+
    œ°³j¶  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$Swin Transformer model configuration)Ústrict)ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringz&microsoft/swin-tiny-patch4-window7-224)Ú
checkpointc                   óž   a a€ ] tR t^t oRtRtRRRR/t^àt^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^ tRtRtV 3R ltV3R ltRtVtV ;t# )Ú
SwinConfiga~  
depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
    Depth of each layer in the Transformer encoder.
num_heads (`list(int)`, *optional*, defaults to `[3, 6, 12, 24]`):
    Number of attention heads in each layer of the Transformer encoder.
window_size (`int`, *optional*, defaults to 7):
    Size of windows.
encoder_stride (`int`, *optional*, defaults to 32):
    Factor to increase the spatial resolution by in the decoder head for masked image modeling.

Example:

```python
>>> from transformers import SwinConfig, SwinModel

>>> # Initializing a Swin microsoft/swin-tiny-patch4-window7-224 style configuration
>>> configuration = SwinConfig()

>>> # Initializing a model (with random weights) from the microsoft/swin-tiny-patch4-window7-224 style configuration
>>> model = SwinModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚswinÚnum_attention_headsÚ	num_headsÚnum_hidden_layersÚ
num_layersg      @Tg        gš™™™™™¹?ÚgeluFg{®Gáz”?gñhãˆµøä>Nc                óÜ  <€ \        V P                  4      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 `,  ! R/ VB  R# u upi )é   ÚstemÚstageÚout_indicesNÚout_features)r   r   © )ÚlenÚdepthsr   ÚintÚ	embed_dimÚhidden_sizeÚrangeÚstage_namesÚ"set_output_features_output_indicesÚpopÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s   &, €Úl/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/swin/configuration_swin.pyr    ÚSwinConfig.__post_init__N   sÂ   ø€ Ü˜dŸk™kÓ*ˆ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
™
 =°$Ó7ÀfÇjÁjÐQ_ÐaeÓFfð 	0ô 	
ô 	‰ÒÑ' Ô'ùò	 '`s   ÂC)c                ón  <€ V ^8„  d   Qh/ S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[ ;R&   S[ ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[ ;R&   S[S[ ,          ;R	&   S[;R
&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[;R&   S[;R&   S[;R&   S[;R&   S[ ;R&   S[S[,          R,          ;R&   S[S[ ,          R,          ;R&   # )r   Ú
image_sizeÚ
patch_sizeÚnum_channelsr   .r   r   Úwindow_sizeÚ	mlp_ratioÚqkv_biasÚhidden_dropout_probÚattention_probs_dropout_probÚdrop_path_rateÚ
hidden_actÚuse_absolute_embeddingsÚinitializer_rangeÚlayer_norm_epsÚencoder_strideNÚ_out_featuresÚ_out_indices)r   ÚlistÚtupleÚfloatÚboolÚstr)ÚformatÚ__classdict__s   "€r%   Ú__annotate__ÚSwinConfig.__annotate__   s�  ø‡ ‚ ñF ‘d™3•i•¡%©©S¨¥/Õ1Ñ7ñG ñH ‘d™3•i•¡%©©S¨¥/Õ1Ñ5ñI ñJ ÑñK ñL ÑñM ñN ‘�I™™c 3˜h�Õ'Ñ6ñO ñP ‘C�y™5¡ c �?Õ*Ñ;ñQ ñR ÑñS ñT ‘s�{Ñ ñU ñV ÑñW ñX ¡�Ñ*ñY ñZ #(©#¥+Ñ3ñ[ ñ\ ™C•KÑ%ñ] ñ^ Ññ_ ñ` "Ñ)ña ñb Ñ#ñc ñd Ñ ñe ñf Ññg ñh ™•9˜tÕ#Ñ*ñi ñj ‘s•)˜dÕ"Ñ)òk ó    )r   r   r   )r   r   é   r   )é   rB   é   é   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚattribute_mapr(   r)   r*   r   r   r   r+   r,   r-   r.   r/   r0   r1   r2   r3   r4   r5   r6   r7   r    Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r$   r>   s   @@r%   r   r      s–   ù‡ € ñð2 €Jð 	˜{Ø˜\ð€Mð
 58€JØ45€JØ€LØ€IØ*6€FØ-;€IØ€KØ €IØ€HØ'*ÐØ03Ð Ø"%€NØ€JØ$)ÐØ#ÐØ €NØ€NØ&*€MØ%)€Lõ	(÷o … rA   r   N)rJ   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   rA   r%   Ú<module>rV      sN   ðñ +å .å 1Ý 3Ý #ñ ÐCÔDØô>(Ð$Ð&6ó >(ó ó Eð>(ðB ˆ.‚rA   