codingutf8 2018 google ai google brain and carnegie mellon university s 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 classes for camembert model import os from shutil import copyfile from typing import list optional tuple from tokenizationutils import addedtoken from tokenizationutilsfast import pretrainedtokenizerfast from utils import issentencepieceavailable logging if issentencepieceavailable from tokenizationcamembert import camemberttokenizer else camemberttokenizer none logger logging getloggername vocabfilesnames vocabfile sentencepiece bpe model tokenizerfile tokenizer json pretrainedvocabfilesmap vocabfile camembertbase https huggingface cocamembertbaseresolvemainsentencepiece bpe model tokenizerfile camembertbase https huggingface cocamembertbaseresolvemaintokenizer json pretrainedpositionalembeddingssizes camembertbase 512 spieceunderline class camemberttokenizerfastpretrainedtokenizerfast vocabfilesnames vocabfilesnames pretrainedvocabfilesmap pretrainedvocabfilesmap maxmodelinputsizes pretrainedpositionalembeddingssizes modelinputnames inputids attentionmask slowtokenizerclass camemberttokenizer def init self vocabfilenone tokenizerfilenone bostokens eostokens septokens clstokens unktokenunk padtokenpad masktokenmask additionalspecialtokenssnotused snotused unknotused kwargs mask token behave like a normal word i e include the space before it will have normalized false masktoken addedtokenmasktoken lstriptrue specialtrue if isinstancemasktoken str else masktoken super init vocabfile tokenizerfiletokenizerfile bostokenbostoken eostokeneostoken septokenseptoken clstokenclstoken unktokenunktoken padtokenpadtoken masktokenmasktoken additionalspecialtokensadditionalspecialtokens kwargs self vocabfile vocabfile property def cansaveslowtokenizerself bool return os path isfileself vocabfile if self vocabfile else false def buildinputswithspecialtokens self tokenids0 listint tokenids1 optionallistint none listint if tokenids1 is none return self clstokenid tokenids0 self septokenid cls self clstokenid sep self septokenid return cls tokenids0 sep sep tokenids1 sep def createtokentypeidsfromsequences self tokenids0 listint tokenids1 optionallistint none listint sep self septokenid cls self clstokenid if tokenids1 is none return lencls tokenids0 sep 0 return lencls tokenids0 sep sep tokenids1 sep 0 def savevocabularyself savedirectory str filenameprefix optionalstr none tuplestr if not self cansaveslowtokenizer raise valueerror your fast tokenizer does not have the necessary information to save the vocabulary for a slow tokenizer if not os path isdirsavedirectory logger errorfvocabulary path savedirectory should be a directory return outvocabfile os path join savedirectory filenameprefix if filenameprefix else vocabfilesnamesvocabfile if os path abspathself vocabfile os path abspathoutvocabfile copyfileself vocabfile outvocabfile return outvocabfile coding utf 8 2018 google ai google brain and carnegie mellon university s 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 classes for camembert model construct a fast camembert tokenizer backed by huggingface s tokenizers library adapted from robertatokenizer and xlnettokenizer based on bpe https huggingface co docs tokenizers python latest components html highlight bpe models this tokenizer inherits from pretrainedtokenizerfast which contains most of the main methods users should refer to this superclass for more information regarding those methods 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 bos_token str optional defaults to s the beginning of sequence token that was used during pretraining can be used a sequence classifier token tip 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 tip eos_token str optional defaults to s the end of sequence token tip 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 tip sep_token str optional defaults to s 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 cls_token str optional defaults to s 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 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 pad_token str optional defaults to pad the token used for padding for example when batching sequences of different lengths 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 additional_special_tokens list str optional defaults to s notused s notused additional special tokens used by the tokenizer mask token behave like a normal word i e include the space before it will have normalized false build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens an camembert sequence has the following format single sequence s x s pair of sequences s a s s b s 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 create a mask from the two sequences passed to be used in a sequence pair classification task camembert like roberta does not make use of token type ids therefore a list of zeros is returned 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 zeros
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_camembert import CamembertTokenizer
else:
    CamembertTokenizer = None


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "camembert-base": 512,
}

SPIECE_UNDERLINE = "▁"


class CamembertTokenizerFast(PreTrainedTokenizerFast):
    

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]
    slow_tokenizer_class = CamembertTokenizer

    def __init__(
        self,
        vocab_file=None,
        tokenizer_file=None,
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        additional_special_tokens=["<s>NOTUSED", "</s>NOTUSED", "<unk>NOTUSED"],
        **kwargs,
    ):
        
        mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
        super().__init__(
            vocab_file,
            tokenizer_file=tokenizer_file,
            bos_token=bos_token,
            eos_token=eos_token,
            sep_token=sep_token,
            cls_token=cls_token,
            unk_token=unk_token,
            pad_token=pad_token,
            mask_token=mask_token,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )

        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: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        

        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 + sep + token_ids_1 + sep

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        
        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 + sep + token_ids_1 + sep) * [0]

    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,)