o i1@sJdZddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddl Z ddlZddlmZddlmZddlmZmZmZddlmZddlmZdd lmZmZmZmZm Z m!Z!dd l"m#Z#m$Z$e!%e&Z'd e(d e(fd dZ)e *Z+ddZ,d ee(ej-fd dfddZ.dee(ej-fd e/e(fddZ0dee(ej-fd e/e(fddZ1dee(ej-fd e/e(fddZ2dee(ej-fd e/e(fddZ3ddde(d ee(ej-fd!e4d e5fd"d#Z6         dAd$ee(ej-fde(d%eee(ej-fd&e4d'ee4d(ee7e(e(fd)eee4e(fd*ee(d+e4d,ee(d-ee(d e(fd.d/Z8         dAd0e(d$ee(ej-fd%eee(ej-fd&e4d'ee4d(ee7e(e(fd)eee4e(fd*ee(d+e4d,ee(d1ee(d e5fd2d3Z9dBd4ed5ee(ej-fd6ee7d e/e(fd7d8Z:d9d:Z;d;Z< dCddS)Ez3Utilities to dynamically load objects from the Hub.N)Path) ModuleType)AnyOptionalUnion)try_to_load_from_cache)version)HF_MODULES_CACHE TRANSFORMERS_DYNAMIC_MODULE_NAME cached_fileextract_commit_hashis_offline_modelogging)VersionComparisonsplit_package_versionnamereturncCsx|dddd}|r|drd|}t|r*td|d|d |S|s:td|d|d |S) a Tries to sanitize a module name so that it can be used as a Python module. The following transformations are applied: 1. 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Returns: `list[str]`: The list of relative imports in the file. z&Encountered exception while importing z: zNo module namedNrz\This modeling file requires the following packages that were not found in your environment: z, z. Run `pip install  `) r~r. import_module ImportErrorrrrGr)rOjoinrE)rUimportsmissing_packagesimp exceptionr!r!r" check_importss,     rF force_reload class_namerJrc Cstj|}|d}|tjjd}tt|}tf|r)t j |dt t j |}t jj||d}|gtttt|}tddd|D}|durbt j|} | t j |<n|} t| dd |kru|j| || _t| |WdS1swYdS) a Import a module on the cache directory for modules and extract a class from it. Args: class_name (`str`): The name of the class to import. module_path (`str` or `os.PathLike`): The path to the module to import. force_reload (`bool`, *optional*, defaults to `False`): Whether to reload the dynamic module from file if it already exists in `sys.modules`. Otherwise, the module is only reloaded if the file has changed. Returns: `typing.Type`: The class looked for. rFrN)locationcss |] }t||VqdSN)bytes read_bytesrLr!r!r" ,sz&get_class_in_module..__transformers_module_hash__rW)r*r(normpath removesuffixrseprr _HF_REMOTE_CODE_LOCKr'modulespopr.r/getutilspec_from_file_locationr|maprThashlibsha256r hexdigestmodule_from_specraloader exec_moduler) rrJrrr6 cached_module module_spec module_files module_hashrnr!r!r"get_class_in_module s*       $rpretrained_model_name_or_path cache_dirforce_downloadresume_downloadproxiestokenrevisionlocal_files_only repo_type _commit_hashc  Ks8| dd} | durtdt|durtd| }tr&|s&tdd}t|}t j |} | r;t t j |}nt j jtt |d}t|||| | d}g}zt|||||||||| | d }| sn||krn||Wntytd |d |d wt|}tt j j|}t|tt|}|t t j |kr||rt|t||s||jjddd t !|||t"#|D]/}t|j|d}t j ||}||rt|t||st !|||t"#qnct$|| }||}|t j j|}t j ||}tt|j||s/t !|||t"#|D].}||j|ds^t%|t|j|d||||||||d ||dq1t&|dkr|durddd|D}| dur}dn| d}d||}t'd|d|dt j ||S)a Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached Transformers module. 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/`. module_file (`str`): The name of the module file containing the class to look for. 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. resume_download: Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers. 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. repo_type (`str`, *optional*): Specify the repo type (useful when downloading from a space for instance). 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Args: class_reference (`str`): The full name of the class to load, including its module and optionally its repo. 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/`. This is used when `class_reference` does not specify another repo. module_file (`str`): The name of the module file containing the class to look for. class_name (`str`): The name of the class to import in the module. 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. resume_download: Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers. 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. repo_type (`str`, *optional*): Specify the repo type (useful when downloading from a space for instance). code_revision (`str`, *optional*, defaults to `"main"`): The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. 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. Passing `token=True` is required when you want to use a private model. Returns: `typing.Type`: The class, dynamically imported from the module. Examples: ```python # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this # module. cls = get_class_from_dynamic_module("modeling.MyBertModel", "sgugger/my-bert-model") # Download module `modeling.py` from a given repo and cache then extract the class `MyBertModel` from this # module. cls = get_class_from_dynamic_module("sgugger/my-bert-model--modeling.MyBertModel", "sgugger/another-bert-model") ```rNrrz--rrF)rrrrrrrrr)rrrrrrjrr)rrrrrrrrrrrkwargsrrepo_idr6r final_moduler!r!r"get_class_from_dynamic_modules8 Z robjfolderconfigc sjdkrtdd|ddSfdd}t|ttfr*|D]}||q"n|dur2||g}tjjj}t |t |j }t ||| |t|D]}t |t |j }t ||| |qS|S)a  Save the modeling files corresponding to a custom model/configuration/tokenizer etc. in a given folder. Optionally adds the proper fields in a config. Args: obj (`Any`): The object for which to save the module files. folder (`str` or `os.PathLike`): The folder where to save. config (`PretrainedConfig` or dictionary, `optional`): A config in which to register the auto_map corresponding to this custom object. Returns: `list[str]`: The list of files saved. __main__z We can't save the code defining z in z as it's been defined in __main__. 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Please set the option `trust_remote_code=True` to permit loading of this model.)r)signumframer!r!r"_raise_timeout_errorsrc Cs|dur2|durd|d|d|d}ntj|r)d|dtj|d}n d|d|d}|dur|r;d}n||rtd krd}zZz6ttjt}tt|durtt |d |d }| d vrhd }n| dvrpd}|dusUtd Wnt yt |d |dwW|durttj|td n|durttj|td ww|rtdd|r|s|st |d |d|S)a Resolves the `trust_remote_code` argument. If there is remote code to be loaded, the user must opt-in to loading it. Args: trust_remote_code (`bool` or `None`): User-defined `trust_remote_code` value. model_name (`str`): The name of the model repository in huggingface.co. has_local_code (`bool`): Whether the model has local code. has_remote_code (`bool`): Whether the model has remote code. error_message (`str`, *optional*): Custom error message to display if there is remote code to load and the user didn't opt-in. If unset, the error message will be regarding loading a model with custom code. Returns: The resolved `trust_remote_code` value. NzThe repository z% references custom code contained in zm which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/z . zt contains custom code which must be executed to correctly load the model. You can inspect the repository content at z contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/Frz9 You can inspect the repository content at https://hf.co/z. You can avoid this prompt in future by passing the argument `trust_remote_code=True`. Do you wish to run the custom code? [y/N] )yesy1T)non0rWzS. Please pass the argument `trust_remote_code=True` to allow custom code to be run.) r*r(rabspathTIME_OUT_REMOTE_CODEsignalSIGALRMralarminputlower Exceptionr)trust_remote_code model_namehas_local_codehas_remote_code error_message upstream_repoprev_sig_handleranswerr!r!r"resolve_trust_remote_codesl           rrequirements.txtc Ks\g}ztd ||d|}t|d }|}Wdn1s!wY|D]i}|}|r5|dr6q(z t|\}}} WntyN|}d\}} Ynwztj |} Wntjj yj| |dYq(w|dur| durt |t | t | } nd} | s| |d| d q(Wn tyYnw|rtd |d d |dS)a Tries to locate `requirements_file` in a local folder or repo, and confirms that the environment has all the python dependencies installed. Args: path_or_repo_id (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a model repo on huggingface.co. - a path to a *directory* potentially containing the file. kwargs (`dict[str, Any]`, *optional*): Additional arguments to pass to `cached_file`. )path_or_repo_idrUrN#NNz (installed: None)Tz (installed: )z4Missing requirements in your local environment for `z`: rr!)r r; readlinesstriprdrrr.metadatarPackageNotFoundErrorr)r from_stringr{rrr) rrequirements_filerfailed requirementsrB requirement package_name delimiterversion_numberlocal_package_version is_satisfiedr!r!r"check_python_requirementssN      r)) NFNNNNFNNrr)r)?__doc__r[rrr.importlib.metadataimportlib.utilrr*r=rrr' threadingrpathlibrtypesrtypingrrrhuggingface_hubr packagingrrfr r r r rrutils.import_utilsrr get_loggerrrrGr#Lockrr1PathLiker4r@rErTr~rbooltyperrrrrrrrr)r!r!r!r"s       !   7'  3     6     .}L U