text_preprocessing.py

import collections
import unicodedata

import re
import tensorflow as tf


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)

    def mark_unk_tokens(self, tokens, unk_token='[UNK]'):
        return [t if t in self.vocab else unk_token for t in tokens]


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=100):
        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


class InputExample(object):

    def __init__(self, unique_id, text_a, text_b):
        self.unique_id = unique_id
        self.text_a = text_a
        self.text_b = text_b


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, tokens, input_ids, input_mask, input_type_ids):
        # self.unique_id = unique_id
        self.tokens = tokens
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.input_type_ids = input_type_ids


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


def convert_to_unicode(text):
    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)))


def printable_text(text):
    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)))


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 peice of text."""
    text = text.strip()
    if not text:
        return []
    tokens = text.split()
    return tokens


def convert_lst_to_features(lst_str, max_seq_length, max_position_embeddings,
                            tokenizer, is_tokenized=False, mask_cls_sep=False):
    """Loads a data file into a list of `InputBatch`s."""

    examples = read_tokenized_examples(lst_str) if is_tokenized else read_examples(lst_str)

    _tokenize = lambda x: tokenizer.mark_unk_tokens(x) if is_tokenized else tokenizer.tokenize(x)

    all_tokens = [(_tokenize(ex.text_a), _tokenize(ex.text_b) if ex.text_b else []) for ex in examples]

    # user did not specify a meaningful sequence length
    # override the sequence length by the maximum seq length of the current batch
    if max_seq_length is None:
        max_seq_length = max(len(ta) + len(tb) for ta, tb in all_tokens)
        # add special tokens into account
        # case 1: Account for [CLS], tokens_a [SEP], tokens_b [SEP] -> 3 additional tokens
        # case 2: Account for [CLS], tokens_a [SEP] -> 2 additional tokens
        max_seq_length += 3 if any(len(tb) for _, tb in all_tokens) else 2
        max_seq_length = min(max_seq_length, max_position_embeddings)

    for (tokens_a, tokens_b) in all_tokens:
        if tokens_b:
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[0:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0     0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = ['[CLS]'] + tokens_a + ['[SEP]']
        input_type_ids = [0] * len(tokens)
        input_mask = [int(not mask_cls_sep)] + [1] * len(tokens_a) + [int(not mask_cls_sep)]

        if tokens_b:
            tokens += tokens_b + ['[SEP]']
            input_type_ids += [1] * (len(tokens_b) + 1)
            input_mask += [1] * len(tokens_b) + [int(not mask_cls_sep)]

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # Zero-pad up to the sequence length. more pythonic
        pad_len = max_seq_length - len(input_ids)
        input_ids += [0] * pad_len
        input_mask += [0] * pad_len
        input_type_ids += [0] * pad_len

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(input_type_ids) == max_seq_length

        yield InputFeatures(
            # unique_id=example.unique_id,
            tokens=tokens,
            input_ids=input_ids,
            input_mask=input_mask,
            input_type_ids=input_type_ids)


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def read_examples(lst_strs):
    """Read a list of `InputExample`s from a list of strings."""
    unique_id = 0
    for ss in lst_strs:
        line = convert_to_unicode(ss)
        if not line:
            continue
        line = line.strip()
        text_a = None
        text_b = None
        m = re.match(r"^(.*) \|\|\| (.*)$", line)
        if m is None:
            text_a = line
        else:
            text_a = m.group(1)
            text_b = m.group(2)
        yield InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b)
        unique_id += 1


def read_tokenized_examples(lst_strs):
    unique_id = 0
    lst_strs = [[convert_to_unicode(w) for w in s] for s in lst_strs]
    for ss in lst_strs:
        text_a = ss
        text_b = None
        try:
            j = ss.index('|||')
            text_a = ss[:j]
            text_b = ss[(j + 1):]
        except ValueError:
            pass
        yield InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b)
        unique_id += 1

def stub_preprocessor(text):
    return text