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r
  SSKJr  SSKJr  \ R                  " \5      r\\" \R$                  5       " S	 S
\5      5       5       rg)    N)	dataclassfield)Path)OptionalUnion   )GenerationConfig)TrainingArguments)add_start_docstringsc                      ^  \ rS rSr% Sr\" SSS0S9r\\S'   \" SSS0S9r	\\S	'   \" S
SS0S9r
\\   \S'   \" S
SS0S9r\\   \S'   \" S
SS0S9r\\\\\4      \S'   U 4S jrSrU =r$ )Seq2SeqTrainingArguments   aj  
Args:
    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.
Fhelpz%Whether to use SortishSampler or not.)defaultmetadatasortish_samplerzFWhether to use generate to calculate generative metrics (ROUGE, BLEU).predict_with_generateNzThe `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_max_lengthzThe `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_num_beamsz^Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.generation_configc                    > [         TU ]  5       nUR                  5        H.  u  p#[        U[        5      (       d  M  UR                  5       X'   M0     U$ )z
Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
serialization support). It obfuscates the token values by removing their value.
)superto_dictitems
isinstancer	   )selfdkv	__class__s       Z/var/www/auris/envauris/lib/python3.13/site-packages/transformers/training_args_seq2seq.pyr    Seq2SeqTrainingArguments.to_dictP   sE     GOGGIDA!-..yy{       )__name__
__module____qualname____firstlineno____doc__r   r   bool__annotations__r   r   r   intr   r   r   strr   r	   r   __static_attributes____classcell__)r    s   @r!   r   r      s    ( "%6Cj:klOTl"')q r#4  ,1H
,8C=  +0G
+(3-  GLt
Gxc41A&A BC 
 
r#   r   )loggingdataclassesr   r   pathlibr   typingr   r   generation.configuration_utilsr	   training_argsr
   utilsr   	getLoggerr%   loggerr)   r   r$   r#   r!   <module>r9      s^     (  " < , ' 
		8	$ '//0<0 < 1 <r#   