+
    œ°³jG7  ã                   óŽ  € R t ^ RIt^ RIHt ^ RIHt ^RIHt ^RIHt ^RI	H
t
 ^RIHt ^RIHt  ! R	 R
]P                  4      t ! R R]P                  4      t ! R R]P                  4      t ! R R]P                  4      t ! R R]P                  4      t] ! R R]
4      4       t]! RR7       ! R R]4      4       tRR.tR# )zrPyTorch UperNet model. Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.N)Únn)ÚCrossEntropyLoss)Úload_backbone)ÚSemanticSegmenterOutput)ÚPreTrainedModel)Úauto_docstring©ÚUperNetConfigc                   óX   a a€ ] tR t^t oRtRV3R lV 3R llltV3R lR ltRtVtV ;t	# )ÚUperNetConvModulez×
A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
c                óÄ   <€ V ^8„  d   QhRS[ RS[ RS[ S[S[ S[ 3,          ,          RS[ S[S[ S[ 3,          ,          S[,          RS[RS[ S[S[ S[ 3,          ,          RR/# )	é   Úin_channelsÚout_channelsÚkernel_sizeÚpaddingÚbiasÚdilationÚreturnN)ÚintÚtupleÚstrÚbool)ÚformatÚ__classdict__s   "€Úm/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/upernet/modeling_upernet.pyÚ__annotate__ÚUperNetConvModule.__annotate__!   s}   ø€ ÷ $ñ $áð$ñ ð$ñ ™5¡¡c �?Õ*ð	$ñ
 ‘u™S¡#˜X•Õ&©Õ,ð$ñ ð$ñ ™™c¡3˜h�Õ'ð$ð 
ñ$ó    c           	     óÐ   <€ \         SV `  4        \        P                  ! VVVVVVR 7      V n        \        P
                  ! V4      V n        \        P                  ! 4       V n        R# ))r   r   r   r   r   r   N)	ÚsuperÚ__init__r   ÚConv2dÚconvÚBatchNorm2dÚ
batch_normÚReLUÚ
activation)Úselfr   r   r   r   r   r   Ú	__class__s   &&&&&&&€r   r!   ÚUperNetConvModule.__init__!   sQ   ø€ ô 	‰ÑÔÜ—I’IØ#Ø%Ø#ØØØô
ˆŒ	ô Ÿ.š.¨Ó6ˆŒÜŸ'š'›)ˆŽr   c                óN   <€ V ^8„  d   QhRS[ P                  RS[ P                  /# ©r   Úinputr   ©ÚtorchÚTensor)r   r   s   "€r   r   r   6   s#   ø€ ÷ ñ ™UŸ\™\ð ©e¯l©lñ r   c                ól   € V P                  V4      pV P                  V4      pV P                  V4      pV# ©N)r#   r%   r'   )r(   r-   Úoutputs   && r   ÚforwardÚUperNetConvModule.forward6   s1   € Ø—‘˜5Ó!ˆØ—‘ Ó(ˆØ—‘ Ó(ˆàˆr   )r'   r%   r#   )é    Fé   ©
Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r!   r4   Ú__static_attributes__Ú__classdictcell__Ú__classcell__©r)   r   s   @@r   r   r      s#   ù‡ € ñ÷
$õ $÷*÷ ð r   r   c                   óP   a a€ ] tR t^>t oV3R lV 3R lltV3R lR ltRtVtV ;t# )ÚUperNetPyramidPoolingBlockc                ó0   <€ V ^8„  d   QhRS[ RS[ RS[ RR/# )r   Ú
pool_scaler   Úchannelsr   N)r   )r   r   s   "€r   r   Ú'UperNetPyramidPoolingBlock.__annotate__?   s)   ø€ ÷ +ñ +¡3ð +±Sð +ÁCð +ÈDñ +r   c                óæ   <€ \         SV `  4        \        P                  ! V4      \	        W#^R7      .V n        \        V P
                  4       F   w  rEV P                  \        V4      V4       K"  	  R# )r7   ©r   N)	r    r!   r   ÚAdaptiveAvgPool2dr   ÚlayersÚ	enumerateÚ
add_moduler   )r(   rE   r   rF   ÚiÚlayerr)   s   &&&&  €r   r!   Ú#UperNetPyramidPoolingBlock.__init__?   sX   ø€ Ü‰ÑÔä× Ò  Ó,Ü˜kÀÔCð
ˆŒô " $§+¡+Ö.‰HˆAØ�O‰OœC ›F EÖ*ó /r   c                óN   <€ V ^8„  d   QhRS[ P                  RS[ P                  /# r,   r.   )r   r   s   "€r   r   rG   H   s#   ø€ ÷ ñ ™UŸ\™\ð ©e¯l©lñ r   c                ó@   € TpV P                    F  pV! V4      pK  	  V# r2   ©rK   )r(   r-   Úhidden_staterO   s   &&  r   r4   Ú"UperNetPyramidPoolingBlock.forwardH   s%   € ØˆØ—[”[ˆEÙ  Ó.ŠLñ !àÐr   rS   )	r9   r:   r;   r<   r!   r4   r>   r?   r@   rA   s   @@r   rC   rC   >   s   ù‡ € ÷+ó +÷÷ ð r   rC   c                   óT   a a€ ] tR t^Ot oRtV3R lV 3R lltV3R lR ltRtVtV ;t	# )ÚUperNetPyramidPoolingModuleaQ  
Pyramid Pooling Module (PPM) used in PSPNet.

Args:
    pool_scales (`tuple[int]`):
        Pooling scales used in Pooling Pyramid Module.
    in_channels (`int`):
        Input channels.
    channels (`int`):
        Channels after modules, before conv_seg.
    align_corners (`bool`):
        align_corners argument of F.interpolate.
c          
      óJ   <€ V ^8„  d   QhRS[ S[R3,          RS[RS[RS[RR/# )r   Úpool_scales.r   rF   Úalign_cornersr   N)r   r   r   )r   r   s   "€r   r   Ú(UperNetPyramidPoolingModule.__annotate__^   s;   ø€ ÷ 
+ñ 
+¡E©#¨s¨(¥Oð 
+Á#ð 
+ÑQTð 
+Ñeið 
+Ðnrñ 
+r   c                ó  <€ \         SV `  4        Wn        W@n        W n        W0n        . V n        \        V4       FH  w  rV\        WbVR 7      pV P                  P                  V4       V P                  \        V4      V4       KJ  	  R# ))rE   r   rF   N)r    r!   rY   rZ   r   rF   ÚblocksrL   rC   ÚappendrM   r   )	r(   rY   r   rF   rZ   rN   rE   Úblockr)   s	   &&&&&   €r   r!   Ú$UperNetPyramidPoolingModule.__init__^   sn   ø€ Ü‰ÑÔØ&ÔØ*ÔØ&ÔØ ŒØˆŒÜ& {Ö3‰MˆAÜ.¸*ÐhpÔqˆEØ�K‰K×Ñ˜uÔ%Ø�O‰OœC ›F EÖ*ó 4r   c                ó^   <€ V ^8„  d   QhRS[ P                  RS[S[ P                  ,          /# )r   Úxr   )r/   r0   Úlist)r   r   s   "€r   r   r[   j   s(   ø€ ÷ ñ ™Ÿ™ð ©$©u¯|©|Õ*<ñ r   c                óä   € . pV P                    F]  pV! V4      p\        P                  P                  WAP	                  4       R ,          RV P
                  R7      pVP                  V4       K_  	  V# )ºr   NNÚbilinear©ÚsizeÚmoderZ   )r]   r   Ú
functionalÚinterpolaterh   rZ   r^   )r(   rb   Úppm_outsÚppmÚppm_outÚupsampled_ppm_outs   &&    r   r4   Ú#UperNetPyramidPoolingModule.forwardj   se   € ØˆØ—;”;ˆCÙ˜!“fˆGÜ "§¡× 9Ñ 9ØŸf™f›h r�l°È4×K]ÑK]ð !:ó !Ðð �O‰OÐ-Ö.ñ ð ˆr   )rZ   r]   rF   r   rY   r8   rA   s   @@r   rW   rW   O   s#   ù‡ € ñ÷
+ó 
+÷÷ ð r   rW   c                   óN   a a€ ] tR t^ut oRtV 3R ltR tV3R lR ltRtVt	V ;t
# )ÚUperNetHeadz�
Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
[UPerNet](https://huggingface.co/papers/1807.10221).
c                ó$  <€ \         SV `  4        Wn        VP                  V n        W n        VP
                  V n        R V n        \        P                  ! V P                  VP                  ^R7      V n        \        V P                  V P                  R,          V P                  V P                  R7      V n        \        V P                  R,          \        V P                  4      V P                  ,          ,           V P                  ^^R7      V n        \        P"                  ! 4       V n        \        P"                  ! 4       V n        V P                  RR  Fs  p\        W P                  ^R7      p\        V P                  V P                  ^^R7      pV P$                  P)                  V4       V P&                  P)                  V4       Ku  	  \        \        V P                  4      V P                  ,          V P                  ^^R7      V n        R# )FrI   )rZ   ©r   r   Néÿÿÿÿ)r    r!   ÚconfigrY   r   Úhidden_sizerF   rZ   r   r"   Ú
num_labelsÚ
classifierrW   Úpsp_modulesr   ÚlenÚ
bottleneckÚ
ModuleListÚlateral_convsÚ	fpn_convsr^   Úfpn_bottleneck)r(   rv   r   Úl_convÚfpn_convr)   s   &&&  €r   r!   ÚUperNetHead.__init__{   sŠ  ø€ Ü‰ÑÔàŒØ!×-Ñ-ˆÔØ&ÔØ×*Ñ*ˆŒØ"ˆÔÜŸ)š) D§M¡M°6×3DÑ3DÐRSÔTˆŒô 7Ø×ÑØ×Ñ˜RÕ Ø�M‰MØ×,Ñ,ô	
ˆÔô ,Ø×Ñ˜RÕ ¤3 t×'7Ñ'7Ó#8¸4¿=¹=Õ#HÕHØ�M‰MØØô	
ˆŒô  Ÿ]š]›_ˆÔÜŸš›ˆŒØ×+Ñ+¨C¨RÓ0ˆKÜ& {·M±MÈqÔQˆFÜ(¨¯©¸¿¹ÐSTÐ^_Ô`ˆHØ×Ñ×%Ñ% fÔ-Ø�N‰N×!Ñ! (Ö+ñ	 1ô 0Ü�× Ñ Ó! D§M¡MÕ1Ø�M‰MØØô	
ˆÖr   c                ó°   € VR,          pV.pVP                  V P                  V4      4       \        P                  ! V^R7      pV P	                  V4      pV# )r7   ©Údimru   )Úextendrz   r/   Úcatr|   )r(   Úinputsrb   Úpsp_outsr3   s   &&   r   Úpsp_forwardÚUperNetHead.psp_forward¢   sL   € Ø�2�JˆØ�3ˆØ�‰˜×(Ñ(¨Ó+Ô,Ü—9’9˜X¨1Ô-ˆØ—‘ Ó*ˆàˆr   c                óN   <€ V ^8„  d   QhRS[ P                  RS[ P                  /# ©r   Úencoder_hidden_statesr   r.   )r   r   s   "€r   r   ÚUperNetHead.__annotate__«   s#   ø€ ÷ ñ ©U¯\©\ð ¹e¿l¹lñ r   c           	     óÞ  € \        V P                  4       UUu. uF  w  r#V! W,          4      NK  	  pppVP                  V P                  V4      4       \	        V4      p\        V^,
          ^ R4       Fs  pWB^,
          ,          P                  R,          pWB^,
          ,          \        P                  P                  WB,          VRV P                  R7      ,           WB^,
          &   Ku  	  \        V^,
          4       Uu. uF!  q P                  V,          ! WB,          4      NK#  	  ppVP                  VR,          4       \        V^,
          ^ R4       FP  p\        P                  P                  Wr,          V^ ,          P                  R,          RV P                  R7      Wr&   KR  	  \        P                  ! V^R7      pV P                  V4      pV P                  V4      pV# u uppi u upi )r7   re   rf   rg   r…   ru   )rL   r~   r^   r‹   r{   ÚrangeÚshaper   rj   rk   rZ   r   r/   rˆ   r€   ry   )	r(   r�   rN   Úlateral_convÚlateralsÚused_backbone_levelsÚ
prev_shapeÚfpn_outsr3   s	   &&       r   r4   ÚUperNetHead.forward«   s™  € äR[Ð\`×\nÑ\nÔRoÔpÑRo¹¸q‘LÐ!6Õ!9Ö:ÑRoˆÑpà�‰˜×(Ñ(Ð)>Ó?Ô@ô  # 8›}ÐÜÐ+¨aÕ/°°BÖ7ˆAØ! a¥%�×.Ñ.¨rÕ2ˆJØ&¨1¥u�o´·±×0IÑ0IØ• *°:ÈT×M_ÑM_ð 1Jó 1õ ˆH˜•U‹Oñ 8ô =BÐBVÐYZÕBZÔ<[Ó\Ñ<[°q—N‘N 1Ö% h¥kÖ2Ñ<[ˆÐ\à�‰˜ �Ô%äÐ+¨aÕ/°°BÖ7ˆAÜŸ-™-×3Ñ3Ø• (¨1¥+×"3Ñ"3°BÕ"7¸jÐX\×XjÑXjð 4ó ˆH‹Kñ 8ô —9’9˜X¨1Ô-ˆØ×$Ñ$ XÓ.ˆØ—‘ Ó(ˆàˆùó3 qùò ]s   ™G$Ã?'G*)rZ   r|   rF   ry   rv   r€   r   r   r~   rY   rz   )r9   r:   r;   r<   r=   r!   r‹   r4   r>   r?   r@   rA   s   @@r   rr   rr   u   s$   ù‡ € ñõ
%
òN÷÷ ð r   rr   c                   óX   a a€ ] tR t^Ét oRtRV3R lV 3R llltV3R lR ltRtVtV ;t	# )ÚUperNetFCNHeada”  
Fully Convolution Networks for Semantic Segmentation. This head is the implementation of
[FCNNet](https://huggingface.co/papers/1411.4038>).

Args:
    config:
        Configuration.
    in_channels (int):
        Number of input channels.
    kernel_size (int):
        The kernel size for convs in the head. Default: 3.
    dilation (int):
        The dilation rate for convs in the head. Default: 1.
c          	      óV   <€ V ^8„  d   QhRS[ RS[ RS[ S[S[ S[ 3,          ,          RR/# )r   Úin_indexr   r   r   N)r   r   )r   r   s   "€r   r   ÚUperNetFCNHead.__annotate__Ù   sE   ø€ ÷ $Uñ $UÙ-0ð$UÙCFð$UÙVYÑ\aÑbeÑgjÐbjÕ\kÕVkð$Uà	ñ$Ur   c                ó²  <€ \         S	V `  4        Wn        VP                  f	   W#,          MVP                  V n        VP
                  V n        VP                  V n        VP                  V n
        W0n        V^,          V,          p. pVP                  \        V P                  V P                  WFVR7      4       \        V P                  ^,
          4       F5  pVP                  \        V P                  V P                  WFVR7      4       K7  	  V P                  ^ 8X  d   \        P                   ! 4       V n        M\        P$                  ! V!  V n        V P                  '       d@   \        V P                  V P                  ,           V P                  WD^,          R7      V n        \        P(                  ! V P                  VP*                  ^R7      V n        R # )N)r   r   r   rt   rI   )r    r!   rv   Úauxiliary_in_channelsr   Úauxiliary_channelsrF   Úauxiliary_num_convsÚ	num_convsÚauxiliary_concat_inputÚconcat_inputr�   r^   r   r’   r   ÚIdentityÚconvsÚ
SequentialÚconv_catr"   rx   ry   )
r(   rv   r   r�   r   r   Úconv_paddingr§   rN   r)   s
   &&&&&&   €r   r!   ÚUperNetFCNHead.__init__Ù   se  ø€ ô 	‰ÑÔàŒà%+×%AÑ%AÒ%IˆKÖ!Èv×OkÑOkð 	Ôð ×1Ñ1ˆŒØ×3Ñ3ˆŒØ"×9Ñ9ˆÔØ Œà# qÕ(¨HÕ4ˆØˆØ�‰ÜØ× Ñ  $§-¡-¸[Ðiqôô	
ô
 �t—~‘~¨Õ)Ö*ˆAØ�L‰LÜ!Ø—M‘M 4§=¡=¸kÐjrôöñ +ð �>‰>˜QÔÜŸš›ˆD�JäŸš¨Ñ.ˆDŒJØ××ÐÜ-Ø× Ñ  4§=¡=Õ0°$·-±-È[ÐqrÕbrôˆDŒMô Ÿ)š) D§M¡M°6×3DÑ3DÐRSÔTˆŽr   c                óN   <€ V ^8„  d   QhRS[ P                  RS[ P                  /# rŽ   r.   )r   r   s   "€r   r   rž   ÿ   s#   ø€ ÷ ñ ©U¯\©\ð ¹e¿l¹lñ r   c                óâ   € WP                   ,          pV P                  V4      pV P                  '       d)   V P                  \        P
                  ! W#.^R7      4      pV P                  V4      pV# )r7   r…   )r�   r§   r¥   r©   r/   rˆ   ry   )r(   r�   Úhidden_statesr3   s   &&  r   r4   ÚUperNetFCNHead.forwardÿ   sW   € à-¯m©mÕ<ˆØ—‘˜MÓ*ˆØ××ÐØ—]‘]¤5§9¢9¨mÐ-DÈ!Ô#LÓMˆFØ—‘ Ó(ˆØˆr   )	rF   ry   r¥   rv   r©   r§   r   r�   r£   )r   é   r7   r8   rA   s   @@r   r›   r›   É   s&   ù‡ € ñ÷$Uõ $U÷L÷ ð r   r›   c                   ó8   a € ] tR tRt o RtRt. tV 3R ltRtV t	R# )ÚUperNetPreTrainedModeli	  Úpixel_valuesc                ó&   <€ V ^8„  d   Qh/ S[ ;R&   # )r   rv   r   )r   r   s   "€r   r   Ú#UperNetPreTrainedModel.__annotate__	  s   ø‡ ‚ áÑò r   © N)Úimage)
r9   r:   r;   r<   Úmain_input_nameÚinput_modalitiesÚ_no_split_modulesÚ__annotate_func__r>   r?   )r   s   @r   r²   r²   	  s   ø‡ € ð %€OØ!ÐØÐ÷ ƒ r   r²   zW
    UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.
    )Úcustom_introc                   óR   a a€ ] tR tRt oV 3R lt]RV3R lR ll4       tRtVtV ;t	# )ÚUperNetForSemanticSegmentationi  c                ó"  <€ \         SV `  V4       \        V4      V n        \	        WP                  P
                  R 7      V n        VP                  '       d!   \        WP                  P
                  R 7      MRV n	        V P                  4        R# ))r   N)r    r!   r   Úbackbonerr   rF   Údecode_headÚuse_auxiliary_headr›   Úauxiliary_headÚ	post_init)r(   rv   r)   s   &&€r   r!   Ú'UperNetForSemanticSegmentation.__init__  si   ø€ Ü‰Ñ˜Ô ä% fÓ-ˆŒô ' v¿=¹=×;QÑ;QÔRˆÔàJP×Jc×JcÐJcŒN˜6¯}©}×/EÑ/EÕFÐimð 	Ôð
 	�‰Ör   c                ó¼   <€ V ^8„  d   QhRS[ P                  R,          RS[R,          RS[R,          RS[ P                  R,          RS[R,          RS[S[,          /# )r   r³   NÚoutput_attentionsÚoutput_hidden_statesÚlabelsÚreturn_dictr   )r/   r0   r   r   r   )r   r   s   "€r   r   Ú+UperNetForSemanticSegmentation.__annotate__&  ss   ø€ ÷ Q
ñ Q
á—l‘l TÕ)ðQ
ñ   $�;ðQ
ñ # T�kð	Q
ñ
 —‘˜tÕ#ðQ
ñ ˜D•[ðQ
ñ 
Ñ(Õ	(ñQ
r   c                ó  € Ve'   V P                   P                  ^8X  d   \        R4      hVe   TMV P                   P                  pVe   TMV P                   P                  pVe   TMV P                   P
                  pV P                  P                  WVR7      pVP                  pV P                  V4      p	\        P                  P                  W‘P                  R,          RRR7      p	Rp
V P                  eE   V P                  V4      p
\        P                  P                  W¡P                  R,          RRR7      p
RpVeX   \        V P                   P                   R7      pV! W”4      pV
e,   V! W¤4      pW°P                   P"                  V,          ,          pV'       g<   V'       d   V	3VR	,          ,           pMV	3VR,          ,           pVe   V3V,           # T# \%        VV	VP&                  VP(                  R
7      # )a(  
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
    Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
    config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).

Examples:
```python
>>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
>>> from PIL import Image
>>> from huggingface_hub import hf_hub_download

>>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny")
>>> model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny")

>>> filepath = hf_hub_download(
...     repo_id="hf-internal-testing/fixtures_ade20k", filename="ADE_val_00000001.jpg", repo_type="dataset"
... )
>>> image = Image.open(filepath).convert("RGB")

>>> inputs = image_processor(images=image, return_tensors="pt")

>>> outputs = model(**inputs)

>>> logits = outputs.logits  # shape (batch_size, num_labels, height, width)
>>> list(logits.shape)
[1, 150, 512, 512]
```Nz/The number of labels should be greater than one)rÈ   rÇ   re   rf   Frg   )Úignore_index:r7   NN)ÚlossÚlogitsr®   Ú
attentions)rv   rx   Ú
ValueErrorrÊ   rÈ   rÇ   rÀ   Úforward_with_filtered_kwargsÚfeature_mapsrÁ   r   rj   rk   r“   rÃ   r   Úloss_ignore_indexÚauxiliary_loss_weightr   r®   rÐ   )r(   r³   rÇ   rÈ   rÉ   rÊ   ÚkwargsÚoutputsÚfeaturesrÏ   Úauxiliary_logitsrÎ   Úloss_fctÚauxiliary_lossr3   s   &&&&&&,        r   r4   Ú&UperNetForSemanticSegmentation.forward%  sß  € ðJ Ò $§+¡+×"8Ñ"8¸AÔ"=ÜÐNÓOÐOà%0Ò%<‘kÀ$Ç+Á+×BYÑBYˆà$8Ò$DÑ È$Ï+É+×JjÑJjð 	ð 2CÒ1NÑ-ÐTX×T_ÑT_×TqÑTqÐà—-‘-×<Ñ<ØÐWhð =ó 
ˆð ×'Ñ'ˆà×!Ñ! (Ó+ˆÜ—‘×*Ñ*¨6×8JÑ8JÈ2Õ8NÐU_ÐotÐ*ÓuˆàÐØ×ÑÒ*Ø#×2Ñ2°8Ó<ÐÜ!Ÿ}™}×8Ñ8Ø ×'9Ñ'9¸"Õ'=ÀJÐ^cð  9ó  Ðð ˆØÒä'°T·[±[×5RÑ5RÔSˆHÙ˜FÓ+ˆDØÒ+Ù!)Ð*:Ó!C�ØŸ™×9Ñ9¸NÕJÕJ�çß#Ø ˜ W¨R¥[Õ0‘à ˜ W¨R¥[Õ0�Ø)-Ò)9�T�G˜fÕ$ÐE¸vÐEä&ØØØ!×/Ñ/Ø×)Ñ)ô	
ð 	
r   )rÃ   rÀ   rÁ   )NNNNN)
r9   r:   r;   r<   r!   r   r4   r>   r?   r@   rA   s   @@r   r¾   r¾     s,   ù‡ € õð ÷Q
ñ Q
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r   r¾   )r=   r/   r   Útorch.nnr   Úbackbone_utilsr   Úmodeling_outputsr   Úmodeling_utilsr   Úutilsr   Úconfiguration_upernetr	   ÚModuler   rC   rW   rr   r›   r²   r¾   Ú__all__r¶   r   r   Ú<module>rå      sË   ðñ yã Ý Ý %å +Ý 7Ý -Ý #Ý 0ô ˜Ÿ	™	ô  ôF §¡ô ô"# "§)¡)ô #ôLQ�"—)‘)ô Qôh=�R—Y‘Yô =ð@ ô˜_ó ó ðñ ðôô
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