subword-nmt/subword_nmt/subword_nmt.py
2019-11-07 13:30:05 +09:00

98 lines
4.0 KiB
Python
Executable File

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import io
import sys
import codecs
import argparse
from .learn_bpe import learn_bpe
from .apply_bpe import BPE, read_vocabulary
from .get_vocab import get_vocab
from .learn_joint_bpe_and_vocab import learn_joint_bpe_and_vocab
from .learn_bpe import create_parser as create_learn_bpe_parser
from .apply_bpe import create_parser as create_apply_bpe_parser
from .get_vocab import create_parser as create_get_vocab_parser
from .learn_joint_bpe_and_vocab import create_parser as create_learn_joint_bpe_and_vocab_parser
# hack for python2/3 compatibility
argparse.open = io.open
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter,
description="subword-nmt: unsupervised word segmentation for neural machine translation and text generation ")
subparsers = parser.add_subparsers(dest='command',
help="""command to run. Run one of the commands with '-h' for more info.
learn-bpe: learn BPE merge operations on input text.
apply-bpe: apply given BPE operations to input text.
get-vocab: extract vocabulary and word frequencies from input text.
learn-joint-bpe-and-vocab: executes recommended workflow for joint BPE.""")
learn_bpe_parser = create_learn_bpe_parser(subparsers)
apply_bpe_parser = create_apply_bpe_parser(subparsers)
get_vocab_parser = create_get_vocab_parser(subparsers)
learn_joint_bpe_and_vocab_parser = create_learn_joint_bpe_and_vocab_parser(subparsers)
args = parser.parse_args()
if args.command == 'learn-bpe':
# read/write files as UTF-8
if args.input.name != '<stdin>':
args.input = codecs.open(args.input.name, encoding='utf-8')
if args.output.name != '<stdout>':
args.output = codecs.open(args.output.name, 'w', encoding='utf-8')
learn_bpe(args.input, args.output, args.symbols, args.min_frequency, args.verbose,
is_dict=args.dict_input, total_symbols=args.total_symbols)
elif args.command == 'apply-bpe':
# read/write files as UTF-8
args.codes = codecs.open(args.codes.name, encoding='utf-8')
if args.input.name != '<stdin>':
args.input = codecs.open(args.input.name, encoding='utf-8')
if args.output.name != '<stdout>':
args.output = codecs.open(args.output.name, 'w', encoding='utf-8')
if args.vocabulary:
args.vocabulary = codecs.open(args.vocabulary.name, encoding='utf-8')
if args.vocabulary:
vocabulary = read_vocabulary(args.vocabulary, args.vocabulary_threshold)
else:
vocabulary = None
if sys.version_info < (3, 0):
args.separator = args.separator.decode('UTF-8')
if args.glossaries:
args.glossaries = [g.decode('UTF-8') for g in args.glossaries]
bpe = BPE(args.codes, args.merges, args.separator, vocabulary, args.glossaries)
for line in args.input:
args.output.write(bpe.process_line(line, args.dropout))
elif args.command == 'get-vocab':
if args.input.name != '<stdin>':
args.input = codecs.open(args.input.name, encoding='utf-8')
if args.output.name != '<stdout>':
args.output = codecs.open(args.output.name, 'w', encoding='utf-8')
get_vocab(args.input, args.output)
elif args.command == 'learn-joint-bpe-and-vocab':
learn_joint_bpe_and_vocab(args)
if sys.version_info < (3, 0):
args.separator = args.separator.decode('UTF-8')
else:
raise Exception('Invalid command provided')
# python 2/3 compatibility
if sys.version_info < (3, 0):
sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)
sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)
sys.stdin = codecs.getreader('UTF-8')(sys.stdin)
else:
sys.stderr = codecs.getwriter('UTF-8')(sys.stderr.buffer)
sys.stdout = codecs.getwriter('UTF-8')(sys.stdout.buffer)
sys.stdin = codecs.getreader('UTF-8')(sys.stdin.buffer)