o sh@s0dZddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddl Z ddlmZddlmZddlmZmZmZddlmZddlmZdd lmZmZmZmZmZm Z dd l!m"Z"m#Z#e $e%Z&e 'Z(d d Z)d ee*ej+fddfddZ,dee*ej+fde-e*fddZ.dee*ej+fde-e*fddZ/dee*ej+fde-e*fddZ0dee*ej+fde-e*fddZ1ddde*dee*ej+fde2de3fd d!Z4         d?d"ee*ej+fde*d#eee*ej+fd$e2d%ee2d&ee5e*e*fd'eee2e*fd(ee*d)e2d*ee*d+ee*de*fd,d-Z6         d?d.e*d"ee*ej+fd#eee*ej+fd$e2d%ee2d&ee5e*e*fd'eee2e*fd(ee*d)e2d*ee*d/ee*de3fd0d1Z7d@d2ed3ee*ej+fd4ee5de-e*fd5d6Z8d7d8Z9d9Z: dAd:d;Z;dBd=d>Zsz-get_relative_import_files..csg|]}|vr|qSrrr:r4)all_relative_importsrr r=sr)extendr7rr%len)r( no_changefiles_to_check new_importsr4new_import_filesr)r?r<r get_relative_import_filesns     rFfilenamecslt|dd }|}Wdn1swYtddlfddt|}|tS)a Extracts all the libraries (not relative imports this time) that are imported in a file. Args: filename (`str` or `os.PathLike`): The module file to inspect. Returns: `list[str]`: The list of all packages required to use the input module. r)r*Nrcst|tjrdSt|tjr;|j}t|D]#}t|tjr9t|jdd}| dr/| ds6t j j |r9dSqn9t|tjrW|jD]}|jdd}|rU|qDnt|tjrt|jdkrt|jrt|jdd}|rt|t|D]}|qydS)Nid available is_flash_attn.r) isinstanceastTryIftestwalkCallgetattrfuncendswith startswithhasattrutils import_utilsImportnamesr"splitadd ImportFromlevelmoduleiter_child_nodes)noderQcondition_nodecheck_functionalias top_modulechildimported_modulesrecursive_look_for_imports transformersrr rks>         z/get_imports..recursive_look_for_imports)r-r.r3transformers.utilsrNparsesorted)rGr4r5treerrir get_importss  !rqc Cst|}g}|D]3}zt|Wqty;}ztd|d|dt|vr0||nWYd}~qd}~wwt|dkrStdd |dd |d t |S) a. Check if the current Python environment contains all the libraries that are imported in a file. Will raise if a library is missing. Args: filename (`str` or `os.PathLike`): The module file to check. 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  `) rqr import_module ImportErrorloggerwarningr9rrAjoinr7)rGimportsmissing_packagesimp exceptionrrr check_importss,     r}F force_reload class_namer<rc Cs"tj|}|dr|dd}|tjjd}tt|}tf|r/t j |dt t j |}t jj||d}|gtttt|}tddd|D}|durht j|} | t j |<n|} t| d d |kr{|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. r8NrL)locationcss |] }t||VqdSN)bytes read_bytesr>rrr sz&get_class_in_module..__transformers_module_hash__rI)rrnormpathrVreplaceseprr _HF_REMOTE_CODE_LOCKrmodulespoprrgetutilspec_from_file_locationromaprFhashlibsha256rx hexdigestmodule_from_specrTloader exec_moduler) rr<rr"r( cached_module module_spec module_files module_hashrarrr get_class_in_modules,        $rpretrained_model_name_or_path cache_dirforce_downloadresume_downloadproxiestokenrevisionlocal_files_only repo_type _commit_hashc  Ks| dd} | durtdt|durtd| }tr&|s&tdd}t|}t j |} | r9t j |}n| dt j j}t|||| | d}g}zt|||||||||| | d }| sg||krg||Wnty|td |d |d wt|}tt j j|}t|tt|}|t j |kr||rt|t||s||jjddd t|||t |D]/}t|j|d}t j !||}||rt|t||st|||t qn[t"|| }||}|t j j|}t j !||}tt|j||s&t|||t |D]&}||dsMt#||d||||||||d ||dq(t$|dkr|durd!dd|D}| durldn| 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). Passing `token=True` is required when you want to use a private model. Returns: `str`: The path to the module inside the cache. use_auth_tokenNrThe `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.V`token` and `use_auth_token` are both specified. Please set only the argument `token`.z+Offline mode: forcing local_files_only=TrueT/)rrr) rrrrrrrrrzCould not locate the z inside rL)parentsrr8)rrrrrrrrr cSsg|]}d|qS)z- rr>rrr r=sz*get_cached_module_file..rIzs/zhttps://huggingface.co/z9A new version of the following files was downloaded from z: z . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.)&rwarningswarn FutureWarning ValueErrorrrvinfor9rrisdirbasenamerrrr rOSErrorr}r r&rr rfilecmpcmpr%mkdirshutilcopyrrrxr get_cached_module_filerArw)rr(rrrrrrrrrdeprecated_kwargsris_local submoduler new_filesresolved_module_filemodules_neededfull_submodulesubmodule_path module_neededmodule_needed_file commit_hashfull_submodule_module_file_path repo_type_strurlrrr rs >           rclass_reference code_revisionc  Ks| dd} | durtdt|durtd| }d|vr&|d\} }n|} |d\}}| dur9|| kr9|} t| |d|||||| || d }t|||d S) a6 Extracts a class from a module file, present in the local folder or repository of a model. Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should therefore only be called on trusted repos. 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--rLr8)rrrrrrrrr~)rrrrrr]rr)rrrrrrrrrrrkwargsrrepo_idr(r final_modulerrr 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)signumframerrr _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)non0rIzS. Please pass the argument `trust_remote_code=True` to allow custom code to be run.) rrrabspathTIME_OUT_REMOTE_CODEsignalSIGALRMralarminputlower Exceptionr)trust_remote_code model_namehas_local_codehas_remote_code error_message upstream_repoprev_sig_handleranswerrrr resolve_trust_remote_codesl           r requirements.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_idrGrN#NNz (installed: None)Tz (installed: )z4Missing requirements in your local environment for `z`: rr)r r- readlinesstriprWrrrmetadatarPackageNotFoundErrorrr from_stringrnrrurx) r requirements_filerfailed requirementsr4 requirement package_name delimiterversion_numberlocal_package_version is_satisfiedrrr check_python_requirementssN      r) NFNNNNFNNrr)r )=__doc__rNrrrimportlib.metadataimportlib.utilrr/rrr threadingrpathlibrtypesrtypingrrrhuggingface_hubr packagingrrYr r r r rrutils.import_utilsrr get_loggerrrvLockrr!r9PathLiker&r2r7rFrqr}booltyperrrrrrrr rrrrr s          7'  4     6     .}L U