+
    QV-jÆ  ã                   ó  € ^ RI t ^ RIt^ RIt^ RIHtHt ^ RIHt ^ RIt^ RI	H
t
 ^ RIHt ^RIHt ^RIHtHt ^RIHtHtHt ^R	IHt ]P0                  ! ]4      t] ! R
 R4      4       t ! R R]4      t ! R R]4      tR# )é    N)Ú	dataclassÚfield)ÚEnum)ÚFileLock)ÚDataset)ÚPreTrainedTokenizerBase)Úcheck_torch_load_is_safeÚlogging)Ú!glue_convert_examples_to_featuresÚglue_output_modesÚglue_processors©ÚInputFeaturesc                   óÜ   a € ] tR t^"t o Rt]! RRRP                  ]P                  ! 4       4      ,           /R7      t	]! RR/R7      t
]! ^€RR/R7      t]! R	RR
/R7      tR tV 3R ltRtV tR# )ÚGlueDataTrainingArgumentszÓ
Arguments pertaining to what data we are going to input our model for training and eval.

Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
line.
Úhelpz"The name of the task to train on: z, )ÚmetadatazUThe input data dir. Should contain the .tsv files (or other data files) for the task.z‹The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.)Údefaultr   Fz1Overwrite the cached training and evaluation setsc                óD   € V P                   P                  4       V n         R # ©N)Ú	task_nameÚlower©Úselfs   &Úp/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/transformers/data/datasets/glue.pyÚ__post_init__Ú'GlueDataTrainingArguments.__post_init__<   s   € ØŸ™×-Ñ-Ó/ˆŽó    c                óJ   <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[;R&   S[;R&   # )é   r   Údata_dirÚmax_seq_lengthÚoverwrite_cache)ÚstrÚintÚbool)ÚformatÚ__classdict__s   "€r   Ú__annotate__Ú&GlueDataTrainingArguments.__annotate__"   s?   ø‡ ‚ ñ Ñwñ ñ ñ ñ ñ ñ ñ ñ, ñ ò- r   )r   N)Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r   Újoinr   Úkeysr   r!   r"   r#   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__©r(   s   @r   r   r   "   sŠ   ø‡ € ññ  VÐ-QÐTX×T]ÑT]Ð^m×^rÒ^rÓ^tÓTuÕ-uÐ$vÔw€IÙØÐqÐrô€Hñ  ØàðQð
ô€Nñ "Ø Ð)\Ð ]ô€Oò0÷5 ƒ r   r   c                   ó"   € ] tR t^@tRtRtRtRtR# )ÚSplitÚtrainÚdevÚtest© N)r+   r,   r-   r.   r8   r9   r:   r3   r;   r   r   r7   r7   @   s   † Ø€EØ
€CØ„Dr   r7   c                   óz   a € ] tR t^Ft o R]P
                  R3V 3R lR lltR tV 3R lR ltR t	V 3R lt
R	tV tR# )
ÚGlueDatasetNc                ód   <€ V ^8„  d   QhRS[ RS[RS[R,          RS[S[,          RS[R,          /# )r    ÚargsÚ	tokenizerÚlimit_lengthNÚmodeÚ	cache_dir)r   r   r%   r$   r7   )r'   r(   s   "€r   r)   ÚGlueDataset.__annotate__K   sP   ø€ ÷ Hñ Há'ðHñ +ðHñ ˜D•jð	Hñ
 ‘E�kðHñ ˜•:ñHr   c                ó`  € \         P                  ! R \        4       Wn        \        VP
                  ,          ! 4       V n        \        VP
                  ,          V n        \        V\        4      '       d    \        V,          p\        P                  P                  Ve   TMVP                   RVP"                   RVP$                  P&                   RVP(                   RVP
                   24      pV P                  P+                  4       pVP
                  R9   d5   VP$                  P&                  R9   d   V^,          V^,          uV^&   V^&   Wpn        VR,           p\/        V4      ;_uu_ 4        \        P                  P1                  V4      '       d…   VP2                  '       gs   \4        P4                  ! 4       p	\7        4        \8        P:                  ! VRR7      V n        \>        PA                  RV R	2\4        P4                  ! 4       V	,
          4       EM\\>        PA                  R
VP                    24       V\        PB                  8X  d'   V P                  PE                  VP                   4      p
M`V\        PF                  8X  d'   V P                  PI                  VP                   4      p
M%V P                  PK                  VP                   4      p
Ve   V
RV p
\M        V
VVP(                  VV P                  R7      V n        \4        P4                  ! 4       p	\8        PN                  ! V P<                  V4       \>        PA                  RV R\4        P4                  ! 4       V	,
          R R24       RRR4       R#   \         d    \        R4      hi ; i  + '       g   i     R# ; i)a  This dataset will be removed from the library soon, preprocessing should be handled with the Hugging Face Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.pyzmode is not a valid split nameNÚcached_Ú_z.lockT)Úweights_onlyz"Loading features from cached file z [took %.3f s]z'Creating features from dataset file at )Ú
max_lengthÚ
label_listÚoutput_modez!Saving features into cached file z [took z.3fz s])Úmnlizmnli-mm)ÚRobertaTokenizerÚXLMRobertaTokenizerÚBartTokenizerÚBartTokenizerFast)(ÚwarningsÚwarnÚFutureWarningr?   r   r   Ú	processorr   rK   Ú
isinstancer$   r7   ÚKeyErrorÚosÚpathr0   r!   ÚvalueÚ	__class__r+   r"   Ú
get_labelsrJ   r   Úexistsr#   Útimer	   ÚtorchÚloadÚfeaturesÚloggerÚinfor9   Úget_dev_examplesr:   Úget_test_examplesÚget_train_examplesr   Úsave)r   r?   r@   rA   rB   rC   Úcached_features_filerJ   Ú	lock_pathÚstartÚexampless   &&&&&&     r   Ú__init__ÚGlueDataset.__init__K   sà  € ô 	�Šðuô ô		
ð Œ	Ü(¨¯©Ö8Ó:ˆŒÜ,¨T¯^©^Õ<ˆÔÜ�dœC× Ò ðAÜ˜T•{�ô  "Ÿw™wŸ|™|Ø"Ò.‰I°D·M±MØ�d—j‘j�\  9×#6Ñ#6×#?Ñ#?Ð"@ÀÀ$×BUÑBUÐAVÐVWÐX\×XfÑXfÐWgÐhó 
Ðð —^‘^×.Ñ.Ó0ˆ
Ø�>‰>Ð0Ô0°Y×5HÑ5H×5QÑ5Qð V
ô 6
ð ,6°a­=¸*ÀQ½-Ð(ˆJ�q‰M˜: a™=Ø$Œð )¨7Õ2ˆ	Ü�i× Õ Ü�w‰w�~‰~Ð2×3Ò3¸D×<P×<PÐ<PÜŸ	š	›�Ü(Ô*Ü %§
¢
Ð+?ÈdÔ S�”Ü—‘Ø8Ð9MÐ8NÈnÐ]Ô_c×_hÒ_hÓ_jÐmrÕ_röô —‘ÐEÀdÇmÁmÀ_ÐUÔVàœ5Ÿ9™9Ô$Ø#Ÿ~™~×>Ñ>¸t¿}¹}ÓM‘HØœUŸZ™ZÔ'Ø#Ÿ~™~×?Ñ?ÀÇÁÓN‘Hà#Ÿ~™~×@Ñ@ÀÇÁÓO�HØÒ+Ø'¨¨Ð6�HÜ AØØØ#×2Ñ2Ø)Ø $× 0Ñ 0ô!�”ô Ÿ	š	›�Ü—
’
˜4Ÿ=™=Ð*>Ô?ä—‘Ø7Ð8LÐ7MÈWÔUY×U^ÒU^ÓU`ÐchÕUhÐilÐTmÐmpÐqô÷; !Ñ øô+ ô AÜÐ?Ó@Ð@ðAú÷* !× Ð ús   Á6N Å27NÆ*GNÎNÎN-	c                ó,   € \        V P                  4      # r   )Úlenr`   r   s   &r   Ú__len__ÚGlueDataset.__len__•   s   € Ü�4—=‘=Ó!Ð!r   c                ó    <€ V ^8„  d   QhRS[ /# )r    Úreturnr   )r'   r(   s   "€r   r)   rD   ˜   s   ø€ ÷  ñ  ¡ñ  r   c                ó(   € V P                   V,          # r   )r`   )r   Úis   &&r   Ú__getitem__ÚGlueDataset.__getitem__˜   s   € Ø�}‰}˜QÕÐr   c                ó   € V P                   # r   )rJ   r   s   &r   r[   ÚGlueDataset.get_labels›   s   € Ø�‰Ðr   c                óN   <€ V ^8„  d   Qh/ S[ ;R&   S[;R&   S[S[,          ;R&   # )r    r?   rK   r`   )r   r$   Úlistr   )r'   r(   s   "€r   r)   rD   F   s-   ø‡ ‚ Ù
#Ñ#ñ áÑñ ñ ‘=Õ!Ñ!ò r   )r?   r`   rJ   rK   rT   )r+   r,   r-   r.   r7   r8   rk   ro   ru   r[   r2   r3   r4   r5   s   @r   r=   r=   F   s=   ø‡ € ð $(Ø!ŸK™KØ $÷Hò HòT"÷ ð  ò÷k ƒ r   r=   )rW   r]   rQ   Údataclassesr   r   Úenumr   r^   Úfilelockr   Útorch.utils.datar   Útokenization_utils_baser   Úutilsr	   r
   Úprocessors.gluer   r   r   Úprocessors.utilsr   Ú
get_loggerr+   ra   r   r7   r=   r;   r   r   Ú<module>r„      ss   ðó 
Û Û ß (Ý ã Ý Ý $å >ß 6ß cÑ cÝ ,ð 
×	Ò	˜HÓ	%€ð ÷0ð 0ó ð0ô:ˆDô ôV�'ö Vr   