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 tokenization classes for camembert model import os from shutil import copyfile from typing import any dict list optional tuple import sentencepiece as spm from tokenizationutils import addedtoken pretrainedtokenizer from utils import logging logger logging getloggername vocabfilesnames vocabfile sentencepiece bpe model pretrainedvocabfilesmap vocabfile camembertbase https huggingface cocamembertbaseresolvemainsentencepiece bpe model pretrainedpositionalembeddingssizes camembertbase 512 spieceunderline class camemberttokenizerpretrainedtokenizer vocabfilesnames vocabfilesnames pretrainedvocabfilesmap pretrainedvocabfilesmap maxmodelinputsizes pretrainedpositionalembeddingssizes modelinputnames inputids attentionmask def init self vocabfile bostokens eostokens septokens clstokens unktokenunk padtokenpad masktokenmask additionalspecialtokenssnotused snotused unknotused spmodelkwargs optionaldictstr any none kwargs none mask token behave like a normal word i e include the space before it masktoken addedtokenmasktoken lstriptrue rstripfalse normalizedfalse specialtrue if isinstancemasktoken str else masktoken self spmodelkwargs if spmodelkwargs is none else spmodelkwargs self spmodel spm sentencepieceprocessorself spmodelkwargs self spmodel loadstrvocabfile self vocabfile vocabfile hack these tokens were added by the for an obscure reason as they were already part of the sentencepiece vocabulary this is the case for s and s and unk in this case it is recommended to properly set the tokens by hand self addedtokensdecoder 0 addedtokensnotused specialtrue 1 addedtokenpadtoken specialtrue if isinstancepadtoken str else padtoken 2 addedtokensnotused specialtrue 3 addedtokenunktoken specialtrue if isinstanceunktoken str else unktoken 4 addedtokenunknotused specialtrue self fairseqoffset 4 3 tokens are newly added but the offset starts from 4 legacy camemebert is a particular case were we have to make sure unknotused is here if addedtokensdecoder in kwargs this is the only class that requires this unfortunately the reason is that the fast version has a whole kwargsaddedtokensdecoder updateself addedtokensdecoder super init bostokenbostoken eostokeneostoken unktokenunktoken septokenseptoken clstokenclstoken padtokenpadtoken masktokenmasktoken additionalspecialtokensadditionalspecialtokens spmodelkwargsself spmodelkwargs kwargs property def vocabsizeself the length of the vocabulary without added tokens is lenself spmodel but the added tokens are added at the beginning return lenself spmodel def getvocabself vocab self convertidstotokensi i for i in rangeself vocabsize self fairseqoffset vocab updateself addedtokensencoder return vocab def tokenizeself text str liststr return self spmodel encodetext outtypestr def converttokentoidself token specifi to camembert both 3 and 4 point to the unk token convert sentence piece unk token to fairseq unk token index converts an index integer in a token str using the vocab return self spmodel idtopieceindex self fairseqoffset def converttokenstostringself tokens todo decode outputs do not match between fast and slow make sure that special tokens are not decoded using sentencepiece model for backward compatibility 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 ss b s 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 retrieve sequence ids from a token list that has no special tokens added this method is called when adding special tokens using the tokenizer prepareformodel method 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 camembert like roberta does not make use of token type ids therefore a list of zeros is returned args tokenids0 listint list of ids tokenids1 listint optional optional second list of ids for sequence pairs returns listint list of zeros 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 tokenization classes for camembert model adapted from robertatokenizer and xlnettokenizer construct a camembert tokenizer based on sentencepiece https github com google sentencepiece this tokenizer inherits from pretrainedtokenizer 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 unk notused additional special tokens used by the 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 attributes sp_model sentencepieceprocessor the sentencepiece processor that is used for every conversion string tokens and ids mask token behave like a normal word i e include the space before it hack these tokens were added by the for an obscure reason as they were already part of the sentencepiece vocabulary this is the case for s and s and unk in this case it is recommended to properly set the tokens by hand 3 tokens are newly added but the offset starts from 4 legacy camemebert is a particular case were we have to make sure unk notused is here this is the only class that requires this unfortunately the reason is that the fast version has a whole the length of the vocabulary without added tokens is len self sp_model but the added tokens are added at the beginning converts a token str in an id using the vocab specifi to camembert both 3 and 4 point to the unk token convert sentence piece unk token to fairseq unk token index converts an index integer in a token str using the vocab converts a sequence of tokens string in a single string todo decode outputs do not match between fast and slow make sure that special tokens are not decoded using sentencepiece model for backward compatibility 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 retrieve 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 method 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 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 Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}

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

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

SPIECE_UNDERLINE = "▁"


class CamembertTokenizer(PreTrainedTokenizer):
    

    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"]

    def __init__(
        self,
        vocab_file,
        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"],
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs,
    ) -> None:
        
        mask_token = (
            AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False, special=True)
            if isinstance(mask_token, str)
            else mask_token
        )

        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(str(vocab_file))
        self.vocab_file = vocab_file

        
        
        
        self._added_tokens_decoder = {
            0: AddedToken("<s>NOTUSED", special=True),
            1: AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token,
            2: AddedToken("</s>NOTUSED", special=True),
            3: AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token,
            4: AddedToken("<unk>NOTUSED", special=True),
        }

        self.fairseq_offset = 4  

        
        if "added_tokens_decoder" in kwargs:
            
            
            kwargs["added_tokens_decoder"].update(self._added_tokens_decoder)

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            additional_special_tokens=additional_special_tokens,
            sp_model_kwargs=self.sp_model_kwargs,
            **kwargs,
        )

    @property
    def vocab_size(self):
        
        return len(self.sp_model)

    def get_vocab(self):
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.fairseq_offset)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text: str) -> List[str]:
        return self.sp_model.encode(text, out_type=str)

    def _convert_token_to_id(self, token):
        
        
        if self.sp_model.PieceToId(token) == 0:
            
            return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(token)

    def _convert_id_to_token(self, index):
        
        return self.sp_model.IdToPiece(index - self.fairseq_offset)

    def convert_tokens_to_string(self, tokens):
        
        
        current_sub_tokens = []
        out_string = ""
        prev_is_special = False
        for token in tokens:
            
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(current_sub_tokens) + token
                prev_is_special = True
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
                prev_is_special = False
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string.strip()

    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        return state

    def __setstate__(self, d):
        self.__dict__ = d

        
        if not hasattr(self, "sp_model_kwargs"):
            self.sp_model_kwargs = {}

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(self.vocab_file)

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        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) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)

    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 get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        
        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 None:
            return [1] + ([0] * len(token_ids_0)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]

    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]