+
    œ°³j¼	  ã                   óz   € R 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ConvNeXT model configuration)Ústrict)ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringzfacebook/convnext-tiny-224)Ú
checkpointc                   óz   a a€ ] tR t^t oRtRt^t^t^tRt	Rt
RtRtRtRtRt^àtRtRtV 3R	 ltV3R
 ltRtVtV ;t# )ÚConvNextConfigaç  
num_stages (`int`, *optional*, defaults to 4):
    The number of stages in the model.

Example:
```python
>>> from transformers import ConvNextConfig, ConvNextModel

>>> # Initializing a ConvNext convnext-tiny-224 style configuration
>>> configuration = ConvNextConfig()

>>> # Initializing a model (with random weights) from the convnext-tiny-224 style configuration
>>> model = ConvNextModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚconvnextÚgelug{®Gáz”?gê-�™—q=g�íµ ÷Æ°>g        Nc                ó$  <€ 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       \        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superÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s   &, €Út/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/convnext/configuration_convnext.pyr   ÚConvNextConfig.__post_init__<   s‡   ø€ Ø"˜8ÄÀaÌÈTÏ[É[ÓIYÐ\]ÕI]Ô@^Ó&_Ñ@^¸¨¨s¨e£}Ñ@^Ñ&_Õ_ˆÔØ×/Ñ/ØŸ
™
 =°$Ó7ÀfÇjÁjÐQ_ÐaeÓFfð 	0ô 	
ô 	‰ÒÑ' Ô'ùò	 '`s   ¬Bc                ó  <€ V ^8„  d   Qh/ S[ ;R&   S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[ ;R&   S[S[ ,          S[S[ R3,          ,          R,          ;R&   S[S[ ,          S[S[ R3,          ,          R,          ;R&   S[;R&   S[;R	&   S[;R
&   S[;R&   S[S[ ,          ;R&   S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[S[,          R,          ;R&   S[S[ ,          R,          ;R&   # )é   Únum_channelsÚ
patch_sizeÚ
num_stages.NÚhidden_sizesr   Ú
hidden_actÚinitializer_rangeÚlayer_norm_epsÚlayer_scale_init_valueÚdrop_path_rateÚ
image_sizeÚ_out_featuresÚ_out_indices)ÚintÚlistÚtupleÚstrÚfloat)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚConvNextConfig.__annotate__   s"  ø‡ ‚ ñ. Ññ/ ñ0 ‘d™3•i•¡%©©S¨¥/Õ1Ñ5ñ1 ñ2 Ññ3 ñ4 ‘s•)™e¡C¨ H�oÕ-°Õ4ÑJñ5 ñ6 ‘�I™™c 3˜h�Õ'¨$Õ.Ñ=ñ7 ñ8 Ññ9 ñ: Ñ#ñ; ñ< Ñ!ñ= ñ> "Ñ(ñ? ñ@ ™C•KÑ%ñA ñB ‘d™3•i•¡%©©S¨¥/Õ1Ñ7ñC ñD ™•9˜tÕ#Ñ*ñE ñF ‘s•)˜dÕ"Ñ)òG ó    )r   )é`   éÀ   i€  i   )é   r9   é	   r9   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer!   r"   r#   r$   r   r%   r&   r'   r(   r)   r*   r+   r,   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r3   s   @@r   r   r      sa   ù‡ € ñð$ €Jà€LØ45€JØ€JØ7J€LØ1=€FØ€JØ#ÐØ!€NØ$(ÐØ"%€NØ47€JØ&*€MØ%)€Lõ(÷K … r6   r   N)r?   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r6   r   Ú<module>rJ      sN   ðñ #å .å 1Ý 3Ý #ñ Ð7Ô8Øô((Ð(Ð*:ó ((ó ó 9ð((ðV Ð
‚r6   