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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_CLASSci|]\}}|| Sr).0kvs h/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py rs@@@41a!Q@@@)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-*ct|dd5}tj|cdddS#1swxYwYdS)z*Loads a vocabulary file into a dictionary.rutf-8encodingN)openjsonload) vocab_filereaders r load_vocabrs j# 0 0 0!Fy  !!!!!!!!!!!!!!!!!!s 488c(g}t|dd5}|D]a}|}|rI|ds4|t |b dddn #1swxYwY|S)z Loads a merges file into a list.rrr#N)rstrip startswithappendtuplesplit) merges_filemergesrlines r load_mergesrs F k3 1 1 13V 3 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 rrrs4===  ,,,&z22111+J77(((  )@(E(E(G(G$ _ j ( (3K@@Krrr"888"01OQ_``"01B[1B1BDYZZ  44%flDAAAFGvQTQ\G\G\"{62HHfo&>GGGK**6G+;XFFF !    # )('5<<>> 9j$ / /: = =    > ).99K{J''0{J///6"":(CRC999 4s1BD44 EEFrrforce_downloadproxiesrrrrc R|d} t|t|||||||ddd|  } | tdiSt | | } t | d5} tj| } dddn #1swxYwY| | d<| S)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 # rsss %&:KHHK "W 5 5 5#6""###############(F> Ms6BBBcveZdZdZdZeeedee zfdZ e ddZ dS) 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). c td)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 _   rrc |dd}d|d<|dd}|dd}|dd}|d}|t|d} | 8td |d d d tDd t | } | td| d| j|g|Ri|S|r7t||fi|} t| dd} tj d2i| }nA|? tj|fd|i|}n)#ttf$rtj|fi|}YnwxYw|j } t|dr|jnd}t!|fi|}|dd}d}d|vrGt#|dt$t&fr |d}n|ddd}t#t)|ddx}t*r|nd|t)| d-ddkrd|vs t=|fi|st.} | j|g|Ri|S|dd}||d.kr|dr |dd!}t |} | '|dst |dz} | | jd'krt.} | t.} | j|g|Ri|Std/|j.d0d d1tBDd )3a 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, c3K|]}|VdSNrrcs r z0AutoTokenizer.from_pretrained..s" D Dq D D D D D DrrzTokenizer class z is not currently imported.F)return_tensors model_namerauto_mapr _name_or_pathrc3BK|]}tj|VdSr)fnmatch)rp_config_name_or_paths rrz0AutoTokenizer.from_pretrained..s0ffGO$8!<<ffffffrr)r PythonBackendPreTrainedTokenizerFastr)rr r rfix_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_revisionr r z 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. 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