Ë
    S^(ho‹  ã                   óR  — d Z ddlZddlZddlZddlZddlmZ ddlmZ ddl	m
Z
mZmZ ddlZddlZddlmZ ddlmZ dd	lmZ dd
lmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$ ddl%m&Z& ddl'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/ eeeeeeee e"e#ei e¥e!¥e$edœZ0 e/jb                  e2«      Z3 G d„ d«      Z4dZ5dZ6i dd“dd“dd“dd“dd“dd“dd“d d!“d"d#“d$d%“d&d'“d(d)“d*d+“d,d-“d.d/“d0d1“Z7g d2¢Z8d3„ Z9d4„ Z:d5„ Z;d6„ Z<d7„ Z=d8„ Z>e G d9„ d:«      «       Z?d;„ Z@d<„ ZAd=„ ZBdDd>„ZCd?„ ZDd@„ ZEdA„ ZFg dB¢ZGdC„ ZHy)Ez'Configuration base class and utilities.é    N)Ú	dataclass)ÚPath)ÚAnyÚOptionalÚUnion)Ú
model_info)ÚHFValidationErroré   )Ú__version__)Ú,MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMESÚ!MODEL_FOR_CAUSAL_LM_MAPPING_NAMESÚMODEL_FOR_CTC_MAPPING_NAMESÚ,MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMESÚ*MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMESÚ*MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMESÚ!MODEL_FOR_MASKED_LM_MAPPING_NAMESÚ(MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMESÚ*MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMESÚ,MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMESÚ/MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMESÚ(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMESÚ0MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMESÚ,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMESÚ6MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES)ÚParallelMode)ÚMODEL_CARD_NAMEÚcached_fileÚis_datasets_availableÚis_offline_modeÚis_tf_availableÚis_tokenizers_availableÚis_torch_availableÚlogging)útext-generationúimage-classificationúimage-segmentationú	fill-maskúobject-detectionúquestion-answeringútext2text-generationútext-classificationútable-question-answeringútoken-classificationúaudio-classificationúautomatic-speech-recognitionzzero-shot-image-classificationzimage-text-to-textc                   ój   — e Zd ZdZd„ Zd„ Zed„ «       Zed„ «       Zed„ «       Z	d„ Z
d„ Zd	„ Zd
„ Zd„ Zy)Ú	ModelCarda  
    Structured Model Card class. Store model card as well as methods for loading/downloading/saving model cards.

    Please read the following paper for details and explanation on the sections: "Model Cards for Model Reporting" by
    Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer,
    Inioluwa Deborah Raji and Timnit Gebru for the proposal behind model cards. Link: https://arxiv.org/abs/1810.03993

    Note: A model card can be loaded and saved to disk.
    c           
      ó„  — t        j                  dt        «       |j                  di «      | _        |j                  di «      | _        |j                  di «      | _        |j                  di «      | _        |j                  di «      | _        |j                  di «      | _	        |j                  di «      | _
        |j                  d	i «      | _        |j                  d
i «      | _        |j                  «       D ]  \  }}	 t        | ||«       Œ y # t        $ r%}t         j#                  d|› d|› d| › �«       |‚d }~ww xY w)NzTThe class `ModelCard` is deprecated and will be removed in version 5 of TransformersÚmodel_detailsÚintended_useÚfactorsÚmetricsÚevaluation_dataÚtraining_dataÚquantitative_analysesÚethical_considerationsÚcaveats_and_recommendationsz
Can't set z with value z for )ÚwarningsÚwarnÚFutureWarningÚpopr3   r4   r5   r6   r7   r8   r9   r:   r;   ÚitemsÚsetattrÚAttributeErrorÚloggerÚerror)ÚselfÚkwargsÚkeyÚvalueÚerrs        úT/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/modelcard.pyÚ__init__zModelCard.__init__[   s&  € Ü�‰ØbÔdqô	
ð $ŸZ™Z¨¸Ó<ˆÔØ"ŸJ™J ~°rÓ:ˆÔØ—z‘z )¨RÓ0ˆŒØ—z‘z )¨RÓ0ˆŒØ%Ÿz™zÐ*;¸RÓ@ˆÔØ#ŸZ™Z¨¸Ó<ˆÔØ%+§Z¡ZÐ0GÈÓ%LˆÔ"Ø&,§j¡jÐ1IÈ2Ó&NˆÔ#Ø+1¯:©:Ð6SÐUWÓ+XˆÔ(ð !Ÿ,™,›.ò 	‰JˆC�ðÜ˜˜c 5Õ)ñ	øô "ò Ü—‘˜z¨#¨¨l¸5¸'ÀÀtÀfÐMÔNØ�	ûðús   ÄDÄ	D?Ä D:Ä:D?c                 óâ   — t         j                  j                  |«      r%t         j                  j                  |t        «      }n|}| j                  |«       t        j                  d|› �«       y)zKSave a model card object to the directory or file `save_directory_or_file`.zModel card saved in N)ÚosÚpathÚisdirÚjoinr   Úto_json_filerC   Úinfo)rE   Úsave_directory_or_fileÚoutput_model_card_files      rJ   Úsave_pretrainedzModelCard.save_pretrainedr   sT   € ä�7‰7�=‰=Ð/Ô0ä%'§W¡W§\¡\Ð2HÌ/Ó%ZÑ"à%;Ð"à×ÑÐ0Ô1Ü�‰Ð*Ð+AÐ*BÐCÕDó    c                 ó6  — |j                  dd«      }|j                  dd«      }|j                  dd«      }|j                  dd«      }ddi}|�||d	<   t        j                  j                  |«      }t        j                  j	                  |«      r|}	d
}n`	 t        |t        |||¬«      }	|rt        j                  d|	› �«       nt        j                  dt        › d|	› �«       | j                  |	«      }
g }|j                  «       D ]0  \  }}t        
|«      sŒt        |
||«       |j!                  |«       Œ2 |D ]  }|j                  |d«       Œ t        j                  d
› �«       |r|
|fS |
S # t        t        j                  f$ r
  | «       }
Y Œ w xY w)a˜	  
        Instantiate a [`ModelCard`] from a pre-trained model model card.

        Parameters:
            pretrained_model_name_or_path: either:

                - a string, the *model id* of a pretrained model card hosted inside a model repo on huggingface.co.
                - a path to a *directory* containing a model card file saved using the [`~ModelCard.save_pretrained`]
                  method, e.g.: `./my_model_directory/`.
                - a path or url to a saved model card JSON *file*, e.g.: `./my_model_directory/modelcard.json`.

            cache_dir: (*optional*) string:
                Path to a directory in which a downloaded pre-trained model card should be cached if the standard cache
                should not be used.

            kwargs: (*optional*) dict: key/value pairs with which to update the ModelCard object after loading.

                - The values in kwargs of any keys which are model card attributes will be used to override the loaded
                  values.
                - Behavior concerning key/value pairs whose keys are *not* model card attributes is controlled by the
                  *return_unused_kwargs* keyword parameter.

            proxies: (*optional*) dict, default None:
                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.

            return_unused_kwargs: (*optional*) bool:

                - If False, then this function returns just the final model card object.
                - If True, then this functions returns a tuple *(model card, unused_kwargs)* where *unused_kwargs* is a
                  dictionary consisting of the key/value pairs whose keys are not model card attributes: ie the part of
                  kwargs which has not been used to update *ModelCard* and is otherwise ignored.

        Examples:

        ```python
        # Download model card from huggingface.co and cache.
        modelcard = ModelCard.from_pretrained("google-bert/bert-base-uncased")
        # Model card was saved using *save_pretrained('./test/saved_model/')*
        modelcard = ModelCard.from_pretrained("./test/saved_model/")
        modelcard = ModelCard.from_pretrained("./test/saved_model/modelcard.json")
        modelcard = ModelCard.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False)
        ```Ú	cache_dirNÚproxiesÚreturn_unused_kwargsFÚ_from_pipelineÚ	file_typeÚ
model_cardÚusing_pipelineT)ÚfilenamerX   rY   Ú
user_agentzloading model card file z from cache at zModel card: )r?   rM   rN   rO   Úisfiler   r   rC   rR   Úfrom_json_fileÚOSErrorÚjsonÚJSONDecodeErrorr@   ÚhasattrrA   Úappend)ÚclsÚpretrained_model_name_or_pathrF   rX   rY   rZ   Úfrom_pipeliner`   Úis_localÚresolved_model_card_fileÚ	modelcardÚ	to_removerG   rH   s                 rJ   Úfrom_pretrainedzModelCard.from_pretrained}   s©  € ðZ —J‘J˜{¨DÓ1ˆ	Ø—*‘*˜Y¨Ó-ˆØ%Ÿz™zÐ*@À%ÓHÐØŸ
™
Ð#3°TÓ:ˆà! <Ð0ˆ
ØÐ$Ø+8ˆJÐ'Ñ(ä—7‘7—=‘=Ð!>Ó?ˆÜ�7‰7�>‰>Ð7Ô8Ø'DÐ$Ø‰Hð"ä+6Ø1Ü,Ø'Ø#Ø)ô,Ð(ñ Ü—K‘KÐ":Ð;SÐ:TÐ UÕVä—K‘KÐ":¼?Ð:KÈ?Ð[sÐZtÐ uÔvà×.Ñ.Ð/GÓH�	ð ˆ	Ø Ÿ,™,›.ò 	&‰JˆC�Ü�y #Õ&Ü˜	 3¨Ô.Ø× Ñ  Õ%ð	&ð ò 	"ˆCØ�J‰J�s˜DÕ!ð	"ô 	�‰�l 9 +Ð.Ô/ÙØ˜fÐ$Ð$àÐøô# œT×1Ñ1Ð2ò "á›E’	ð"ús   ÂAE5 Å5 FÆFc                 ó   —  | di |¤ŽS )z@Constructs a `ModelCard` from a Python dictionary of parameters.© rq   )rh   Újson_objects     rJ   Ú	from_dictzModelCard.from_dictÛ   s   € ñ Ñ!�[Ñ!Ð!rV   c                 ó    — t        |d¬«      5 }|j                  «       }ddd«       t        j                  «      } | di |¤ŽS # 1 sw Y   Œ&xY w)z8Constructs a `ModelCard` from a json file of parameters.úutf-8©ÚencodingNrq   )ÚopenÚreadrd   Úloads)rh   Ú	json_fileÚreaderÚtextÚdict_objs        rJ   rb   zModelCard.from_json_fileà   sJ   € ô �) gÔ.ð 	!°&Ø—;‘;“=ˆD÷	!ä—:‘:˜dÓ#ˆÙ‰�X‰Ð÷	!ð 	!ús   ŽAÁAc                 ó4   — | j                   |j                   k(  S ©N)Ú__dict__)rE   Úothers     rJ   Ú__eq__zModelCard.__eq__è   s   € Ø�}‰} §¡Ñ.Ð.rV   c                 ó4   — t        | j                  «       «      S r€   )ÚstrÚto_json_string©rE   s    rJ   Ú__repr__zModelCard.__repr__ë   s   € Ü�4×&Ñ&Ó(Ó)Ð)rV   c                 óD   — t        j                  | j                  «      }|S )z0Serializes this instance to a Python dictionary.)ÚcopyÚdeepcopyr�   )rE   Úoutputs     rJ   Úto_dictzModelCard.to_dictî   s   € ä—‘˜tŸ}™}Ó-ˆØˆrV   c                 óT   — t        j                  | j                  «       dd¬«      dz   S )z*Serializes this instance to a JSON string.é   T)ÚindentÚ	sort_keysú
)rd   Údumpsr�   r‡   s    rJ   r†   zModelCard.to_json_stringó   s    € ä�z‰z˜$Ÿ,™,›.°¸dÔCÀdÑJÐJrV   c                 óˆ   — t        |dd¬«      5 }|j                  | j                  «       «       ddd«       y# 1 sw Y   yxY w)z"Save this instance to a json file.Úwru   rv   N)rx   Úwriter†   )rE   Újson_file_pathÚwriters      rJ   rQ   zModelCard.to_json_file÷   s:   € ä�. #°Ô8ð 	0¸FØ�L‰L˜×,Ñ,Ó.Ô/÷	0÷ 	0ñ 	0ús	   � 8¸AN)Ú__name__Ú
__module__Ú__qualname__Ú__doc__rK   rU   Úclassmethodro   rs   rb   rƒ   rˆ   r�   r†   rQ   rq   rV   rJ   r1   r1   P   sk   „ ñòò.	Eð ñ[ó ð[ðz ñ"ó ð"ð ñó ðò/ò*òò
Kó0rV   r1   z¼
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
z¶
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
r'   zMasked Language Modelingr%   zImage Classificationr&   zImage Segmentationzmultiple-choicezMultiple Choicer(   zObject Detectionr)   zQuestion AnsweringÚsummarizationÚSummarizationr,   zTable Question Answeringr+   zText Classificationr$   zCausal Language Modelingr*   z&Sequence-to-sequence Language Modelingr-   zToken ClassificationÚtranslationÚTranslationzzero-shot-classificationzZero Shot Classificationr/   zAutomatic Speech Recognitionr.   zAudio Classification)ÚaccuracyÚbleuÚf1Úmatthews_correlationÚpearsonrÚ	precisionÚrecallÚrougeÚ	sacrebleuÚ	spearmanrÚwerc                 ó4   — | €g S t        | t        «      r| gS | S r€   )Ú
isinstancer…   )Úobjs    rJ   Ú_listifyr°   +  s"   € Ø
€{Øˆ	Ü	�CœÔ	Øˆuˆàˆ
rV   c                 óŒ   — |€| S t        |t        «      r|g}|D �cg c]  }|€Œ|‘Œ	 }}t        |«      dk(  r| S || |<   | S c c}w )Nr   )r®   r…   Úlen)ÚmetadataÚnameÚvaluesÚvs       rJ   Ú_insert_values_as_listr·   4  sX   € Ø€~ØˆÜ�&œ#ÔØ�ˆØÖ1�A 1¡=ŠaÐ1€FÐ1Ü
ˆ6ƒ{�aÒØˆØ€HˆT�NØ€Oùò	 2s
   œA¤Ac                 ó  — | €i S i }| j                  «       D ]e  }|j                  «       j                  dd«      t        v r$|||j                  «       j                  dd«      <   ŒM|j                  «       dk(  sŒa||d<   Œg |S )Nú Ú_Úrouge1r©   )ÚkeysÚlowerÚreplaceÚMETRIC_TAGS)Úeval_resultsÚresultrG   s      rJ   Ú#infer_metric_tags_from_eval_resultsrÂ   @  s€   € ØÐØˆ	Ø€FØ× Ñ Ó"ò "ˆØ�9‰9‹;×Ñ˜s CÓ(¬KÑ7Ø47ˆF�3—9‘9“;×&Ñ& s¨CÓ0Ò1Ø�Y‰Y‹[˜HÓ$Ø!ˆF�7ŠOð	"ð
 €MrV   c                 ó   — |€| S || |<   | S r€   rq   )r³   r´   rH   s      rJ   Ú_insert_valuerÄ   L  s   € Ø€}ØˆØ€HˆT�NØ€OrV   c                 óD   — t        «       syddlm}m} t	        | ||f«      S )NFr   )ÚDatasetÚIterableDataset)r   ÚdatasetsrÆ   rÇ   r®   )ÚdatasetrÆ   rÇ   s      rJ   Úis_hf_datasetrÊ   S  s    € Ü Ô"Øç1ä�g ¨Ð9Ó:Ð:rV   c                 ó    — g }| j                  «       D ]8  }t        |t        t        f«      r|t        |«      z  }Œ(|j	                  |«       Œ: |S r€   )rµ   r®   ÚtupleÚlistrg   )ÚmappingrÁ   r¶   s      rJ   Ú_get_mapping_valuesrÏ   \  sL   € Ø€FØ�^‰^Óò ˆÜ�aœ%¤˜Ô'Ø”d˜1“gÑ‰Fà�M‰M˜!Õð	ð
 €MrV   c                   ó  — e Zd ZU eed<   dZeeeee   f      ed<   dZ	ee   ed<   dZ
eeeee   f      ed<   dZee   ed<   dZeeeee   f      ed<   dZeeeee   f      ed<   dZeeeee   f      ed	<   dZeeeee   f      ed
<   dZeeeef      ed<   dZeeeef      ed<   dZeee      ed<   dZeeeef      ed<   dZee   ed<   d„ Zd„ Zd„ Zd„ Ze	 	 	 	 	 	 	 	 	 	 dd„«       Ze	 	 	 	 	 	 	 	 	 dd„«       Zy)ÚTrainingSummaryÚ
model_nameNÚlanguageÚlicenseÚtagsÚfinetuned_fromÚtasksrÉ   Údataset_tagsÚdataset_argsÚdataset_metadatarÀ   Ú
eval_linesÚhyperparametersÚtrainerÚsourcec                 óŠ  — | j                   €ut        «       sj| j                  �]t        | j                  «      dkD  rD	 t	        | j                  «      }|j
                  D ]  }|j                  d«      sŒ|dd  | _         Œ  y y y y y # t        j                  j                  t        j                  j                  t        f$ r Y y w xY w)Nr   zlicense:é   )rÔ   r   rÖ   r²   r   rÕ   Ú
startswithÚrequestsÚ
exceptionsÚ	HTTPErrorÚConnectionErrorr	   )rE   rR   Útags      rJ   Ú__post_init__zTrainingSummary.__post_init__w  s¼   € ð �L‰LÐ Ü#Ô%Ø×#Ñ#Ð/Ü�D×'Ñ'Ó(¨1Ò,ðÜ! $×"5Ñ"5Ó6�ØŸ9™9ò /�CØ—~‘~ jÕ1Ø'*¨1¨2 w˜�ñ/ð	 -ð 0ð &ð !øô ×'Ñ'×1Ñ1´8×3FÑ3F×3VÑ3VÔXiÐjò Ùðús   ¼5B Á2B Â<CÃCc                 óš  — d| j                   i}t        | j                  «      }t        | j                  «      }t        | j                  «      }t        | j
                  «      }t        |«      t        |«      k  r|d gt        |«      t        |«      z
  z  z   }t        t        ||«      «      }t        t        ||«      «      }t        t        ||«      «      }	t        | j                  «      D �
ci c]  }
|
t        v sŒ|
t        |
   “Œ }}
g |d<   t        |«      dk(  rt        |«      dk(  r|gS t        |«      dk(  rd d i}t        |«      dk(  rd d i}|D ��cg c]  }|D ]  }||f‘Œ Œ }}}|D ]Î  \  }}i }|�||   |dœ|d<   |�/|	j                  |i «      }||   |dœ|¥|d<   ||   �||   |d   d<   t        |«      dkD  rBg |d<   |j                  «       D ]*  \  }}|d   j                  ||| j                  |   d	œ«       Œ, d|v rd|v rd|v r|d   j                  |«       Œ·t        j!                  d
|› �«       ŒÐ |gS c c}
w c c}}w )Nr´   Úresultsr   )r´   ÚtypeÚtaskrÉ   Úargsr6   )r´   rê   rH   zLDropping the following result as it does not have all the necessary fields:
)rÒ   r°   rÉ   rØ   rÙ   rÚ   r²   ÚdictÚzipr×   ÚTASK_TAG_TO_NAME_MAPPINGÚgetr@   rg   rÀ   rC   rR   )rE   Úmetric_mappingÚmodel_indexÚdataset_namesrØ   rÙ   rÚ   Údataset_mappingÚdataset_arg_mappingÚdataset_metadata_mappingrë   Útask_mappingÚtask_tagÚds_tagÚall_possibilitiesrÁ   r³   Ú
metric_tagÚmetric_names                      rJ   Úcreate_model_indexz"TrainingSummary.create_model_index‡  s¾  € Ø˜tŸ™Ð/ˆô ! §¡Ó.ˆÜ × 1Ñ 1Ó2ˆÜ × 1Ñ 1Ó2ˆÜ# D×$9Ñ$9Ó:ÐÜˆ|Óœs <Ó0Ò0Ø'¨4¨&´C¸Ó4EÌÈLÓHYÑ4YÑ*ZÑZˆLÜœs <°Ó?Ó@ˆÜ"¤3 |°\Ó#BÓCÐÜ#'¬¨LÐ:JÓ(KÓ#LÐ ô >FÀdÇjÁjÓ=Qö
Ø59ÐUYÔ]uÒUuˆDÔ*¨4Ñ0Ñ0ð
ˆð 
ð "$ˆ�IÑäˆ|Ó Ò!¤c¨/Ó&:¸aÒ&?Ø�=Ð Üˆ|Ó Ò!Ø  $˜<ˆLÜˆÓ 1Ò$Ø# T˜lˆOð AM×k°HÐ[jÒkÐQW˜h¨Ò/ÐkÐ/ÐkÐÑkØ 1ò 	vÑˆH�fØˆFØÐ#Ø*6°xÑ*@È(Ñ!S��v‘àÐ!Ø3×7Ñ7¸ÀÓC�à+¨FÑ3Ø"ñ%ð ð%��yÑ!ð
 ' vÑ.Ð:Ø0CÀFÑ0K�F˜9Ñ% fÑ-ä�>Ó" QÒ&Ø$&��yÑ!Ø/=×/CÑ/CÓ/Eò Ñ+�J Ø˜9Ñ%×,Ñ,à$/Ø$.Ø%)×%6Ñ%6°{Ñ%Cñõðð ˜Ñ I°Ñ$7¸IÈÑ<OØ˜IÑ&×-Ñ-¨fÕ5ä—‘ÐkÐlrÐksÐtÕuð=	vð@ ˆ}Ðùò_
ùó ls   Ã.IÃ<IÅIc                 ó   — t        | j                  «      }i }t        |dd«      }t        |d| j                  «      }t        |d| j
                  «      }| j                  �It        | j                  t        «      r/t        | j                  «      dkD  rt        |d| j                  «      }t        |d| j                  «      }t        |d| j                  «      }t        |d	t        |j                  «       «      «      }| j                  |«      |d
<   |S )NÚlibrary_nameÚtransformersrÓ   rÔ   r   Ú
base_modelrÕ   rÈ   r6   zmodel-index)rÂ   rÀ   rÄ   r·   rÓ   rÔ   rÖ   r®   r…   r²   rÕ   rØ   rÍ   r¼   rý   )rE   rñ   r³   s      rJ   Úcreate_metadatazTrainingSummary.create_metadataÆ  sé   € Ü<¸T×=NÑ=NÓOˆàˆÜ  ¨>¸>ÓJˆÜ)¨(°JÀÇÁÓNˆÜ  ¨9°d·l±lÓCˆØ×ÑÐ*¬z¸$×:MÑ:MÌsÔ/SÔX[Ð\`×\oÑ\oÓXpÐstÒXtÜ$ X¨|¸T×=PÑ=PÓQˆHÜ)¨(°F¸D¿I¹IÓFˆÜ)¨(°JÀ×@QÑ@QÓRˆÜ)¨(°I¼tÀN×DWÑDWÓDYÓ?ZÓ[ˆØ"&×"9Ñ"9¸.Ó"Iˆ�ÑàˆrV   c                 óü  — d}t        j                  | j                  «       d¬«      }t        |«      dkD  rd|› d�}| j                  dk(  r
|t
        z  }n	|t        z  }|d| j                  › d�z  }| j                  €|d	z  }n |d
| j                  › d| j                  › d�z  }| j                  �2t        | j                  t        «      rt        | j                  «      dk(  r|dz  }nÃt        | j                  t        «      r|d| j                  › d�z  }n•t        | j                  t        t        f«      r/t        | j                  «      dk(  r|d| j                  d   › d�z  }nF|dj                  | j                  d d D �cg c]  }d|› �‘Œ	 c}«      d| j                  d   › d�z   z  }| j                  �S|dz  }|dj                  | j                  j!                  «       D ��cg c]  \  }}d|› dt#        |«      › �‘Œ c}}«      z  }|dz  }|dz  }|dz  }|dz  }|dz  }|dz  }| j$                  �P|dz  }|dj                  | j$                  j!                  «       D ��cg c]  \  }}d|› d|› �‘Œ c}}«      z  }|dz  }n|dz  }| j&                  �"|d z  }|t)        | j&                  «      z  }|dz  }|d!z  }|d"t*        › d�z  }| j                  dk(  r"t-        «       rdd l}|d#|j*                  › d�z  }n0| j                  d$k(  r!t1        «       rdd l}|d%|j*                  › d�z  }t5        «       rdd l}|d&|j*                  › d�z  }t9        «       rdd l}	|d'|	j*                  › d�z  }|S c c}w c c}}w c c}}w )(NÚ F)r‘   r   z---
rÝ   z
# z

z'This model was trained from scratch on z'This model is a fine-tuned version of [z](https://huggingface.co/z) on zan unknown dataset.zthe z	 dataset.r
   z, éÿÿÿÿz	 and the z
 datasets.z:
It achieves the following results on the evaluation set:
r’   z- z: z/
## Model description

More information needed
z9
## Intended uses & limitations

More information needed
z:
## Training and evaluation data

More information needed
z
## Training procedure
z
### Training hyperparameters
z:
The following hyperparameters were used during training:
z
More information needed
z
### Training results

z
### Framework versions

z- Transformers z
- Pytorch Úkerasz- TensorFlow z- Datasets z- Tokenizers )ÚyamlÚdumpr  r²   rÞ   ÚAUTOGENERATED_TRAINER_COMMENTÚAUTOGENERATED_KERAS_COMMENTrÒ   rÖ   rÉ   r®   rÍ   r…   rÌ   rP   rÀ   r@   Ú_maybe_roundrÜ   rÛ   Úmake_markdown_tabler   r"   Útorchr    Ú
tensorflowr   rÈ   r!   Ú
tokenizers)
rE   r]   r³   Údsr´   rH   r  ÚtfrÈ   r  s
             rJ   Úto_model_cardzTrainingSummary.to_model_cardÖ  s¼  € Øˆ
ä—9‘9˜T×1Ñ1Ó3¸uÔEˆÜˆx‹=˜1ÒØ   
¨%Ð0ˆJð �;‰;˜)Ò#ØÔ7Ñ7‰JàÔ5Ñ5ˆJà˜˜TŸ_™_Ð-¨TÐ2Ñ2ˆ
à×ÑÐ&ØÐCÑC‰JàðØ×(Ñ(Ð)Ð)BÀ4×CVÑCVÐBWÐW\ð^ñˆJð
 �<‰<Ð¤J¨t¯|©|¼TÔ$BÄsÈ4Ï<É<ÓGXÐ\]ÒG]ØÐ/Ñ/‰Jä˜$Ÿ,™,¬Ô,Ø  T§\¡\ N°)Ð<Ñ<‘
Ü˜DŸL™L¬5´$¨-Ô8¼SÀÇÁÓ=NÐRSÒ=SØ  T§\¡\°!¡_Ð$5°YÐ?Ñ?‘
àØ—I‘I°T·\±\À#À2Ð5FÖG¨r  b Tš{ÒGÓHÈYÐW[×WcÑWcÐdfÑWgÐVhÐhrÐKsÑsñ�
ð ×ÑÐ(ØÐXÑXˆJØ˜$Ÿ)™)Ð[_×[lÑ[l×[rÑ[rÓ[t×$uÉKÈDÐRW r¨$¨¨r´,¸uÓ2EÐ1FÒ%GÓ$uÓvÑvˆJØ�dÑˆ
àÐKÑKˆ
ØÐUÑUˆ
ØÐVÑVˆ
àÐ1Ñ1ˆ
ØÐ8Ñ8ˆ
Ø×ÑÐ+ØÐXÑXˆJØ˜$Ÿ)™)ÈT×MaÑMa×MgÑMgÓMi×$j¹k¸dÀE r¨$¨¨r°%°Ò%9Ó$jÓkÑkˆJØ˜$Ñ‰JàÐ7Ñ7ˆJà�?‰?Ð&ØÐ6Ñ6ˆJØÔ-¨d¯o©oÓ>Ñ>ˆJØ˜$ÑˆJàÐ4Ñ4ˆ
Ø˜¬ }°BÐ7Ñ7ˆ
à�;‰;˜)Ò#Ô(:Ô(<Ûà˜J u×'8Ñ'8Ð&9¸Ð<Ñ<‰JØ�[‰[˜GÒ#¬Ô(9Û#à˜M¨"¯.©.Ð)9¸Ð<Ñ<ˆJÜ Ô"Ûà˜K¨×(<Ñ(<Ð'=¸RÐ@Ñ@ˆJÜ"Ô$Ûà˜M¨*×*@Ñ*@Ð)AÀÐDÑDˆJàÐùò] Hùó
 %vùó %ks   ÆM-
Ç$M2É!M8c                 ó
  — |j                   �|j                   n|j                  }t        |«      rO|�|�|	€I|j                  }|dvr9|	€#|j                  t        |j                  «      dœg}	|€|g}|€|j                  g}|
€|�|}
|€}t        |j                  j                  d«      r]t        j                  j                  |j                  j                  j                  «      s |j                  j                  j                  }|€L|j                  j                  j                  }t         j#                  «       D ]  \  }}|t%        |«      v sŒ|}Œ |€)t'        |j(                  j*                  «      j,                  }t/        |«      dk(  r|}|€dg}n/t1        |t
        «      r
|dk7  r|dg}nd|vr|j3                  d«       t5        |j6                  j8                  «      \  }}}t;        |«      } | |||||||
|||	|||¬«      S )N©Úcsvrd   ÚpandasÚparquetr}   )ÚconfigÚsplitÚ_name_or_pathr   Úgenerated_from_trainer)rÓ   rÔ   rÕ   rÒ   rÖ   r×   rÉ   rØ   rÙ   rÚ   rÀ   rÛ   rÜ   )Úeval_datasetÚtrain_datasetrÊ   Úbuilder_nameÚconfig_namer…   r  rf   Úmodelr  rM   rN   rO   r  Ú	__class__r™   ÚTASK_MAPPINGr@   rÏ   r   rì   Ú
output_dirr´   r²   r®   rg   Úparse_log_historyÚstateÚlog_historyÚ$extract_hyperparameters_from_trainer)rh   rÝ   rÓ   rÔ   rÕ   rÒ   rÖ   r×   rØ   rÚ   rÉ   rÙ   Úone_datasetÚdefault_tagÚmodel_class_namerë   rÎ   rº   rÛ   rÀ   rÜ   s                        rJ   Úfrom_trainerzTrainingSummary.from_trainer&  s  € ð  /6×.BÑ.BÐ.N�g×*Ò*ÐT[×TiÑTiˆÜ˜Ô%¨<Ð+?À<ÐCWÐ[kÐ[sØ%×2Ñ2ˆKàÐ"NÑNØ#Ð+Ø3>×3JÑ3JÔUXÐYd×YjÑYjÓUkÑ(lÐ'mÐ$ØÐ'Ø$/ =�LØÐ'Ø$/×$;Ñ$;Ð#<�Làˆ?˜|Ð7Ø"ˆGð Ð"Ü˜Ÿ™×,Ñ,¨oÔ>Ü—G‘G—M‘M '§-¡-×"6Ñ"6×"DÑ"DÔEà$Ÿ]™]×1Ñ1×?Ñ?ˆNð ˆ=Ø&Ÿ}™}×6Ñ6×?Ñ?ÐÜ!-×!3Ñ!3Ó!5ò !‘��gØ#Ô':¸7Ó'CÒCØ ‘Eð!ð ÐÜ˜gŸl™l×5Ñ5Ó6×;Ñ;ˆJÜˆz‹?˜aÒØ'ˆJð ˆ<Ø,Ð-‰DÜ˜œcÔ" tÐ/GÒ'GØÐ2Ð3‰DØ%¨TÑ1Ø�K‰KÐ0Ô1ä&7¸¿¹×8QÑ8QÓ&RÑ#ˆˆ:�|Ü>¸wÓGˆáØØØØ!Ø)ØØØ%Ø%Ø-Ø%Ø!Ø+ô
ð 	
rV   c                 ó˜  — |
�3t        |
«      r(|	�|€$|
j                  }|dvr|	€|g}	|€|
j                  g}|
€|	�|	}
|€_t        |j                  d«      rIt
        j                  j                  |j                  j                  «      s|j                  j                  }|€B|j                  j                  }t        j                  «       D ]  \  }}|t        |«      v sŒ|}Œ |€dg}n/t        |t        «      r
|dk7  r|dg}nd|vr|j!                  d«       |�t#        |«      \  }}}ng }i }t%        |«      } | |||||||	|
||||d¬«      S )Nr  r  Úgenerated_from_keras_callbackr  )rÓ   rÔ   rÕ   rÒ   rÖ   r×   rØ   rÉ   rÙ   rÀ   rÛ   rÜ   rÞ   )rÊ   r  r  rf   r  rM   rN   rO   r  r!  r™   r"  r@   rÏ   r®   r…   rg   Úparse_keras_historyÚ"extract_hyperparameters_from_keras)rh   r   rÒ   Úkeras_historyrÓ   rÔ   rÕ   rÖ   r×   rØ   rÉ   rÙ   r)  r*  rë   rÎ   rº   rÛ   rÀ   rÜ   s                       rJ   Ú
from_keraszTrainingSummary.from_kerast  s…  € ð  ÐÜ˜WÔ%¨<Ð+?À<ÐCWØ%×2Ñ2�àÐ&RÑRØ#Ð+Ø(3 }˜Ø#Ð+Ø(/×(;Ñ(;Ð'<˜àˆ?˜|Ð7Ø"ˆGð Ð"Ü˜Ÿ™ oÔ6Ü—G‘G—M‘M %§,¡,×"<Ñ"<Ô=à"Ÿ\™\×7Ñ7ˆNð ˆ=Ø$Ÿ™×7Ñ7ÐÜ!-×!3Ñ!3Ó!5ò !‘��gØ#Ô':¸7Ó'CÒCØ ‘Eð!ð
 ˆ<Ø3Ð4‰DÜ˜œcÔ" tÐ/NÒ'NØÐ9Ð:‰DØ,°DÑ8Ø�K‰KÐ7Ô8àÐ$Ü*=¸mÓ*LÑ'ˆAˆz™<àˆJØˆLÜ<¸UÓCˆáØØØØ!Ø)ØØ%ØØ%Ø%Ø!Ø+Øô
ð 	
rV   )
NNNNNNNNNN)	NNNNNNNNN)r™   rš   r›   r…   Ú__annotations__rÓ   r   r   rÍ   rÔ   rÕ   rÖ   r×   rÉ   rØ   rÙ   rÚ   rí   r   rÀ   ÚfloatrÛ   rÜ   rÞ   rç   rý   r  r  r�   r+  r1  rq   rV   rJ   rÑ   rÑ   f  s´  … àƒOØ04€Hˆh�u˜S $ s¡)˜^Ñ,Ñ-Ó4Ø!€GˆX�c‰]Ó!Ø,0€Dˆ(�5˜˜d 3™i˜Ñ(Ñ
)Ó0Ø$(€N�H˜S‘MÓ(Ø-1€Eˆ8�E˜#˜t C™y˜.Ñ)Ñ*Ó1Ø/3€GˆX�e˜C  c¡˜NÑ+Ñ,Ó3Ø48€L�(˜5  d¨3¡i Ñ0Ñ1Ó8Ø48€L�(˜5  d¨3¡i Ñ0Ñ1Ó8Ø15Ð�h˜t C¨ H™~Ñ.Ó5Ø/3€L�(˜4  U 
Ñ+Ñ,Ó3Ø&*€J�˜˜c™Ñ#Ó*Ø04€O�X˜d 3¨ 8™nÑ-Ó4Ø%€FˆH�S‰MÓ%òò =ò~ò Nð` ð ØØØØØØØØØòK
ó ðK
ðZ ð
 ØØØØØØØØòH
ó ñH
rV   rÑ   c           
      óÀ  — t        | d«      r7t        | d«      sdg i fS | j                  | j                  d<   | j                  } n&| d   D ��ci c]  }|| D �cg c]  }||   ‘Œ	 c}“Œ } }}g }t        t	        | d   «      «      D ]º  }| j                  «       D ��ci c]  \  }}|||   “Œ }}}i }|j                  «       D ]l  \  }}	|j                  d«      r	d|dd z   }n
|dk7  rd|z   }|j                  d	«      }
d
j                  |
D �cg c]  }|j                  «       ‘Œ c}«      }|	||<   Œn |j                  |«       Œ¼ |d   }| ||fS c c}w c c}}w c c}}w c c}w )zê
    Parse the `logs` of either a `keras.History` object returned by `model.fit()` or an accumulated logs `dict`
    passed to the `PushToHubCallback`. Returns lines and logs compatible with those returned by `parse_log_history`.
    ÚhistoryÚepochNr   Úval_Úvalidation_é   Útrain_rº   r¹   r  )rf   r6  r5  Úranger²   r@   rá   r  rP   Ú
capitalizerg   )ÚlogsÚlog_keyÚsingle_dictÚlinesÚiÚlog_value_listÚ
epoch_dictrµ   Úkr¶   ÚsplitsÚpartr´   rÀ   s                 rJ   r.  r.  À  s|  € ô
 ˆt�YÔä�t˜WÔ%à˜˜R�<ÐØ $§
¡
ˆ�‰�WÑØ�|‰|‰ð X\Ð\]ÑW^×_ÈG�À$ÖG°;˜+ gÓ.ÒGÑGÐ_ˆÑ_à€EÜ”3�t˜G‘}Ó%Ó&ò ˆØPT×PZÑPZÓP\×]Ñ5L°W¸n�g˜~¨aÑ0Ñ0Ð]ˆ
Ñ]ØˆØ×$Ñ$Ó&ò 	‰DˆAˆqØ�|‰|˜FÔ#Ø! A a b EÑ)‘Ø�g’Ø˜q‘L�Ø—W‘W˜S“\ˆFØ—8‘8¸6ÖB°4˜TŸ_™_Õ.ÒBÓCˆDØˆF�4ŠLð	ð 	�‰�VÕðð ˜‘9€Là�˜Ð$Ð$ùò% HùÓ_ùó ^ùò Cs$   Á
EÁE
Á"EÂEÄEÅ
Ec           	      óæ  — d}|t        | «      k  r"d| |   vr|dz  }|t        | «      k  rd| |   vrŒ|t        | «      k(  r1|dz  }|dk\  rd| |   vr|dz  }|dk\  rd| |   vrŒ|dk\  rdd| |   fS y| |   }g }d}t        |«      D �]+  }d| |   v r| |   d   }d| |   v sŒ| |   j                  «       }|j                  d	d«      }|j                  d
d«      }|j                  dd«      }	|j                  dd«      }|j                  dd«      }|j                  dd«      }|j                  dd«      }|||	dœ}
|j	                  «       D ]V  \  }}|dk(  r||
d<   Œ|j                  d«      }dj                  |dd D �cg c]  }|j                  «       ‘Œ c}«      }||
|<   ŒX |j                  |
«       �Œ. t        | «      dz
  }|dk\  rd| |   vr|dz  }|dk\  rd| |   vrŒ|dkD  r~i }| |   j	                  «       D ]a  \  }}|j                  d«      r|dd }|dvsŒ!dj                  |j                  d«      D �cg c]  }|j                  «       ‘Œ c}«      }|||<   Œc |||fS ||dfS c c}w c c}w )zd
    Parse the `log_history` of a Trainer to get the intermediate and final evaluation results.
    r   Útrain_runtimer
   Ú	eval_lossN)NNNzNo logÚlossÚ
total_flosr6  ÚstepÚeval_runtimeÚeval_samples_per_secondÚeval_steps_per_secondÚeval_jit_compilation_time)zTraining LossÚEpochÚStepzValidation Lossrº   r¹   Úeval_é   )ÚruntimeÚsamples_per_secondÚsteps_per_secondr6  rL  )
r²   r;  rŠ   r?   r@   r  rP   r<  rg   rá   )r&  ÚidxÚ	train_logr@  Útraining_lossrA  r6   rº   r6  rL  rµ   rD  r¶   rE  rF  r´   rÀ   rG   rH   Úcamel_cased_keys                       rJ   r$  r$  ã  sï  € ð €CØ
”�KÓ Ò
  _¸KÈÑ<LÑ%LØˆq‰ˆð ”�KÓ Ò
  _¸KÈÑ<LÒ%Lð Œc�+ÓÒØˆq‰ˆØ�QŠh˜;¨k¸#Ñ.>Ñ>Ø�1‰HˆCð �QŠh˜;¨k¸#Ñ.>Ò>ð �!Š8Ø˜˜{¨3Ñ/Ð/Ð/à#ð ˜CÑ €IØ€EØ€MÜ�3‹Zó !ˆØ�[ ‘^Ñ#Ø'¨™N¨6Ñ2ˆMØ˜+ a™.Ò(Ø! !‘n×)Ñ)Ó+ˆGØ—‘˜L¨$Ó/ˆAØ—K‘K ¨Ó.ˆEØ—;‘;˜v tÓ,ˆDØ—‘˜N¨DÓ1ˆAØ—‘Ð5°tÓ<ˆAØ—‘Ð3°TÓ:ˆAØ—‘Ð7¸Ó>ˆAØ'4¸uÈdÑSˆFØŸ™›ò %‘��1Ø˜Ò#Ø01�FÐ,Ò-àŸW™W S›\�FØŸ8™8À6È!È"À:Ö$N¸4 T§_¡_Õ%6Ò$NÓO�DØ#$�F˜4’Lð%ð �L‰L˜Ö ð)!ô, ˆkÓ
˜QÑ
€CØ
�Š(�{¨+°cÑ*:Ñ:Øˆq‰ˆð �Š(�{¨+°cÑ*:Ò:ð ˆQ‚wØˆØ% cÑ*×0Ñ0Ó2ò 	6‰JˆC�Ø�~‰~˜gÔ&Ø˜!˜"�g�ØÐ`Ò`Ø"%§(¡(È#Ï)É)ÐTWË.Ö+YÀ$¨D¯O©OÕ,=Ò+YÓ"Z�Ø05�˜_Ò-ð	6ð ˜% Ð-Ð-à˜% Ð%Ð%ùò% %Oùò ,Zs   Å<I)È:I.
c                 óÚ   — ddl m} i }t        | d«      r*| j                  �| j                  j	                  «       |d<   nd |d<   |j
                  j                  «       j                  |d<   |S )Nr
   )r  Ú	optimizerÚtraining_precision)Úmodeling_tf_utilsr  rf   r]  Ú
get_configÚmixed_precisionÚglobal_policyr´   )r   r  rÜ   s      rJ   r/  r/  !  sd   € Ý(à€OÜˆu�kÔ" u§¡Ð'BØ',§¡×'AÑ'AÓ'Cˆ˜Ò$à'+ˆ˜Ñ$Ø,1×,AÑ,A×,OÑ,OÓ,Q×,VÑ,V€OÐ(Ñ)àÐrV   c                 óæ   — t        | t        «      rWt        t        | «      j	                  d«      «      dkD  r1t        t        | «      j	                  d«      d   «      |kD  r| d|› d�›S t        | «      S )Nú.r
   Úf)r®   r3  r²   r…   r  )r¶   Údecimalss     rJ   r  r  .  sb   € Ü�!”UÔ¤¤C¨£F§L¡L°Ó$5Ó 6¸Ò :¼sÄ3ÀqÃ6Ç<Á<ÐPSÓCTÐUVÑCWÓ?XÐ[cÒ?cØ�A�h�Z˜q�=Ð!Ð"Üˆq‹6€MrV   c           
      ó¢   — t        | |«      D ��cg c]  \  }}d|› �d|t        |«      z
  dz   z  z   ‘Œ! }}}dj                  |«      dz   S c c}}w )Nz| r¹   r
   r  ú|
)rî   r²   rP   )rµ   Ú
col_widthsr¶   r•   Úvalues_with_spaces        rJ   Ú_regular_table_linerk  4  sY   € ÜGJÈ6ÐS]ÓG^×_¹t¸qÀ!˜2˜a˜S˜ C¨1¬s°1«v©:¸©>Ñ$:Ó:Ð_ÐÑ_Ø�7‰7Ð$Ó%¨Ñ-Ð-ùó `s   �$Ac                 ód   — | D �cg c]  }dd|z  z   dz   ‘Œ }}dj                  |«      dz   S c c}w )Nz|:ú-ú:r  rh  )rP   )ri  r•   rµ   s      rJ   Ú_second_table_linero  9  s:   € Ø,6Ö7 qˆd�S˜1‘W‰n˜sÓ"Ð7€FÐ7Ø�7‰7�6‹?˜UÑ"Ð"ùò 8s   …-c           
      ó¬  — | �t        | «      dk(  ry| d   j                  «       D �ci c]  }|t        t        |«      «      “Œ }}| D ]L  }|j                  «       D ]7  \  }}||   t        t	        |«      «      k  sŒ!t        t	        |«      «      ||<   Œ9 ŒN t        t        | d   j                  «       «      t        |j                  «       «      «      }|t        t        |j                  «       «      «      z  }| D ]M  }|t        |j                  «       D �cg c]  }t	        |«      ‘Œ c}t        |j                  «       «      «      z  }ŒO |S c c}w c c}w )zC
    Create a nice Markdown table from the results in `lines`.
    r   r  )	r²   r¼   r…   r@   r  rk  rÍ   rµ   ro  )r@  rG   ri  ÚlinerH   Útabler¶   s          rJ   r  r  >  s2  € ð €}œ˜E›
 ašØØ05°a±·±³Ö@¨�#”sœ3˜s›8“}Ñ$Ð@€JÐ@Øò ;ˆØŸ*™*›,ò 	;‰JˆC�Ø˜#‰¤¤\°%Ó%8Ó!9Ó9Ü"%¤l°5Ó&9Ó":�
˜3’ñ	;ð;ô
  ¤ U¨1¡X§]¡]£_Ó 5´t¸J×<MÑ<MÓ<OÓ7PÓQ€EØ	Ô¤ Z×%6Ñ%6Ó%8Ó 9Ó:Ñ:€EØò jˆØÔ$¸t¿{¹{»}Ö%M¸!¤l°1¥oÒ%MÌtÐT^×TeÑTeÓTgÓOhÓiÑi‰ðjà€Lùò Aùò &Ns   §EÄE)Úlearning_rateÚtrain_batch_sizeÚeval_batch_sizeÚseedc           
      ód  — t         D �ci c]  }|t        | j                  |«      “Œ }}| j                  j                  t        j
                  t        j                  fvrL| j                  j                  t        j                  k(  rdn| j                  j                  j                  |d<   | j                  j                  dkD  r| j                  j                  |d<   | j                  j                  dkD  r| j                  j                  |d<   | j                  j                  | j                  j                  z  | j                  j                  z  }||d   k7  r||d<   | j                  j                  | j                  j                  z  }||d   k7  r||d	<   | j                  j                  r²| j                  j                  }| j                  j                  r| j                  j                  nd
}d|j                  «       v rQd|› d| j                  j                   › d| j                  j"                  › d| j                  j$                  › d|› �
|d<   nd|› d|› �|d<   | j                  j&                  j                  |d<   | j                  j(                  dk7  r| j                  j(                  |d<   | j                  j*                  dk7  r| j                  j*                  |d<   | j                  j,                  dk7  r| j                  j,                  |d<   n| j                  j.                  |d<   | j                  j0                  r.| j2                  rd| j                  j4                  › �|d<   nd|d<   | j                  j6                  dk7  r| j                  j6                  |d<   |S c c}w )Nz	multi-GPUÚdistributed_typer
   Únum_devicesÚgradient_accumulation_stepsrt  Útotal_train_batch_sizeru  Útotal_eval_batch_sizez!No additional optimizer argumentsÚadamzUse z with betas=(ú,z) and epsilon=z and optimizer_args=r]  z and the args are:
Úlr_scheduler_typeg        Úlr_scheduler_warmup_ratioÚlr_scheduler_warmup_stepsr  Útraining_stepsÚ
num_epochszApex, opt level Úmixed_precision_trainingz
Native AMPÚlabel_smoothing_factor)Ú_TRAINING_ARGS_KEYSÚgetattrrì   Úparallel_moder   ÚNOT_PARALLELÚNOT_DISTRIBUTEDÚDISTRIBUTEDrH   Ú
world_sizerz  rt  ru  ÚoptimÚ
optim_argsr½   Ú
adam_beta1Ú
adam_beta2Úadam_epsilonr  Úwarmup_ratioÚwarmup_stepsÚ	max_stepsÚnum_train_epochsÚfp16Úuse_apexÚfp16_opt_levelr…  )rÝ   rD  rÜ   r{  r|  Úoptimizer_nameÚoptimizer_argss          rJ   r'  r'  Y  s,  € Ü<OÖP°q�qœ' '§,¡,°Ó2Ñ2ÐP€OÐPà‡|�|×!Ñ!¬,×*CÑ*CÄ\×EaÑEaÐ)bÑbà"Ÿ<™<×5Ñ5¼×9QÑ9QÒQ‰KÐW^×WcÑWc×WqÑWq×WwÑWwð 	Ð*Ñ+ð ‡|�|×Ñ Ò"Ø)0¯©×)@Ñ)@ˆ˜Ñ&Ø‡|�|×/Ñ/°!Ò3Ø9@¿¹×9aÑ9aˆÐ5Ñ6ð 	�‰×%Ñ%¨¯©×(?Ñ(?Ñ?À'Ç,Á,×BjÑBjÑjð ð  Ð1CÑ!DÒDØ4JˆÐ0Ñ1Ø#ŸL™L×8Ñ8¸7¿<¹<×;RÑ;RÑRÐØ Ð0AÑ BÒBØ3HˆÐ/Ñ0à‡|�|×ÒØ Ÿ™×+Ñ+ˆØ4;·L±L×4KÒ4K˜Ÿ™×0Ò0ÐQtˆà�^×)Ñ)Ó+Ñ+à�~Ð& m°G·L±L×4KÑ4KÐ3LÈAÈgÏlÉl×NeÑNeÐMfð gØ#ŸL™L×5Ñ5Ð6Ð6JÈ>ÐJZð\ð ˜KÒ(ð
 .2°.Ð1AÐAUÐVdÐUeÐ+fˆO˜KÑ(à+2¯<©<×+IÑ+I×+OÑ+O€OÐ'Ñ(Ø‡|�|× Ñ  CÒ'Ø7>·|±|×7PÑ7PˆÐ3Ñ4Ø‡|�|× Ñ  CÒ'Ø7>·|±|×7PÑ7PˆÐ3Ñ4Ø‡|�|×Ñ Ò#Ø,3¯L©L×,BÑ,BˆÐ(Ò)à(/¯©×(EÑ(Eˆ˜Ñ%à‡|�|×ÒØ×ÒØ<LÈWÏ\É\×MhÑMhÐLiÐ:jˆOÐ6Ò7à:FˆOÐ6Ñ7à‡|�|×*Ñ*¨cÒ1Ø4;·L±L×4WÑ4WˆÐ0Ñ1àÐùòg Qs   ‰N-)r9  )Irœ   rŠ   rd   rM   r<   Údataclassesr   Úpathlibr   Útypingr   r   r   râ   r  Úhuggingface_hubr   Úhuggingface_hub.utilsr	   r  r   Úmodels.auto.modeling_autor   r   r   r   r   r   r   r   r   r   r   r   r   r   r   Útraining_argsr   Úutilsr   r   r   r   r    r!   r"   r#   r"  Ú
get_loggerr™   rC   r1   r	  r
  rï   r¿   r°   r·   rÂ   rÄ   rÊ   rÏ   rÑ   r.  r$  r/  r  rk  ro  r  r†  r'  rq   rV   rJ   ú<module>r¤     s  ðñ .ã Û Û 	Û Ý !Ý ß 'Ñ 'ã Û Ý &Ý 3å ÷÷ ÷ ÷ ñ õ" (÷	÷ 	ó 	ð 9ØHØDØ2Ø@ØDØHØJØ PØHØHØ$oÐ'BÐ$oÐFnÐ$oØ&\ØDñ€ð" 
ˆ×	Ñ	˜HÓ	%€÷j0ñ j0ðZ!Ð ð
Ð ðØÐ+ðàÐ2ðð Ð.ðð Ð(ð	ð
 Ð*ðð Ð.ðð �_ðð Ð :ðð Ð0ðð Ð1ðð ÐDðð Ð2ðð �=ðð Ð :ðð #Ð$Bðð  Ð2ð!Ð ò(€òò	ò	òò;òð ÷V
ð V
ó ðV
òr
 %òF;&ò|
óò.ò
#ò
ò&Ð ó4rV   