgt_bpemodel="${tgt_bpeprefix}".model
tgt_bpetoken_list="${tgt_bpedir}"/tokens.txt
tgt_chartoken_list="${token_listdir}"/char/tgt_tokens.txt
if "${token_joint}"; then
    # if token_joint, the bpe training will use both src_lang and tgt_lang to train a single bpe model
    src_bpedir="${tgt_bpedir}"
    src_bpeprefix="${tgt_bpeprefix}"
    src_bpemodel="${tgt_bpemodel}"
    src_bpetoken_list="${tgt_bpetoken_list}"
    src_chartoken_list="${tgt_chartoken_list}"
else
    src_bpedir="${token_listdir}/src_bpe_${src_bpemode}${src_nbpe}"
    src_bpeprefix="${src_bpedir}"/bpe
    src_bpemodel="${src_bpeprefix}".model
    src_bpetoken_list="${src_bpedir}"/tokens.txt
    src_chartoken_list="${token_listdir}"/char/src_tokens.txt
fi

# NOTE: keep for future development.
# shellcheck disable=SC2034
tgt_wordtoken_list="${token_listdir}"/word/tgt_tokens.txt
if "${token_joint}"; then
    src_wordtoken_list="${tgt_wordtoken_list}"
else
    src_wordtoken_list="${token_listdir}"/word/src_tokens.txt
fi

# Set token types for src and tgt langs
if [ "${src_token_type}" = bpe ]; then
    src_token_list="${src_bpetoken_list}"
elif [ "${src_token_type}" = char ]; then
    src_token_list="${src_chartoken_list}"
    src_bpemodel=none
elif [ "${src_token_type}" = word ]; then
    src_token_list="${src_wordtoken_list}"
    src_bpemodel=none
else
    log "Error: not supported --src_token_type '${src_token_type}'"
    exit 2
fi
if [ "${tgt_token_type}" = bpe ]; then
    tgt_token_list="${tgt_bpetoken_list}"
elif [ "${tgt_token_type}" = char ]; then
    tgt_token_list="${tgt_chartoken_list}"
    tgt_bpemodel=none
elif [ "${tgt_token_type}" = word ]; then
    tgt_token_list="${tgt_wordtoken_list}"
    tgt_bpemodel=none
else
    log "Error: not supported --tgt_token_type '${tgt_token_type}'"
    exit 2
fi
if ${use_word_lm}; then
    log "Error: Word LM is not supported yet"
    exit 2

    lm_token_list="${tgt_wordtoken_list}"
    lm_token_type=word
else
    lm_token_list="${tgt_token_list}"
    lm_token_type="${tgt_token_type}"
fi


# Set tag for naming of model directory
if [ -z "${mt_tag}" ]; then
    if [ -n "${mt_config}" ]; then
        mt_tag="$(basename "${mt_config}" .yaml)_${feats_type}"
    else
        mt_tag="train_${feats_type}"
    fi
    if [ "${lang}" != noinfo ]; then
        mt_tag+="_${lang}_${tgt_token_type}_${tgt_case}"
    else
        mt_tag+="_${tgt_token_type}_${tgt_case}"
    fi
    if [ "${tgt_token_type}" = bpe ]; then
        mt_tag+="${tgt_nbpe}"
    fi
    # Add overwritten arg's info
    if [ -n "${mt_args}" ]; then
        mt_tag+="$(echo "${mt_args}" | sed -e "s/--/\_/g" -e "s/[ |=/]//g")"
    fi
fi
if [ -z "${lm_tag}" ]; then
    if [ -n "${lm_config}" ]; then
        lm_tag="$(basename "${lm_config}" .yaml)"
    else
        lm_tag="train"
    fi
    if [ "${lang}" != noinfo ]; then
        lm_tag+="_${lang}_${lm_token_type}"
    else
        lm_tag+="_${lm_token_type}"
    fi
    if [ "${lm_token_type}" = bpe ]; then
        lm_tag+="${tgt_nbpe}"
    fi
    # Add overwritten arg's info
    if [ -n "${lm_args}" ]; then
        lm_tag+="$(echo "${lm_args}" | sed -e "s/--/\_/g" -e "s/[ |=/]//g")"
    fi
fi

# The directory used for collect-stats mode
if [ -z "${mt_stats_dir}" ]; then
    if [ "${lang}" != noinfo ]; then
        mt_stats_dir="${expdir}/mt_stats_${feats_type}_${lang}_${tgt_token_type}"
    else
        mt_stats_dir="${expdir}/mt_stats_${feats_type}_${tgt_token_type}"
    fi
    if [ "${tgt_token_type}" = bpe ]; then
        mt_stats_dir+="${tgt_nbpe}"
    fi
fi
if [ -z "${lm_stats_dir}" ]; then
    if [ "${lang}" != noinfo ]; then
        lm_stats_dir="${expdir}/lm_stats_${lang}_${lm_token_type}"
    else
        lm_stats_dir="${expdir}/lm_stats_${lm_token_type}"
    fi
    if [ "${lm_token_type}" = bpe ]; then
        lm_stats_dir+="${tgt_nbpe}"
    fi
fi
# The directory used for training commands
if [ -z "${mt_exp}" ]; then
    mt_exp="${expdir}/mt_${mt_tag}"
fi
if [ -z "${lm_exp}" ]; then
    lm_exp="${expdir}/lm_${lm_tag}"
fi
if [ -z "${ngram_exp}" ]; then
    ngram_exp="${expdir}/ngram"
fi


if [ -z "${inference_tag}" ]; then
    if [ -n "${inference_config}" ]; then
        inference_tag="$(basename "${inference_config}" .yaml)"
    else
        inference_tag=inference
    fi
    # Add overwritten arg's info
    if [ -n "${inference_args}" ]; then
        inference_tag+="$(echo "${inference_args}" | sed -e "s/--/\_/g" -e "s/[ |=]//g")"
    fi
    if "${use_lm}"; then
        inference_tag+="_lm_$(basename "${lm_exp}")_$(echo "${inference_lm}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
    fi
    if "${use_ngram}"; then
        inference_tag+="_ngram_$(basename "${ngram_exp}")_$(echo "${inference_ngram}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
    fi
    inference_tag+="_mt_model_$(echo "${inference_mt_model}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"

    if "${use_k2}"; then
      inference_tag+="_use_k2"
    fi
fi

# ========================== Main stages start from here. ==========================

if ! "${skip_data_prep}"; then
    if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
        log "Stage 1: Data preparation for data/${train_set}, data/${valid_set}, etc."
        # [Task dependent] Need to create data.sh for new corpus
        local/data.sh ${local_data_opts}

    fi

    if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
        if [ "${feats_type}" = raw ]; then
            log "Stage 2: data/ -> ${data_feats}"

            for dset in "${train_set}" "${valid_set}" ${test_sets}; do
                if [ "${dset}" = "${train_set}" ] || [ "${dset}" = "${valid_set}" ]; then
                    _suf="/org"
                else
                    _suf=""
                fi
                mkdir -p "${data_feats}${_suf}/${dset}"

                for extra_file in ${utt_extra_files}; do
                    # with regex to suuport multi-references
                    for single_file in $(ls data/"${dset}"/${extra_file}*); do
                        cp ${single_file} "${data_feats}${_suf}/${dset}"
                    done
                done
                echo "${feats_type}" > "${data_feats}${_suf}/${dset}/feats_type"
            done
        else
            log "Error: not supported: --feats_type ${feats_type}"
            exit 2
        fi
    fi


    if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
        log "Stage 3: Data filtering: ${data_feats}/org -> ${data_feats}"

        # NOTE(kamo): Not applying to test_sets to keep original data
        for dset in "${train_set}" "${valid_set}"; do
            # Copy data dir
            mkdir -p "${data_feats}/${dset}"
            cp "${data_feats}/org/${dset}/feats_type" "${data_feats}/${dset}/feats_type"

            for utt_extra_file in ${utt_extra_files}; do
                cp "${data_feats}/org/${dset}/${utt_extra_file}" "${data_feats}/${dset}"
            done
            # TODO: Maybe Remove empty text
            # TODO: Add other data cleaning -- currently being done as part of data.sh
        done

        # shellcheck disable=SC2002
        cat ${lm_train_text} | awk ' { if( NF != 1 ) print $0; } ' > "${data_feats}/lm_train.txt"
    fi


    if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then

        if "${token_joint}"; then
            log "Merge src and target data if joint BPE"

            cat $tgt_bpe_train_text > ${data_feats}/${train_set}/text.${src_lang}_${tgt_lang}
            [ ! -z "${src_bpe_train_text}" ] && cat ${src_bpe_train_text} >> ${data_feats}/${train_set}/text.${src_lang}_${tgt_lang}
            # Set the new text as the target text
            tgt_bpe_train_text="${data_feats}/${train_set}/text.${src_lang}_${tgt_lang}"
        fi

        # First generate tgt lang
        if [ "${tgt_token_type}" = bpe ]; then
            log "Stage 4a: Generate token_list from ${tgt_bpe_train_text} using BPE for tgt_lang"

            mkdir -p "${tgt_bpedir}"
            # shellcheck disable=SC2002
            cat ${tgt_bpe_train_text} | cut -f 2- -d" "  > "${tgt_bpedir}"/train.txt

            if [ -n "${tgt_bpe_nlsyms}" ]; then
                _opts_spm="--user_defined_symbols=${tgt_bpe_nlsyms}"
            else
                _opts_spm=""
            fi

            spm_train \
                --input="${tgt_bpedir}"/train.txt \
                --vocab_size="${tgt_nbpe}" \
                --model_type="${tgt_bpemode}" \
                --model_prefix="${tgt_bpeprefix}" \
                --character_coverage=${tgt_bpe_char_cover} \
                --input_sentence_size="${tgt_bpe_input_sentence_size}" \
                ${_opts_spm}

            {
            echo "${blank}"
            echo "${oov}"
            # Remove <unk>, <s>, </s> from the vocabulary
            <"${tgt_bpeprefix}".vocab awk '{ if( NR != 1 && NR != 2 && NR != 3 ){ print $1; } }'
            echo "${sos_eos}"
            } > "${tgt_token_list}"

        elif [ "${tgt_token_type}" = char ] || [ "${tgt_token_type}" = word ]; then
            log "Stage 4a: Generate character level token_list from ${tgt_bpe_train_text}  for tgt_lang"

            _opts="--non_linguistic_symbols ${nlsyms_txt}"

            # shellcheck disable=SC2002
            cat ${tgt_bpe_train_text} | cut -f 2- -d" "  > "${data_feats}"/token_train.txt

            # The first symbol in token_list must be "<blank>" and the last must be also sos/eos:
            # 0 is reserved for CTC-blank for MT and also used as ignore-index in the other task
            ${python} -m espnet2.bin.tokenize_text  \
                --token_type "${tgt_token_type}" \
                --input "${data_feats}/token_train.txt" --output "${tgt_token_list}" ${_opts} \
                --field 2- \
                --cleaner "${cleaner}" \
                --g2p "${g2p}" \
                --write_vocabulary true \
                --add_symbol "${blank}:0" \
                --add_symbol "${oov}:1" \
                --add_symbol "${sos_eos}:-1"

        else
            log "Error: not supported --token_type '${tgt_token_type}'"
            exit 2
        fi

        # Create word-list for word-LM training
        if ${use_word_lm} && [ "${tgt_token_type}" != word ]; then
            log "Generate word level token_list from ${data_feats}/lm_train.txt"
            ${python} -m espnet2.bin.tokenize_text \
                --token_type word \
                --input "${data_feats}/lm_train.txt" --output "${lm_token_list}" \
                --field 2- \
                --cleaner "${cleaner}" \
                --g2p "${g2p}" \
                --write_vocabulary true \
                --vocabulary_size "${word_vocab_size}" \
                --add_symbol "${blank}:0" \
                --add_symbol "${oov}:1" \
                --add_symbol "${sos_eos}:-1"
        fi

        # Then generate src lang
        if "${token_joint}"; then
            log "Stage 4b: Skip separate token construction for src_lang when setting ${token_joint} as true"
        else
            if [ "${src_token_type}" = bpe ]; then
                log "Stage 4b: Generate token_list from ${src_bpe_train_text} using BPE for src_lang"

                mkdir -p "${src_bpedir}"
                # shellcheck disable=SC2002
                cat ${src_bpe_train_text} | cut -f 2- -d" "  > "${src_bpedir}"/train.txt

                if [ -n "${src_bpe_nlsyms}" ]; then
                    _opts_spm="--user_defined_symbols=${src_bpe_nlsyms}"
                else
                    _opts_spm=""
                fi

                spm_train \
                    --input="${src_bpedir}"/train.txt \
                    --vocab_size="${src_nbpe}" \
                    --model_type="${src_bpemode}" \
                    --model_prefix="${src_bpeprefix}" \
                    --character_coverage=${src_bpe_char_cover} \
                    --input_sentence_size="${src_bpe_input_sentence_size}" \
                    ${_opts_spm}

                {
                echo "${blank}"
                echo "${oov}"
                # Remove <unk>, <s>, </s> from the vocabulary
                <"${src_bpeprefix}".vocab awk '{ if( NR != 1 && NR != 2 && NR != 3 ){ print $1; } }'
                echo "${sos_eos}"
                } > "${src_token_list}"

            elif [ "${src_token_type}" = char ] || [ "${src_token_type}" = word ]; then
                log "Stage 4b: Generate character level token_list from ${src_bpe_train_text}  for src_lang"

                _opts="--non_linguistic_symbols ${nlsyms_txt}"

                # shellcheck disable=SC2002
                cat ${src_bpe_train_text} | cut -f 2- -d" "  > "${data_feats}"/token_train.txt

                # The first symbol in token_list must be "<blank>" and the last must be also sos/eos:
                # 0 is reserved for CTC-blank for MT and also used as ignore-index in the other task
                ${python} -m espnet2.bin.tokenize_text  \
                    --token_type "${src_token_type}" \
                    --input "${data_feats}/token_train.txt" --output "${src_token_list}" ${_opts} \
                    --field 2- \
                    --cleaner "${cleaner}" \
                    --g2p "${g2p}" \
                    --write_vocabulary true \
                    --add_symbol "${blank}:0" \
                    --add_symbol "${oov}:1" \
                    --add_symbol "${sos_eos}:-1"

            else
                log "Error: not supported --token_type '${src_token_type}'"
                exit 2
            fi
        fi
    fi

else
    log "Skip the stages for data preparation"
fi


# ========================== Data preparation is done here. ==========================


if ! "${skip_train}"; then
    if "${use_lm}"; then
        if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
            log "Stage 5: LM collect stats: train_set=${data_feats}/lm_train.txt, dev_set=${lm_dev_text}"

            _opts=
            if [ -n "${lm_config}" ]; then
                # To generate the config file: e.g.
                #   % python3 -m espnet2.bin.lm_train --print_config --optim adam
                _opts+="--config ${lm_config} "
            fi

            # 1. Split the key file
            _logdir="${lm_stats_dir}/logdir"
            mkdir -p "${_logdir}"
            # Get the minimum number among ${nj} and the number lines of input files
            _nj=$(min "${nj}" "$(<${data_feats}/lm_train.txt wc -l)" "$(<${lm_dev_text} wc -l)")

            key_file="${data_feats}/lm_train.txt"
            split_scps=""
            for n in $(seq ${_nj}); do
                split_scps+=" ${_logdir}/train.${n}.scp"
            done
            # shellcheck disable=SC2086
            utils/split_scp.pl "${key_file}" ${split_scps}

            key_file="${lm_dev_text}"
            split_scps=""
            for n in $(seq ${_nj}); do
                split_scps+=" ${_logdir}/dev.${n}.scp"
            done
            # shellcheck disable=SC2086
            utils/split_scp.pl "${key_file}" ${split_scps}

            # 2. Generate run.sh
            log "Generate '${lm_stats_dir}/run.sh'. You can resume the process from stage 6 using this script"
            mkdir -p "${lm_stats_dir}"; echo "${run_args} --stage 6 \"\$@\"; exit \$?" > "${lm_stats_dir}/run.sh"; chmod +x "${lm_stats_dir}/run.sh"

            # 3. Submit jobs
            log "LM collect-stats started... log: '${_logdir}/stats.*.log'"
            # NOTE: --*_shape_file doesn't require length information if --batch_type=unsorted,
            #       but it's used only for deciding the sample ids.
            # shellcheck disable=SC2046,SC2086
            ${train_cmd} JOB=1:"${_nj}" "${_logdir}"/stats.JOB.log \
                ${python} -m espnet2.bin.lm_train \
                    --collect_stats true \
                    --use_preprocessor true \
                    --bpemodel "${tgt_bpemodel}" \
                    --token_type "${lm_token_type}"\
                    --token_list "${lm_token_list}" \
                    --non_linguistic_symbols "${nlsyms_txt}" \
                    --cleaner "${cleaner}" \
                    --g2p "${g2p}" \
                    --train_data_path_and_name_and_type "${data_feats}/lm_train.txt,text,text" \
                    --valid_data_path_and_name_and_type "${lm_dev_text},text,text" \
                    --train_shape_file "${_logdir}/train.JOB.scp" \
                    --valid_shape_file "${_logdir}/dev.JOB.scp" \
                    --output_dir "${_logdir}/stats.JOB" \
                    ${_opts} ${lm_args} || { cat $(grep -l -i error "${_logdir}"/stats.*.log) ; exit 1; }

            # 4. Aggregate shape files
            _opts=
            for i in $(seq "${_nj}"); do
                _opts+="--input_dir ${_logdir}/stats.${i} "
            done
            # shellcheck disable=SC2086
            ${python} -m espnet2.bin.aggregate_stats_dirs ${_opts} --output_dir "${lm_stats_dir}"

            # Append the num-tokens at the last dimensions. This is used for batch-bins count
            <"${lm_stats_dir}/train/text_shape" \
                awk -v N="$(<${lm_token_list} wc -l)" '{ print $0 "," N }' \
                >"${lm_stats_dir}/train/text_shape.${lm_token_type}"

            <"${lm_stats_dir}/valid/text_shape" \
                awk -v N="$(<${lm_token_list} wc -l)" '{ print $0 "," N }' \
                >"${lm_stats_dir}/valid/text_shape.${lm_token_type}"
        fi


        if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
            log "Stage 6: LM Training: train_set=${data_feats}/lm_train.txt, dev_set=${lm_dev_text}"

            _opts=
            if [ -n "${lm_config}" ]; then
                # To generate the config file: e.g.
                #   % python3 -m espnet2.bin.lm_train --print_config --optim adam
                _opts+="--config ${lm_config} "
            fi

            if [ "${num_splits_lm}" -gt 1 ]; then
                # If you met a memory error when parsing text files, this option may help you.
                # The corpus is split into subsets and each subset is used for training one by one in order,
                # so the memory footprint can be limited to the memory required for each dataset.

                _split_dir="${lm_stats_dir}/splits${num_splits_lm}"
                if [ ! -f "${_split_dir}/.done" ]; then
                    rm -f "${_split_dir}/.done"
                    ${python} -m espnet2.bin.split_scps \
                      --scps "${data_feats}/lm_train.txt" "${lm_stats_dir}/train/text_shape.${lm_token_type}" \
                      --num_splits "${num_splits_lm}" \
                      --output_dir "${_split_dir}"
                    touch "${_split_dir}/.done"
                else
                    log "${_split_dir}/.done exists. Spliting is skipped"
                fi

                _opts+="--train_data_path_and_name_and_type ${_split_dir}/lm_train.txt,text,text "
                _opts+="--train_shape_file ${_split_dir}/text_shape.${lm_token_type} "
                _opts+="--multiple_iterator true "

            else
                _opts+="--train_data_path_and_name_and_type ${data_feats}/lm_train.txt,text,text "
                _opts+="--train_shape_file ${lm_stats_dir}/train/text_shape.${lm_token_type} "
            fi

            # NOTE(kamo): --fold_length is used only if --batch_type=folded and it's ignored in the other case

            log "Generate '${lm_exp}/run.sh'. You can resume the process from stage 7 using this script"
            mkdir -p "${lm_exp}"; echo "${run_args} --stage 7 \"\$@\"; exit \$?" > "${lm_exp}/run.sh"; chmod +x "${lm_exp}/run.sh"

            log "LM training started... log: '${lm_exp}/train.log'"
            if echo "${cuda_cmd}" | grep -e queue.pl -e queue-freegpu.pl &> /dev/null; then
                # SGE can't include "/" in a job name
                jobname="$(basename ${lm_exp})"
            else
                jobname="${lm_exp}/train.log"
            fi

            # shellcheck disable=SC2086
            ${python} -m espnet2.bin.launch \
                --cmd "${cuda_cmd} --name ${jobname}" \
                --log "${lm_exp}"/train.log \
                --ngpu "${ngpu}" \
                --num_nodes "${num_nodes}" \
                --init_file_prefix "${lm_exp}"/.dist_init_ \
                --multiprocessing_distributed true -- \
                ${python} -m espnet2.bin.lm_train \
                    --ngpu "${ngpu}" \
                    --use_preprocessor true \
                    --bpemodel "${tgt_bpemodel}" \
                    --token_type "${lm_token_type}"\
                    --token_list "${lm_token_list}" \
                    --non_linguistic_symbols "${nlsyms_txt}" \
                    --cleaner "${cleaner}" \
                    --g2p "${g2p}" \
                    --valid_data_path_and_name_and_type "${lm_dev_text},text,text" \
                    --valid_shape_file "${lm_stats_dir}/valid/text_shape.${lm_token_type}" \
                    --fold_length "${lm_fold_length}" \
                    --resume true \
                    --output_dir "${lm_exp}" \
                    ${_opts} ${lm_args}

        fi


        if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
            log "Stage 7: Calc perplexity: ${lm_test_text}"
            _opts=
            # TODO(kamo): Parallelize?
            log "Perplexity calculation started... log: '${lm_exp}/perplexity_test/lm_calc_perplexity.log'"
            # shellcheck disable=SC2086
            ${cuda_cmd} --gpu "${ngpu}" "${lm_exp}"/perplexity_test/lm_calc_perplexity.log \
                ${python} -m espnet2.bin.lm_calc_perplexity \
                    --ngpu "${ngpu}" \
                    --data_path_and_name_and_type "${lm_test_text},text,text" \
                    --train_config "${lm_exp}"/config.yaml \
                    --model_file "${lm_exp}/${inference_lm}" \
                    --output_dir "${lm_exp}/perplexity_test" \
                    ${_opts}
            log "PPL: ${lm_test_text}: $(cat ${lm_exp}/perplexity_test/ppl)"

        fi

    else
        log "Stage 5-7: Skip lm-related stages: use_lm=${use_lm}"
    fi


    if "${use_ngram}"; then
        mkdir -p ${ngram_exp}
    fi
    if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
        if "${use_ngram}"; then
            log "Stage 8: Ngram Training: train_set=${data_feats}/lm_train.txt"
            cut -f 2 -d " " ${data_feats}/lm_train.txt | lmplz -S "20%" --discount_fallback -o ${ngram_num} - >${ngram_exp}/${ngram_num}gram.arpa
            build_binary -s ${ngram_exp}/${ngram_num}gram.arpa ${ngram_exp}/${ngram_num}gram.bin
        else
            log "Stage 8: Skip ngram stages: use_ngram=${use_ngram}"
        fi
    fi


    if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
        _mt_train_dir="${data_feats}/${train_set}"
        _mt_valid_dir="${data_feats}/${valid_set}"
        log "Stage 9: MT collect stats: train_set=${_mt_train_dir}, valid_set=${_mt_valid_dir}"

        _opts=
        if [ -n "${mt_config}" ]; then
            # To generate the config file: e.g.
            #   % python3 -m espnet2.bin.mt_train --print_config --optim adam
            _opts+="--config ${mt_config} "
        fi

        # 1. Split the key file
        _logdir="${mt_stats_dir}/logdir"
        mkdir -p "${_logdir}"

        _scp=text.${src_case}.${src_lang}

        # Get the minimum number among ${nj} and the number lines of input files
        _nj=$(min "${nj}" "$(<${_mt_train_dir}/${_scp} wc -l)" "$(<${_mt_valid_dir}/${_scp} wc -l)")

        key_file="${_mt_train_dir}/${_scp}"
        split_scps=""
        for n in $(seq "${_nj}"); do
            split_scps+=" ${_logdir}/train.${n}.scp"
        done
        # shellcheck disable=SC2086
        utils/split_scp.pl "${key_file}" ${split_scps}

        key_file="${_mt_valid_dir}/${_scp}"
        split_scps=""
        for n in $(seq "${_nj}"); do
            split_scps+=" ${_logdir}/valid.${n}.scp"
        done
        # shellcheck disable=SC2086
        utils/split_scp.pl "${key_file}" ${split_scps}

        # 2. Generate run.sh
        log "Generate '${mt_stats_dir}/run.sh'. You can resume the process from stage 9 using this script"
        mkdir -p "${mt_stats_dir}"; echo "${run_args} --stage 9 \"\$@\"; exit \$?" > "${mt_stats_dir}/run.sh"; chmod +x "${mt_stats_dir}/run.sh"

        # 3. Submit jobs
        log "MT collect-stats started... log: '${_logdir}/stats.*.log'"

        # NOTE: --*_shape_file doesn't require length information if --batch_type=unsorted,
        #       but it's used only for deciding the sample ids.

        # TODO(jiatong): fix different bpe model
        # shellcheck disable=SC2086
        ${train_cmd} JOB=1:"${_nj}" "${_logdir}"/stats.JOB.log \
            ${python} -m espnet2.bin.mt_train \
                --collect_stats true \
                --use_preprocessor true \
                --bpemodel "${tgt_bpemodel}" \
                --src_bpemodel "${src_bpemodel}" \
                --token_type "${tgt_token_type}" \
                --src_token_type "${src_token_type}" \
                --token_list "${tgt_token_list}" \
                --src_token_list "${src_token_list}" \
                --non_linguistic_symbols "${nlsyms_txt}" \
                --cleaner "${cleaner}" \
                --g2p "${g2p}" \
                --train_data_path_and_name_and_type "${_mt_train_dir}/text.${tgt_case}.${tgt_lang},text,text" \
                --train_data_path_and_name_and_type "${_mt_train_dir}/text.${src_case}.${src_lang},src_text,text" \
                --valid_data_path_and_name_and_type "${_mt_valid_dir}/text.${tgt_case}.${tgt_lang},text,text" \
                --valid_data_path_and_name_and_type "${_mt_valid_dir}/text.${src_case}.${src_lang},src_text,text" \
                --train_shape_file "${_logdir}/train.JOB.scp" \
                --valid_shape_file "${_logdir}/valid.JOB.scp" \
                --output_dir "${_logdir}/stats.JOB" \
                ${_opts} ${mt_args} || { cat "${_logdir}"/stats.1.log; exit 1; }

        # 4. Aggregate shape files
        _opts=
        for i in $(seq "${_nj}"); do
            _opts+="--input_dir ${_logdir}/stats.${i} "
        done
        # shellcheck disable=SC2086
        ${python} -m espnet2.bin.aggregate_stats_dirs ${_opts} --output_dir "${mt_stats_dir}"

        # Append the num-tokens at the last dimensions. This is used for batch-bins count
        <"${mt_stats_dir}/train/text_shape" \
            awk -v N="$(<${tgt_token_list} wc -l)" '{ print $0 "," N }' \
            >"${mt_stats_dir}/train/text_shape.${tgt_token_type}"

        <"${mt_stats_dir}/train/src_text_shape" \
            awk -v N="$(<${src_token_list} wc -l)" '{ print $0 "," N }' \
            >"${mt_stats_dir}/train/src_text_shape.${src_token_type}"

        <"${mt_stats_dir}/valid/text_shape" \
            awk -v N="$(<${tgt_token_list} wc -l)" '{ print $0 "," N }' \
            >"${mt_stats_dir}/valid/text_shape.${tgt_token_type}"

        <"${mt_stats_dir}/valid/src_text_shape" \
            awk -v N="$(<${src_token_list} wc -l)" '{ print $0 "," N }' \
            >"${mt_stats_dir}/valid/src_text_shape.${src_token_type}"
    fi


    if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
        _mt_train_dir="${data_feats}/${train_set}"
        _mt_valid_dir="${data_feats}/${valid_set}"
        log "Stage 10: MT Training: train_set=${_mt_train_dir}, valid_set=${_mt_valid_dir}"

        _opts=
        if [ -n "${mt_config}" ]; then
            # To generate the config file: e.g.
            #   % python3 -m espnet2.bin.mt_train --print_config --optim adam
            _opts+="--config ${mt_config} "
        fi

        if [ "${num_splits_mt}" -gt 1 ]; then
            # If you met a memory error when parsing text files, this option may help you.
            # The corpus is split into subsets and each subset is used for training one by one in order,
            # so the memory footprint can be limited to the memory required for each dataset.

            _split_dir="${mt_stats_dir}/splits${num_splits_mt}"
            if [ ! -f "${_split_dir}/.done" ]; then
                rm -f "${_split_dir}/.done"
                ${python} -m espnet2.bin.split_scps \
                  --scps \
                      "${_mt_train_dir}/${_scp}" \
                      "${_mt_train_dir}/text.${tgt_case}.${tgt_lang}" \
                      "${_mt_train_dir}/text.${src_case}.${src_lang}" \
                      "${mt_stats_dir}/train/text_shape.${tgt_token_type}" \
                      "${mt_stats_dir}/train/src_text_shape.${src_token_type}" \
                  --num_splits "${num_splits_mt}" \
                  --output_dir "${_split_dir}"
                touch "${_split_dir}/.done"
            else
                log "${_split_dir}/.done exists. Spliting is skipped"
            fi

            _opts+="--train_data_path_and_name_and_type ${_split_dir}/text.${tgt_case}.${tgt_lang},text,text "
            _opts+="--train_data_path_and_name_and_type ${_split_dir}/text.${src_case}.${src_lang},src_text,text "
            _opts+="--train_shape_file ${_split_dir}/text_shape.${tgt_token_type} "
            _opts+="--train_shape_file ${_split_dir}/src_text_shape.${src_token_type} "
            _opts+="--multiple_iterator true "
        else
            _opts+="--train_data_path_and_name_and_type ${_mt_train_dir}/text.${tgt_case}.${tgt_lang},text,text "
            _opts+="--train_data_path_and_name_and_type ${_mt_train_dir}/text.${src_case}.${src_lang},src_text,text "
            _opts+="--train_shape_file ${mt_stats_dir}/train/text_shape.${tgt_token_type} "
            _opts+="--train_shape_file ${mt_stats_dir}/train/src_text_shape.${src_token_type} "
        fi

        log "Generate '${mt_exp}/run.sh'. You can resume the process from stage 10 using this script"
        mkdir -p "${mt_exp}"; echo "${run_args} --stage 10 \"\$@\"; exit \$?" > "${mt_exp}/run.sh"; chmod +x "${mt_exp}/run.sh"

        # NOTE(kamo): --fold_length is used only if --batch_type=folded and it's ignored in the other case
        log "MT training started... log: '${mt_exp}/train.log'"
        if echo "${cuda_cmd}" | grep -e queue.pl -e queue-freegpu.pl &> /dev/null; then
            # SGE can't include "/" in a job name
            jobname="$(basename ${mt_exp})"
        else
            jobname="${mt_exp}/train.log"
        fi

        # TODO(jiatong): fix bpe
        # shellcheck disable=SC2086
        ${python} -m espnet2.bin.launch \
            --cmd "${cuda_cmd} --name ${jobname}" \
            --log "${mt_exp}"/train.log \
            --ngpu "${ngpu}" \
            --num_nodes "${num_nodes}" \
            --init_file_prefix "${mt_exp}"/.dimt_init_ \
            --multiprocessing_distributed true -- \
            ${python} -m espnet2.bin.mt_train \
                --use_preprocessor true \
                --bpemodel "${tgt_bpemodel}" \
                --token_type "${tgt_token_type}" \
                --token_list "${tgt_token_list}" \
                --src_bpemodel "${src_bpemodel}" \
                --src_token_type "${src_token_type}" \
                --src_token_list "${src_token_list}" \
                --non_linguistic_symbols "${nlsyms_txt}" \
                --cleaner "${cleaner}" \
                --g2p "${g2p}" \
                --valid_data_path_and_name_and_type "${_mt_valid_dir}/text.${tgt_case}.${tgt_lang},text,text" \
                --valid_data_path_and_name_and_type "${_mt_valid_dir}/text.${src_case}.${src_lang},src_text,text" \
                --valid_shape_file "${mt_stats_dir}/valid/text_shape.${tgt_token_type}" \
                --valid_shape_file "${mt_stats_dir}/valid/src_text_shape.${src_token_type}" \
                --resume true \
                --ignore_init_mismatch ${ignore_init_mismatch} \
                --fold_length "${mt_text_fold_length}" \
                --fold_length "${mt_text_fold_length}" \
                --output_dir "${mt_exp}" \
                ${_opts} ${mt_args}

    fi
else
    log "Skip the training stages"
fi


if [ -n "${download_model}" ]; then
    log "Use ${download_model} for decoding and evaluation"
    mt_exp="${expdir}/${download_model}"
    mkdir -p "${mt_exp}"

    # If the model already exists, you can skip downloading
    espnet_model_zoo_download --unpack true "${download_model}" > "${mt_exp}/config.txt"

    # Get the path of each file
    _mt_model_file=$(<"${mt_exp}/config.txt" sed -e "s/.*'mt_model_file': '\([^']*\)'.*$/\1/")
    _mt_train_config=$(<"${mt_exp}/config.txt" sed -e "s/.*'mt_train_config': '\([^']*\)'.*$/\1/")

    # Create symbolic links
    ln -sf "${_mt_model_file}" "${mt_exp}"
    ln -sf "${_mt_train_config}" "${mt_exp}"
    inference_mt_model=$(basename "${_mt_model_file}")

    if [ "$(<${mt_exp}/config.txt grep -c lm_file)" -gt 0 ]; then
        _lm_file=$(<"${mt_exp}/config.txt" sed -e "s/.*'lm_file': '\([^']*\)'.*$/\1/")
        _lm_train_config=$(<"${mt_exp}/config.txt" sed -e "s/.*'lm_train_config': '\([^']*\)'.*$/\1/")

        lm_exp="${expdir}/${download_model}/lm"
        mkdir -p "${lm_exp}"

        ln -sf "${_lm_file}" "${lm_exp}"
        ln -sf "${_lm_train_config}" "${lm_exp}"
        inference_lm=$(basename "${_lm_file}")
    fi

fi


if ! "${skip_eval}"; then
    if [ ${stage} -le 11 ] && [ ${stop_stage} -ge 11 ]; then
        log "Stage 11: Decoding: training_dir=${mt_exp}"

        if ${gpu_inference}; then
            _cmd="${cuda_cmd}"
            _ngpu=1
        else
            _cmd="${decode_cmd}"
            _ngpu=0
        fi

        _opts=
        if [ -n "${inference_config}" ]; then
            _opts+="--config ${inference_config} "
        fi
        if "${use_lm}"; then
            if "${use_word_lm}"; then
                _opts+="--word_lm_train_config ${lm_exp}/config.yaml "
                _opts+="--word_lm_file ${lm_exp}/${inference_lm} "
            else
                _opts+="--lm_train_config ${lm_exp}/config.yaml "
                _opts+="--lm_file ${lm_exp}/${inference_lm} "
            fi
        fi
        if "${use_ngram}"; then
             _opts+="--ngram_file ${ngram_exp}/${inference_ngram}"
        fi

        # 2. Generate run.sh
        log "Generate '${mt_exp}/${inference_tag}/run.sh'. You can resume the process from stage 11 using this script"
        mkdir -p "${mt_exp}/${inference_tag}"; echo "${run_args} --stage 11 \"\$@\"; exit \$?" > "${mt_exp}/${inference_tag}/run.sh"; chmod +x "${mt_exp}/${inference_tag}/run.sh"

        for dset in ${test_sets}; do
            _data="${data_feats}/${dset}"
            _dir="${mt_exp}/${inference_tag}/${dset}"
            _logdir="${_dir}/logdir"
            mkdir -p "${_logdir}"

            _scp=text.${src_case}.${src_lang}

            # 1. Split the key file
            key_file=${_data}/${_scp}
            split_scps=""
            _nj=$(min "${inference_nj}" "$(<${key_file} wc -l)")
            mt_inference_tool="espnet2.bin.mt_inference"

            for n in $(seq "${_nj}"); do
                split_scps+=" ${_logdir}/keys.${n}.scp"
            done
            # shellcheck disable=SC2086
            utils/split_scp.pl "${key_file}" ${split_scps}

            # 2. Submit decoding jobs
            log "Decoding started... log: '${_logdir}/mt_inference.*.log'"
            # shellcheck disable=SC2046,SC2086
            ${_cmd} --gpu "${_ngpu}" JOB=1:"${_nj}" "${_logdir}"/mt_inference.JOB.log \
                ${python} -m ${mt_inference_tool} \
                    --batch_size ${batch_size} \
                    --ngpu "${_ngpu}" \
                    --data_path_and_name_and_type "${_data}/${_scp},src_text,text" \
                    --key_file "${_logdir}"/keys.JOB.scp \
                    --mt_train_config "${mt_exp}"/config.yaml \
                    --mt_model_file "${mt_exp}"/"${inference_mt_model}" \
                    --output_dir "${_logdir}"/output.JOB \
                    ${_opts} ${inference_args} || { cat $(grep -l -i error "${_logdir}"/mt_inference.*.log) ; exit 1; }

            # 3. Concatenates the output files from each jobs
            for f in token token_int score text; do
                for i in $(seq "${_nj}"); do
                    cat "${_logdir}/output.${i}/1best_recog/${f}"
                done | LC_ALL=C sort -k1 >"${_dir}/${f}"
            done
        done
    fi

    if [ ${stage} -le 12 ] && [ ${stop_stage} -ge 12 ]; then
        log "Stage 12: Scoring"

        for dset in ${test_sets}; do
            _data="${data_feats}/${dset}"
            _dir="${mt_exp}/${inference_tag}/${dset}"

            # TODO(jiatong): add asr scoring and inference

            _scoredir="${_dir}/score_bleu"
            mkdir -p "${_scoredir}"

            <"${_data}/text.${tgt_case}.${tgt_lang}" \
                ${python} -m espnet2.bin.tokenize_text  \
                    -f 2- --input - --output - \
                    --token_type word \
                    --non_linguistic_symbols "${nlsyms_txt}" \
                    --remove_non_linguistic_symbols true \
                    --cleaner "${cleaner}" \
            >"${_scoredir}/ref.trn"

            #paste \
            #    <(<"${_data}/text.${tgt_case}.${tgt_lang}" \
            #        ${python} -m espnet2.bin.tokenize_text  \
            #            -f 2- --input - --output - \
            #            --token_type word \
            #            --non_linguistic_symbols "${nlsyms_txt}" \
            #            --remove_non_linguistic_symbols true \
            #            --cleaner "${cleaner}" \
            #            ) \
            #    <(<"${_data}/text.${tgt_case}.${tgt_lang}" awk '{ print "(" $2 "-" $1 ")" }') \
            #        >"${_scoredir}/ref.trn.org"

            # NOTE(kamo): Don't use cleaner for hyp
            <"${_dir}/text"  \
                    ${python} -m espnet2.bin.tokenize_text  \
                    -f 2- --input - --output - \
                    --token_type word \
                    --non_linguistic_symbols "${nlsyms_txt}" \
                    --remove_non_linguistic_symbols true \
            >"${_scoredir}/hyp.trn"

            #paste \
            #    <(<"${_dir}/text"  \
            #            ${python} -m espnet2.bin.tokenize_text  \
            #                -f 2- --input - --output - \
            #                --token_type word \
            #                --non_linguistic_symbols "${nlsyms_txt}" \
            #                --remove_non_linguistic_symbols true \
            #                ) \
            #    <(<"${_data}/text.${tgt_case}.${tgt_lang}" awk '{ print "(" $2 "-" $1 ")" }') \
            #        >"${_scoredir}/hyp.trn.org"

            # remove utterance id
            #perl -pe 's/\([^\)]+\)//g;' "${_scoredir}/ref.trn.org" > "${_scoredir}/ref.trn"
            #perl -pe 's/\([^\)]+\)//g;' "${_scoredir}/hyp.trn.org" > "${_scoredir}/hyp.trn"

            # detokenizer
            detokenizer.perl -l ${tgt_lang} -q < "${_scoredir}/ref.trn" > "${_scoredir}/ref.trn.detok"
            detokenizer.perl -l ${tgt_lang} -q < "${_scoredir}/hyp.trn" > "${_scoredir}/hyp.trn.detok"

            if [ ${tgt_case} = "tc" ]; then
                echo "Case sensitive BLEU result (single-reference)" > ${_scoredir}/result.tc.txt
                sacrebleu "${_scoredir}/ref.trn.detok" \
                          -i "${_scoredir}/hyp.trn.detok" \
                          -m bleu chrf ter \
                          >> ${_scoredir}/result.tc.txt

                log "Write a case-sensitive BLEU (single-reference) result in ${_scoredir}/result.tc.txt"
            fi

            # detokenize & remove punctuation except apostrophe
            scripts/utils/remove_punctuation.pl < "${_scoredir}/ref.trn.detok" > "${_scoredir}/ref.trn.detok.lc.rm"
            scripts/utils/remove_punctuation.pl < "${_scoredir}/hyp.trn.detok" > "${_scoredir}/hyp.trn.detok.lc.rm"
            echo "Case insensitive BLEU result (single-reference)" > ${_scoredir}/result.lc.txt
            sacrebleu -lc "${_scoredir}/ref.trn.detok.lc.rm" \
                      -i "${_scoredir}/hyp.trn.detok.lc.rm" \
                      -m bleu chrf ter \
                      >> ${_scoredir}/result.lc.txt
            log "Write a case-insensitve BLEU (single-reference) result in ${_scoredir}/result.lc.txt"

            # process multi-references cases
            multi_references=$(ls "${_data}/text.${tgt_case}.${tgt_lang}".* || echo "")
            if [ "${multi_references}" != "" ]; then
                case_sensitive_refs=""
                case_insensitive_refs=""
                for multi_reference in ${multi_references}; do
                    ref_idx="${multi_reference##*.}"
                    paste \
                        <(<${multi_reference} \
                            ${python} -m espnet2.bin.tokenize_text  \
                                -f 2- --input - --output - \
                                --token_type word \
                                --non_linguistic_symbols "${nlsyms_txt}" \
                                --remove_non_linguistic_symbols true \
                                --cleaner "${cleaner}" \
                                ) \
                        <(<"${_data}/text.${tgt_case}.${tgt_lang}" awk '{ print "(" $2 "-" $1 ")" }') \
                            >"${_scoredir}/ref.trn.org.${ref_idx}"

                    #
                    perl -pe 's/\([^\)]+\)//g;' "${_scoredir}/ref.trn.org.${ref_idx}" > "${_scoredir}/ref.trn.${ref_idx}"
                    detokenizer.perl -l ${tgt_lang} -q < "${_scoredir}/ref.trn.${ref_idx}" > "${_scoredir}/ref.trn.detok.${ref_idx}"
                    scripts/utils/remove_punctuation.pl < "${_scoredir}/ref.trn.detok.${ref_idx}" > "${_scoredir}/ref.trn.detok.lc.rm.${ref_idx}"
                    case_sensitive_refs="${case_sensitive_refs} ${_scoredir}/ref.trn.detok.${ref_idx}"
                    case_insensitive_refs="${case_insensitive_refs} ${_scoredir}/ref.trn.detok.lc.rm.${ref_idx}"
                done

                if [ ${tgt_case} = "tc" ]; then
                    echo "Case sensitive BLEU result (multi-references)" >> ${_scoredir}/result.tc.txt
                    sacrebleu ${case_sensitive_refs} \
                        -i ${_scoredir}/hyp.trn.detok.lc.rm -m bleu chrf ter \
                        >> ${_scoredir}/result.tc.txt
                    log "Write a case-sensitve BLEU (multi-reference) result in ${_scoredir}/result.tc.txt"
                fi

                echo "Case insensitive BLEU result (multi-references)" >> ${_scoredir}/result.lc.txt
                sacrebleu -lc ${case_insensitive_refs} \
                    -i ${_scoredir}/hyp.trn.detok.lc.rm -m bleu chrf ter \
                    >> ${_scoredir}/result.lc.txt
                log "Write a case-insensitve BLEU (multi-reference) result in ${_scoredir}/result.lc.txt"
            fi
        done

        # Show results in Markdown syntax
        scripts/utils/show_translation_result.sh --case $tgt_case "${mt_exp}" > "${mt_exp}"/RESULTS.md
        cat "${mt_exp}"/RESULTS.md
    fi
else
    log "Skip the evaluation stages"
fi


packed_model="${mt_exp}/${mt_exp##*/}_${inference_mt_model%.*}.zip"
if ! "${skip_upload}"; then
    if [ ${stage} -le 13 ] && [ ${stop_stage} -ge 13 ]; then
        log "Stage 13: Pack model: ${packed_model}"

        _opts=
        if "${use_lm}"; then
            _opts+="--lm_train_config ${lm_exp}/config.yaml "
            _opts+="--lm_file ${lm_exp}/${inference_lm} "
            _opts+="--option ${lm_exp}/perplexity_test/ppl "
            _opts+="--option ${lm_exp}/images "
        fi
        if [ "${tgt_token_type}" = bpe ]; then
            _opts+="--option ${tgt_bpemodel} "
            _opts+="--option ${src_bpemodel} "
        fi
        if [ "${nlsyms_txt}" != none ]; then
            _opts+="--option ${nlsyms_txt} "
        fi
        # shellcheck disable=SC2086
        ${python} -m espnet2.bin.pack mt \
            --mt_train_config "${mt_exp}"/config.yaml \
            --mt_model_file "${mt_exp}"/"${inference_mt_model}" \
            ${_opts} \
            --option "${mt_exp}"/RESULTS.md \
            --option "${mt_exp}"/RESULTS.md \
            --option "${mt_exp}"/images \
            --outpath "${packed_model}"
    fi
else
    log "Skip the packing stages"
fi

if ! "${skip_upload_hf}"; then
    if [ ${stage} -le 14 ] && [ ${stop_stage} -ge 14 ]; then
        [ -z "${hf_repo}" ] && \
            log "ERROR: You need to setup the variable hf_repo with the name of the repository located at HuggingFace" && \
            exit 1
        log "Stage 14: Upload model to HuggingFace: ${hf_repo}"

        if [ ! -f "${packed_model}" ]; then
            log "ERROR: ${packed_model} does not exist. Please run stage 13 first."
            exit 1
        fi

        gitlfs=$(git lfs --version 2> /dev/null || true)
        [ -z "${gitlfs}" ] && \
            log "ERROR: You need to install git-lfs first" && \
            exit 1

        dir_repo=${expdir}/hf_${hf_repo//"/"/"_"}
        [ ! -d "${dir_repo}" ] && git clone https://huggingface.co/${hf_repo} ${dir_repo}

        if command -v git &> /dev/null; then
            _creator_name="$(git config user.name)"
            _checkout="git checkout $(git show -s --format=%H)"
        else
            _creator_name="$(whoami)"
            _checkout=""
        fi
        # /some/where/espnet/egs2/foo/asr1/ -> foo/asr1
        _task="$(pwd | rev | cut -d/ -f2 | rev)"
        # foo/asr1 -> foo
        _corpus="${_task%/*}"
        _model_name="${_creator_name}/${_corpus}_$(basename ${packed_model} .zip)"

        # copy files in ${dir_repo}
        unzip -o ${packed_model} -d ${dir_repo}
        # Generate description file
        # shellcheck disable=SC2034
        hf_task=machine-translation
        # shellcheck disable=SC2034
        espnet_task=MT
        # shellcheck disable=SC2034
        task_exp=${mt_exp}
        eval "echo \"$(cat scripts/utils/TEMPLATE_HF_Readme.md)\"" > "${dir_repo}"/README.md

        this_folder=${PWD}
        cd ${dir_repo}
        if [ -n "$(git status --porcelain)" ]; then
            git add .
            git commit -m "Update model"
        fi
        git push
        cd ${this_folder}
    fi
else
    log "Skip the uploading to HuggingFace stage"
fi

log "Successfully finished. [elapsed=${SECONDS}s]"
