#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Rico Sennrich
# flake8: noqa
"""Use operations learned with learn_bpe.py to encode a new text.
The text will not be smaller, but use only a fixed vocabulary, with rare words
encoded as variable-length sequences of subword units.
Reference:
Rico Sennrich, Barry Haddow and Alexandra Birch (2015). Neural Machine Translation of Rare Words with Subword Units.
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany.
"""
# This file is retrieved from https://github.com/rsennrich/subword-nmt
from __future__ import unicode_literals, division
import sys
import codecs
import io
import argparse
import json
import re
from collections import defaultdict
# hack for python2/3 compatibility
from io import open
argparse.open = open
class BPE(object):
def __init__(self, codes, separator='@@', vocab=None, glossaries=None):
# check version information
firstline = codes.readline()
if firstline.startswith('#version:'):
self.version = tuple([int(x) for x in re.sub(r'(\.0+)*$','', firstline.split()[-1]).split(".")])
else:
self.version = (0, 1)
codes.seek(0)
self.bpe_codes = [tuple(item.split()) for item in codes]
# some hacking to deal with duplicates (only consider first instance)
self.bpe_codes = dict([(code,i) for (i,code) in reversed(list(enumerate(self.bpe_codes)))])
self.bpe_codes_reverse = dict([(pair[0] + pair[1], pair) for pair,i in self.bpe_codes.items()])
self.separator = separator
self.vocab = vocab
self.glossaries = glossaries if glossaries else []
self.cache = {}
def segment(self, sentence):
"""segment single sentence (whitespace-tokenized string) with BPE encoding"""
output = []
for word in sentence.split():
new_word = [out for segment in self._isolate_glossaries(word)
for out in encode(segment,
self.bpe_codes,
self.bpe_codes_reverse,
self.vocab,
self.separator,
self.version,
self.cache,
self.glossaries)]
for item in new_word[:-1]:
output.append(item + self.separator)
output.append(new_word[-1])
return ' '.join(output)
def _isolate_glossaries(self, word):
word_segments = [word]
for gloss in self.glossaries:
word_segments = [out_segments for segment in word_segments
for out_segments in isolate_glossary(segment, gloss)]
return word_segments
def create_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="learn BPE-based word segmentation")
parser.add_argument(
'--input', '-i', type=argparse.FileType('r'), default=sys.stdin,
metavar='PATH',
help="Input file (default: standard input).")
parser.add_argument(
'--codes', '-c', type=argparse.FileType('r'), metavar='PATH',
required=True,
help="File with BPE codes (created by learn_bpe.py).")
parser.add_argument(
'--output', '-o', type=argparse.FileType('w'), default=sys.stdout,
metavar='PATH',
help="Output file (default: standard output)")
parser.add_argument(
'--separator', '-s', type=str, default='@@', metavar='STR',
help="Separator between non-final subword units (default: '%(default)s'))")
parser.add_argument(
'--vocabulary', type=argparse.FileType('r'), default=None,
metavar="PATH",
help="Vocabulary file (built with get_vocab.py). If provided, this script reverts any merge operations that produce an OOV.")
parser.add_argument(
'--vocabulary-threshold', type=int, default=None,
metavar="INT",
help="Vocabulary threshold. If vocabulary is provided, any word with frequency < threshold will be treated as OOV")
parser.add_argument(
'--glossaries', type=str, nargs='+', default=None,
metavar="STR",
help="Glossaries. The strings provided in glossaries will not be affected"+
"by the BPE (i.e. they will neither be broken into subwords, nor concatenated with other subwords")
return parser
def get_pairs(word):
"""Return set of symbol pairs in a word.
word is represented as tuple of symbols (symbols being variable-length strings)
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
def encode(orig, bpe_codes, bpe_codes_reverse, vocab, separator, version, cache, glossaries=None):
"""Encode word based on list of BPE merge operations, which are applied consecutively
"""
if orig in cache:
return cache[orig]
if orig in glossaries:
cache[orig] = (orig,)
return (orig,)
if version == (0, 1):
word = tuple(orig) + ('</w>',)
elif version == (0, 2): # more consistent handling of word-final segments
word = tuple(orig[:-1]) + ( orig[-1] + '</w>',)
else:
raise NotImplementedError
pairs = get_pairs(word)
if not pairs:
return orig
while True:
bigram = min(pairs, key = lambda pair: bpe_codes.get(pair, float('inf')))
if bigram not in bpe_codes:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word)-1 and word[i+1] == second:
new_word.append(first+second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
# don't print end-of-word symbols
if word[-1] == '</w>':
word = word[:-1]
elif word[-1].endswith('</w>'):
word = word[:-1] + (word[-1].replace('</w>',''),)
if vocab:
word = check_vocab_and_split(word, bpe_codes_reverse, vocab, separator)
cache[orig] = word
return word
def recursive_split(segment, bpe_codes, vocab, separator, final=False):
"""Recursively split segment into smaller units (by reversing BPE merges)
until all units are either in-vocabulary, or cannot be split futher."""
try:
if final:
left, right = bpe_codes[segment + '</w>']
right = right[:-4]
else:
left, right = bpe_codes[segment]
except:
#sys.stderr.write('cannot split {0} further.\n'.format(segment))
yield segment
return
if left + separator in vocab:
yield left
else:
for item in recursive_split(left, bpe_codes, vocab, separator, False):
yield item
if (final and right in vocab) or (not final and right + separator in vocab):
yield right
else:
for item in recursive_split(right, bpe_codes, vocab, separator, final):
yield item
def check_vocab_and_split(orig, bpe_codes, vocab, separator):
"""Check for each segment in word if it is in-vocabulary,
and segment OOV segments into smaller units by reversing the BPE merge operations"""
out = []
for segment in orig[:-1]:
if segment + separator in vocab:
out.append(segment)
else:
#sys.stderr.write('OOV: {0}\n'.format(segment))
for item in recursive_split(segment, bpe_codes, vocab, separator, False):
out.append(item)
segment = orig[-1]
if segment in vocab:
out.append(segment)
else:
#sys.stderr.write('OOV: {0}\n'.format(segment))
for item in recursive_split(segment, bpe_codes, vocab, separator, True):
out.append(item)
return out
def read_vocabulary(vocab_file, threshold):
"""read vocabulary file produced by get_vocab.py, and filter according to frequency threshold.
"""
vocabulary = set()
for line in vocab_file:
word, freq = line.split()
freq = int(freq)
if threshold == None or freq >= threshold:
vocabulary.add(word)
return vocabulary
def isolate_glossary(word, glossary):
"""
Isolate a glossary present inside a word.
Returns a list of subwords. In which all 'glossary' glossaries are isolated
For example, if 'USA' is the glossary and '1934USABUSA' the word, the return value is:
['1934', 'USA', 'B', 'USA']
"""
if word == glossary or glossary not in word:
return [word]
else:
splits = word.split(glossary)
segments = [segment.strip() for split in splits[:-1] for segment in [split, glossary] if segment != '']
return segments + [splits[-1].strip()] if splits[-1] != '' else segments
if __name__ == '__main__':
# 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.stdin = io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8')
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', write_through=True, line_buffering=True)
parser = create_parser()
args = parser.parse_args()
# 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
bpe = BPE(args.codes, args.separator, vocabulary, args.glossaries)
for line in args.input:
args.output.write(bpe.segment(line).strip())
args.output.write('\n')
