+
    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RegNet model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzfacebook/regnet-y-040)Ú
checkpointc                   ób   a € ] tR t^t o RtRtRR.t^t^ tRt	Rt
^@tRtRtRtR tV 3R ltR	tV tR
# )ÚRegNetConfiga¢  
groups_width (`int`, *optional*, defaults to 64):
    Width of group for each stage.
layer_type (`str`, *optional*, defaults to `"y"`):
    The layer to use, it can be either `"x" or `"y"`. An `x` layer is a ResNet's BottleNeck layer with
    `reduction` fixed to `1`. While a `y` layer is a `x` but with squeeze and excitation. Please refer to the
    paper for a detailed explanation of how these layers were constructed.
downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
    If `True`, the first stage will downsample the inputs using a `stride` of 2.

Example:
```python
>>> from transformers import RegNetConfig, RegNetModel

>>> # Initializing a RegNet regnet-y-40 style configuration
>>> configuration = RegNetConfig()
>>> # Initializing a model from the regnet-y-40 style configuration
>>> model = RegNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
ÚregnetÚxÚyÚreluTc                ó¤   € V P                   V P                  9  d5   \        RV P                    RRP                  V P                  4       24      hR# )zOPart of `@strict`-powered validation. Validates the architecture of the config.zlayer_type=z is not one of Ú,N)Ú
layer_typeÚlayer_typesÚ
ValueErrorÚjoin)Úselfs   &Úp/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/regnet/configuration_regnet.pyÚvalidate_architectureÚ"RegNetConfig.validate_architecture<   sG   € à�?‰? $×"2Ñ"2Ô2Ü˜{¨4¯?©?Ð*;¸?È3Ï8É8ÐTX×TdÑTdÓKeÐJfÐgÓhÐhñ 3ó    c                óâ   <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[ ;R&   S[;R&   S[;R&   S[;R	&   # )
é   Únum_channelsÚembedding_size.Úhidden_sizesÚdepthsÚgroups_widthr   Ú
hidden_actÚdownsample_in_first_stage)ÚintÚlistÚtupleÚstrÚbool)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚRegNetConfig.__annotate__   s™   ø‡ ‚ ñ: Ññ; ñ< Ññ= ñ> ‘s•)™e¡C¨ H�oÕ-ÑEñ? ñ@ ‘�I™™c 3˜h�Õ'Ñ7ñA ñB ÑñC ñD ÑñE ñF ÑñG ñH  $Ñ*òI r   © N)é€   éÀ   i   i@  )r   é   é   r   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   r   r   r   r   r   r   r   r   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__)r&   s   @r   r   r      sQ   ø‡ € ñð. €JØ˜�*€Kà€LØ€NØ0E€LØ*7€FØ€LØ€JØ€JØ&*Ðòi÷M ƒ r   r   N)	r2   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r)   r   r   Ú<module>r;      sK   ðñ !å .å 3Ý #ñ Ð2Ô3Øô'iÐ#ó 'ió ó 4ð'iðT Ð
‚r   