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file into a list.rrr#N)rstrip startswithappendtuplesplit) merges_filemergesrlines r load_mergesrs F k3 1 1 13V 3 3D::<BloomTokenizerBloomTokenizerFastr)rrrrrrrDrz.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 EEFpretrained_model_name_or_path cache_dirforce_downloadproxiestokenrevisionlocal_files_only subfolderc 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) getrr loggerinfor rrr) rrrrrrrrkwargs commit_hashresolved_config_filerrs rget_tokenizer_configrsJ**^,,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.)OSError)selfs r__init__zAutoTokenizer.__init__>s _   rrc D|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 d+i| }nA|? tj|fd|i|}n)#ttf$rtj|fi|}YnwxYw|j } t|fi|}|dd}d}d|vrGt|dt t"fr |d}n|ddd}||| | dkrt| t| d|dkrt| d}|dvr8| t&vr|n|}t |} | | jdvr| j|g|Ri|St*t+j|g|Ri|Std|dd|vr |d|d<|r|dr |dd}|du}t/|t0vp(|duo$t |dupt |dzdu}|oSt/|t0vo=|duo9t |pt |dzjd }|r | t&vrd}d}|rN|d |d}n|d}d|vr|dd}nd}t9|||||}|rs|rq|so|r"t |dt;||fi|} |d d}| | j|g|Rd|i|S|h|}t |} | '|dst |dz} | | jd!krt*} | t*} | j|g|Ri|St?|ddrJ|j }d"|vr|dr |dd}t |} | j|g|Ri|St|tBrdt/|j"t/|j#ur5tH%d#|j#j&d$|j"j&d%|j#}tOt/|jpt?|d&d}|@t0t/|t*} | | j|g|Ri|S|dd}||d'kr|dr |dd}t |} | '|dst |dz} | | jd!krt*} | t*} | j|g|Ri|Std(|j&d)d d*t0Dd ),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, c3K|]}|VdSNr{r|cs r z0AutoTokenizer.from_pretrained..s" D Dq D D D D D DrrzTokenizer class z is not currently imported.F)return_tensorsrauto_maprrr)r PythonBackendPreTrainedTokenizerFastzTokenizer 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_typerz!Unrecognized configuration class z8 to build an AutoTokenizer. 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