fThWSrSSKrSSKrSSKrSSKJrJrJr SSKJ r J r J r J r J r SSKJr \R "\5rSS0rS rS r"S S \ 5r"S S5r"SS5rS /rg)zTokenization classes for MPNet.N)ListOptionalTuple) AddedTokenPreTrainedTokenizer _is_control_is_punctuation_is_whitespace)logging vocab_filez vocab.txtc[R"5n[USSS9nUR5nSSS5 [ W5HupEUR S5nXAU'M U$!,(df  N9=f)z*Loads a vocabulary file into a dictionary.rutf-8encodingN ) collections OrderedDictopen readlines enumeraterstrip)r vocabreadertokensindextokens d/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/mpnet/tokenization_mpnet.py load_vocabr sg  # # %E j# 0F!!# 1!&)  T"e * L 1 0s A%% A3cXUR5nU(d/$UR5nU$)z@Runs basic whitespace cleaning and splitting on a piece of text.)stripsplit)textrs rwhitespace_tokenizer%+s% ::"`): The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token. 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`. eos_token (`str`, *optional*, defaults to `""`): The end of sequence token. 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`. 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 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 `""`): 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 masked language modeling. This is the token which the model will try to predict. tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this [issue](https://github.com/huggingface/transformers/issues/328)). strip_accents (`bool`, *optional*): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT). clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces. input_idsattention_maskc >[U[5(a [USS9OUn[U[5(a [USS9OUn[U[5(a [USS9OUn[U[5(a [USS9OUn[U [5(a [U SS9OU n [U [5(a [U SS9OU n [U [5(a [U SSS9OU n [RR U5(d[ SUS35e[U5Ul[R"URR5VVs/sH unnUU4PM snn5Ul X0l U(a[UUU U S9Ul[!UR[U 5S9Ul[$TU]L"S UUUUUU UUU U U U US. UD6 gs snnf) NT)special)lstripr-z&Can't find a vocabulary file at path 'z'. To load the vocabulary from a Google pretrained model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`) do_lower_case never_splittokenize_chinese_chars strip_accents)r unk_token) r/do_basic_tokenizer0 bos_token eos_tokenr3 sep_token cls_token pad_token mask_tokenr1r2clean_up_tokenization_spaces) isinstancestrrospathisfile ValueErrorr rrritems ids_to_tokensr4BasicTokenizerbasic_tokenizerWordpieceTokenizerwordpiece_tokenizersuper__init__)selfr r/r4r0r5r6r7r8r3r9r:r1r2r;kwargstokids __class__s rrJMPNetTokenizer.__init__ws$>@ r& token_ids_0 token_ids_1returncUcUR/U-UR/-$UR/nUR/nX1-U-U-U-U-$)a3 Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A MPNet sequence has the following format: - single sequence: ` X ` - pair of sequences: ` A B ` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens. ) cls_token_id sep_token_id)rKrurvclsseps r build_inputs_with_special_tokens/MPNetTokenizer.build_inputs_with_special_tokensse&  %%&48I8I7JJ J  !  ! 3&,{:S@@r&already_has_special_tokensc>U(a[TU]XSS9$UcS/S/[U5--S/-$S/S/[U5--SS/-S/[U5--S/-$)ae 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` methods. 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`): Set to True if 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)rurvrr)rIget_special_tokens_maskrV)rKrurvrrOs rr&MPNetTokenizer.get_special_tokens_masks$ &72']a3   31#K 001QC7 7sqcC ,,-A61#K@P:PQUVTWWWr&cUR/nUR/nUc[XA-U-5S/-$[XA-U-U-U-U-5S/-$)a{ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet 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. r)rzryrV)rKrurvr|r{s r$create_token_type_ids_from_sequences3MPNetTokenizer.create_token_type_ids_from_sequencessi   !  !  s(3./1#5 53$s*S0;>DEKKr&save_directoryfilename_prefixcSn[RRU5(a6[RRX(aUS-OS[S-5nOU(aUS-OSU-n[ USSS9n[ URR5SS 9H>upgX7:wa[RS US 35 UnURUS -5 US - nM@ SSS5 U4$!,(df  U4$=f)Nr-ror wrrc US$)Nrr<)kvs r0MPNetTokenizer.save_vocabulary..1s PRSTPUr&)keyzSaving vocabulary to z\: vocabulary indices are not consecutive. Please check that the vocabulary is not corrupted!rr) r?r@isdirrpVOCAB_FILES_NAMESrsortedrrCloggerwarningwrite)rKrrrr writerr token_indexs rsave_vocabularyMPNetTokenizer.save_vocabulary(s 77== ( (/3!6rUfgsUt tJ4C/C/n\J *cG 4&,TZZ-=-=-?EU&V"'NN/ |rrz[UNK]zzTNTrR)NF)__name__ __module__ __qualname____firstlineno____doc__rvocab_files_namesmodel_input_namesrJpropertyr/rWr]rdrhrkrsrintrr}boolrrr>rr__static_attributes__ __classcell__)rOs@rr(r(4sr=~*$&67 #%)< |22 E= JNA9A3;DI3FA cA4sxX9X3;DI3FXkoX cXX8JNL9L3;DI3FL cL.cHSM]bcf]gr&r(cX\rSrSrSrS SjrS SjrSrS SjrSr S r S r S r g)rEi>ab Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. never_split (`Iterable`, *optional*): Collection of tokens which will never be split during tokenization. Only has an effect when `do_basic_tokenize=True` tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this [issue](https://github.com/huggingface/transformers/issues/328)). strip_accents (`bool`, *optional*): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT). do_split_on_punc (`bool`, *optional*, defaults to `True`): In some instances we want to skip the basic punctuation splitting so that later tokenization can capture the full context of the words, such as contractions. Nc^Uc/nXl[U5UlX0lX@lXPlgrR)r/setr0r1r2do_split_on_punc)rKr/r0r1r2rs rrJBasicTokenizer.__init__Us4  K*{+&<#* 0r&czU(a$URR[U55O URnURU5nUR(aUR U5n[ R"SU5n[U5n/nUHnXb;adUR(a1UR5nURSLaURU5nO"UR(aURU5nURURXb55 M [SRU55nU$)a: Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer. Args: never_split (`List[str]`, *optional*) Kept for backward compatibility purposes. Now implemented directly at the base class level (see [`PreTrainedTokenizer.tokenize`]) List of token not to split. NFCFrn)r0unionr _clean_textr1_tokenize_chinese_chars unicodedata normalizer%r/lowerr2_run_strip_accentsextend_run_split_on_puncrp)rKr$r0unicode_normalized_text orig_tokensrcr output_tokenss rr`BasicTokenizer.tokenizees CNd&&,,S-=>SWScSc %  & &//5D"-"7"7t"D)*AB   E'%%!KKME))6 $ 7 7 >'' 33E:E    7 7 K L!,CHH\,BC r&c[R"SU5n/nUH2n[R"U5nUS:XaM!URU5 M4 SR U5$)z$Strips accents from a piece of text.NFDMnro)rrcategoryrbrp)rKr$outputcharcats rr!BasicTokenizer._run_strip_accentssY$$UD1D&&t,Cd{ MM$   wwvr&cUR(aUbX;aU/$[U5nSnSn/nU[U5:amX4n[U5(aUR U/5 SnO.U(aUR /5 SnUSR U5 US- nU[U5:aMmUVs/sHnSR U5PM sn$s snf)z&Splits punctuation on a piece of text.rTFrro)rlistrVr rbrp) rKr$r0charsistart_new_wordrrxs rr!BasicTokenizer._run_split_on_puncs$$)@TEX6MT  #e*n8Dt$$ tf%!%!MM"%!&r !!$' FA#e*n%++Fq F+++s.C c/nUHjn[U5nURU5(a5URS5 URU5 URS5 MYURU5 Ml SRU5$)z)Adds whitespace around any CJK character.rnro)ord_is_chinese_charrbrprKr$rrcps rr&BasicTokenizer._tokenize_chinese_charsskDTB$$R(( c" d# c" d#wwvr&cUS:aUS::dTUS:aUS::dHUS:aUS::d<US:aUS::d0US :aUS ::d$US :aUS ::dUS :aUS::d US:aUS::agg)z6Checks whether CP is the codepoint of a CJK character.iNii4iMiiߦii?i@ii iiiiiTFr<)rKrs rrBasicTokenizer._is_chinese_charsr6\bFlf vg "-g "-g "-g "-f vg "-r&c/nUH`n[U5nUS:XdUS:Xd[U5(aM,[U5(aURS5 MOURU5 Mb SR U5$)zBPerforms invalid character removal and whitespace cleanup on text.rirnro)rr r rbrprs rrBasicTokenizer._clean_textsgDTBQw",+d*;*;d## c" d#wwvr&)r/rr0r2r1)TNTNTrR) rrrrrrJr`rrrrrrr<r&rrErE>s<0# 1 $L ,, 0 r&rEc(\rSrSrSrSSjrSrSrg)rGizRuns WordPiece tokenization.c(XlX lX0lgrR)rr3max_input_chars_per_word)rKrr3rs rrJWordpieceTokenizer.__init__s "(@%r&c8/n[U5GHn[U5n[U5UR:aUR UR 5 MESnSn/nU[U5:ax[U5nSn Xh:a<SR XFU5n US:aSU -n XR;aU n O US-nXh:aM<U cSnO$UR U 5 UnU[U5:aMxU(aUR UR 5 MURU5 GM U$)a Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example, `input = "unaffable"` will return as output `["un", "##aff", "##able"]`. Args: text: A single token or whitespace separated tokens. This should have already been passed through *BasicTokenizer*. Returns: A list of wordpiece tokens. FrNroz##rT) r%rrVrrbr3rprr) rKr$rrris_badstart sub_tokensend cur_substrsubstrs rr`WordpieceTokenizer.tokenizes (.EKE5zD999$$T^^4FEJ#e*$%j! kWWU%56Fqy!%+%+ 1HCk%!F!!*-#e*$"$$T^^4$$Z0;/<r&)rr3rN)d)rrrrrrJr`rr<r&rrGrGs&A .r&rG)rrr?rtypingrrrtokenization_utilsrrr r r utilsr get_loggerrrrr r%r(rErG__all__r<r&rrs{ & ((oo   H %!;/F(FT^^D66r  r&