+
    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&Swinv2 Transformer model configuration)Ústrict)ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringz(microsoft/swinv2-tiny-patch4-window8-256)Ú
checkpointc                   ó¢   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Rt^ tRtRtV 3R ltV3R ltRtVtV ;t# )ÚSwinv2Configa  
window_size (`int`, *optional*, defaults to 7):
    Size of windows.
pretrained_window_sizes (`list(int)`, *optional*, defaults to `[0, 0, 0, 0]`):
    Size of windows during pretraining.
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 Swinv2Config, Swinv2Model

>>> # Initializing a Swinv2 microsoft/swinv2-tiny-patch4-window8-256 style configuration
>>> configuration = Swinv2Config()

>>> # Initializing a model (with random weights) from the microsoft/swinv2-tiny-patch4-window8-256 style configuration
>>> model = Swinv2Model(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úswinv2Únum_attention_headsÚ	num_headsÚnum_hidden_layersÚ
num_layersg      @Tg        gš™™™™™¹?ÚgeluFg{®Gáz”?gñhãˆµøä>Nc                óÜ  <€ \        V P                  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       \        V P                  ^\        V P                  4      ^,
          ,          ,          4      V n	        \        SV `,  ! R/ VB  R# u upi )ÚstemÚstageÚout_indicesNÚout_features)r   r   © )ÚlenÚdepthsr   ÚrangeÚstage_namesÚ"set_output_features_output_indicesÚpopÚintÚ	embed_dimÚhidden_sizeÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s   &, €Úp/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/swinv2/configuration_swinv2.pyr   ÚSwinv2Config.__post_init__M   sÀ   ø€ Ü˜dŸk™kÓ*ˆŒØ"˜8ÄÀaÌÈTÏ[É[ÓIYÐ\]ÕI]Ô@^Ó&_Ñ@^¸¨¨s¨e£}Ñ@^Ñ&_Õ_ˆÔØ×/Ñ/ØŸ
™
 =°$Ó7ÀfÇjÁjÐQ_ÐaeÓFfð 	0ô 	
ô
 ˜tŸ~™~°´c¸$¿+¹+Ó6FÈÕ6JÕ0KÕKÓLˆÔÜ‰ÒÑ' Ô'ùò '`s   ÁC)c                óž  <€ 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[ ,          S[S[ R3,          ,          ;R	&   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&   # )é   Ú
image_sizeÚ
patch_sizeÚnum_channelsr   .r   r   Úwindow_sizeÚpretrained_window_sizesÚ	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$   Ú__annotate__ÚSwinv2Config.__annotate__   s¬  ø‡ ‚ ñB ‘d™3•i•¡%©©S¨¥/Õ1Ñ7ñC ñD ‘d™3•i•¡%©©S¨¥/Õ1Ñ5ñE ñF ÑñG ñH ÑñI ñJ ‘�I™™c 3˜h�Õ'Ñ6ñK ñL ‘C�y™5¡ c �?Õ*Ñ;ñM ñN ÑñO ñP "¡#�Y©©s°C¨x­Õ8ÑGñQ ñR ÑñS ñT ÑñU ñV ¡�Ñ*ñW ñX #(©#¥+Ñ3ñY ñZ ™C•KÑ%ñ[ ñ\ Ññ] ñ^ "Ñ)ñ_ ñ` Ñ#ña ñb Ñ ñc ñd Ññe ñf ™•9˜tÕ#Ñ*ñg ñh ‘s•)˜dÕ"Ñ)òi ó    )r   r   r   )r'   r'   é   r'   )é   rC   é   é   )é    rG   rG   rG   ) Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚattribute_mapr(   r)   r*   r   r   r   r+   r,   r-   r.   r/   r0   r1   r2   r3   r4   r5   r6   r7   r8   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r#   r?   s   @@r$   r   r      sœ   ù‡ € ñð. €Jð 	˜{Ø˜\ð€Mð
 58€JØ45€JØ€LØ€IØ*6€FØ-;€IØ€KØ;GÐØ€IØ€HØ'*ÐØ03Ð Ø"%€NØ€JØ$)ÐØ#ÐØ €NØ€NØ&*€MØ%)€Lõ	(÷m … rB   r   N)rL   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   rB   r$   Ú<module>rX      sO   ðñ -å .å 1Ý 3Ý #ñ ÐEÔFØô=(Ð&Ð(8ó =(ó ó Gð=(ð@ Ð
‚rB   