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(3- ""D"r)ru model_classc[U[[-5(a*SRUVs/sH ocMSUS3PM sn5$SUS3$s snf)Nz or [``]) isinstancerMrojoin)rcs r'_get_class_namersP+te|,,{{ M 1Jb2J MNN  }B Ns A  A c LUcU(d [S5eU(aUc/[R"5VVs0sH up4USUS3_M nnnO:UR5VVs0sHup6U[;dMU[U5_M nnn[ UR 55Vs/sHnUSUSXSS[US3PM nnOUR5VVs0sH$upHU[;dM[U[U5_M& n nn[R"5VVs0sHup4U[U_M n nn[ U R 55V s/sHn USU S XSXS3PM nn S R U5$s snnfs snnfs snfs snnfs snnfs sn f) NzFUsing `use_model_types=False` requires a `config_to_class` dictionary.rrz- **z** -- z (z model)z- [`z`] configuration class:  )rcrr+rsortedrNr) indentconfig_to_classuse_model_typesr}r/model_type_to_namerlinesclasconfig_to_nameconfig_to_model_name config_names r'_list_model_optionsrsabb  "XlXrXrXt!uXtBT**6("o"=Xt !u 0?/D/D/F"/F+J!559 OK88/F "%%7%<%<%>? ? hd:,f-?-K,LBOcdnOoNppw x?  !0 5 5 7 7 -- @  (/$*? ? 7  PdOiOiOk Ok9KF(4 4Ok  &n&9&9&;<  = hd;-(+,B/C/P.QQX Z<  99U 3"v"     s/F"F 6F $!FF/F!FF!rc^^UU4SjnU$)Nc>URnUcU$URS5nSnU[U5:aJ[R"SX#5c0US- nU[U5:a[R"SX#5cM0U[U5:aU[R"SX#5R 5SnT(aUS3n[ UTTS9X#'SRU5nO[SUSU35eXlU$) Nrrz^(\s*)List options\s*$rz )rrz The function z\ should have an empty 'List options' in its docstring as placeholder, current docstring is: ) rlsplitlenresearchgroupsrrrc)fn docstringsrirrrs r'docstring_decorator>replace_list_option_in_docstrings..docstring_decoratorsZZ  I  & #e*n+Deh!O!W FA#e*n+Deh!O!W s5z>YY8%(CJJLQOF"84*6?dstEH5)Jt$##-,0   r)rs)rrrs`` r'!replace_list_option_in_docstringsrs. r)c\rSrSrSrS Sjr\S\S\4Sj5r \\ "5S\\ R\-4S j55r \S S S jj5rS rg) AutoConfigia  This is a generic configuration class that will be instantiated as one of the configuration classes of the library when created with the [`~AutoConfig.from_pretrained`] class method. 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The configuration class to instantiate is selected based on the `model_type` property of the config object that is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: List options Args: pretrained_model_name_or_path (`str` or `os.PathLike`): 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 [`~PreTrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method, e.g., `./my_model_directory/`. - a path to a saved configuration JSON *file*, e.g., `./my_model_directory/configuration.json`. 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. return_unused_kwargs (`bool`, *optional*, defaults to `False`): If `False`, then this function returns just the final configuration object. If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the part of `kwargs` which has not been used to update `config` and is otherwise ignored. 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*): The values in kwargs of any keys which are configuration attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter. Examples: ```python >>> from transformers import AutoConfig >>> # Download configuration from huggingface.co and cache. >>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased") >>> # Download configuration from huggingface.co (user-uploaded) and cache. >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased") >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*). >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/") >>> # Load a specific configuration file. >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json") >>> # Change some config attributes when loading a pretrained config. >>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False) >>> config.output_attentions True >>> config, unused_kwargs = AutoConfig.from_pretrained( ... "google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True ... ) >>> config.output_attentions True >>> unused_kwargs {'foo': False} ``` T _from_auto name_or_pathtrust_remote_codeN code_revisionauto_maprr}z transformers.z--rmistral layer_typeszhDetected mistral model with layer_types, treating as ministral for alternating attention compatibility. ministralz6The checkpoint you are trying to load has model type `a%` but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date. You can update Transformers with the command `pip install --upgrade transformers`. If this does not work, and the checkpoint is very new, then there may not be a release version that supports this model yet. 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