text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # Author: Rico Sennrich
  
  """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.
  """
  
  from __future__ import unicode_literals, division
  
  import sys
  import os
  import inspect
  import codecs
  import io
  import argparse
  import re
  import warnings
  
  # hack for python2/3 compatibility
  from io import open
  argparse.open = open
  
  class BPE(object):
  
      def __init__(self, codes, merges=-1, separator='@@', vocab=None, glossaries=None):
  
          codes.seek(0)
          offset=1
  
          # 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(".")])
              offset += 1
          else:
              self.version = (0, 1)
              codes.seek(0)
  
          self.bpe_codes = [tuple(item.strip('\r\n ').split(' ')) for (n, item) in enumerate(codes) if (n < merges or merges == -1)]
  
          for i, item in enumerate(self.bpe_codes):
              if len(item) != 2:
                  sys.stderr.write('Error: invalid line {0} in BPE codes file: {1}\n'.format(i+offset, ' '.join(item)))
                  sys.stderr.write('The line should exist of exactly two subword units, separated by whitespace\n')
                  sys.exit(1)
  
          # 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 process_line(self, line):
          """segment line, dealing with leading and trailing whitespace"""
  
          out = ""
  
          leading_whitespace = len(line)-len(line.lstrip('\r\n '))
          if leading_whitespace:
              out += line[:leading_whitespace]
  
          out += self.segment(line)
  
          trailing_whitespace = len(line)-len(line.rstrip('\r\n '))
          if trailing_whitespace and trailing_whitespace != len(line):
              out += line[-trailing_whitespace:]
  
          return out
  
      def segment(self, sentence):
          """segment single sentence (whitespace-tokenized string) with BPE encoding"""
          segments = self.segment_tokens(sentence.strip('\r\n ').split(' '))
          return ' '.join(segments)
  
      def segment_tokens(self, tokens):
          """segment a sequence of tokens with BPE encoding"""
          output = []
          for word in tokens:
              # eliminate double spaces
              if not word:
                  continue
              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 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(subparsers=None):
  
      if subparsers:
          parser = subparsers.add_parser('apply-bpe',
              formatter_class=argparse.RawDescriptionHelpFormatter,
              description="learn BPE-based word segmentation")
      else:
          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(
          '--merges', '-m', type=int, default=-1,
          metavar='INT',
          help="Use this many BPE operations (<= number of learned symbols)"+
               "default: Apply all the learned merge operations")
      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. Words matching any of the words/regex provided in glossaries will not be affected "+
               "by the BPE (i.e. they will neither be broken into subwords, nor concatenated with other subwords. "+
               "Can be provided as a list of words/regex after the --glossaries argument. Enclose each regex in quotes.")
  
      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 re.match('^({})$'.format('|'.join(glossaries)), orig):
          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.strip('\r\n ').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']
      """
      # regex equivalent of (if word == glossary or glossary not in word)
      if re.match('^'+glossary+'$', word) or not re.search(glossary, word):
          return [word]
      else:
          segments = re.split(r'({})'.format(glossary), word)
          segments, ending = segments[:-1], segments[-1]
          segments = list(filter(None, segments)) # Remove empty strings in regex group.
          return segments + [ending.strip('\r\n ')] if ending != '' else segments
  
  if __name__ == '__main__':
  
      currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
      newdir = os.path.join(currentdir, 'subword_nmt')
      if os.path.isdir(newdir):
          warnings.simplefilter('default')
          warnings.warn(
              "this script's location has moved to {0}. This symbolic link will be removed in a future version. Please point to the new location, or install the package and use the command 'subword-nmt'".format(newdir),
              DeprecationWarning
          )
  
      # 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
  
      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))
  