codingutf8 2020 ecole polytechnique 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 the barthez 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 moussakammbarthez https huggingface comoussakammbarthezresolvemainsentencepiece bpe model moussakambarthez https huggingface comoussakambarthezresolvemainsentencepiece bpe model moussakambarthezorangesumtitle https huggingface comoussakambarthezorangesumtitleresolvemainsentencepiece bpe model pretrainedpositionalembeddingssizes moussakammbarthez 1024 moussakambarthez 1024 moussakambarthezorangesumtitle 1024 spieceunderline todo this class is useless this is the most standard sentencpiece model let s find which one is closest and nuke this class bartheztokenizerpretrainedtokenizer vocabfilesnames vocabfilesnames pretrainedvocabfilesmap pretrainedvocabfilesmap maxmodelinputsizes pretrainedpositionalembeddingssizes modelinputnames inputids attentionmask def init self vocabfile bostokens eostokens septokens clstokens unktokenunk padtokenpad masktokenmask spmodelkwargs optionaldictstr any none kwargs none mask token behave like a normal word i e include the space before it will have normalizedfalse by default this way masktoken addedtokenmasktoken lstriptrue specialtrue if isinstancemasktoken str else masktoken self spmodelkwargs if spmodelkwargs is none else spmodelkwargs self vocabfile vocabfile self spmodel spm sentencepieceprocessorself spmodelkwargs self spmodel loadstrvocabfile super init bostokenbostoken eostokeneostoken unktokenunktoken septokenseptoken clstokenclstoken padtokenpadtoken masktokenmasktoken spmodelkwargsself spmodelkwargs kwargs 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 getspecialtokensmask self tokenids0 listint tokenids1 optionallistint none alreadyhasspecialtokens bool false listint if alreadyhasspecialtokens return super getspecialtokensmask tokenids0tokenids0 tokenids1tokenids1 alreadyhasspecialtokenstrue if tokenids1 is none return 1 0 lentokenids0 1 return 1 0 lentokenids0 1 1 0 lentokenids1 1 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 property def vocabsizeself return lenself spmodel def getvocabself vocab self convertidstotokensi i for i in rangeself vocabsize vocab updateself addedtokensencoder return vocab def tokenizeself text str liststr return self spmodel encodetext outtypestr def converttokentoidself token converts an index integer in a token str using the vocab return self spmodel idtopieceindex def converttokenstostringself tokens make sure that special tokens are not decoded using sentencepiece model for backward compatibility coding utf 8 2020 ecole polytechnique 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 the barthez model todo this class is useless this is the most standard sentencpiece model let s find which one is closest and nuke this adapted from camemberttokenizer and barttokenizer construct a barthez 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 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 will have normalized false by default this way build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens a barthez 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 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 converts a token str in an id using the vocab converts an index integer in a token str using the vocab converts a sequence of tokens string in a single string make sure that special tokens are not decoded using sentencepiece model for backward compatibility
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": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

SPIECE_UNDERLINE = "▁"




class BarthezTokenizer(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>",
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs,
    ) -> None:
        
        mask_token = AddedToken(mask_token, lstrip=True, 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.vocab_file = vocab_file
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(str(vocab_file))
        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,
            sp_model_kwargs=self.sp_model_kwargs,
            **kwargs,
        )

    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]

    @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)}
        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):
        
        return self.sp_model.PieceToId(token)

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

    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,)