+
     °³j˜  ã                   ó–   € ^ RI t^ RIt^ RIHt ^ RIHtHtHtH	t	 ^ RI
t^ RIHt ^RIHtHt ^RIHt  ! R R]4      t ! R	 R
]4      tR# )é    N)ÚPath)ÚAnyÚCallableÚOptionalÚUnion)ÚImage)Úcheck_integrityÚdownload_and_extract_archive)ÚVisionDatasetc                   óþ   a a€ ] tR t^t oRtRtRtRtRtRR.RR	.R
R.RR.RR..t	RR..t
RRRRRR/tR'V3R lV 3R llltV3R lR ltV3R lR ltV3R lR ltV3R  lR! ltV3R" lR# ltV3R$ lR% ltR&tVtV ;t# )(ÚCIFAR10a.  `CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

Args:
    root (str or ``pathlib.Path``): Root directory of dataset where directory
        ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
    train (bool, optional): If True, creates dataset from training set, otherwise
        creates from test set.
    transform (callable, optional): A function/transform that takes in a PIL image
        and returns a transformed version. E.g, ``transforms.RandomCrop``
    target_transform (callable, optional): A function/transform that takes in the
        target and transforms it.
    download (bool, optional): If true, downloads the dataset from the internet and
        puts it in root directory. If dataset is already downloaded, it is not
        downloaded again.

zcifar-10-batches-pyz7https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gzzcifar-10-python.tar.gzÚ c58f30108f718f92721af3b95e74349aÚdata_batch_1Ú c99cafc152244af753f735de768cd75fÚdata_batch_2Ú d4bba439e000b95fd0a9bffe97cbabecÚdata_batch_3Ú 54ebc095f3ab1f0389bbae665268c751Údata_batch_4Ú 634d18415352ddfa80567beed471001aÚdata_batch_5Ú 482c414d41f54cd18b22e5b47cb7c3cbÚ
test_batchÚ 40351d587109b95175f43aff81a1287eÚfilenamezbatches.metaÚkeyÚlabel_namesÚmd5Ú 5ff9c542aee3614f3951f8cda6e48888c                ór   <€ V ^8„  d   QhRS[ S[S[3,          RS[RS[S[,          RS[S[,          RS[RR/# )é   ÚrootÚtrainÚ	transformÚtarget_transformÚdownloadÚreturnN)r   Ústrr   Úboolr   r   )ÚformatÚ__classdict__s   "€Ú[/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchvision/datasets/cifar.pyÚ__annotate__ÚCIFAR10.__annotate__4   sZ   ø€ ÷ )ñ )á‘C™�IÕð)ñ ð)ñ ™HÕ%ð	)ñ
 #¡8Õ,ð)ñ ð)ð 
ñ)ó    c                óØ  <€ \         SV `  WVR 7       W n        V'       d   V P                  4        V P	                  4       '       g   \        R4      hV P                  '       d   V P                  pMV P                  p. V n        . V n	        V FÝ  w  rx\        P                  P                  V P                  V P                  V4      p	\        V	R4      ;_uu_ 4       p
\         P"                  ! V
RR7      pV P                  P%                  VR,          4       RV9   d$   V P                  P'                  VR,          4       M"V P                  P'                  VR,          4       RRR4       Kß  	  \(        P*                  ! V P                  4      P-                  R	^^ ^ 4      V n        V P                  P/                  R
4      V n        V P1                  4        R#   + '       g   i     EK\  ; i))r$   r%   zHDataset not found or corrupted. You can use download=True to download itÚrbÚlatin1©ÚencodingÚdataÚlabelsÚfine_labelsNéÿÿÿÿ)r   r!   é   é   )ÚsuperÚ__init__r#   r&   Ú_check_integrityÚRuntimeErrorÚ
train_listÚ	test_listr5   ÚtargetsÚosÚpathÚjoinr"   Úbase_folderÚopenÚpickleÚloadÚappendÚextendÚnpÚvstackÚreshapeÚ	transposeÚ
_load_meta)Úselfr"   r#   r$   r%   r&   Údownloaded_listÚ	file_nameÚchecksumÚ	file_pathÚfÚentryÚ	__class__s   &&&&&&      €r,   r<   ÚCIFAR10.__init__4   sa  ø€ ô 	‰Ñ˜ÐEUÐÔVàŒ
çØ�M‰MŒOà×$Ñ$×&Ò&ÜÐiÓjÐjà�:�:ˆ:Ø"Ÿo™o‰Oà"Ÿn™nˆOàˆŒ	ØˆŒó $3ÑˆIÜŸ™Ÿ™ T§Y¡Y°×0@Ñ0@À)ÓLˆIÜ�i ×&Ô&¨!ÜŸš A°Ô9�Ø—	‘	× Ñ   v¥Ô/Ø˜uÔ$Ø—L‘L×'Ñ'¨¨h­Õ8à—L‘L×'Ñ'¨¨mÕ(<Ô=÷ 'Ñ&ñ $3ô —I’I˜dŸi™iÓ(×0Ñ0°°Q¸¸BÓ?ˆŒ	Ø—I‘I×'Ñ'¨Ó5ˆŒ	à�‰Ö÷ '×&Ñ&ús   ÃBGÇG)c                ó   <€ V ^8„  d   QhRR/# ©r!   r'   N© )r*   r+   s   "€r,   r-   r.   _   s   ø€ ÷ Qñ Q˜Dñ Qr/   c                ó   € \         P                  P                  V P                  V P                  V P
                  R ,          4      p\        WP
                  R,          4      '       g   \        R4      h\        VR4      ;_uu_ 4       p\        P                  ! VRR7      pW0P
                  R,          ,          V n        RRR4       \        V P                  4       UUu/ uF  w  rEWTbK	  	  uppV n        R#   + '       g   i     L@; iu uppi )r   r   zVDataset metadata file not found or corrupted. You can use download=True to download itr1   r2   r3   r   N)rB   rC   rD   r"   rE   Úmetar	   r>   rF   rG   rH   ÚclassesÚ	enumerateÚclass_to_idx)rP   rC   Úinfiler5   ÚiÚ_classs   &     r,   rO   ÚCIFAR10._load_meta_   s¹   € Ü�w‰w�|‰|˜DŸI™I t×'7Ñ'7¸¿¹À:Õ9NÓOˆÜ˜t§Y¡Y¨uÕ%5×6Ò6ÜÐwÓxÐxÜ�$˜×Ô Ü—;’;˜v°Ô9ˆDØ§	¡	¨%Õ 0Õ1ˆDŒL÷ ô 9BÀ$Ç,Á,Ô8OÔPÑ8O©9¨1˜VšYÑ8OÒPˆÖ÷ ×üó Qs   Â7C7ÃD
Ã7D	c                ó<   <€ V ^8„  d   QhRS[ RS[S[S[3,          /# )r!   Úindexr'   )ÚintÚtupler   )r*   r+   s   "€r,   r-   r.   h   s#   ø€ ÷ ñ ¡ð ©©s±C¨x­ñ r/   c                óú   € V P                   V,          V P                  V,          r2\        P                  ! V4      pV P                  e   V P	                  V4      pV P
                  e   V P                  V4      pW#3# )zn
Args:
    index (int): Index

Returns:
    tuple: (image, target) where target is index of the target class.
)r5   rA   r   Ú	fromarrayr$   r%   )rP   rf   ÚimgÚtargets   &&  r,   Ú__getitem__ÚCIFAR10.__getitem__h   si   € ð —i‘i Õ&¨¯©°UÕ(;ˆVô �oŠo˜cÓ"ˆà�>‰>Ò%Ø—.‘. Ó%ˆCà× Ñ Ò,Ø×*Ñ*¨6Ó2ˆFàˆ{Ðr/   c                ó    <€ V ^8„  d   QhRS[ /# ©r!   r'   )rg   )r*   r+   s   "€r,   r-   r.   ~   s   ø€ ÷ ñ ™ñ r/   c                ó,   € \        V P                  4      # )N)Úlenr5   ©rP   s   &r,   Ú__len__ÚCIFAR10.__len__~   s   € Ü�4—9‘9‹~Ðr/   c                ó    <€ V ^8„  d   QhRS[ /# rp   )r)   )r*   r+   s   "€r,   r-   r.   �   s   ø€ ÷ ñ ¡$ñ r/   c                óä   € V P                   V P                  ,            FN  w  r\        P                  P	                  V P
                  V P                  V4      p\        W24      '       d   KM   R # 	  R# )FT)r?   r@   rB   rC   rD   r"   rE   r	   )rP   r   r   Úfpaths   &   r,   r=   ÚCIFAR10._check_integrity�   sQ   € Ø!Ÿ_™_¨t¯~©~×=Ð=‰MˆHÜ—G‘G—L‘L §¡¨D×,<Ñ,<¸hÓGˆEÜ" 5×.Ô.Úñ >ñ r/   c                ó   <€ V ^8„  d   QhRR/# rZ   r[   )r*   r+   s   "€r,   r-   r.   ˆ   s   ø€ ÷ dñ d˜$ñ dr/   c                ó¤   € V P                  4       '       d   R # \        V P                  V P                  V P                  V P
                  R7       R # )N)r   r   )r=   r
   Úurlr"   r   Útgz_md5rs   s   &r,   r&   ÚCIFAR10.downloadˆ   s5   € Ø× Ñ ×"Ò"ÙÜ$ T§X¡X¨t¯y©yÀ4Ç=Á=ÐVZ×VbÑVb×cr/   c                ó    <€ V ^8„  d   QhRS[ /# rp   )r(   )r*   r+   s   "€r,   r-   r.   �   s   ø€ ÷ !ñ !™Cñ !r/   c                ó4   € V P                   R J d   RMRpRV 2# )TÚTrainÚTestzSplit: )r#   )rP   Úsplits   & r,   Ú
extra_reprÚCIFAR10.extra_repr�   s!   € ØŸ:™:¨Ó-‘°6ˆØ˜˜Ð Ð r/   )r`   r^   r5   rA   r#   )TNNF)Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__rE   r|   r   r}   r?   r@   r]   r<   rO   rm   rt   r=   r&   r„   Ú__static_attributes__Ú__classdictcell__Ú__classcell__)rW   r+   s   @@r,   r   r      sÏ   ù‡ € ñð" (€KØ
C€CØ'€HØ0€Gà	Ð;Ð<Ø	Ð;Ð<Ø	Ð;Ð<Ø	Ð;Ð<Ø	Ð;Ð<ð€Jð 
Ð9Ð:ð€Ið 	�NØˆ}ØÐ1ð€D÷)õ )÷VQð Q÷ð ÷,ð ÷ð ÷dð d÷
!÷ !ð !r/   r   c                   óN   € ] tR t^’tRtRtRtRtRtRR..t	RR	..t
R
RRRRR/tRtR# )ÚCIFAR100zq`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

This is a subclass of the `CIFAR10` Dataset.
zcifar-100-pythonz8https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gzzcifar-100-python.tar.gzÚ eb9058c3a382ffc7106e4002c42a8d85r#   Ú 16019d7e3df5f24257cddd939b257f8dÚtestÚ f0ef6b0ae62326f3e7ffdfab6717acfcr   r]   r   Úfine_label_namesr   Ú 7973b15100ade9c7d40fb424638fde48r[   N)r†   r‡   rˆ   r‰   rŠ   rE   r|   r   r}   r?   r@   r]   r‹   r[   r/   r,   r�   r�   ’   sY   † ñð
 %€KØ
D€CØ(€HØ0€Gà	Ð4Ð5ð€Jð
 
Ð3Ð4ð€Ið 	�FØÐ!ØÐ1ð„Dr/   r�   )Úos.pathrB   rG   Úpathlibr   Útypingr   r   r   r   ÚnumpyrK   ÚPILr   Úutilsr	   r
   Úvisionr   r   r�   r[   r/   r,   Ú<module>r�      s;   ðÛ Û Ý ß 1Ó 1ã Ý ç @Ý !ôB!ˆmô B!ôJˆwö r/   