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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. | |
"""Fast Tokenization class for model DeBERTa.""" | |
import os | |
from shutil import copyfile | |
from typing import Optional, Tuple | |
from ...file_utils import is_sentencepiece_available | |
from ...tokenization_utils_fast import PreTrainedTokenizerFast | |
from ...utils import logging | |
if is_sentencepiece_available(): | |
from .tokenization_deberta_v2 import DebertaV2Tokenizer | |
else: | |
DebertaV2Tokenizer = None | |
logger = logging.get_logger(__name__) | |
VOCAB_FILES_NAMES = {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"} | |
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}, | |
} | |
class DebertaV2TokenizerFast(PreTrainedTokenizerFast): | |
r""" | |
Constructs a DeBERTa-v2 fast 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 | |
slow_tokenizer_class = DebertaV2Tokenizer | |
def __init__( | |
self, | |
vocab_file=None, | |
tokenizer_file=None, | |
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]", | |
**kwargs, | |
) -> None: | |
super().__init__( | |
vocab_file, | |
tokenizer_file=tokenizer_file, | |
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, | |
**kwargs, | |
) | |
self.do_lower_case = do_lower_case | |
self.split_by_punct = split_by_punct | |
self.vocab_file = vocab_file | |
def can_save_slow_tokenizer(self) -> bool: | |
return os.path.isfile(self.vocab_file) if self.vocab_file else False | |
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 save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: | |
if not self.can_save_slow_tokenizer: | |
raise ValueError( | |
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " | |
"tokenizer." | |
) | |
if not os.path.isdir(save_directory): | |
logger.error(f"Vocabulary path ({save_directory}) should be a directory") | |
return | |
out_vocab_file = os.path.join( | |
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] | |
) | |
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): | |
copyfile(self.vocab_file, out_vocab_file) | |
return (out_vocab_file,) | |