a hI@sddlZddlmZddlmZmZddlZddlm Z m Z m Z ddl m Z ddlmZe eZdZd d d Zgd gd dZddddddddZeddGddde ZdgZdS)N)copyfile)AnyOptional) AddedToken BatchEncodingPreTrainedTokenizer)logging)requiresu▁zsentencepiece.bpe.modelztokenizer.json) vocab_filetokenizer_file)__java__ __python__ __en_XX__)r rr__javascript____php____ruby____go__)basemultir rrrrrr)javapythonen_XX javascriptphprubygo) sentencepiece)backendscseZdZUdZeZddgZgZee e d<gZ ee e d<dFe e eefdfdd ZddZddZeddZeedddZejed dddZdGee e ee eee dfdd ZdHee e ee ee d d!d"ZdIee e ee ee d d#d$Zee ee ed%d&d'Zd(d)Zeeed*d+d,Zd-d.Zd/d0Zd1d2Z dJee ee!ed3d4d5Z"dKeeee eeee#d8fd9d: Z$d;d<Z%d=d>Z&d dd?d@Z'ed dAdBdCZ(eedAdDdEZ)Z*S)LPLBartTokenizera Construct an PLBART tokenizer. Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). The tokenization method is ` ` for source language documents, and ` ` for target language documents. Args: vocab_file (`str`): Path to the vocabulary file. src_lang (`str`, *optional*): A string representing the source language. tgt_lang (`str`, *optional*): A string representing the target language. bos_token (`str`, *optional*, defaults to `""`): The start of sequence token. eos_token (`str`, *optional*, defaults to `""`): The end of sequence token. sep_token (`str`, *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 (`str`, *optional*, defaults to `""`): The cls token, which is a special token used as the first token for all tasks. unk_token (`str`, *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 (`str`, *optional*, defaults to `""`): The token used for padding, for example when batching sequences of different lengths. mask_token(`str`, *optional*, defaults to `""`): The token used for masking values. This is the token used when training this model with masking tasks. This is only used in the `"base"` tokenizer type. For `"multi"` tokenizer, masking is never done for the downstream tasks. language_codes (`str`, *optional*, defaults to `"base"`): What language codes to use. Should be one of `"base"` or `"multi"`. 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. Examples: ```python >>> from transformers import PLBartTokenizer >>> tokenizer = PLBartTokenizer.from_pretrained("uclanlp/plbart-python-en_XX", src_lang="python", tgt_lang="en_XX") >>> example_python_phrase = "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])" >>> expected_translation_english = "Returns the maximum value of a b c." >>> inputs = tokenizer(example_python_phrase, text_target=expected_translation_english, return_tensors="pt") ```Z input_idsZattention_mask prefix_tokens suffix_tokensrNT)sp_model_kwargsc st|trt|dddn|}| dur(in| _| } | } tjfij_jt||_ | _ t j }ddddd_ d_ tj_fd d t|D_d d jD_j d krtjtjj j d <j jdd j D_tj|durJfdd|Dj d kr~| _jdurtjjnj_n"| dur| nd_jj_tjf|||||||| | | | j|d|| _jdS)NTF)lstriprstriprr)r"r%r#r$cs"i|]\}}|j|jqS) sp_model_sizefairseq_offset).0icodeselfr,j/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/plbart/tokenization_plbart.py sz,PLBartTokenizer.__init__..cSsi|]\}}||qSr,r,r/kvr,r,r4r5rr&cSsi|]\}}||qSr,r,r6r,r,r4r5r9csg|]}|vr|qSr,r,)r/t)_additional_special_tokensr,r4 r9z,PLBartTokenizer.__init__..r) bos_token eos_token unk_token sep_token cls_token pad_token mask_tokenlanguage_codesr src_langtgt_langadditional_special_tokensr'clean_up_tokenization_spaces) isinstancestrrr'!_convert_lang_code_special_formatspmSentencePieceProcessorsp_modelLoadr rDFAIRSEQ_LANGUAGE_CODESfairseq_tokens_to_idsr.lenr- enumeratelang_code_to_iditemsZid_to_lang_codeupdatefairseq_ids_to_tokenslistkeysextend _src_langZcur_lang_code_idsuper__init__rFset_src_lang_special_tokens)r3r r=r>r@rAr?rBrCrDr rErFr'rGrHkwargsZfairseq_language_codes __class__)r;r3r4r]wsf         zPLBartTokenizer.__init__cCs$|j}d|d<|j|d<|S)NrNsp_model_proto)__dict__copyrNserialized_model_proto)r3stater,r,r4 __getstate__s zPLBartTokenizer.__getstate__cCs<||_t|dsi|_tjfi|j|_|j|jdS)Nr')rchasattrr'rLrMrNZLoadFromSerializedProtorb)r3dr,r,r4 __setstate__s  zPLBartTokenizer.__setstate__cCsF|jdkr(t|jt|j|jdSt|jt|j|jSdS)Nrr*)rDrRrNrTr.r2r,r,r4 vocab_sizes zPLBartTokenizer.vocab_size)returncCs|jSN)r[r2r,r,r4rEszPLBartTokenizer.src_lang) new_src_langrlcCs ||}||_||jdSrm)rKr[r^)r3rnr,r,r4rEs F) token_ids_0 token_ids_1already_has_special_tokensrlcsx|rtj||ddSdgt|j}dgt|j}|durT|dgt||S|dgt|dgt||S)a 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. T)rorprqr*Nr)r\get_special_tokens_maskrRr r!)r3rorprqZ prefix_onesZ suffix_onesr`r,r4rrsz'PLBartTokenizer.get_special_tokens_mask)rorprlcCs,|dur|j||jS|j|||jS)ac Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An PLBART sequence has the following format, where `X` represents the sequence: - `input_ids` (for encoder) `X [eos, src_lang_code]` - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]` BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. 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. N)r r!)r3rorpr,r,r4 build_inputs_with_special_tokenssz0PLBartTokenizer.build_inputs_with_special_tokenscCsP|jg}|jg}|dur.t|||dgSt||||||dgS)a Create a mask from the two sequences passed to be used in a sequence-pair classification task. PLBart 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. Nr)Z sep_token_idZ cls_token_idrR)r3rorpsepclsr,r,r4$create_token_type_ids_from_sequences1s z4PLBartTokenizer.create_token_type_ids_from_sequences)return_tensorsrErFcKs^|dus|durtd|||_|||_||fd|d|}||j}||d<|S)zIUsed by translation pipeline, to prepare inputs for the generate functionNzATranslation requires a `src_lang` and a `tgt_lang` for this modelT)Zadd_special_tokensrwZforced_bos_token_id) ValueErrorrKrErFZconvert_tokens_to_ids)r3Z raw_inputsrwrErF extra_kwargsinputsZ tgt_lang_idr,r,r4_build_translation_inputsIs   z)PLBartTokenizer._build_translation_inputscs(fddtjD}|j|S)Ncsi|]}||qSr,)Zconvert_ids_to_tokens)r/r0r2r,r4r5Wr9z-PLBartTokenizer.get_vocab..)rangerkrVZadded_tokens_encoder)r3Zvocabr,r2r4 get_vocabVs zPLBartTokenizer.get_vocab)textrlcCs|jj|tdS)N)Zout_type)rNencoderJ)r3r~r,r,r4 _tokenize[szPLBartTokenizer._tokenizecCs4||jvr|j|S|j|}|r.||jS|jS)z0Converts a token (str) in an id using the vocab.)rQrNZ PieceToIdr.Z unk_token_id)r3tokenZspm_idr,r,r4_convert_token_to_id^s   z$PLBartTokenizer._convert_token_to_idcCs&||jvr|j|S|j||jS)z=Converts an index (integer) in a token (str) using the vocab.)rWrNZ IdToPiecer.)r3indexr,r,r4_convert_id_to_tokengs  z$PLBartTokenizer._convert_id_to_tokencCsd|td}|S)zIConverts a sequence of tokens (strings for sub-words) in a single string. )joinreplaceSPIECE_UNDERLINEstrip)r3tokensZ out_stringr,r,r4convert_tokens_to_stringmsz(PLBartTokenizer.convert_tokens_to_string)save_directoryfilename_prefixrlcCstj|s"td|ddStj||r6|dndtd}tj|jtj|krztj |jrzt |j|nLtj |jst |d$}|j }||Wdn1s0Y|fS)NzVocabulary path (z) should be a directory-rr wb)ospathisdirloggererrorrVOCAB_FILES_NAMESabspathr isfileropenrNrewrite)r3rrZout_vocab_filefiZcontent_spiece_modelr,r,r4save_vocabularyrs (  (zPLBartTokenizer.save_vocabularyrr) src_textsrE tgt_textsrFrlc s.|||_|||_tj||fi|Srm)rKrErFr\prepare_seq2seq_batch)r3rrErrFr_r`r,r4rs  z%PLBartTokenizer.prepare_seq2seq_batchcCs ||jSrm)r^rEr2r,r,r4_switch_to_input_modesz%PLBartTokenizer._switch_to_input_modecCs ||jSrm)set_tgt_lang_special_tokensrFr2r,r,r4_switch_to_target_modesz&PLBartTokenizer._switch_to_target_modecCsP||}|dur|j|nd|_g|_|jdurB|j|jg|_n |jg|_dS)z_Reset the special tokens to the source lang setting. 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