§ ñ§gÔãóÖ—ddlZddlmZmZddlmZddlmZmZddl m Z ddl m Z ddl mZeje¦«Zeee j¦«Gd „d e ¦«¦«¦«ZdS) éN)Ú dataclassÚfield)ÚPath)ÚOptionalÚUnioné)ÚGenerationConfig)ÚTrainingArguments)Úadd_start_docstringscó"‡—eZdZUdZedddi¬¦«Zeed<edddi¬¦«Zeed<ed dd i¬¦«Z e e ed <ed dd i¬¦«Z e e ed <ed ddi¬¦«Z e eeeefed<ˆfd„ZˆxZS)ÚSeq2SeqTrainingArgumentsaJ Args: sortish_sampler (`bool`, *optional*, defaults to `False`): Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset* for now but will become generally available in the near future. It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for the training set. predict_with_generate (`bool`, *optional*, defaults to `False`): Whether to use generate to calculate generative metrics (ROUGE, BLEU). generation_max_length (`int`, *optional*): The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. generation_num_beams (`int`, *optional*): The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. generation_config (`str` or `Path` or [`~generation.GenerationConfig`], *optional*): Allows to load a [`~generation.GenerationConfig`] from the `from_pretrained` method. 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 [`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`. - a [`~generation.GenerationConfig`] object. FÚhelpz%Whether to use SortishSampler or not.)ÚdefaultÚmetadataÚsortish_samplerzFWhether to use generate to calculate generative metrics (ROUGE, BLEU).Úpredict_with_generateNz•The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration.Úgeneration_max_lengthz“The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration.Úgeneration_num_beamsz^Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.Úgeneration_configcóÔ•—t¦« ¦«}| ¦«D]1\}}t|t¦«r| ¦«||<Œ2|S)zØ Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON serialization support). It obfuscates the token values by removing their value. )ÚsuperÚto_dictÚitemsÚ isinstancer )ÚselfÚdÚkÚvÚ __class__s €ú^/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/training_args_seq2seq.pyrz Seq2SeqTrainingArguments.to_dictVs]ø€õ ‰GŒGOŠOÑ Ô ˆØ—G’G‘I”Ið #ð #‰DˆAˆqݘ!Õ-Ñ.Ô.ð #Ø—y’y‘{”{!‘ø؈ó)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrÚboolÚ__annotations__rrrÚintrrrÚstrrr rÚ __classcell__)rs@r r r skø€€€€€€ððð4"˜E¨%¸6ÐCjÐ:kÐlÑlÔl€OTÐlÐlÑlØ"' %Ø Ð)qÐ rð#ñ#ô#И4ððñð,1¨5Øà ðHð ð,ñ,ô,И8 Cœ=ððñð+0¨%Øà ðGð ð+ñ+ô+И( 3œ-ððñðGLÀeØà Ðtð ðGñGôGÐx  c¨4Ð1AÐ&AÔ BÔCððñð ð ð ð ð ð ð ð ð r!r )ÚloggingÚ dataclassesrrÚpathlibrÚtypingrrÚgeneration.configuration_utilsr Ú training_argsr Úutilsr Ú getLoggerr"Úloggerr%r ©r!r úr5sðð€€€Ø(Ð(Ð(Ð(Ð(Ð(Ð(Ð(ØÐÐÐÐÐØ"Ð"Ð"Ð"Ð"Ð"Ð"Ð"à<Ð<Ð<Ð<Ð<Ð<Ø,Ð,Ð,Ð,Ð,Ð,Ø'Ð'Ð'Ð'Ð'Ð'ð ˆÔ ˜8Ñ $Ô $€ð ØÐÐ'Ô/Ñ0Ô0ðBðBðBðBðBÐ0ñBôBñ1Ô0ñ „ðBðBðBr!