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Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. - a path to a *directory* containing a configuration file saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. proxies (`dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `hf auth login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, will only try to load the tokenizer configuration from local files. subfolder (`str`, *optional*, defaults to `""`): In case the tokenizer config is located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. Passing `token=True` is required when you want to use a private model. Returns: `dict`: The configuration of the tokenizer. Examples: ```python # Download configuration from huggingface.co and cache. tokenizer_config = get_tokenizer_config("google-bert/bert-base-uncased") # This model does not have a tokenizer config so the result will be an empty dict. tokenizer_config = get_tokenizer_config("FacebookAI/xlm-roberta-base") # Save a pretrained tokenizer locally and you can reload its config from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased") tokenizer.save_pretrained("tokenizer-test") tokenizer_config = get_tokenizer_config("tokenizer-test") ``` _commit_hashF) rzr{r|r}r~rr _raise_exceptions_for_gated_repo%_raise_exceptions_for_missing_entries'_raise_exceptions_for_connection_errorsrNz\Could not locate the tokenizer configuration file, will try to use the model config instead.r6r7) getrr loggerinfor r9r:r;) ryrzr{r|r}r~rrkwargs commit_hashresolved_config_filer=rss &&&&&&&&, r>get_tokenizer_configrsJ**^,K&%%))..305 # rs %&:HK "W 5 56" 6(F> M 6 5s )B B cpa]tRtRtoRtRt]]!]4V3RlRl44t ] R Rl4t Rt Vt R#) AutoTokenizeri6a This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when created with the [`AutoTokenizer.from_pretrained`] class method. This class cannot be instantiated directly using `__init__()` (throws an error). c\R4h)z}AutoTokenizer is designed to be instantiated using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method.)OSError)selfs&r>__init__AutoTokenizer.__init__>s _  rSc0<V^8dQhRS[S[,/#)rLrN)rr)rQ __classdict__s"r>rRAutoTokenizer.__annotate__FsT T 1 1T rSc  *VPRR4pRVR&VPRR4pVPRR4pVPRR4pVPR4pVe{\PVR4p V f-\R V R R P R \44 R 24h\ V 4p V f\RV R24hV P !V.VO5/VB#V'd8\W3/VBp \V RR7R,p \P!R'/V BpMVf\P !V3RV/VBpVPp \V3/VBpVPRR4pRpRV9dG\VR,\ \"34'd VR,pMVR,PRR4pVEfVeV eV R8wd\PV 4e\PV 4P%R4VP%R48wd\PV 4P%R4pVR(9dFV \&9dTMTp\ V4p V e(V P(R(9dV P !V.VO5/VB#\*e\*P !V.VO5/VB#\RV R24hRV9d VR,VR&V'dVP-R4'dVRR)pVRJp\/V4\09;'g6VRJ;'d*\ V4RJ;'g\ VR,4RJpT;'dj\/V4\09;'dPVRJ;'dD\ V4;'g\ VR,4P2P5R4'*pV'dV \&9dRpRpV'dOV^,e V^,pM V^,pRV9dVP7R4^,pMRp\9WqVVV4pV'dzV'drV'gjV'd\ VP%R44\;XV3/VBp VPRR4pV P=4V P !V.VO5RV/VB#VevTp\ V4p V f*VP-R4'g\ VR,4p V eV P(R8Xd\*p V f\*p V P !V.VO5/VB#\?VRR4'dQVP@pRV9dVP-R4'dVRR)p\ V4p V P !V.VO5/VB#\V\B4'd}\/VPD4\/VPF4JdE\HPKR VPFPL R!VPDPL R"24VPFp\O\/V4P(4;'g\?VR#R4pVe>\0P\/V4\*4p V eV P !V.VO5/VB#VPRR4pVeVR8wdVP-R4'dVRR)p\ V4p V f*VP-R4'g\ VR,4p V eV P(R8Xd\*p V f\*p V P !V.VO5/VB#\R$VPL R%R P R&\044 R 24h \\3d\P !T3/TBpELWi;i)*a Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary. The tokenizer class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: List options Params: pretrained_model_name_or_path (`str` or `os.PathLike`): Can be either: - A string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co. - A path to a *directory* containing vocabulary files required by the tokenizer, for instance saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. - a path to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (like Bert or XLNet), e.g.: `./my_model_directory/vocab.txt`. (Not applicable to all derived classes) inputs (additional positional arguments, *optional*): Will be passed along to the Tokenizer `__init__()` method. config ([`PreTrainedConfig`], *optional*) The configuration object used to determine the tokenizer class to instantiate. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download the model weights and configuration files and override the cached versions if they exist. proxies (`dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*): In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for facebook/rag-token-base), specify it here. tokenizer_type (`str`, *optional*): Tokenizer type to be loaded. backend (`str`, *optional*, defaults to `"tokenizers"`): Backend to use for tokenization. Valid options are: - `"tokenizers"`: Use the HuggingFace tokenizers library backend (default) - `"sentencepiece"`: Use the SentencePiece backend trust_remote_code (`bool`, *optional*, defaults to `False`): Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. kwargs (additional keyword arguments, *optional*): Will be passed to the Tokenizer `__init__()` method. Can be used to set special tokens like `bos_token`, `eos_token`, `unk_token`, `sep_token`, `pad_token`, `cls_token`, `mask_token`, `additional_special_tokens`. See parameters in the `__init__()` for more details. Examples: ```python >>> from transformers import AutoTokenizer >>> # Download vocabulary from huggingface.co and cache. >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> # Download vocabulary from huggingface.co (user-uploaded) and cache. >>> tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") >>> # If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*) >>> # tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/") >>> # Download vocabulary from huggingface.co and define model-specific arguments >>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base", add_prefix_space=True) >>> # Explicitly use the tokenizers backend >>> tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer", backend="tokenizers") >>> # Explicitly use the sentencepiece backend >>> tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer", backend="sentencepiece") ```configNT _from_autouse_fasttokenizer_typetrust_remote_code gguf_filezPassed `tokenizer_type` z3 does not exist. `tokenizer_type` should be one of z, c3$"TFqxK R#5iN.0cs& r> 0AutoTokenizer.from_pretrained..s D,Cq,CsrWzTokenizer class z is not currently imported.F)return_tensorsrqauto_maprrYr PythonBackendPreTrainedTokenizerFastzTokenizer class 'zf' specified in the tokenizer config was not found. The tokenizer may need to be converted or re-saved.rz transformers.z-- code_revisionz The encoder model config class: z3 is different from the decoder model config class: z. It is not recommended to use the `AutoTokenizer.from_pretrained()` method in this case. Please use the encoder and decoder specific tokenizer classes. model_typez!Unrecognized configuration class z8 to build an AutoTokenizer. Model type should be one of c38"TFqPxK R#5ir)r[rs& r>rrYs4[IZAZZIZsr)rrrr])(poprr` ValueErrorjoinrofrom_pretrainedrrr for_modelrrrr isinstancerElist removesuffix)MODELS_WITH_INCORRECT_HUB_TOKENIZER_CLASSr[rrnrPrjrXrCrFrrregister_for_auto_classrdrqrdecoderencoderrwarning __class__r)clsryinputsrr_rrrtokenizer_class_namerq gguf_path config_dictconfig_model_typetokenizer_configtokenizer_config_classtokenizer_auto_mapregistered_class_namerMhas_remote_codehas_local_codeexplicit_local_code class_ref upstream_repotokenizer_class_candidate_classrs&&*, r>rAutoTokenizer.from_pretrainedDsdHd+#| JJz4 ($4d;"JJ':DAJJ{+   %#:#>#>~t#T #+ .~.>>qyy D,C DDEQH 88LMO& #34H3IId!eff"223PdSYd]cd d #$AWPVWI.yOPXYK))8K8F ^ c#331EVZ` #--00MXQWX!1!5!56G!N" ) )*:6 FF%5j%A"%5j%A%E%EoW[%\"  &&2!-!R''++,=>J(,,->?LLVT&33F;=%<$?$?@Q$R$_$_`f$g !$,mm),UU*/ # " !&<&E&Ef&M&M%;CR%@ ",D8f)::  "$ .  )*@AMZZ,-Cf-LMUYY    V $55  'd299-.DERR01G&1PQ*ZZ89  04]]#O!%  !!$0.q1 .q1 y ) 5a 8 $ 9!.Racp!  09L%)*@*M*Mf*UV;IGdohnoO ?D1A  3 3 5"22-06J[_e $ /(> %78QRO&/H/Q/QRX/Y/Y";?22 ^^F/V 0E0EFmm'RXZfhlJm  !/33DLBSTO*&667ThW]haghh"2!5!56G!N ! -%)<rAutoTokenizer.register\s  "#/"6%1"6 !GHH.oVI$CL,Y-?-?@W +0D0PEY #$8$A$A B""<8"TrSr)NNNF)r[rX __qualname____firstlineno____doc__r classmethodrr`r staticmethodr__static_attributes____classdictcell__)rs@r>rr6sK &'>?T @T lUUrSrrjcV^8dQh/^\9d&\\\\,3,;R&^\9d&\\\\,3,;R&^\9d\ \,;R&#)rLr_r^r)__conditional_annotations__rrOrPrset)rQs"r>rRrRsf^87d3S >27_`32c49n-2a` &&3s8&a rS)bartphoBartphoTokenizer)z bert-japaneseBertJapaneseTokenizer)bertweetBertweetTokenizer)biogptBioGptTokenizer)zblenderbot-smallBlenderbotSmallTokenizer) bridgetowerr%)byt5 ByT5Tokenizer)canineCanineTokenizer)clapr%)clvp ClvpTokenizer)cpmantCpmAntTokenizer)ctrl CTRLTokenizer)zdata2vec-audioWav2Vec2CTCTokenizer)z data2vec-textr%)dia DiaTokenizer)esm EsmTokenizer)flaubertFlaubertTokenizer)fsmt FSMTTokenizer)gpt_neox_japaneseGPTNeoXJapaneseTokenizer)hubertr)ibertr%)luke LukeTokenizer)megar%)zmgp-strMgpstrTokenizer)mrar%)myt5 MyT5Tokenizer) perceiverPerceiverTokenizer)phobertPhobertTokenizer) prophetnetProphetNetTokenizer)rag RagTokenizer)robertar%)zroberta-prelayernormr%)roc_bertRoCBertTokenizer)splinterSplinterTokenizer)tapasTapasTokenizer) unispeechr)z unispeech-satr)vits VitsTokenizer)wav2vec2r)z wav2vec2-bertr)zwav2vec2-conformerr)wav2vec2_phonemeWav2Vec2PhonemeCTCTokenizer)xlm XLMTokenizer)NFNNNFr)>rrrbr:rre collectionsrtypingrtransformers.utils.import_utilsrconfiguration_utilsrdynamic_module_utilsrrmodeling_gguf_pytorch_utilsrtokenization_utils_baser utilsr r r r r utils.hubrencoder_decoderr auto_factoryrconfiguration_autorrrrrtokenization_utils_tokenizersr tokenization_utils_sentencepiecer get_loggerr[rr_r^rOr`rrrjraCONFIG_TO_TYPEr?rJrorr__all__rR)kvrs00@r>r7s  #G3\?<%2*BH   H %687022%c3:o6X %<%>%>/DIX (?(A(A$tLX %<%>%>/DIX /F/H/H+dS X ,C,E,E(4P X $;$=$=4H X '>'@'@#dKX *A*C*C&NX (X $;$=$=4HX 9S9U9U5[_`X 3X *X +B+D+D'$OX 2I2K2K.QUVX &!X" 0G0I0I,tT#X$ 9%X& $;$=$=4H'X( &=&?&??TJ)X* ,+X, $;$=$=4H-X. "/X0 .E.G.G*TR1X2 &3X4 ,C,E,E4P5X6 %7X8 $;$=$=4H9X: '>'@'@OdK;X< "=X> /F/H/H+dS?X@ '>'@'@OdKAXB (?(A(A$tLCXD )@)B)B%MEXF )@)B)B%MGXH (?(A(A_tLIXJ "9";";FKXL &MXN "OXP 3QXR .SXT $;$=$=4HUXV *A*C*C&NWXX /F/H/H+dSYXZ [X\ *A*C*CN]X^ 1H1J1J-PTU_X` '>'@'@OdKaXb $;$=$=4HcXd %<%>%>/DIeXf gXh /F/H/H+dSiXj !EXEZEZ"A`dekXl *mXn %<%>%>/DIoXp )@)B)BoMqXr )@)B)BoMsXt $;$=$=4HuXv "wXx (?(A(A$tLyXz &=&?&?"TJ{X| '>'@'@#dK}X~ '>'@'@#dKX@ ,C,E,E(4PAXB (?(A(A$tLCXD -D-F-F)DQEXF #:#<#<$GGXH '>'@'@#dKIXJ (?(A(A$tLKXL ,C,E,E(4PMXN 1H1J1J-PTUOXP )@)B)B%MQXR -D-F-F)DQSXT -D-F-F)DQUXV *A*C*C&NWXX ,C,E,E(4PYXZ )C)E)E%4P[X\ $;$=$=4H]X^ +B+D+D$O_X` '>'@'@OdKaXb +B+D+D'$OcXd :eXf $;$=$=4HgXh +B+D+D'$OiXj .E.G.G*TRkXl 4K4M4M0SWXmXn 4K4M4M0SWXoXp .E.G.G?TRqXr (?(A(A_tLsXt *A*C*C&NuXv +wXx &yXz (?(A(A$tL{X| )@)B)B%M}X~ ,C,E,E4PX@ 1H1J1JoPTUAXB )@)B)B%MCXD %<%>%>/DIEXF 8O8Q8Q4W[\GXH .E.G.G*TRIXJ (?(A(A_tLKXL ,C,E,E4PMXN (?(A(A_tLOXP 0G0I0I,tTQXR 0G0I0I,tTSXT .E.G.G*TRUXV "9";";FWXX 0G0I0I,tTYXZ '>'@'@#dK[X\ -D-F-F)DQ]X^ "_X` (?(A(A$tLaXb )C)E)E%4PcXd (?(A(A$tLeXf )@)B)B%MgXh (B(D(D$$OiXj ,C,E,E(4PkXl &=&?&?"TJmXn *A*C*C&NoXp %qXr -D-F-F/DQsXt 0G0I0I,tTuXv 'wXx /F/H/H+dSyX| *,, #)@)B)B%  {XH *,, #)@)B)B%  GXT *,, #)@)B)B%  SX` *,, #)@)B)B%  _Xl *,, #)@)B)B%  kXv &@&B&B"MwXx 1H1J1JoPTUyXz 0G0I0I,tT{X| &=&?&?"TJ}X~ &=&?&?"TJX@ $AXB !8!:!: ECXD &=&?&?]TJEXF -D-F-FMDQGXH "9";";FIXJ "KXL %<%>%>/DIMXN $;$=$=4HOXP (?(A(A_tLQXR *A*C*CNSXT (?(A(A$tLUXV /F/H/H+dSWXX '>'@'@#dKYXZ (?(A(A$tL[X\ )@)B)B%M]X^ /F/H/H+dS_X` (?(A(A$tLaXb +B+D+D$OcXd )@)B)BoMeXf /F/H/H+dSgXh #:#<#<$GiXj &=&?&?"TJkXl %<%>%>/DImXn &=&?&??TJoXp 0G0I0I,tTqXr 0G0I0I,tTsXt *A*C*C&NuXv ,C,E,E(4PwXx ,yXz #:#<#<$G{X| (}X~ (?(A(A}tLXB *,, #)@)B)B%  AXL (?(A(A$tLMXN /F/H/H+dSOXP .QXR '>'@'@OdKSXT ,C,E,E(4PUXV &=&?&?"TJWXX -D-F-F)DQYXZ +B+D+D'$O[X\ ,C,E,E(4P]X^ *A*C*C&N_X` )@)B)B%MaXb &=&?&?"TJcXd *A*C*C&NeXf .E.G.G*TRgXh *A*C*C&NiXj +B+D+D'$OkXl /F/H/H+dSmXn )@)B)B%MoXp -D-F-F)DQqXr sXt %<%>%>/DIuXv 0G0I0I,tTwXx ,C,E,E(4PyXz *A*C*C&N{X| )@)B)BoM}X~ (X@ 5AXB )CXD ,C,E,E(4PEXF '>'@'@#dKGXH $;$=$=4HIXJ *A*C*CNKXL 3J3L3L/RVWMXN 6M6O6O2UYZOXP -D-F-F)DQQXR (B(D(D$$OSXT *A*C*C&NUXV 5O5Q5Q1W[\WXX ,F,H,H(dSYXZ *[X\ +B+D+D$O]X^ +B+D+D'$O_X` *A*C*CNaXb 1H1J1J PTUcXd  7 9 9}tDeXf (?(A(A$tLgXh $iXj +B+D+D'$OkXl #:#<#<$GmXn $;$=$=4HoXp "9";";FqXr .sXt 2uXv $;$=$=4HwXx +B+D+D$OyXz "{X~ *,, #)@)B)B%  }XJ *,, #)@)B)B%  IXT -UXV 2WXX 7YXZ <[X\ *A*C*C&N]X^ %<%>%>/DI_X` $;$=$=4HaXb cXd 1H1J1J-PTUeXf 4K4M4M0SWXgXh &=&?&?"TJiXj (?(A(A$tLkXl *A*C*C&NmXn &=&?&?"TJoXZ|&7 &7&7 &7 &7  &7  &7&7&7&7&7&7 &7&7&7&7 !&7" #&7$ %&7& '&7()&7*+&7,-&7./&70 1&72 3&745&76 7&789&7:;&7< =&7> ?&7@ A&7B C&7DE&7FG&7HI&7JK&7)&P