+
    œ°³j½  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>Dilated Neighborhood Attention Transformer model configuration)Ústrict)ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringzshi-labs/dinat-mini-in1k-224)Ú
checkpointc                   óš   a a€ ] tR t^t oRtRtRRRR/t^t^t^@t	Rt
Rt^tRtRtR	tR
tR
tRtRtRtRtR
tRtRtV 3R ltV3R ltRtVtV ;t# )ÚDinatConfigaR  
dilations (`list[list[int]]`, *optional*, defaults to `[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]`):
    Dilation value of each NA layer in the Transformer encoder.

Example:

```python
>>> from transformers import DinatConfig, DinatModel

>>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration
>>> configuration = DinatConfig()

>>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration
>>> model = DinatModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚdinatÚnum_attention_headsÚ	num_headsÚnum_hidden_layersÚ
num_layersNg      @Tg        gš™™™™™¹?Úgelug{®Gáz”?gñhãˆµøä>c                ó*  <€ \        V P                  4      V n        V P                  ;'       g    . RO. RO. R	O. R
O.V n        \	        V P
                  ^\        V P                  4      ^,
          ,          ,          4      V n        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 `0  ! R/ VB  R# u upi )é   ÚstemÚstageÚout_indicesNÚout_features)r   r   )r   é   r   )r   é   r   r   )r   é   r   r   r   r   )r   r   r   r   r   © )ÚlenÚdepthsr   Ú	dilationsÚintÚ	embed_dimÚhidden_sizeÚrangeÚstage_namesÚ"set_output_features_output_indicesÚpopÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s   &, €Ún/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/dinat/configuration_dinat.pyr$   ÚDinatConfig.__post_init__G   sà   ø€ Ü˜dŸk™kÓ*ˆŒØŸ™×iÐiªI²|ÒEWÒYhÐ+iˆŒô ˜tŸ~™~°´c¸$¿+¹+Ó6FÈÕ6JÕ0KÕKÓLˆÔØ"˜8ÄÀaÌÈTÏ[É[ÓIYÐ\]ÕI]Ô@^Ó&_Ñ@^¸¨¨s¨e£}Ñ@^Ñ&_Õ_ˆÔØ×/Ñ/ØŸ
™
 =°$Ó7ÀfÇjÁjÐQ_ÐaeÓFfð 	0ô 	
ô 	‰ÒÑ' Ô'ùò	 '`s   Â/Dc                ó*  <€ V ^8„  d   Qh/ S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[ ;R&   S[ ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[ ;R&   S[S[,          R,          ;R	&   S[;R
&   S[;R&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[;R&   S[;R&   S[;R&   S[;R&   S[S[,          R,          ;R&   S[S[ ,          R,          ;R&   # )r   Ú
patch_sizeÚnum_channelsr   .r   r   Úkernel_sizeNr   Ú	mlp_ratioÚqkv_biasÚhidden_dropout_probÚattention_probs_dropout_probÚdrop_path_rateÚ
hidden_actÚinitializer_rangeÚlayer_norm_epsÚlayer_scale_init_valueÚ_out_featuresÚ_out_indices)r   ÚlistÚtupleÚfloatÚboolÚstr)ÚformatÚ__classdict__s   "€r)   Ú__annotate__ÚDinatConfig.__annotate__   sk  ø‡ ‚ ñ: ‘d™3•i•¡%©©S¨¥/Õ1Ñ5ñ; ñ< Ññ= ñ> Ññ? ñ@ ‘�I™™c 3˜h�Õ'Ñ6ñA ñB ‘C�y™5¡ c �?Õ*Ñ:ñC ñD ÑñE ñF ‘e�|˜dÕ"Ñ)ñG ñH ÑñI ñJ ÑñK ñL ¡�Ñ*ñM ñN #(©#¥+Ñ3ñO ñP ™C•KÑ%ñQ ñR ÑñS ñT Ñ#ñU ñV Ñ ñW ñX "Ñ'ñY ñZ ™•9˜tÕ#Ñ*ñ[ ñ\ ‘s•)˜dÕ"Ñ)ò] ó    )r   r   r   r    )é   r   é   é   )r   r   r   é   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚattribute_mapr,   r-   r   r   r   r.   r   r/   r0   r1   r2   r3   r4   r5   r6   r7   r8   r9   r$   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r(   r@   s   @@r)   r   r      s‘   ù‡ € ñð& €Jð 	˜{Ø˜\ð€Mð
 56€JØ€LØ€IØ*6€FØ-:€IØ€KØ%)€IØ€IØ€HØ'*ÐØ03Ð Ø"%€NØ€JØ#ÐØ €NØ$'ÐØ&*€MØ%)€Lõ(÷a … rC   r   N)rL   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   rC   r)   Ú<module>rX      sN   ðñ Eå .å 1Ý 3Ý #ñ Ð9Ô:Øô9(Ð%Ð'7ó 9(ó ó ;ð9(ðx ˆ/‚rC   