+
    œ°³jF  ã                   ó†   € R t ^ RIHt ^ RIHt ^RIHt ^RIHt ^RI	H
t
 ]
! RR7      ] ! R R	]]4      4       4       tR	.tR
# )zResNet model configuration)ÚClassVar)Ústrict)ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringzmicrosoft/resnet-50)Ú
checkpointc                   ót   a a€ ] tR t^t oRtRtRR.t^t^@tRt	Rt
RtRtRtRtV 3R ltR tV3R	 ltR
tVtV ;t# )ÚResNetConfiga¬  
layer_type (`str`, *optional*, defaults to `"bottleneck"`):
    The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or
    `"bottleneck"` (used for larger models like resnet-50 and above).
downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
    If `True`, the first stage will downsample the inputs using a `stride` of 2.
downsample_in_bottleneck (`bool`, *optional*, defaults to `False`):
    If `True`, the first conv 1x1 in ResNetBottleNeckLayer will downsample the inputs using a `stride` of 2.

Example:
```python
>>> from transformers import ResNetConfig, ResNetModel

>>> # Initializing a ResNet resnet-50 style configuration
>>> configuration = ResNetConfig()

>>> # Initializing a model (with random weights) from the resnet-50 style configuration
>>> model = ResNetModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```
ÚresnetÚbasicÚ
bottleneckÚreluFc                óX  <€ R .\        ^\        V P                  4      ^,           4       Uu. uF  pRV 2NK
  	  up,           V n        V P	                  VP                  RR4      VP                  RR4      R7       \        V P                  4      V n        \        SV `$  ! R/ VB  R# u upi )ÚstemÚstageÚout_indicesNÚout_features)r   r   © )
ÚrangeÚlenÚdepthsÚstage_namesÚ"set_output_features_output_indicesÚpopÚlistÚhidden_sizesÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s   &, €Úp/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/resnet/configuration_resnet.pyr   ÚResNetConfig.__post_init__@   sš   ø€ Ø"˜8ÄÀaÌÈTÏ[É[ÓIYÐ\]ÕI]Ô@^Ó&_Ñ@^¸¨¨s¨e£}Ñ@^Ñ&_Õ_ˆÔØ×/Ñ/ØŸ
™
 =°$Ó7ÀfÇjÁjÐQ_ÐaeÓFfð 	0ô 	
ô ! ×!2Ñ!2Ó3ˆÔÜ‰ÒÑ' Ô'ùò '`s   ¬B'c                ó¤   € V P                   V P                  9  d5   \        RV P                    RRP                  V P                  4       24      hR# )z.Check that `layer_types` is correctly defined.zlayer_type=z is not one of Ú,N)Ú
layer_typeÚlayer_typesÚ
ValueErrorÚjoin)r   s   &r"   Úvalidate_layer_typeÚ ResNetConfig.validate_layer_typeH   sG   € à�?‰? $×"2Ñ"2Ô2Ü˜{¨4¯?©?Ð*;¸?È3Ï8É8ÐTX×TdÑTdÓKeÐJfÐgÓhÐhñ 3ó    c                ó*  <€ V ^8„  d   Qh/ S[ S[S[,          ,          ;R&   S[;R&   S[;R&   S[S[,          S[S[R3,          ,          R,          ;R&   S[S[,          S[S[R3,          ,          R,          ;R&   S[;R&   S[;R	&   S[;R
&   S[;R&   # )é   r'   Únum_channelsÚembedding_size.Nr   r   r&   Ú
hidden_actÚdownsample_in_first_stageÚdownsample_in_bottleneck)r   r   ÚstrÚintÚtupleÚbool)ÚformatÚ__classdict__s   "€r"   Ú__annotate__ÚResNetConfig.__annotate__   s»   ø‡ ‚ ñ8 ™$™s�)Õ$Ñ>ñ9 ñ< Ññ= ñ> Ññ? ñ@ ‘s•)™e¡C¨ H�oÕ-°Õ4ÑMñA ñB ‘�I™™c 3˜h�Õ'¨$Õ.Ñ=ñC ñD Ñ"ñE ñF ÑñG ñH  $Ñ+ñI ñJ #Ñ*òK r,   )r   r   )é   i   i   i   )é   é   é   r=   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer'   r/   r0   r   r   r&   r1   r2   r3   r   r*   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r!   r9   s   @@r"   r	   r	      sX   ù‡ € ñð0 €JØ(/°Ð'>€Kà€LØ€NØ7M€LØ1=€FØ"€JØ€JØ&+ÐØ%*Ðõ(òi÷_ … r,   r	   N)rD   Útypingr   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r   r,   r"   Ú<module>rP      sT   ðñ !å å .å 1Ý 3Ý #ñ Ð0Ô1Øô0iÐ&Ð(8ó 0ió ó 2ð0iðf Ð
‚r,   