codingutf8 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 orglicenseslicense2 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 fileutils import issentencepieceavailable from tokenizationutilsfast import pretrainedtokenizerfast from utils import logging if issentencepieceavailable from tokenizationdebertav2 import debertav2tokenizer else debertav2tokenizer none logger logging getloggername vocabfilesnames vocabfile spm model tokenizerfile tokenizer json pretrainedvocabfilesmap vocabfile microsoftdebertav2xlarge https huggingface comicrosoftdebertav2xlargeresolvemainspm model microsoftdebertav2xxlarge https huggingface comicrosoftdebertav2xxlargeresolvemainspm model microsoftdebertav2xlargemnli https huggingface comicrosoftdebertav2xlargemnliresolvemainspm model microsoftdebertav2xxlargemnli https huggingface comicrosoftdebertav2xxlargemnliresolvemainspm model pretrainedpositionalembeddingssizes microsoftdebertav2xlarge 512 microsoftdebertav2xxlarge 512 microsoftdebertav2xlargemnli 512 microsoftdebertav2xxlargemnli 512 pretrainedinitconfiguration microsoftdebertav2xlarge dolowercase false microsoftdebertav2xxlarge dolowercase false microsoftdebertav2xlargemnli dolowercase false microsoftdebertav2xxlargemnli dolowercase false class debertav2tokenizerfastpretrainedtokenizerfast r constructs a debertav2 fast tokenizer based on sentencepiecehttps github comgooglesentencepiece args vocabfile str sentencepiecehttps github comgooglesentencepiece file generally has a spm extension that contains the vocabulary necessary to instantiate a tokenizer dolowercase bool optional defaults to false whether or not to lowercase the input when tokenizing bostoken string optional defaults to cls the beginning of sequence token that was used during pretraining 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 clstoken eostoken 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 septoken unktoken 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 septoken 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 padtoken str optional defaults to pad the token used for padding for example when batching sequences of different lengths clstoken str optional defaults to cls the classifier token which is used when doing sequence classification classification of the whole sequence instead of pertoken classification it is the first token of the sequence when built with special tokens masktoken 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 spmodelkwargs dict optional will be passed to the sentencepieceprocessor init method the python wrapper for sentencepiecehttps github comgooglesentencepiecetreemasterpython can be used among other things to set enablesampling enable subword regularization nbestsize sampling parameters for unigram invalid for bpedropout nbestsize 0 1 no sampling is performed nbestsize 1 samples from the nbestsize results nbestsize 0 assuming that nbestsize is infinite and samples from the all hypothesis lattice using forwardfilteringandbackwardsampling algorithm alpha smoothing parameter for unigram sampling and dropout probability of merge operations for bpedropout 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 tokenids0 listint list of ids to which the special tokens will be added tokenids1 listint optional optional second list of ids for sequence pairs returns listint list of input ids glossaryinputids with the appropriate special tokens retrieves sequence ids from a token list that has no special tokens added this method is called when adding special tokens using the tokenizer prepareformodel or encodeplus methods args tokenids0 listint list of ids tokenids1 listint optional optional second list of ids for sequence pairs alreadyhasspecialtokens bool optional defaults to false whether or not the token list is already formatted with special tokens for the model returns listint a list of integers in the range 0 1 1 for a special token 0 for a sequence token create a mask from the two sequences passed to be used in a sequencepair 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 tokenids1 is none this method only returns the first portion of the mask 0s args tokenids0 listint list of ids tokenids1 listint optional optional second list of ids for sequence pairs returns listint list of token type ids glossarytokentypeids according to the given sequences coding utf 8 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 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 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 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 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
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

    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

    @property
    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):
        

        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):
        

        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):
        
        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,)