+
    œ°³jV  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BiT model configuration)Ústrict)ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringzgoogle/bit-50)Ú
checkpointc                   ó”   a a€ ] tR t^t oRtRtRR.t. ROt^t^@t	Rt
RtRtRtRt^ tRtRt^ t^tRtRtV 3R	 ltR
 tV3R ltRtVtV ;t# )Ú	BitConfigaÒ  
layer_type (`str`, *optional*, defaults to `"preactivation"`):
    The layer to use, it can be either `"preactivation"` or `"bottleneck"`.
global_padding (`str`, *optional*):
    Padding strategy to use for the convolutional layers. Can be either `"valid"`, `"same"`, or `None`.
num_groups (`int`, *optional*, defaults to 32):
    Number of groups used for the `BitGroupNormActivation` layers.
embedding_dynamic_padding (`bool`, *optional*, defaults to `False`):
    Whether or not to make use of dynamic padding for the embedding layer.
width_factor (`int`, *optional*, defaults to 1):
    The width factor for the model.

Example:
```python
>>> from transformers import BitConfig, BitModel

>>> # Initializing a BiT bit-50 style configuration
>>> configuration = BitConfig()

>>> # Initializing a model (with random weights) from the bit-50 style configuration
>>> model = BitModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```
ÚbitÚpreactivationÚ
bottleneckNÚrelug        Fc                óæ  <€ \        V P                  4      V n        \        V P                  4      V n        V P                  e    V P                  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       \        SV `,  ! R/ VB  R # u upi )NÚstemÚstageÚout_indicesÚout_features)r   r   © )ÚlistÚhidden_sizesÚdepthsÚglobal_paddingÚupperÚrangeÚlenÚstage_namesÚ"set_output_features_output_indicesÚpopÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s   &, €Új/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/bit/configuration_bit.pyr   ÚBitConfig.__post_init__H   sÍ   ø€ Ü  ×!2Ñ!2Ó3ˆÔÜ˜4Ÿ;™;Ó'ˆŒà×ÑÒ*Ø"&×"5Ñ"5×";Ñ";Ó"=ˆDÔà"˜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                ó  € V P                   V P                  9  d5   \        RV P                    RRP                  V P                  4       24      hV P                  V P
                  9  d   \        RV P                   R24      hR# )zOPart of `@strict`-powered validation. Validates the architecture of the config.zlayer_type=z is not one of Ú,zPadding strategy z not supportedN)Ú
layer_typeÚlayer_typesÚ
ValueErrorÚjoinr   Úsupported_padding)r   s   &r#   Úvalidate_architectureÚBitConfig.validate_architectureV   sw   € à�?‰? $×"2Ñ"2Ô2Ü˜{¨4¯?©?Ð*;¸?È3Ï8É8ÐTX×TdÑTdÓKeÐJfÐgÓhÐhà×Ñ d×&<Ñ&<Ô<ÜÐ0°×1DÑ1DÐ0EÀ^ÐTÓUÐUñ =ó    c                ó„  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[;R&   S[;R&   S[R,          ;R	&   S[ ;R
&   S[S[ ,          ;R&   S[;R&   S[ ;R&   S[ ;R&   S[S[,          R,          ;R&   S[S[ ,          R,          ;R&   # )é   Únum_channelsÚembedding_size.r   r   r'   Ú
hidden_actNr   Ú
num_groupsÚdrop_path_rateÚembedding_dynamic_paddingÚoutput_strideÚwidth_factorÚ_out_featuresÚ_out_indices)Úintr   ÚtupleÚstrÚfloatÚbool)ÚformatÚ__classdict__s   "€r#   Ú__annotate__ÚBitConfig.__annotate__   s  ø‡ ‚ ñD ÑñE ñF ÑñG ñH ‘s•)™e¡C¨ H�oÕ-ÑFñI ñJ ‘�I™™c 3˜h�Õ'Ñ6ñK ñL Ñ%ñM ñN ÑñO ñP ˜$•JÑ%ñQ ñR ÑñS ñT ™C•KÑ%ñU ñV  $Ñ+ñW ñX ÑñY ñZ Ññ[ ñ\ ™•9˜tÕ#Ñ*ñ] ñ^ ‘s•)˜dÕ"Ñ)ò_ r.   )r   r   r   r   )NÚSAMEÚVALID)é   i   i   i   )é   é   é   rG   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer(   r+   r1   r2   r   r   r'   r3   r   r4   r5   r6   r7   r8   r9   r:   r   r,   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r"   rA   s   @@r#   r   r      s{   ù‡ € ñð6 €JØ" LÐ1€KÚ/Ðà€LØ€NØ0F€LØ*6€FØ%€JØ€JØ!%€NØ€JØ"%€NØ&+ÐØ€MØ€LØ&*€MØ%)€Lõ(òV÷ … r.   r   N)rN   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r.   r#   Ú<module>rY      sR   ðñ å .å 1Ý 3Ý #ñ ˜?Ô+ØôCVÐ#Ð%5ó CVó ó ,ðCVðL ˆ-‚r.   