Ë «q±iÔãóº—ddlZddlmZmZddlmZddlmZddlm Z ddl m Z eje «Zee e j«Gd„d e «««Zy) éN)Ú dataclassÚfield)ÚPathé)ÚGenerationConfig)ÚTrainingArguments)Úadd_start_docstringscó쇗eZdZUdZedddi¬«Zeed<edddi¬«Zeed<ed dd i¬«Z e d zed <ed dd i¬«Z e d zed <ed ddi¬«Z e ezezd zed<ˆfd„ZˆxZS)ÚSeq2SeqTrainingArgumentsa@ 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_configc󜕗t‰|«}|j«D])\}}t|t«sŒ|j«||<Œ+|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 €úT/opt/pipecat/venv/lib/python3.12/site-packages/transformers/training_args_seq2seq.pyrz Seq2SeqTrainingArguments.to_dictTsKø€ô ‰G‰OÓ ˆØ—G‘G“Iò #‰DˆAˆqܘ!Ô-Õ.Ø—y‘y“{!’ð #ðˆó)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrÚboolÚ__annotations__rrÚintrrÚstrrrrÚ __classcell__)rs@rr r sÌø…ññ2"¨%¸6ÐCjÐ:kÔl€OTÓlÙ"'Ø Ð)qÐ rô#И4óñ).Øà ðHð ô)И3 ™:óñ(-Øà ðGð ô(И# ™*óñ?DØà Ðtð ô?Ðs˜T‘zÐ$4Ñ4°tÑ;ó÷ ð rr )ÚloggingÚ dataclassesrrÚpathlibrÚgeneration.configuration_utilsrÚ training_argsrÚutilsr Ú getLoggerr Úloggerr#r ©rrúr2s`ðóß(Ýå<Ý,Ý'ð ˆ× Ñ ˜8Ó $€ð ÙÐ'×/Ñ/Ó0ôAÐ0óAó1ó ñAr