Basic, End-to-end and Word-piece tokenization.

tokenizer.py

import collections
import re
import unicodedata
import six
import tensorflow as tf


def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
  """Checks whether the casing config is consistent with the checkpoint name."""

  # The casing has to be passed in by the user and there is no explicit check
  # as to whether it matches the checkpoint. The casing information probably
  # should have been stored in the bert_config.json file, but it's not, so
  # we have to heuristically detect it to validate.

  if not init_checkpoint:
    return

  m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
  if m is None:
    return

  model_name = m.group(1)

  lower_models = [
      "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
      "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
  ]

  cased_models = [
      "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
      "multi_cased_L-12_H-768_A-12"
  ]

  is_bad_config = False
  if model_name in lower_models and not do_lower_case:
    is_bad_config = True
    actual_flag = "False"
    case_name = "lowercased"
    opposite_flag = "True"

  if model_name in cased_models and do_lower_case:
    is_bad_config = True
    actual_flag = "True"
    case_name = "cased"
    opposite_flag = "False"

  if is_bad_config:
    raise ValueError(
        "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
        "However, `%s` seems to be a %s model, so you "
        "should pass in `--do_lower_case=%s` so that the fine-tuning matches "
        "how the model was pre-training. If this error is wrong, please "
        "just comment out this check." % (actual_flag, init_checkpoint,
                                          model_name, case_name, opposite_flag))


def convert_to_unicode(text):
  """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
  if six.PY3:
    if isinstance(text, str):
      return text
    elif isinstance(text, bytes):
      return text.decode("utf-8", "ignore")
    else:
      raise ValueError("Unsupported string type: %s" % (type(text)))
  elif six.PY2:
    if isinstance(text, str):
      return text.decode("utf-8", "ignore")
    elif isinstance(text, unicode):
      return text
    else:
      raise ValueError("Unsupported string type: %s" % (type(text)))
  else:
    raise ValueError("Not running on Python2 or Python 3?")


def printable_text(text):
  """Returns text encoded in a way suitable for print or `tf.logging`."""

  # These functions want `str` for both Python2 and Python3, but in one case
  # it's a Unicode string and in the other it's a byte string.
  if six.PY3:
    if isinstance(text, str):
      return text
    elif isinstance(text, bytes):
      return text.decode("utf-8", "ignore")
    else:
      raise ValueError("Unsupported string type: %s" % (type(text)))
  elif six.PY2:
    if isinstance(text, str):
      return text
    elif isinstance(text, unicode):
      return text.encode("utf-8")
    else:
      raise ValueError("Unsupported string type: %s" % (type(text)))
  else:
    raise ValueError("Not running on Python2 or Python 3?")


def load_vocab(vocab_file):
  """Loads a vocabulary file into a dictionary."""
  vocab = collections.OrderedDict()
  index = 0
  with tf.gfile.GFile(vocab_file, "r") as reader:
    while True:
      token = convert_to_unicode(reader.readline())
      if not token:
        break
      token = token.strip()
      vocab[token] = index
      index += 1
  return vocab


def convert_by_vocab(vocab, items):
  """Converts a sequence of [tokens|ids] using the vocab."""
  output = []
  for item in items:
    output.append(vocab[item])
  return output


def convert_tokens_to_ids(vocab, tokens):
  return convert_by_vocab(vocab, tokens)


def convert_ids_to_tokens(inv_vocab, ids):
  return convert_by_vocab(inv_vocab, ids)


def whitespace_tokenize(text):
  """Runs basic whitespace cleaning and splitting on a piece of text."""
  text = text.strip()
  if not text:
    return []
  tokens = text.split()
  return tokens


class FullTokenizer(object):
  """Runs end-to-end tokenziation."""

  def __init__(self, vocab_file, do_lower_case=True):
    self.vocab = load_vocab(vocab_file)
    self.inv_vocab = {v: k for k, v in self.vocab.items()}
    self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
    self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)

  def tokenize(self, text):
    split_tokens = []
    for token in self.basic_tokenizer.tokenize(text):
      for sub_token in self.wordpiece_tokenizer.tokenize(token):
        split_tokens.append(sub_token)

    return split_tokens

  def convert_tokens_to_ids(self, tokens):
    return convert_by_vocab(self.vocab, tokens)

  def convert_ids_to_tokens(self, ids):
    return convert_by_vocab(self.inv_vocab, ids)


class BasicTokenizer(object):
  """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""

  def __init__(self, do_lower_case=True):
    """Constructs a BasicTokenizer.
    Args:
      do_lower_case: Whether to lower case the input.
    """
    self.do_lower_case = do_lower_case

  def tokenize(self, text):
    """Tokenizes a piece of text."""
    text = convert_to_unicode(text)
    text = self._clean_text(text)

    # This was added on November 1st, 2018 for the multilingual and Chinese
    # models. This is also applied to the English models now, but it doesn't
    # matter since the English models were not trained on any Chinese data
    # and generally don't have any Chinese data in them (there are Chinese
    # characters in the vocabulary because Wikipedia does have some Chinese
    # words in the English Wikipedia.).
    text = self._tokenize_chinese_chars(text)

    orig_tokens = whitespace_tokenize(text)
    split_tokens = []
    for token in orig_tokens:
      if self.do_lower_case:
        token = token.lower()
        token = self._run_strip_accents(token)
      split_tokens.extend(self._run_split_on_punc(token))

    output_tokens = whitespace_tokenize(" ".join(split_tokens))
    return output_tokens

  def _run_strip_accents(self, text):
    """Strips accents from a piece of text."""
    text = unicodedata.normalize("NFD", text)
    output = []
    for char in text:
      cat = unicodedata.category(char)
      if cat == "Mn":
        continue
      output.append(char)
    return "".join(output)

  def _run_split_on_punc(self, text):
    """Splits punctuation on a piece of text."""
    chars = list(text)
    i = 0
    start_new_word = True
    output = []
    while i < len(chars):
      char = chars[i]
      if _is_punctuation(char):
        output.append([char])
        start_new_word = True
      else:
        if start_new_word:
          output.append([])
        start_new_word = False
        output[-1].append(char)
      i += 1

    return ["".join(x) for x in output]

  def _tokenize_chinese_chars(self, text):
    """Adds whitespace around any CJK character."""
    output = []
    for char in text:
      cp = ord(char)
      if self._is_chinese_char(cp):
        output.append(" ")
        output.append(char)
        output.append(" ")
      else:
        output.append(char)
    return "".join(output)

  def _is_chinese_char(self, cp):
    """Checks whether CP is the codepoint of a CJK character."""
    # This defines a "chinese character" as anything in the CJK Unicode block:
    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
    #
    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
    # despite its name. The modern Korean Hangul alphabet is a different block,
    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
    # space-separated words, so they are not treated specially and handled
    # like the all of the other languages.
    if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
        (cp >= 0x3400 and cp <= 0x4DBF) or  #
        (cp >= 0x20000 and cp <= 0x2A6DF) or  #
        (cp >= 0x2A700 and cp <= 0x2B73F) or  #
        (cp >= 0x2B740 and cp <= 0x2B81F) or  #
        (cp >= 0x2B820 and cp <= 0x2CEAF) or
        (cp >= 0xF900 and cp <= 0xFAFF) or  #
        (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
      return True

    return False

  def _clean_text(self, text):
    """Performs invalid character removal and whitespace cleanup on text."""
    output = []
    for char in text:
      cp = ord(char)
      if cp == 0 or cp == 0xfffd or _is_control(char):
        continue
      if _is_whitespace(char):
        output.append(" ")
      else:
        output.append(char)
    return "".join(output)


class WordpieceTokenizer(object):
  """Runs WordPiece tokenziation."""

  def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
    self.vocab = vocab
    self.unk_token = unk_token
    self.max_input_chars_per_word = max_input_chars_per_word

  def tokenize(self, text):
    """Tokenizes a piece of text into its word pieces.
    This uses a greedy longest-match-first algorithm to perform tokenization
    using the given vocabulary.
    For example:
      input = "unaffable"
      output = ["un", "##aff", "##able"]
    Args:
      text: A single token or whitespace separated tokens. This should have
        already been passed through `BasicTokenizer.
    Returns:
      A list of wordpiece tokens.
    """

    text = convert_to_unicode(text)

    output_tokens = []
    for token in whitespace_tokenize(text):
      chars = list(token)
      if len(chars) > self.max_input_chars_per_word:
        output_tokens.append(self.unk_token)
        continue

      is_bad = False
      start = 0
      sub_tokens = []
      while start < len(chars):
        end = len(chars)
        cur_substr = None
        while start < end:
          substr = "".join(chars[start:end])
          if start > 0:
            substr = "##" + substr
          if substr in self.vocab:
            cur_substr = substr
            break
          end -= 1
        if cur_substr is None:
          is_bad = True
          break
        sub_tokens.append(cur_substr)
        start = end

      if is_bad:
        output_tokens.append(self.unk_token)
      else:
        output_tokens.extend(sub_tokens)
    return output_tokens


def _is_whitespace(char):
  """Checks whether `chars` is a whitespace character."""
  # \t, \n, and \r are technically contorl characters but we treat them
  # as whitespace since they are generally considered as such.
  if char == " " or char == "\t" or char == "\n" or char == "\r":
    return True
  cat = unicodedata.category(char)
  if cat == "Zs":
    return True
  return False


def _is_control(char):
  """Checks whether `chars` is a control character."""
  # These are technically control characters but we count them as whitespace
  # characters.
  if char == "\t" or char == "\n" or char == "\r":
    return False
  cat = unicodedata.category(char)
  if cat.startswith("C"):
    return True
  return False


def _is_punctuation(char):
  """Checks whether `chars` is a punctuation character."""
  cp = ord(char)
  # We treat all non-letter/number ASCII as punctuation.
  # Characters such as "^", "$", and "`" are not in the Unicode
  # Punctuation class but we treat them as punctuation anyways, for
  # consistency.
  if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
      (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
    return True
  cat = unicodedata.category(char)
  if cat.startswith("P"):
    return True
return False

tokenizer_test.py

import os
import six
import tempfile

import tensorflow as tf

import tokenizer as tokenization


class TokenizationTest(tf.test.TestCase):

  def test_full_tokenizer(self):
    vocab_tokens = [
        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
        "##ing", ","
    ]
    with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
      if six.PY2:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
      else:
        vocab_writer.write("".join(
            [x + "\n" for x in vocab_tokens]).encode("utf-8"))

      vocab_file = vocab_writer.name

    tokenizer = tokenization.FullTokenizer(vocab_file)
    os.unlink(vocab_file)

    tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
    self.assertAllEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])

    self.assertAllEqual(
        tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])

  def test_chinese(self):
    tokenizer = tokenization.BasicTokenizer()

    self.assertAllEqual(
        tokenizer.tokenize(u"ah\u535A\u63A8zz"),
        [u"ah", u"\u535A", u"\u63A8", u"zz"])

  def test_basic_tokenizer_lower(self):
    tokenizer = tokenization.BasicTokenizer(do_lower_case=True)

    self.assertAllEqual(
        tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
        ["hello", "!", "how", "are", "you", "?"])
    self.assertAllEqual(tokenizer.tokenize(u"H\u00E9llo"), ["hello"])

  def test_basic_tokenizer_no_lower(self):
    tokenizer = tokenization.BasicTokenizer(do_lower_case=False)

    self.assertAllEqual(
        tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
        ["HeLLo", "!", "how", "Are", "yoU", "?"])

  def test_wordpiece_tokenizer(self):
    vocab_tokens = [
        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
        "##ing"
    ]

    vocab = {}
    for (i, token) in enumerate(vocab_tokens):
      vocab[token] = i
    tokenizer = tokenization.WordpieceTokenizer(vocab=vocab)

    self.assertAllEqual(tokenizer.tokenize(""), [])

    self.assertAllEqual(
        tokenizer.tokenize("unwanted running"),
        ["un", "##want", "##ed", "runn", "##ing"])

    self.assertAllEqual(
        tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])

  def test_convert_tokens_to_ids(self):
    vocab_tokens = [
        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
        "##ing"
    ]

    vocab = {}
    for (i, token) in enumerate(vocab_tokens):
      vocab[token] = i

    self.assertAllEqual(
        tokenization.convert_tokens_to_ids(
            vocab, ["un", "##want", "##ed", "runn", "##ing"]), [7, 4, 5, 8, 9])

  def test_is_whitespace(self):
    self.assertTrue(tokenization._is_whitespace(u" "))
    self.assertTrue(tokenization._is_whitespace(u"\t"))
    self.assertTrue(tokenization._is_whitespace(u"\r"))
    self.assertTrue(tokenization._is_whitespace(u"\n"))
    self.assertTrue(tokenization._is_whitespace(u"\u00A0"))

    self.assertFalse(tokenization._is_whitespace(u"A"))
    self.assertFalse(tokenization._is_whitespace(u"-"))

  def test_is_control(self):
    self.assertTrue(tokenization._is_control(u"\u0005"))

    self.assertFalse(tokenization._is_control(u"A"))
    self.assertFalse(tokenization._is_control(u" "))
    self.assertFalse(tokenization._is_control(u"\t"))
    self.assertFalse(tokenization._is_control(u"\r"))

  def test_is_punctuation(self):
    self.assertTrue(tokenization._is_punctuation(u"-"))
    self.assertTrue(tokenization._is_punctuation(u"$"))
    self.assertTrue(tokenization._is_punctuation(u"`"))
    self.assertTrue(tokenization._is_punctuation(u"."))

    self.assertFalse(tokenization._is_punctuation(u"A"))
    self.assertFalse(tokenization._is_punctuation(u" "))


if __name__ == "__main__":
  tf.test.main()