|
- # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-
- """ Tokenization classes (It's exactly the same code as Google BERT code """
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import collections
- import unicodedata
- import six
-
-
- 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 open(vocab_file, "rb") 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_tokens_to_ids(vocab, tokens):
- """Converts a sequence of tokens into ids using the vocab."""
- ids = []
- for token in tokens:
- ids.append(vocab[token])
- return 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
-
-
- class FullTokenizer(object):
- """Runs end-to-end tokenziation."""
-
- def __init__(self, vocab_file, do_lower_case=True):
- self.vocab = load_vocab(vocab_file)
- 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_tokens_to_ids(self.vocab, tokens)
-
- def convert_to_unicode(self, text):
- return convert_to_unicode(text)
-
-
-
- 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)
- 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 _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 tokenization."""
-
- 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
-
-
- 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
|