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# coding=utf-8 | |
# Copyright 2020 Microsoft and the HuggingFace Inc. team. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" Tokenization class for model DeBERTa.""" | |
import os | |
import unicodedata | |
from typing import Any, Dict, List, Optional, Tuple | |
import sentencepiece as sp | |
from ...tokenization_utils import AddedToken, PreTrainedTokenizer | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
PRETRAINED_VOCAB_FILES_MAP = { | |
"vocab_file": { | |
"microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model", | |
"microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model", | |
"microsoft/deberta-v2-xlarge-mnli": ( | |
"https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model" | |
), | |
"microsoft/deberta-v2-xxlarge-mnli": ( | |
"https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model" | |
), | |
} | |
} | |
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { | |
"microsoft/deberta-v2-xlarge": 512, | |
"microsoft/deberta-v2-xxlarge": 512, | |
"microsoft/deberta-v2-xlarge-mnli": 512, | |
"microsoft/deberta-v2-xxlarge-mnli": 512, | |
} | |
PRETRAINED_INIT_CONFIGURATION = { | |
"microsoft/deberta-v2-xlarge": {"do_lower_case": False}, | |
"microsoft/deberta-v2-xxlarge": {"do_lower_case": False}, | |
"microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False}, | |
"microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False}, | |
} | |
VOCAB_FILES_NAMES = {"vocab_file": "spm.model"} | |
class DebertaV2Tokenizer(PreTrainedTokenizer): | |
r""" | |
Constructs a DeBERTa-v2 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). | |
Args: | |
vocab_file (`str`): | |
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that | |
contains the vocabulary necessary to instantiate a tokenizer. | |
do_lower_case (`bool`, *optional*, defaults to `False`): | |
Whether or not to lowercase the input when tokenizing. | |
bos_token (`string`, *optional*, defaults to `"[CLS]"`): | |
The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token. | |
When building a sequence using special tokens, this is not the token that is used for the beginning of | |
sequence. The token used is the `cls_token`. | |
eos_token (`string`, *optional*, defaults to `"[SEP]"`): | |
The end of sequence token. When building a sequence using special tokens, this is not the token that is | |
used for the end of sequence. The token used is the `sep_token`. | |
unk_token (`str`, *optional*, defaults to `"[UNK]"`): | |
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this | |
token instead. | |
sep_token (`str`, *optional*, defaults to `"[SEP]"`): | |
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for | |
sequence classification or for a text and a question for question answering. It is also used as the last | |
token of a sequence built with special tokens. | |
pad_token (`str`, *optional*, defaults to `"[PAD]"`): | |
The token used for padding, for example when batching sequences of different lengths. | |
cls_token (`str`, *optional*, defaults to `"[CLS]"`): | |
The classifier token which is used when doing sequence classification (classification of the whole sequence | |
instead of per-token classification). It is the first token of the sequence when built with special tokens. | |
mask_token (`str`, *optional*, defaults to `"[MASK]"`): | |
The token used for masking values. This is the token used when training this model with masked language | |
modeling. This is the token which the model will try to predict. | |
sp_model_kwargs (`dict`, *optional*): | |
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for | |
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, | |
to set: | |
- `enable_sampling`: Enable subword regularization. | |
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. | |
- `nbest_size = {0,1}`: No sampling is performed. | |
- `nbest_size > 1`: samples from the nbest_size results. | |
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) | |
using forward-filtering-and-backward-sampling algorithm. | |
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for | |
BPE-dropout. | |
""" | |
vocab_files_names = VOCAB_FILES_NAMES | |
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION | |
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
def __init__( | |
self, | |
vocab_file, | |
do_lower_case=False, | |
split_by_punct=False, | |
bos_token="[CLS]", | |
eos_token="[SEP]", | |
unk_token="[UNK]", | |
sep_token="[SEP]", | |
pad_token="[PAD]", | |
cls_token="[CLS]", | |
mask_token="[MASK]", | |
sp_model_kwargs: Optional[Dict[str, Any]] = None, | |
**kwargs, | |
) -> None: | |
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs | |
if not os.path.isfile(vocab_file): | |
raise ValueError( | |
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained" | |
" model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" | |
) | |
self.do_lower_case = do_lower_case | |
self.split_by_punct = split_by_punct | |
self.vocab_file = vocab_file | |
self._tokenizer = SPMTokenizer( | |
vocab_file, None, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs | |
) | |
unk_token = AddedToken(unk_token, normalized=True, lstrip=False, rstrip=False) | |
super().__init__( | |
do_lower_case=do_lower_case, | |
bos_token=bos_token, | |
eos_token=eos_token, | |
unk_token=unk_token, | |
sep_token=sep_token, | |
pad_token=pad_token, | |
cls_token=cls_token, | |
mask_token=mask_token, | |
split_by_punct=split_by_punct, | |
sp_model_kwargs=self.sp_model_kwargs, | |
**kwargs, | |
) | |
self._tokenizer.special_tokens = self.all_special_tokens | |
def vocab_size(self): | |
return len(self.vocab) | |
def vocab(self): | |
return self._tokenizer.vocab | |
def get_vocab(self): | |
vocab = self.vocab.copy() | |
vocab.update(self.get_added_vocab()) | |
return vocab | |
def _tokenize(self, text: str) -> List[str]: | |
"""Take as input a string and return a list of strings (tokens) for words/sub-words""" | |
if self.do_lower_case: | |
text = text.lower() | |
return self._tokenizer.tokenize(text) | |
def _convert_token_to_id(self, token): | |
"""Converts a token (str) in an id using the vocab.""" | |
return self._tokenizer.spm.PieceToId(token) | |
def _convert_id_to_token(self, index): | |
"""Converts an index (integer) in a token (str) using the vocab.""" | |
return self._tokenizer.spm.IdToPiece(index) if index < self.vocab_size else self.unk_token | |
def convert_tokens_to_string(self, tokens): | |
"""Converts a sequence of tokens (string) in a single string.""" | |
return self._tokenizer.decode(tokens) | |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): | |
""" | |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and | |
adding special tokens. A DeBERTa sequence has the following format: | |
- single sequence: [CLS] X [SEP] | |
- pair of sequences: [CLS] A [SEP] B [SEP] | |
Args: | |
token_ids_0 (`List[int]`): | |
List of IDs to which the special tokens will be added. | |
token_ids_1 (`List[int]`, *optional*): | |
Optional second list of IDs for sequence pairs. | |
Returns: | |
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. | |
""" | |
if token_ids_1 is None: | |
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] | |
cls = [self.cls_token_id] | |
sep = [self.sep_token_id] | |
return cls + token_ids_0 + sep + token_ids_1 + sep | |
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): | |
""" | |
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding | |
special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods. | |
Args: | |
token_ids_0 (`List[int]`): | |
List of IDs. | |
token_ids_1 (`List[int]`, *optional*): | |
Optional second list of IDs for sequence pairs. | |
already_has_special_tokens (`bool`, *optional*, defaults to `False`): | |
Whether or not the token list is already formatted with special tokens for the model. | |
Returns: | |
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. | |
""" | |
if already_has_special_tokens: | |
return super().get_special_tokens_mask( | |
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True | |
) | |
if token_ids_1 is not None: | |
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] | |
return [1] + ([0] * len(token_ids_0)) + [1] | |
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): | |
""" | |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa | |
sequence pair mask has the following format: | |
``` | |
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | |
| first sequence | second sequence | | |
``` | |
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). | |
Args: | |
token_ids_0 (`List[int]`): | |
List of IDs. | |
token_ids_1 (`List[int]`, *optional*): | |
Optional second list of IDs for sequence pairs. | |
Returns: | |
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). | |
""" | |
sep = [self.sep_token_id] | |
cls = [self.cls_token_id] | |
if token_ids_1 is None: | |
return len(cls + token_ids_0 + sep) * [0] | |
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] | |
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): | |
add_prefix_space = kwargs.pop("add_prefix_space", False) | |
if is_split_into_words or add_prefix_space: | |
text = " " + text | |
return (text, kwargs) | |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: | |
return self._tokenizer.save_pretrained(save_directory, filename_prefix=filename_prefix) | |
class SPMTokenizer: | |
r""" | |
Constructs a tokenizer based on [SentencePiece](https://github.com/google/sentencepiece). | |
Args: | |
vocab_file (`str`): | |
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that | |
contains the vocabulary necessary to instantiate a tokenizer. | |
sp_model_kwargs (`dict`, *optional*): | |
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for | |
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, | |
to set: | |
- `enable_sampling`: Enable subword regularization. | |
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. | |
- `nbest_size = {0,1}`: No sampling is performed. | |
- `nbest_size > 1`: samples from the nbest_size results. | |
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) | |
using forward-filtering-and-backward-sampling algorithm. | |
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for | |
BPE-dropout. | |
""" | |
def __init__( | |
self, vocab_file, special_tokens, split_by_punct=False, sp_model_kwargs: Optional[Dict[str, Any]] = None | |
): | |
self.split_by_punct = split_by_punct | |
self.vocab_file = vocab_file | |
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs | |
spm = sp.SentencePieceProcessor(**self.sp_model_kwargs) | |
if not os.path.exists(vocab_file): | |
raise FileNotFoundError(f"{vocab_file} does not exist!") | |
spm.load(vocab_file) | |
bpe_vocab_size = spm.GetPieceSize() | |
# Token map | |
# <unk> 0+1 | |
# <s> 1+1 | |
# </s> 2+1 | |
self.vocab = {spm.IdToPiece(i): i for i in range(bpe_vocab_size)} | |
self.ids_to_tokens = [spm.IdToPiece(i) for i in range(bpe_vocab_size)] | |
# self.vocab['[PAD]'] = 0 | |
# self.vocab['[CLS]'] = 1 | |
# self.vocab['[SEP]'] = 2 | |
# self.vocab['[UNK]'] = 3 | |
self.spm = spm | |
self.special_tokens = special_tokens | |
def __getstate__(self): | |
state = self.__dict__.copy() | |
state["spm"] = None | |
return state | |
def __setstate__(self, d): | |
self.__dict__ = d | |
# for backward compatibility | |
if not hasattr(self, "sp_model_kwargs"): | |
self.sp_model_kwargs = {} | |
self.spm = sp.SentencePieceProcessor(**self.sp_model_kwargs) | |
self.spm.Load(self.vocab_file) | |
def tokenize(self, text): | |
return self._encode_as_pieces(text) | |
def convert_ids_to_tokens(self, ids): | |
tokens = [] | |
for i in ids: | |
tokens.append(self.ids_to_tokens[i]) | |
return tokens | |
def decode(self, tokens, start=-1, end=-1, raw_text=None): | |
if raw_text is None: | |
current_sub_tokens = [] | |
out_string = "" | |
prev_is_special = False | |
for token in tokens: | |
# make sure that special tokens are not decoded using sentencepiece model | |
if token in self.special_tokens: | |
if not prev_is_special: | |
out_string += " " | |
out_string += self.spm.decode_pieces(current_sub_tokens) + token | |
prev_is_special = True | |
current_sub_tokens = [] | |
else: | |
current_sub_tokens.append(token) | |
prev_is_special = False | |
out_string += self.spm.decode_pieces(current_sub_tokens) | |
return out_string.strip() | |
else: | |
words = self.split_to_words(raw_text) | |
word_tokens = [self.tokenize(w) for w in words] | |
token2words = [0] * len(tokens) | |
tid = 0 | |
for i, w in enumerate(word_tokens): | |
for k, t in enumerate(w): | |
token2words[tid] = i | |
tid += 1 | |
word_start = token2words[start] | |
word_end = token2words[end] if end < len(tokens) else len(words) | |
text = "".join(words[word_start:word_end]) | |
return text | |
# TODO add a deprecation cycle as this can have different behaviour from our API | |
def add_special_token(self, token): | |
if token not in self.special_tokens: | |
self.special_tokens.append(token) | |
if token not in self.vocab: | |
self.vocab[token] = len(self.vocab) - 1 | |
self.ids_to_tokens.append(token) | |
return self.id(token) | |
def part_of_whole_word(self, token, is_bos=False): | |
logger.warning_once( | |
"The `DebertaTokenizer.part_of_whole_word` method is deprecated and will be removed in `transformers==4.35`" | |
) | |
if is_bos: | |
return True | |
if ( | |
len(token) == 1 | |
and (_is_whitespace(list(token)[0]) or _is_control(list(token)[0]) or _is_punctuation(list(token)[0])) | |
) or token in self.special_tokens: | |
return False | |
word_start = b"\xe2\x96\x81".decode("utf-8") | |
return not token.startswith(word_start) | |
def pad(self): | |
return "[PAD]" | |
def bos(self): | |
return "[CLS]" | |
def eos(self): | |
return "[SEP]" | |
def unk(self): | |
return "[UNK]" | |
def mask(self): | |
return "[MASK]" | |
def sym(self, id): | |
return self.ids_to_tokens[id] | |
def id(self, sym): | |
logger.warning_once( | |
"The `DebertaTokenizer.id` method is deprecated and will be removed in `transformers==4.35`" | |
) | |
return self.vocab[sym] if sym in self.vocab else 1 | |
def _encode_as_pieces(self, text): | |
text = convert_to_unicode(text) | |
if self.split_by_punct: | |
words = self._run_split_on_punc(text) | |
pieces = [self.spm.encode(w, out_type=str) for w in words] | |
return [p for w in pieces for p in w] | |
else: | |
return self.spm.encode(text, out_type=str) | |
def split_to_words(self, text): | |
pieces = self._encode_as_pieces(text) | |
word_start = b"\xe2\x96\x81".decode("utf-8") | |
words = [] | |
offset = 0 | |
prev_end = 0 | |
for i, p in enumerate(pieces): | |
if p.startswith(word_start): | |
if offset > prev_end: | |
words.append(text[prev_end:offset]) | |
prev_end = offset | |
w = p.replace(word_start, "") | |
else: | |
w = p | |
try: | |
s = text.index(w, offset) | |
pn = "" | |
k = i + 1 | |
while k < len(pieces): | |
pn = pieces[k].replace(word_start, "") | |
if len(pn) > 0: | |
break | |
k += 1 | |
if len(pn) > 0 and pn in text[offset:s]: | |
offset = offset + 1 | |
else: | |
offset = s + len(w) | |
except Exception: | |
offset = offset + 1 | |
if prev_end < offset: | |
words.append(text[prev_end:offset]) | |
return words | |
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 save_pretrained(self, path: str, filename_prefix: str = None): | |
filename = VOCAB_FILES_NAMES[list(VOCAB_FILES_NAMES.keys())[0]] | |
if filename_prefix is not None: | |
filename = filename_prefix + "-" + filename | |
full_path = os.path.join(path, filename) | |
with open(full_path, "wb") as fs: | |
fs.write(self.spm.serialized_model_proto()) | |
return (full_path,) | |
def _is_whitespace(char): | |
"""Checks whether `chars` is a whitespace character.""" | |
# \t, \n, and \r are technically control 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): | |
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" | |
if isinstance(text, str): | |
return text | |
elif isinstance(text, bytes): | |
return text.decode("utf-8", "ignore") | |
else: | |
raise ValueError(f"Unsupported string type: {type(text)}") | |