+
    œ°³j§  ã                   ól   € R 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'Swin2SR Transformer model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringz caidas/swin2sr-classicalsr-x2-64)Ú
checkpointc                   óª   a a€ ] tR t^t oRtRtRRRRRR/t^@t^t^t	R	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RtV 3R ltV3R ltRtVtV ;t # )ÚSwin2SRConfigaÎ  
num_channels_out (`int`, *optional*, defaults to `num_channels`):
    The number of output channels. If not set, it will be set to `num_channels`.
depths (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`):
    Depth of each layer in the Transformer encoder.
num_heads (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`):
    Number of attention heads in each layer of the Transformer encoder.
window_size (`int`, *optional*, defaults to 8):
    Size of windows.
upscale (`int`, *optional*, defaults to 2):
    The upscale factor for the image. 2/3/4/8 for image super resolution, 1 for denoising and compress artifact
    reduction
img_range (`float`, *optional*, defaults to 1.0):
    The range of the values of the input image.
resi_connection (`str`, *optional*, defaults to `"1conv"`):
    The convolutional block to use before the residual connection in each stage.
upsampler (`str`, *optional*, defaults to `"pixelshuffle"`):
    The reconstruction reconstruction module. Can be 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None.

Example:

```python
>>> from transformers import Swin2SRConfig, Swin2SRModel

>>> # Initializing a Swin2SR caidas/swin2sr-classicalsr-x2-64 style configuration
>>> configuration = Swin2SRConfig()

>>> # Initializing a model (with random weights) from the caidas/swin2sr-classicalsr-x2-64 style configuration
>>> model = Swin2SRModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úswin2srÚhidden_sizeÚ	embed_dimÚnum_attention_headsÚ	num_headsÚnum_hidden_layersÚ
num_layersNg       @Tg        gš™™™™™¹?ÚgeluFg{®Gáz”?gñhãˆµøä>g      ð?Ú1convÚpixelshufflec                ó²   <€ V P                   f   V P                  MV P                   V n         \        V P                  4      V n        \
        SV `  ! R/ VB  R # )N© )Únum_channels_outÚnum_channelsÚlenÚdepthsr   ÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s   &,€Úr/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/swin2sr/configuration_swin2sr.pyr   ÚSwin2SRConfig.__post_init__Y   sE   ø€ Ø59×5JÑ5JÒ5R × 1Ò 1ÐX\×XmÑXmˆÔÜ˜dŸk™kÓ*ˆŒÜ‰ÒÑ' Ô'ó    c                óH  <€ 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,          ;R&   S[ ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[S[ ,          S[S[ R3,          ,          ;R	&   S[ ;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[;R&   S[;R&   S[;R&   # )é   Ú
image_sizeÚ
patch_sizer   Nr   r
   .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upscaleÚ	img_rangeÚresi_connectionÚ	upsampler)ÚintÚlistÚtupleÚfloatÚboolÚstr)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚSwin2SRConfig.__annotate__   sœ  ø‡ ‚ ñZ ‘d™3•i•¡%©©S¨¥/Õ1Ñ6ñ[ ñ\ ‘d™3•i•¡%©©S¨¥/Õ1Ñ5ñ] ñ^ Ññ_ ñ` ˜D•jÑ'ña ñb Ññc ñd ‘�I™™c 3˜h�Õ'Ñ<ñe ñf ‘C�y™5¡ c �?Õ*Ñ?ñg ñh Ññi ñj Ññk ñl Ññm ñn ¡�Ñ*ño ñp #(©#¥+Ñ3ñq ñr ™C•KÑ%ñs ñt Ññu ñv "Ñ)ñw ñx Ñ#ñy ñz Ñ ñ{ ñ| Ññ} ñ~ Ññ ñ@ Ñ"ñA ñB Ñ#òC r   )r   r   )é   r<   r<   r<   r<   r<   )!Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚattribute_mapr"   r#   r   r   r
   r   r   r$   r%   r&   r'   r(   r)   r*   r+   r,   r-   r.   r/   r0   r1   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r9   s   @@r   r   r      s§   ù‡ € ñ ðD €Jð 	�{Ø˜{Ø˜\ð€Mð 57€JØ45€JØ€LØ#'ÐØ€IØ*<€FØ-?€IØ€KØ€IØ€HØ'*ÐØ03Ð Ø"%€NØ€JØ$)ÐØ#ÐØ €NØ€GØ€IØ"€OØ#€Iõ(÷G … r   r   N)	rA   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r   r   Ú<module>rL      sK   ðñ .å .å 3Ý #ñ Ð=Ô>ØôD(Ð$ó D(ó ó ?ðD(ðN Ð
‚r   