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tokenizers_pegasus.py
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1 |
+
|
2 |
+
from fengshen.examples.pegasus.data_utils import (
|
3 |
+
_is_control,
|
4 |
+
_is_punctuation,
|
5 |
+
_is_whitespace,
|
6 |
+
_is_chinese_char)
|
7 |
+
from transformers import PreTrainedTokenizer
|
8 |
+
from transformers import logging
|
9 |
+
from typing import List, Optional, Tuple, Union
|
10 |
+
import collections
|
11 |
+
import os
|
12 |
+
import unicodedata
|
13 |
+
import re
|
14 |
+
import jieba
|
15 |
+
import sys
|
16 |
+
|
17 |
+
sys.path.append("../../../../")
|
18 |
+
|
19 |
+
jieba.dt.tmp_dir = os.path.expanduser(
|
20 |
+
"/cognitive_comp/dongxiaoqun/software/jieba/tmp/")
|
21 |
+
# jieba.enable_parallel(8)
|
22 |
+
jieba.initialize()
|
23 |
+
|
24 |
+
logger = logging.get_logger(__name__)
|
25 |
+
|
26 |
+
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
|
27 |
+
|
28 |
+
|
29 |
+
def load_vocab(vocab_file):
|
30 |
+
"""Loads a vocabulary file into a dictionary."""
|
31 |
+
vocab = collections.OrderedDict()
|
32 |
+
with open(vocab_file, "r", encoding="utf-8") as reader:
|
33 |
+
tokens = reader.readlines()
|
34 |
+
for index, token in enumerate(tokens):
|
35 |
+
token = token.rstrip("\n")
|
36 |
+
vocab[token] = index
|
37 |
+
return vocab
|
38 |
+
|
39 |
+
|
40 |
+
def whitespace_tokenize(text):
|
41 |
+
"""Runs basic whitespace cleaning and splitting on a piece of text."""
|
42 |
+
text = text.strip()
|
43 |
+
if not text:
|
44 |
+
return []
|
45 |
+
tokens = text.split()
|
46 |
+
return tokens
|
47 |
+
|
48 |
+
|
49 |
+
class PegasusTokenizer(PreTrainedTokenizer):
|
50 |
+
# copy from BertTokenizer
|
51 |
+
r"""
|
52 |
+
Construct a Pegasus tokenizer. Based on WordPiece.
|
53 |
+
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
|
54 |
+
this superclass for more information regarding those methods.
|
55 |
+
Args:
|
56 |
+
vocab_file (`str`):
|
57 |
+
File containing the vocabulary.
|
58 |
+
do_lower_case (`bool`, *optional*, defaults to `True`):
|
59 |
+
Whether or not to lowercase the input when tokenizing.
|
60 |
+
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
|
61 |
+
Whether or not to do basic tokenization before WordPiece.
|
62 |
+
never_split (`Iterable`, *optional*):
|
63 |
+
Collection of tokens which will never be split during tokenization. Only has an effect when
|
64 |
+
`do_basic_tokenize=True`
|
65 |
+
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
|
66 |
+
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
67 |
+
token instead.
|
68 |
+
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
|
69 |
+
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
|
70 |
+
sequence classification or for a text and a question for question answering. It is also used as the last
|
71 |
+
token of a sequence built with special tokens.
|
72 |
+
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
|
73 |
+
The token used for padding, for example when batching sequences of different lengths.
|
74 |
+
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
|
75 |
+
The classifier token which is used when doing sequence classification (classification of the whole sequence
|
76 |
+
instead of per-token classification). It is the first token of the sequence when built with special tokens.
|
77 |
+
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
|
78 |
+
The token used for masking values. This is the token used when training this model with masked language
|
79 |
+
modeling. This is the token which the model will try to predict.
|
80 |
+
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
|
81 |
+
Whether or not to tokenize Chinese characters.
|
82 |
+
This should likely be deactivated for Japanese (see this
|
83 |
+
[issue](https://github.com/huggingface/transformers/issues/328)).
|
84 |
+
strip_accents (`bool`, *optional*):
|
85 |
+
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
|
86 |
+
value for `lowercase` (as in the original BERT).
|
87 |
+
"""
|
88 |
+
|
89 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
90 |
+
model_input_names = ["input_ids", "attention_mask"]
|
91 |
+
|
92 |
+
# pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
93 |
+
# pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
|
94 |
+
# max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
95 |
+
|
96 |
+
def __init__(self,
|
97 |
+
vocab_file,
|
98 |
+
do_lower_case=True,
|
99 |
+
do_basic_tokenize=True,
|
100 |
+
never_split=None,
|
101 |
+
pad_token="<pad>",
|
102 |
+
eos_token="</s>",
|
103 |
+
unk_token="<unk>",
|
104 |
+
mask_token="<mask_2>",
|
105 |
+
mask_token_sent="<mask_1>",
|
106 |
+
additional_special_tokens=None,
|
107 |
+
sep_token="[SEP]",
|
108 |
+
cls_token="[CLS]",
|
109 |
+
tokenize_chinese_chars=True,
|
110 |
+
strip_accents=None,
|
111 |
+
offset=100,
|
112 |
+
pre_tokenizer=lambda x: jieba.cut(x, HMM=False),
|
113 |
+
**kwargs):
|
114 |
+
self.offset = offset
|
115 |
+
|
116 |
+
if additional_special_tokens is not None:
|
117 |
+
if not isinstance(additional_special_tokens, list):
|
118 |
+
raise TypeError(
|
119 |
+
f"additional_special_tokens should be of type {type(list)}, \
|
120 |
+
but is {type(additional_special_tokens)}"
|
121 |
+
)
|
122 |
+
|
123 |
+
additional_special_tokens_extended = (
|
124 |
+
([mask_token_sent] + additional_special_tokens)
|
125 |
+
if mask_token_sent not in additional_special_tokens
|
126 |
+
and mask_token_sent is not None else additional_special_tokens)
|
127 |
+
|
128 |
+
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
|
129 |
+
additional_special_tokens_extended += [
|
130 |
+
f"<unk_{i}>" for i in range(
|
131 |
+
len(additional_special_tokens_extended), self.offset - 1)
|
132 |
+
]
|
133 |
+
|
134 |
+
if len(set(additional_special_tokens_extended)) != len(
|
135 |
+
additional_special_tokens_extended):
|
136 |
+
raise ValueError(
|
137 |
+
f"Please make sure that the provided additional_special_tokens \
|
138 |
+
do not contain an incorrectly shifted list of <unk_x> tokens. \
|
139 |
+
Found {additional_special_tokens_extended}."
|
140 |
+
)
|
141 |
+
additional_special_tokens = additional_special_tokens_extended
|
142 |
+
else:
|
143 |
+
additional_special_tokens = [
|
144 |
+
mask_token_sent
|
145 |
+
] if mask_token_sent is not None else []
|
146 |
+
# additional_special_tokens += [f"<unk_{i}>" for i in range(3, self.offset)]
|
147 |
+
|
148 |
+
# print("additional_special_tokens: ", additional_special_tokens)
|
149 |
+
|
150 |
+
if not os.path.isfile(vocab_file):
|
151 |
+
raise ValueError(
|
152 |
+
f"Can't find a vocabulary file at path '{vocab_file}'. \
|
153 |
+
To load the vocabulary from a Google pretrained "
|
154 |
+
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
|
155 |
+
)
|
156 |
+
|
157 |
+
super().__init__(
|
158 |
+
do_lower_case=do_lower_case,
|
159 |
+
do_basic_tokenize=do_basic_tokenize,
|
160 |
+
never_split=never_split,
|
161 |
+
unk_token=unk_token,
|
162 |
+
sep_token=sep_token,
|
163 |
+
pad_token=pad_token,
|
164 |
+
cls_token=cls_token,
|
165 |
+
mask_token=mask_token,
|
166 |
+
eos_token=eos_token,
|
167 |
+
tokenize_chinese_chars=tokenize_chinese_chars,
|
168 |
+
additional_special_tokens=additional_special_tokens,
|
169 |
+
strip_accents=strip_accents,
|
170 |
+
**kwargs,
|
171 |
+
)
|
172 |
+
|
173 |
+
self.pre_tokenizer = pre_tokenizer
|
174 |
+
self.mask_token_sent = mask_token_sent
|
175 |
+
self.vocab = load_vocab(vocab_file)
|
176 |
+
|
177 |
+
self.vocab[self.eos_token] = self.vocab.pop("[unused1]")
|
178 |
+
# self.vocab[self.eos_token] = self.vocab.pop("[unused2]")
|
179 |
+
self.vocab[self.pad_token] = self.vocab.pop("[PAD]")
|
180 |
+
self.vocab[self.unk_token] = self.vocab.pop("[UNK]")
|
181 |
+
|
182 |
+
if self.mask_token_sent is not None:
|
183 |
+
self.vocab[self.mask_token] = self.vocab.pop("[unused3]")
|
184 |
+
self.vocab[self.mask_token_sent] = self.vocab.pop("[unused2]")
|
185 |
+
|
186 |
+
self.ids_to_tokens = collections.OrderedDict([
|
187 |
+
(ids, tok) for tok, ids in self.vocab.items()
|
188 |
+
])
|
189 |
+
self.do_basic_tokenize = do_basic_tokenize
|
190 |
+
if do_basic_tokenize:
|
191 |
+
self.basic_tokenizer = BasicTokenizer(
|
192 |
+
do_lower_case=do_lower_case,
|
193 |
+
never_split=never_split,
|
194 |
+
tokenize_chinese_chars=tokenize_chinese_chars,
|
195 |
+
strip_accents=strip_accents,
|
196 |
+
)
|
197 |
+
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab,
|
198 |
+
unk_token=self.unk_token)
|
199 |
+
|
200 |
+
@property
|
201 |
+
def do_lower_case(self):
|
202 |
+
return self.basic_tokenizer.do_lower_case
|
203 |
+
|
204 |
+
@property
|
205 |
+
def vocab_size(self):
|
206 |
+
return len(self.vocab)
|
207 |
+
|
208 |
+
def get_vocab(self):
|
209 |
+
return dict(self.vocab, **self.added_tokens_encoder)
|
210 |
+
|
211 |
+
def _tokenize(self, text):
|
212 |
+
split_tokens = []
|
213 |
+
# print("pegasus_tokenizer: ", text)
|
214 |
+
for text in self.pre_tokenizer(text):
|
215 |
+
if text in self.vocab:
|
216 |
+
split_tokens.append(text)
|
217 |
+
else:
|
218 |
+
if self.do_basic_tokenize:
|
219 |
+
for token in self.basic_tokenizer.tokenize(
|
220 |
+
text, never_split=self.all_special_tokens):
|
221 |
+
|
222 |
+
# If the token is part of the never_split set
|
223 |
+
if token in self.basic_tokenizer.never_split:
|
224 |
+
split_tokens.append(token)
|
225 |
+
else:
|
226 |
+
split_tokens += self.wordpiece_tokenizer.tokenize(
|
227 |
+
token)
|
228 |
+
else:
|
229 |
+
split_tokens = self.wordpiece_tokenizer.tokenize(text)
|
230 |
+
return split_tokens
|
231 |
+
|
232 |
+
def _convert_token_to_id(self, token):
|
233 |
+
"""Converts a token (str) in an id using the vocab."""
|
234 |
+
return self.vocab.get(token, self.vocab.get(self.unk_token))
|
235 |
+
|
236 |
+
def _convert_id_to_token(self, index):
|
237 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
238 |
+
return self.ids_to_tokens.get(index, self.unk_token)
|
239 |
+
|
240 |
+
@staticmethod
|
241 |
+
def _cjk_punctuation():
|
242 |
+
return u'\uff02\uff03\uff04\uff05\uff06\uff07\uff08\uff09\uff0a\uff0b\uff0c\uff0d\uff0f\uff1a\uff1b\uff1c\uff1d\
|
243 |
+
\uff1e\uff20\uff3b\uff3c\uff3d\uff3e\uff3f\uff40\uff5b\uff5c\uff5d\uff5e\uff5f\uff60\uff62\
|
244 |
+
\uff63\uff64\u3000\u3001\u3003\u3008\u3009\u300a\u300b\u300c\u300d\u300e\u300f\u3010\u3011\u3014\
|
245 |
+
\u3015\u3016\u3017\u3018\u3019\u301a\u301b\u301c\u301d\u301e\u301f\u3030\u303e\u303f\u2013\u2014\
|
246 |
+
\u2018\u2019\u201b\u201c\u201d\u201e\u201f\u2026\u2027\ufe4f\ufe51\ufe54\u00b7\uff01\uff1f\uff61\u3002'
|
247 |
+
|
248 |
+
def convert_ids_to_tokens(
|
249 |
+
self,
|
250 |
+
ids: Union[int, List[int]],
|
251 |
+
skip_special_tokens: bool = False) -> Union[str, List[str]]:
|
252 |
+
"""
|
253 |
+
Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
|
254 |
+
added tokens.
|
255 |
+
Args:
|
256 |
+
ids (`int` or `List[int]`):
|
257 |
+
The token id (or token ids) to convert to tokens.
|
258 |
+
skip_special_tokens (`bool`, *optional*, defaults to `False`):
|
259 |
+
Whether or not to remove special tokens in the decoding.
|
260 |
+
Returns:
|
261 |
+
`str` or `List[str]`: The decoded token(s).
|
262 |
+
"""
|
263 |
+
if isinstance(ids, int):
|
264 |
+
if ids in self.added_tokens_decoder:
|
265 |
+
return self.added_tokens_decoder[ids]
|
266 |
+
else:
|
267 |
+
return self._convert_id_to_token(ids)
|
268 |
+
tokens = []
|
269 |
+
for index in ids:
|
270 |
+
index = int(index)
|
271 |
+
if skip_special_tokens and index in self.all_special_ids and index != 2:
|
272 |
+
continue
|
273 |
+
if index in self.added_tokens_decoder:
|
274 |
+
tokens.append(self.added_tokens_decoder[index])
|
275 |
+
else:
|
276 |
+
tokens.append(self._convert_id_to_token(index))
|
277 |
+
return tokens
|
278 |
+
|
279 |
+
def convert_tokens_to_string(self, tokens):
|
280 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
281 |
+
# for token in
|
282 |
+
# tokens = tokens or self.ids_to_tokens(ids)
|
283 |
+
# tokens = [token for token in tokens if not self._is_special(token)]
|
284 |
+
|
285 |
+
text = ''
|
286 |
+
for i, token in enumerate(tokens):
|
287 |
+
if token[:2] == '##':
|
288 |
+
text += token[2:]
|
289 |
+
elif len(token) == 1 and _is_chinese_char(ord(token)):
|
290 |
+
text += token
|
291 |
+
elif len(token) == 1 and _is_punctuation(token):
|
292 |
+
text += token
|
293 |
+
text += ' '
|
294 |
+
elif i > 0 and _is_chinese_char(ord(text[-1])):
|
295 |
+
text += token
|
296 |
+
elif tokens == "</s>":
|
297 |
+
continue
|
298 |
+
else:
|
299 |
+
text += ' '
|
300 |
+
text += token
|
301 |
+
|
302 |
+
text = re.sub(' +', ' ', text)
|
303 |
+
text = re.sub('\' (re|m|s|t|ve|d|ll) ', '\'\\1 ', text)
|
304 |
+
punctuation = re.sub(' +', '', self._cjk_punctuation()).strip() + '+-/={(<['
|
305 |
+
punctuation_regex = '|'.join([re.escape(p) for p in punctuation])
|
306 |
+
punctuation_regex = '(%s) ' % punctuation_regex
|
307 |
+
text = re.sub(punctuation_regex, '\\1', text)
|
308 |
+
text = re.sub(r'(\d\.) (\d)', '\\1\\2', text)
|
309 |
+
|
310 |
+
return text.strip()
|
311 |
+
# out_string = " ".join(tokens).replace(" ##", "").strip()
|
312 |
+
|
313 |
+
def build_inputs_with_special_tokens(
|
314 |
+
self,
|
315 |
+
token_ids_0: List[int],
|
316 |
+
token_ids_1: Optional[List[int]] = None) -> List[int]:
|
317 |
+
"""
|
318 |
+
Build model inputs from a sequence or a pair of sequences for sequence classification tasks by concatenating
|
319 |
+
and adding special tokens. A PEGASUS sequence has the following format, where `X` represents the sequence:
|
320 |
+
- single sequence: `X </s>`
|
321 |
+
- pair of sequences: `A B </s>` (not intended use)
|
322 |
+
BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
|
323 |
+
separator.
|
324 |
+
Args:
|
325 |
+
token_ids_0 (`List[int]`):
|
326 |
+
List of IDs to which the special tokens will be added.
|
327 |
+
token_ids_1 (`List[int]`, *optional*):
|
328 |
+
Optional second list of IDs for sequence pairs.
|
329 |
+
Returns:
|
330 |
+
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
331 |
+
"""
|
332 |
+
if token_ids_1 is None:
|
333 |
+
return token_ids_0 + [self.eos_token_id]
|
334 |
+
return token_ids_0 + token_ids_1 + [self.eos_token_id]
|
335 |
+
|
336 |
+
def _special_token_mask(self, seq):
|
337 |
+
all_special_ids = set(
|
338 |
+
self.all_special_ids) # call it once instead of inside list comp
|
339 |
+
# all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
|
340 |
+
|
341 |
+
return [1 if x in all_special_ids else 0 for x in seq]
|
342 |
+
|
343 |
+
def get_special_tokens_mask(
|
344 |
+
self,
|
345 |
+
token_ids_0: List[int],
|
346 |
+
token_ids_1: Optional[List[int]] = None,
|
347 |
+
already_has_special_tokens: bool = False) -> List[int]:
|
348 |
+
"""
|
349 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
350 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
351 |
+
Args:
|
352 |
+
token_ids_0 (`List[int]`):
|
353 |
+
List of IDs.
|
354 |
+
token_ids_1 (`List[int]`, *optional*):
|
355 |
+
Optional second list of IDs for sequence pairs.
|
356 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
357 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
358 |
+
Returns:
|
359 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
360 |
+
"""
|
361 |
+
|
362 |
+
if already_has_special_tokens:
|
363 |
+
return self._special_token_mask(token_ids_0)
|
364 |
+
elif token_ids_1 is None:
|
365 |
+
return self._special_token_mask(token_ids_0) + [self.eos_token_id]
|
366 |
+
else:
|
367 |
+
return self._special_token_mask(token_ids_0 +
|
368 |
+
token_ids_1) + [self.eos_token_id]
|
369 |
+
|
370 |
+
def num_special_tokens_to_add(self, pair=False):
|
371 |
+
"""Just EOS"""
|
372 |
+
return 1
|
373 |
+
|
374 |
+
def save_vocabulary(self,
|
375 |
+
save_directory: str,
|
376 |
+
filename_prefix: Optional[str] = None) -> Tuple[str]:
|
377 |
+
index = 0
|
378 |
+
if os.path.isdir(save_directory):
|
379 |
+
vocab_file = os.path.join(
|
380 |
+
save_directory,
|
381 |
+
(filename_prefix + "-" if filename_prefix else "") +
|
382 |
+
VOCAB_FILES_NAMES["vocab_file"])
|
383 |
+
else:
|
384 |
+
vocab_file = (filename_prefix +
|
385 |
+
"-" if filename_prefix else "") + save_directory
|
386 |
+
with open(vocab_file, "w", encoding="utf-8") as writer:
|
387 |
+
for token, token_index in sorted(self.vocab.items(),
|
388 |
+
key=lambda kv: kv[1]):
|
389 |
+
if index != token_index:
|
390 |
+
logger.warning(
|
391 |
+
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
|
392 |
+
" Please check that the vocabulary is not corrupted!")
|
393 |
+
index = token_index
|
394 |
+
writer.write(token + "\n")
|
395 |
+
index += 1
|
396 |
+
return (vocab_file, )
|
397 |
+
|
398 |
+
|
399 |
+
class BasicTokenizer(object):
|
400 |
+
"""
|
401 |
+
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
|
402 |
+
Args:
|
403 |
+
do_lower_case (`bool`, *optional*, defaults to `True`):
|
404 |
+
Whether or not to lowercase the input when tokenizing.
|
405 |
+
never_split (`Iterable`, *optional*):
|
406 |
+
Collection of tokens which will never be split during tokenization. Only has an effect when
|
407 |
+
`do_basic_tokenize=True`
|
408 |
+
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
|
409 |
+
Whether or not to tokenize Chinese characters.
|
410 |
+
This should likely be deactivated for Japanese (see this
|
411 |
+
[issue](https://github.com/huggingface/transformers/issues/328)).
|
412 |
+
strip_accents: (`bool`, *optional*):
|
413 |
+
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
|
414 |
+
value for `lowercase` (as in the original BERT).
|
415 |
+
"""
|
416 |
+
|
417 |
+
def __init__(self,
|
418 |
+
do_lower_case=True,
|
419 |
+
never_split=None,
|
420 |
+
tokenize_chinese_chars=True,
|
421 |
+
strip_accents=None):
|
422 |
+
if never_split is None:
|
423 |
+
never_split = []
|
424 |
+
self.do_lower_case = do_lower_case
|
425 |
+
self.never_split = set(never_split)
|
426 |
+
self.tokenize_chinese_chars = tokenize_chinese_chars
|
427 |
+
self.strip_accents = strip_accents
|
428 |
+
|
429 |
+
def tokenize(self, text, never_split=None):
|
430 |
+
"""
|
431 |
+
Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
|
432 |
+
WordPieceTokenizer.
|
433 |
+
Args:
|
434 |
+
never_split (`List[str]`, *optional*)
|
435 |
+
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
|
436 |
+
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
|
437 |
+
"""
|
438 |
+
# union() returns a new set by concatenating the two sets.
|
439 |
+
never_split = self.never_split.union(
|
440 |
+
set(never_split)) if never_split else self.never_split
|
441 |
+
text = self._clean_text(text)
|
442 |
+
|
443 |
+
# This was added on November 1st, 2018 for the multilingual and Chinese
|
444 |
+
# models. This is also applied to the English models now, but it doesn't
|
445 |
+
# matter since the English models were not trained on any Chinese data
|
446 |
+
# and generally don't have any Chinese data in them (there are Chinese
|
447 |
+
# characters in the vocabulary because Wikipedia does have some Chinese
|
448 |
+
# words in the English Wikipedia.).
|
449 |
+
if self.tokenize_chinese_chars:
|
450 |
+
text = self._tokenize_chinese_chars(text)
|
451 |
+
orig_tokens = whitespace_tokenize(text)
|
452 |
+
split_tokens = []
|
453 |
+
for token in orig_tokens:
|
454 |
+
if token not in never_split:
|
455 |
+
if self.do_lower_case:
|
456 |
+
token = token.lower()
|
457 |
+
if self.strip_accents is not False:
|
458 |
+
token = self._run_strip_accents(token)
|
459 |
+
elif self.strip_accents:
|
460 |
+
token = self._run_strip_accents(token)
|
461 |
+
split_tokens.extend(self._run_split_on_punc(token, never_split))
|
462 |
+
|
463 |
+
output_tokens = whitespace_tokenize(" ".join(split_tokens))
|
464 |
+
return output_tokens
|
465 |
+
|
466 |
+
def _run_strip_accents(self, text):
|
467 |
+
"""Strips accents from a piece of text."""
|
468 |
+
text = unicodedata.normalize("NFD", text)
|
469 |
+
output = []
|
470 |
+
for char in text:
|
471 |
+
cat = unicodedata.category(char)
|
472 |
+
if cat == "Mn":
|
473 |
+
continue
|
474 |
+
output.append(char)
|
475 |
+
return "".join(output)
|
476 |
+
|
477 |
+
def _run_split_on_punc(self, text, never_split=None):
|
478 |
+
"""Splits punctuation on a piece of text."""
|
479 |
+
if never_split is not None and text in never_split:
|
480 |
+
return [text]
|
481 |
+
chars = list(text)
|
482 |
+
i = 0
|
483 |
+
start_new_word = True
|
484 |
+
output = []
|
485 |
+
while i < len(chars):
|
486 |
+
char = chars[i]
|
487 |
+
if _is_punctuation(char):
|
488 |
+
output.append([char])
|
489 |
+
start_new_word = True
|
490 |
+
else:
|
491 |
+
if start_new_word:
|
492 |
+
output.append([])
|
493 |
+
start_new_word = False
|
494 |
+
output[-1].append(char)
|
495 |
+
i += 1
|
496 |
+
|
497 |
+
return ["".join(x) for x in output]
|
498 |
+
|
499 |
+
def _tokenize_chinese_chars(self, text):
|
500 |
+
"""Adds whitespace around any CJK character."""
|
501 |
+
output = []
|
502 |
+
for char in text:
|
503 |
+
cp = ord(char)
|
504 |
+
if self._is_chinese_char(cp):
|
505 |
+
output.append(" ")
|
506 |
+
output.append(char)
|
507 |
+
output.append(" ")
|
508 |
+
else:
|
509 |
+
output.append(char)
|
510 |
+
return "".join(output)
|
511 |
+
|
512 |
+
def _is_chinese_char(self, cp):
|
513 |
+
"""Checks whether CP is the codepoint of a CJK character."""
|
514 |
+
# This defines a "chinese character" as anything in the CJK Unicode block:
|
515 |
+
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
|
516 |
+
#
|
517 |
+
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
|
518 |
+
# despite its name. The modern Korean Hangul alphabet is a different block,
|
519 |
+
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
|
520 |
+
# space-separated words, so they are not treated specially and handled
|
521 |
+
# like the all of the other languages.
|
522 |
+
if ((cp >= 0x4E00 and cp <= 0x9FFF)
|
523 |
+
or (cp >= 0x3400 and cp <= 0x4DBF) #
|
524 |
+
or (cp >= 0x20000 and cp <= 0x2A6DF) #
|
525 |
+
or (cp >= 0x2A700 and cp <= 0x2B73F) #
|
526 |
+
or (cp >= 0x2B740 and cp <= 0x2B81F) #
|
527 |
+
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
|
528 |
+
or (cp >= 0xF900 and cp <= 0xFAFF)
|
529 |
+
or (cp >= 0x2F800 and cp <= 0x2FA1F)): #
|
530 |
+
return True
|
531 |
+
|
532 |
+
return False
|
533 |
+
|
534 |
+
def _clean_text(self, text):
|
535 |
+
"""Performs invalid character removal and whitespace cleanup on text."""
|
536 |
+
output = []
|
537 |
+
for char in text:
|
538 |
+
cp = ord(char)
|
539 |
+
if cp == 0 or cp == 0xFFFD or _is_control(char):
|
540 |
+
continue
|
541 |
+
if _is_whitespace(char):
|
542 |
+
output.append(" ")
|
543 |
+
else:
|
544 |
+
output.append(char)
|
545 |
+
return "".join(output)
|
546 |
+
|
547 |
+
|
548 |
+
class WordpieceTokenizer(object):
|
549 |
+
"""Runs WordPiece tokenization."""
|
550 |
+
|
551 |
+
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
|
552 |
+
self.vocab = vocab
|
553 |
+
self.unk_token = unk_token
|
554 |
+
self.max_input_chars_per_word = max_input_chars_per_word
|
555 |
+
|
556 |
+
def tokenize(self, text):
|
557 |
+
"""
|
558 |
+
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
|
559 |
+
tokenization using the given vocabulary.
|
560 |
+
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
|
561 |
+
Args:
|
562 |
+
text: A single token or whitespace separated tokens. This should have
|
563 |
+
already been passed through *BasicTokenizer*.
|
564 |
+
Returns:
|
565 |
+
A list of wordpiece tokens.
|
566 |
+
"""
|
567 |
+
|
568 |
+
output_tokens = []
|
569 |
+
for token in whitespace_tokenize(text):
|
570 |
+
chars = list(token)
|
571 |
+
if len(chars) > self.max_input_chars_per_word:
|
572 |
+
output_tokens.append(self.unk_token)
|
573 |
+
continue
|
574 |
+
|
575 |
+
is_bad = False
|
576 |
+
start = 0
|
577 |
+
sub_tokens = []
|
578 |
+
while start < len(chars):
|
579 |
+
end = len(chars)
|
580 |
+
cur_substr = None
|
581 |
+
while start < end:
|
582 |
+
substr = "".join(chars[start:end])
|
583 |
+
if start > 0:
|
584 |
+
substr = "##" + substr
|
585 |
+
if substr in self.vocab:
|
586 |
+
cur_substr = substr
|
587 |
+
break
|
588 |
+
end -= 1
|
589 |
+
if cur_substr is None:
|
590 |
+
is_bad = True
|
591 |
+
break
|
592 |
+
sub_tokens.append(cur_substr)
|
593 |
+
start = end
|
594 |
+
|
595 |
+
if is_bad:
|
596 |
+
output_tokens.append(self.unk_token)
|
597 |
+
else:
|
598 |
+
output_tokens.extend(sub_tokens)
|
599 |
+
return output_tokens
|