U &ºc[/ã@s’dZddlZddlZddlmZddlmZmZddlZ ddl m Z ddl m Z e e¡Zdd iZdd d iiZd diZd d gZGdd„de ƒZdS)z* Tokenization classes for Camembert model.éN)Úcopyfile)ÚListÚOptionalé)ÚPreTrainedTokenizer)ÚSPIECE_UNDERLINEÚ vocab_filezsentencepiece.bpe.modelzcamembert-basezZhttps://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-sentencepiece.bpe.modelz'Musixmatch/umberto-commoncrawl-cased-v1z'Musixmatch/umberto-wikipedia-uncased-v1c súeZdZdZeZeZeZ dgZ ddddddddd gf‡fd d „ Z d&e e ee e e e d œdd„Zd'e e ee e ee e dœdd„Zd(e e ee e e e d œdd„Zedd„ƒZdd„Zdd„Zdd„Zdd„Zd d!„Zd"d#„Zd$d%„Z‡ZS))ÚCamembertTokenizera! Adapted from RobertaTokenizer and XLNetTokenizer SentencePiece based tokenizer. Peculiarities: - requires `SentencePiece `_ This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users should refer to the superclass for more information regarding methods. Args: vocab_file (:obj:`str`): Path to the vocabulary file. bos_token (:obj:`string`, `optional`, defaults to ""): The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token. .. note:: 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 :obj:`cls_token`. eos_token (:obj:`string`, `optional`, defaults to ""): The end of sequence token. .. note:: 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 :obj:`sep_token`. sep_token (:obj:`string`, `optional`, defaults to ""): 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 (:obj:`string`, `optional`, defaults to ""): 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 (:obj:`string`, `optional`, defaults to ""): 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 (:obj:`string`, `optional`, defaults to ""): The token used for padding, for example when batching sequences of different lengths. mask_token (:obj:`string`, `optional`, defaults to ""): 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 (:obj:`List[str]`, `optional`, defaults to :obj:`["NOTUSED", "NOTUSED"]`): Additional special tokens used by the tokenizer. Attributes: sp_model (:obj:`SentencePieceProcessor`): The `SentencePiece` processor that is used for every conversion (string, tokens and IDs). Zattention_maskzzúúúú NOTUSEDú NOTUSEDc s–tƒjfd|||||||| dœ | —Žt ¡|_|j t|ƒ¡||_dddddœ|_t |jƒ|_ t |jƒt |jƒ|jd<d d „|j  ¡Dƒ|_ dS) Ni) Úmax_lenÚ bos_tokenÚ eos_tokenÚ unk_tokenÚ sep_tokenÚ cls_tokenÚ pad_tokenÚ mask_tokenÚadditional_special_tokensrréé)r r rr r cSsi|]\}}||“qS©r)Ú.0ÚkÚvrrúG/tmp/pip-unpacked-wheel-ymerj3tt/transformers/tokenization_camembert.pyÚ Œsz/CamembertTokenizer.__init__..) ÚsuperÚ__init__ÚspmÚSentencePieceProcessorÚsp_modelÚLoadÚstrrÚfairseq_tokens_to_idsÚlenÚfairseq_offsetÚitemsÚfairseq_ids_to_tokens) ÚselfrrrrrrrrrÚkwargs©Ú __class__rrr!ks( ÷ ö  zCamembertTokenizer.__init__N)Ú token_ids_0Ú token_ids_1ÚreturncCsD|dkr|jg||jgS|jg}|jg}||||||S)aÓ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A CamemBERT sequence has the following format: - single sequence: `` X `` - pair of sequences: `` A B `` Args: token_ids_0 (:obj:`List[int]`): List of IDs to which the special tokens will be added token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. N)Ú cls_token_idÚ sep_token_id)r,r0r1ÚclsÚseprrrÚ build_inputs_with_special_tokensŽs z3CamembertTokenizer.build_inputs_with_special_tokensF)r0r1Úalready_has_special_tokensr2cs||r*|dk rtdƒ‚tt‡fdd„|ƒƒS|dkrLdgdgt|ƒdgSdgdgt|ƒddgdgt|ƒdgS)a  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 (:obj:`List[int]`): List of ids. token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): Optional second list of IDs for sequence pairs. already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): Set to True if the token list is already formatted with special tokens for the model Returns: :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. Nz~You should not supply a second sequence if the provided sequence of ids is already formated with special tokens for the model.cs|ˆjˆjfkrdSdS)Nrr)r4r3)Úx©r,rrÚÁóz.rr)Ú ValueErrorÚlistÚmapr()r,r0r1r8rr:rÚget_special_tokens_mask©sÿz*CamembertTokenizer.get_special_tokens_maskcCsP|jg}|jg}|dkr.t|||ƒdgSt||||||ƒdgS)a Creates 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 (:obj:`List[int]`): List of ids. token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of zeros. Nr)r4r3r()r,r0r1r6r5rrrÚ$create_token_type_ids_from_sequencesÇs z7CamembertTokenizer.create_token_type_ids_from_sequencescCst|jƒt|jƒS©N)r(r'r$r:rrrÚ vocab_sizeßszCamembertTokenizer.vocab_sizecCs |j |¡SrB)r$ZEncodeAsPieces)r,ÚtextrrrÚ _tokenizeãszCamembertTokenizer._tokenizecCs<||jkr|j|S|j |¡dkr*|jS|j|j |¡S)z2 Converts a token (str) in an id using the vocab. r)r'r$Z PieceToIdZ unk_token_idr))r,ÚtokenrrrÚ_convert_token_to_idæs   z'CamembertTokenizer._convert_token_to_idcCs&||jkr|j|S|j ||j¡S)z=Converts an index (integer) in a token (str) using the vocab.)r+r$Z IdToPiecer))r,ÚindexrrrÚ_convert_id_to_tokenïs  z'CamembertTokenizer._convert_id_to_tokencCs|j ¡}d|d<|S)Nr$)Ú__dict__Úcopy)r,ÚstaterrrÚ __getstate__õs zCamembertTokenizer.__getstate__cCsP||_z ddl}Wn tk r2t d¡‚YnX| ¡|_|j |j¡dS)NrzzYou need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiecepip install sentencepiece) rJÚ sentencepieceÚ ImportErrorÚloggerÚwarningr#r$r%r)r,Údr"rrrÚ __setstate__ús ÿ zCamembertTokenizer.__setstate__cCsd |¡ td¡ ¡}|S)zIConverts a sequence of tokens (strings for sub-words) in a single string.Úú )ÚjoinÚreplacerÚstrip)r,ÚtokensZ out_stringrrrÚconvert_tokens_to_stringsz+CamembertTokenizer.convert_tokens_to_stringcCs^tj |¡s t d |¡¡dStj |td¡}tj |j ¡tj |¡krXt |j |ƒ|fS)a2 Save the sentencepiece vocabulary (copy original file) and special tokens file to a directory. Args: save_directory (:obj:`str`): The directory in which to save the vocabulary. Returns: :obj:`Tuple(str)`: Paths to the files saved. z*Vocabulary path ({}) should be a directoryNr) ÚosÚpathÚisdirrPÚerrorÚformatrVÚVOCAB_FILES_NAMESÚabspathrr)r,Zsave_directoryZout_vocab_filerrrÚsave_vocabulary s  z"CamembertTokenizer.save_vocabulary)N)NF)N)Ú__name__Ú __module__Ú __qualname__Ú__doc__r`Zvocab_files_namesÚPRETRAINED_VOCAB_FILES_MAPZpretrained_vocab_files_mapÚ&PRETRAINED_POSITIONAL_EMBEDDINGS_SIZESZmax_model_input_sizesZmodel_input_namesr!rÚintrr7Úboolr@rAÚpropertyrCrErGrIrMrSrZrbÚ __classcell__rrr.rr 3sV2ö$ÿ þ ÿ þ ÿ þ    r )rfÚloggingr[ÚshutilrÚtypingrrrNr"Ztokenization_utilsrZtokenization_xlnetrÚ getLoggerrcrPr`rgrhZSHARED_MODEL_IDENTIFIERSr rrrrÚs*    ÿÿÿü