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preprocess.py
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preprocess.py
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#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
#
import argparse
import os
from itertools import zip_longest
from fairseq import dictionary, indexed_dataset
from fairseq.tokenizer import Tokenizer
def main():
parser = argparse.ArgumentParser(
description='Data pre-processing: Create dictionary and store data in binary format')
parser.add_argument('-s', '--source-lang', default=None, metavar='SRC', help='source language')
parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET', help='target language')
parser.add_argument('--trainpref', metavar='FP', default='train', help='target language')
parser.add_argument('--validpref', metavar='FP', default='valid', help='comma separated, valid language prefixes')
parser.add_argument('--testpref', metavar='FP', default='test', help='comma separated, test language prefixes')
parser.add_argument('--destdir', metavar='DIR', default='data-bin', help='destination dir')
parser.add_argument('--thresholdtgt', metavar='N', default=0, type=int,
help='map words appearing less than threshold times to unknown')
parser.add_argument('--thresholdsrc', metavar='N', default=0, type=int,
help='map words appearing less than threshold times to unknown')
parser.add_argument('--tgtdict', metavar='FP', help='reuse given target dictionary')
parser.add_argument('--srcdict', metavar='FP', help='reuse given source dictionary')
parser.add_argument('--nwordstgt', metavar='N', default=-1, type=int, help='number of target words to retain')
parser.add_argument('--nwordssrc', metavar='N', default=-1, type=int, help='number of source words to retain')
parser.add_argument('--alignfile', metavar='ALIGN', default=None, help='an alignment file (optional)')
args = parser.parse_args()
print(args)
os.makedirs(args.destdir, exist_ok=True)
if args.srcdict:
src_dict = dictionary.Dictionary.load(args.srcdict)
else:
src_dict = Tokenizer.build_dictionary(filename='{}.{}'.format(args.trainpref, args.source_lang))
src_dict.save(os.path.join(args.destdir, 'dict.{}.txt'.format(args.source_lang)),
threshold=args.thresholdsrc, nwords=args.nwordssrc)
if args.tgtdict:
tgt_dict = dictionary.Dictionary.load(args.tgtdict)
else:
tgt_dict = Tokenizer.build_dictionary(filename='{}.{}'.format(args.trainpref, args.target_lang))
tgt_dict.save(os.path.join(args.destdir, 'dict.{}.txt'.format(args.target_lang)),
threshold=args.thresholdtgt, nwords=args.nwordstgt)
def make_dataset(input_prefix, output_prefix, lang):
dict = dictionary.Dictionary.load(os.path.join(args.destdir, 'dict.{}.txt'.format(lang)))
print('| [{}] Dictionary: {} types'.format(lang, len(dict) - 1))
ds = indexed_dataset.IndexedDatasetBuilder(
'{}/{}.{}-{}.{}.bin'.format(args.destdir, output_prefix, args.source_lang,
args.target_lang, lang)
)
def consumer(tensor):
ds.add_item(tensor)
input_file = '{}.{}'.format(input_prefix, lang)
res = Tokenizer.binarize(input_file, dict, consumer)
print('| [{}] {}: {} sents, {} tokens, {:.3}% replaced by {}'.format(
lang, input_file, res['nseq'], res['ntok'],
100 * res['nunk'] / res['ntok'], dict.unk_word))
ds.finalize('{}/{}.{}-{}.{}.idx'.format(
args.destdir, output_prefix,
args.source_lang, args.target_lang, lang))
make_dataset(args.trainpref, 'train', args.source_lang)
make_dataset(args.trainpref, 'train', args.target_lang)
for k, validpref in enumerate(args.validpref.split(',')):
outprefix = 'valid{}'.format(k) if k > 0 else 'valid'
make_dataset(validpref, outprefix, args.source_lang)
make_dataset(validpref, outprefix, args.target_lang)
for k, testpref in enumerate(args.testpref.split(',')):
outprefix = 'test{}'.format(k) if k > 0 else 'test'
make_dataset(testpref, outprefix, args.source_lang)
make_dataset(testpref, outprefix, args.target_lang)
print('| Wrote preprocessed data to {}'.format(args.destdir))
if args.alignfile:
src_file_name = '{}.{}'.format(args.trainpref, args.source_lang)
tgt_file_name = '{}.{}'.format(args.trainpref, args.target_lang)
src_dict = dictionary.Dictionary.load(os.path.join(args.destdir, 'dict.{}.txt'.format(args.source_lang)))
tgt_dict = dictionary.Dictionary.load(os.path.join(args.destdir, 'dict.{}.txt'.format(args.target_lang)))
freq_map = {}
with open(args.alignfile, 'r') as align_file:
with open(src_file_name, 'r') as src_file:
with open(tgt_file_name, 'r') as tgt_file:
for a, s, t in zip_longest(align_file, src_file, tgt_file):
si = Tokenizer.tokenize(s, src_dict, add_if_not_exist=False)
ti = Tokenizer.tokenize(t, tgt_dict, add_if_not_exist=False)
ai = list(map(lambda x: tuple(x.split('-')), a.split()))
for sai, tai in ai:
srcidx = si[int(sai)]
tgtidx = ti[int(tai)]
if srcidx != src_dict.unk() and tgtidx != tgt_dict.unk():
assert srcidx != src_dict.pad()
assert srcidx != src_dict.eos()
assert tgtidx != tgt_dict.pad()
assert tgtidx != tgt_dict.eos()
if srcidx not in freq_map:
freq_map[srcidx] = {}
if tgtidx not in freq_map[srcidx]:
freq_map[srcidx][tgtidx] = 1
else:
freq_map[srcidx][tgtidx] += 1
align_dict = {}
for srcidx in freq_map.keys():
align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get)
with open(os.path.join(args.destdir, 'alignment.{}-{}.txt'.format(
args.source_lang, args.target_lang)), 'w') as f:
for k, v in align_dict.items():
print('{} {}'.format(src_dict[k], tgt_dict[v]), file=f)
if __name__ == '__main__':
main()