translation/OpenNMT-py/tools/apply_bpe.py

#!/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')