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llava_next minimax_m2 modernbert deepseek_v2 deepseek_v3 deepseek_v4 deepseek_vl hyperclovax deepseek_ocr deepseek_v32 ernie4_5_moe paddleocr_vl deepseek_ocr2deepseek_vl_v2hyperclovax_vlmdeepseek_vl_hybrid)MODELS_WITH_INCORRECT_HUB_TOKENIZER_CLASS)z!deepseek-ai/deepseek-r1-distill-*zdeepseek-ai/deepseek-coder-*zallenai/dolma2-tokenizerzgoogle/umt5-smallzsalesforce/blip2-opt-*zsalesforce/blip2-flan-t5-*z!salesforce/instructblip-flan-t5-*ctt|dd5}tj|cdddS#1swYyxYw)z*Loads a vocabulary file into a dictionary.rutf-8encodingN)openjsonload) vocab_filereaders u/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py load_vocabrs1 j# 0!Fyy !!!s.7cg}t|dd5}|D]O}|j}|s|jdr(|jt |j Q ddd|S#1swY|SxYw)z Loads a merges file into a list.rrr#N)rstrip startswithappendtuplesplit) merges_filemergesrlines r load_mergesrsr F k3 13V 3D::BloomTokenizerBloomTokenizerFastr)rrrrrrrUrz.tokenization_mistral_common transformers.ztransformers.models __module__Fast_fast__name__)rrrTOKENIZER_MAPPING_NAMESitemsr importlib import_modulegetattrsysmodulessetattrr setdefaultAttributeErrorTOKENIZER_MAPPING_extra_contentvalueshasattrendswithtokenizer_class_from_name) r module_nametokenizer_classmoduleresultsubmodbase_mod tokenizer main_modules rrrs==  ,,&z2211+J77((  )@(E(E(G$ _ j (3K@Krr"88"001OQ_`"001[M1BDYZ  4%flDAAFAvQTQ\Q\G\"{{62HHfoo&>GKK**6G+;XF #*'55<<> 9j$ /: = )).9K{J'{J//6"(CR99 %"  sA>F## F0/F0rforce_downloadproxiesrrrrc "|jd} t|t|||||||ddd|  } | tj diSt | | } t | d5} tj| } ddd|  d<| S#1swYxYw)aY Loads the tokenizer configuration from a pretrained model tokenizer configuration. 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) rrrrrrr _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.rr) rrr loggerinfor rrr) rrrrrrrrr commit_hashresolved_config_filerrs rget_tokenizer_configr sJ**^,K&%%))..305 # rs %&:KHK "W 5#6"#(F> M##s BBc\eZdZdZdZeeedee zfdZ e ddZ y) AutoTokenizera 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). ctd)Nz}AutoTokenizer is designed to be instantiated using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method.)r)selfs r__init__zAutoTokenizer.__init__ss _  rc |jdd}d|d<|jdd}|jdd}|jdd}|jd}|vtj|d} | ,td |d d j d tDd t | } | td| d| j |g|i|S|r3t||fi|} t| dd} tjd2i| }n| tj |fd|i|}|j} t|dr |jnd}t!|fi|}|jdd}d}d|vr4t#|dt$t&fr|d}n|djdd}t#t)|ddx}t*r|j-nd|7t.1t1fdt2Drt/j |g|i|S|xs t)|dd}|5|2| /| dk7r)tj| tj| j5d|j5dk7rtj| j5d}|dvrE| t6vs|t6vr|n|}t |} | $| j8dvr| j |g|i|S|dk(r=t;r3d|vr/t=|fi|r#t d} | | j |g|i|St.t/j |g|i|Std|dd |vr|d |d <|r|j?dr|dd!}|du}tA|tBvxs%|duxrt |duxst |dzdu}|xrNtA|tBvxr;|duxr5t |xst |dzjDjGd" }|r| t6vr|durd}d}|r<|s:|d#|d#}n|d$}d%|vr|jId%d$}nd}tK|||||}|rg|re|sc|rt |j5dtM|fi|} |jd&d}| jO| j |g|d|i|S|c|}t |} | |j?dst |dz} | | j8d'k(rt.} | t.} | j |g|i|St)|ddrG|jP}d(|vr|j?dr|dd!}t |} | j |g|i|St#|tRrztA|jTtA|jVurDtXj[d)|jVj\d*|jTj\d+|jV}t_tA|j8xs t)|d,d}|atBjtA|t.} | >> 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 K|]}|ywN.0cs r z0AutoTokenizer.from_pretrained..s Dq Ds rzTokenizer class z is not currently imported.F)return_tensors model_namerauto_mapr _name_or_pathrc3JK|]}tj|ywr)fnmatch)rp_config_name_or_paths rrz0AutoTokenizer.from_pretrained..sfGOO$8!<fs #r)r PythonBackendPreTrainedTokenizerFastr)rrrrfix_mistral_regexzTokenizer class 'zf' specified in the tokenizer config was not found. The tokenizer may need to be converted or re-saved.rrz transformers.rrz-- code_revisionrrz 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_typerrz!Unrecognized configuration class z8 to build an AutoTokenizer. Model type should be one of c34K|]}|jywr)rrs rrz0AutoTokenizer.from_pretrained..s4[AQZZ4[sr)0poprr ValueErrorrrfrom_pretrainedrr r for_modelrrr rrr isinstancerlistrstrlowerranyMODEL_IDS_TO_TOKENIZERS_BACKEND removesuffixrrrrrtyperrrrr rregister_for_auto_classrrdecoderencoderrwarning __class__r)clsrinputsrr_rrrtokenizer_class_namer gguf_path config_dictconfig_model_typeconfig_model_nametokenizer_configtokenizer_config_classtokenizer_auto_mapname _hub_classregistered_class_namerhas_remote_codehas_local_codeexplicit_local_code class_ref upstream_repotokenizer_class_candidate_classr rs @rrzAutoTokenizer.from_pretrainedys dHd+#| JJz4 ($4d;"JJ':DAJJ{+   %#:#>#>~t#T #+ .~.>>qyy D,C DDEQH 88LMO& #34H3IId!eff2?223PdSYd]cd d #$A9WPVWI.yOPXYK))8K8F ^ c#331EVZ` #--181NF--TX00MXQWX!1!5!56G!N" ) )*:6 F%5j%A"%5j%A%E%EoW[%\"'QU0V(VY\]DJJLce   &!-fFeff$445RfU[f_ef f ,Wwv?PRV/W  &&!-!R''++,=>J(,,->?LLVT''/1%<$?$?@Q$R$_$_`f$g !$-*-VV,0YY* $ # " !&<&E&Ef&M%;CR%@ ",D8f):: "$ . )*@AMZ,-Cf-LMUYY   V $55 'd29-.DER01G&1PQ*ZZ89  !%NN!-#O!%  #6!!$0.q1 .q1 y ) 5a 8 $ 9!#@.Racp!  09L%)*@*M*Mf*UV;IGdohnoO ?D1A  3 3 52?22-06J[_e $ /(> %78QRO&/H/Q/QRX/Y";?22 ^^F/V 0E0EFm'RXZfhlJm  !/33DLBSTO*?J=AWW'6156S^W]^&7O6667ThW]haghh"2!5!56G!N ! -%)<?~ 1 1~ @~ @ kpUUrrr)NFNNNFr)Fr;rrrrr collectionsrtypingrtransformers.utils.import_utilsrconfiguration_utilsrdynamic_module_utilsrr modeling_gguf_pytorch_utilsr tokenization_utils_baser utilsr r rrr utils.hubrrencoder_decoderr auto_factoryrconfiguration_autorrrrrtokenization_utils_tokenizersr tokenization_utils_sentencepiecer get_loggerrrrdictrr__annotations__rrrsetr rrCONFIG_TO_TYPErrrPathLikeboolrrrr__all__)kvs00rrVs #G3\?</2*BH   H %68d3S >2702c49n-26+c3:o6g /F/H+dSg %<%>/DIg (?(A$tLg %<%>/DI g (?(A$tL g /F/H+dS g ,C,E(4Pg $;$=4Hg '>'@#dKg *A*C&Ng (g $;$=4Hg 9S9U5[_`g 3g *g +B+D'$O!g" 3J3L/RVW#g$ &%g& 0G0I,tT'g( 9)g* $;$=4H+g, &=&??TJ-g. ,/g0 $;$=4H1g2 "3g4 .E.G*TR5g6 &7g8 ,C,E4P9g: %;g< $;$=4H=g> '>'@OdK?g@ "AgB /F/H+dSCgD '>'@OdKEgF (?(A$tLGgH )@)B%MIgJ )@)B%MKgL (?(A_tLMgN -D-F)DQOgP "9";FQgR &SgT "UgV 3WgX .YgZ $;$=4H[g\ *A*C&N]g^ /F/H+dS_g` agb 0G0I,tTcgd *A*CNegf 1H1J-PTUggh '>'@OdKigj $;$=4Hkgl %<%>/DImgn ogp /F/H+dSqgr !EXEZ"A`desgt *ugv %<%>/DIwgx -D-F)DQygz )@)BoM{g| $;$=4H}g~ "g@ (?(A$tLAgB &=&?"TJCgD '>'@#dKEgF '>'@#dKGgH ,C,E(4PIgJ (?(A$tLKgL -D-F)DQMgN #:#<$GOgP '>'@#dKQgR (?(A$tLSgT ,C,E(4PUgV 1H1J-PTUWgX )@)B%MYgZ -D-F)DQ[g\ -D-F)DQ]g^ *A*C&N_g` ,C,E(4Pagb )C)E%4Pcgd $;$=4Hegf /F/H+dSggh '>'@OdKigj +B+D'$Okgl :mgn $;$=4Hogp +B+D'$Oqgr .E.G*TRsgt 4K4M0SWXugv 4K4M0SWXwgx .E.G?TRygz (?(A_tL{g| *A*C&N}g~ +g@ +B+D'$OAgB &CgD (?(A$tLEgF )@)B%MGgH ,C,E4PIgJ 1H1JoPTUKgL )@)B%MMgN %<%>/DIOgP 8O8Q4W[\QgR ,C,E(4PSgT ,C,E(4PUgV (?(A_tLWgX ,C,E4PYgZ (?(A_tL[g\ 0G0I,tT]g^ 0G0I,tT_g` .E.G*TRagb "9";Fcgd 0G0I,tTegf '>'@#dKggh -D-F)DQigj "kgl (?(A$tLmgn )C)E%4Pogp (?(A$tLqgr )@)B%Msgt (B(D$$Ougv ,C,E(4Pwgx &=&?"TJygz *A*C&N{g| %}g~ -D-F/DQg@ 0G0I,tTAgB 'CgD 1H1J-PTUEgF /F/H+dSGgJ *, #)@)B%  IgV *, #)@)B%  Ugb *, #)@)B%  agn *, #)@)B%  mgz *, #)@)B%  ygD &@&B"MEgF 1H1JoPTUGgH 0G0I,tTIgJ &=&?"TJKgL &=&?"TJMgN $OgP !8!: EQgR &=&?]TJSgT -D-FMDQUgV "9";FWgX "YgZ 3J3L/RVW[g\ ":Q:S#6Y]^]g^ %<%>/DI_g` $;$=4Hagb (?(A_tLcgd *A*CNegf (?(A$tLggh 1H1J-PTUigj '>'@#dKkgl )@)B%Mmgn )@)B%Mogp /F/H+dSqgr (?(A$tLsgt +B+D$Ougv )@)BoMwgx /F/H+dSygz #:#<$G{g| &=&?"TJ}g~ %<%>/DIg@ &=&??TJAgB 0G0I,tTCgD 1H1J-PTUEgF 0G0I,tTGgH *A*C&NIgJ -D-F)DQKgL ,MgN -D-F)DQOgP #:#<$GQgR (SgT .E.G*TRUgX *, #)@)B%  Wgb (?(A$tLcgd /F/H+dSegf .ggh '>'@OdKigj ,C,E(4Pkgl &=&?"TJmgn -D-F)DQogp +B+D'$Oqgr ,C,E(4Psgt *A*C&Nugv )@)B%Mwgx &=&?"TJygz *A*C&N{g| .E.G*TR}g~ *A*C&Ng@ *A*C&NAgB +B+D'$OCgD /F/H+dSEgF )@)B%MGgH -D-F)DQIgJ KgL %<%>/DIMgN 0G0I,tTOgP ,C,E(4PQgR *A*C&NSgT )@)BoMUgV (WgX 5YgZ )[g\ ,C,E(4P]g^ '>'@#dK_g` $;$=4Hagb *A*CNcgd 3J3L/RVWegf 6M6O2UYZggh -D-F)DQigj (B(D$$Okgl *A*C&Nmgn 5O5Q1W[\ogp ,F,H(dSqgr *sgt +B+D$Ougv ,C,E(4Pwgx *A*CNygz 1H1J PTU{g|  7 9}tD}g~ (?(A$tLg@ $AgB (B(D$$OCgD +B+D'$OEgF #:#<$GGgH $;$=4HIgJ (?(A$tLKgL .MgN 2OgP 0G0I,tTQgR $;$=4HSgT +B+D$OUgV "WgZ *, #)@)B%  Ygf *, #)@)B%  egp -qgr 2sgt 7ugv /DI{g| $;$=4H}g~ g@ 1H1J-PTUA gB 2I2K.QUVC gD &=&?"TJE gF (?(A$tLG gH *A*C&NI gJ &=&?"TJK gL %<%>!DIM giZ .7)3s8.`<iJ00E\E^.Adh +i%%9;RS#=#7#=#=#?@41a!Q$@#! #&S)9#9 &3#3$s)d2B3p04 %)#"]#&S)9#9]R[[%%,]]#s(^d " ] #:  ] Dj ]]] #s(^]@nUnUb  0qAs v