+
    œ°³j2
  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DeiT model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringz(facebook/deit-base-distilled-patch16-224)Ú
checkpointc                   ó†   a a€ ] tR t^t oRtRtRt^t^tRt	Rt
RtRtRtRt^àt^t^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
DeiTConfiga  
encoder_stride (`int`, *optional*, defaults to 16):
    Factor to increase the spatial resolution by in the decoder head for masked image modeling.
pooler_output_size (`int`, *optional*):
    Dimensionality of the pooler layer. If None, defaults to `hidden_size`.
pooler_act (`str`, *optional*, defaults to `"tanh"`):
    The activation function to be used by the pooler.

Example:

```python
>>> from transformers import DeiTConfig, DeiTModel

>>> # Initializing a DeiT deit-base-distilled-patch16-224 style configuration
>>> configuration = DeiTConfig()

>>> # Initializing a model (with random weights) from the deit-base-distilled-patch16-224 style configuration
>>> model = DeiTModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Údeiti   i   Úgelug        g{®Gáz”?gê-�™—q=TNÚtanhc                ó†   <€ V P                   '       d   V P                   MV P                  V n         \        SV `  ! R/ VB  R # )N© )Úpooler_output_sizeÚhidden_sizeÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s   &,€Úl/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/deit/configuration_deit.pyr   ÚDeiTConfig.__post_init__C   s5   ø€ Ø=A×=T×=TÐ=T $×"9Ò"9ÐZ^×ZjÑZjˆÔÜ‰ÒÑ' Ô'ó    c                ó”  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[;R&   S[;R	&   S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R
&   S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[ ;R&   S[;R&   S[ ;R&   S[ R,          ;R&   S[;R&   # )é   r   Únum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ
hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚinitializer_rangeÚlayer_norm_epsÚ
image_sizeÚ
patch_sizeÚnum_channelsÚqkv_biasÚencoder_strideNr   Ú
pooler_act)ÚintÚstrÚfloatÚlistÚtupleÚbool)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚDeiTConfig.__annotate__   s#  ø‡ ‚ ñ8 Ññ9 ñ: Ññ; ñ< Ñ!ñ= ñ> Ñ!ñ? ñ@ ÑñA ñB ¡�Ñ*ñC ñD #(©#¥+Ñ3ñE ñF Ñ#ñG ñH Ñ!ñI ñJ ‘d™3•i•¡%©©S¨¥/Õ1Ñ7ñK ñL ‘d™3•i•¡%©©S¨¥/Õ1Ñ6ñM ñN ÑñO ñP ÑñQ ñR ÑñS ñT ˜d�
Ñ)ñU ñV ÑòW r   )r   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   r   r   r   r   r   r   r   r    r!   r"   r#   r$   r%   r   r&   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r.   s   @@r   r   r      su   ù‡ € ñð. €Jà€KØÐØ!ÐØ!ÐØ€JØ'*ÐØ03Ð Ø#ÐØ!€NØ47€JØ46€JØ€LØ€HØ€NØ%)ÐØ€Jõ(÷[ … r   r   N)	r5   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r   r   Ú<module>r?      sH   ðñ å .å 3Ý #ñ ÐEÔFØô-(Ð!ó -(ó ó Gð-(ð` ˆ.‚r   