+
    œ°³jR
  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ViViT model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzgoogle/vivit-b-16x2-kinetics400)Ú
checkpointc                   ó†   a a€ ] tR t^t oRtRt^àt^ tRt^t	Rt
^t^tRtRtRtRtRtRtR	tR
tRtV 3R ltV3R ltRtVtV ;t# )ÚVivitConfigaE  
num_frames (`int`, *optional*, defaults to 32):
    The number of frames in each video.
tubelet_size (`list[int]`, *optional*, defaults to `[2, 16, 16]`):
    The size (resolution) of each tubelet.
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 VivitConfig, VivitModel

>>> # Initializing a ViViT google/vivit-b-16x2-kinetics400 style configuration
>>> configuration = VivitConfig()

>>> # Initializing a model (with random weights) from the google/vivit-b-16x2-kinetics400 style configuration
>>> model = VivitModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úviviti   i   Ú	gelu_fastg        g{®Gáz”?g�íµ ÷Æ°>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   &,€Ún/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/vivit/configuration_vivit.pyr   ÚVivitConfig.__post_init__E   s5   ø€ Ø=A×=T×=TÐ=T $×"9Ò"9ÐZ^×ZjÑZjˆÔÜ‰ÒÑ' Ô'ó    c                ó‚  <€ V ^8„  d   Qh/ S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[ ;R&   S[S[ ,          S[S[ R3,          ,          ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R	&   S[;R
&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[;R&   S[;R&   S[;R&   S[ R,          ;R&   S[;R&   # )é   Ú
image_sizeÚ
num_frames.Útubelet_sizeÚnum_channelsr   Únum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ
hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚinitializer_rangeÚlayer_norm_epsÚqkv_biasNr   Ú
pooler_act)ÚintÚlistÚtupleÚstrÚfloatÚbool)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚVivitConfig.__annotate__   s#  ø‡ ‚ ñ< ‘d™3•i•¡%©©S¨¥/Õ1Ñ7ñ= ñ> Ññ? ñ@ ‘s•)™e¡C¨ H�oÕ-Ñ;ñA ñB ÑñC ñD ÑñE ñF ÑñG ñH Ñ!ñI ñJ Ñ!ñK ñL Ñ!ñM ñN ¡�Ñ*ñO ñP #(©#¥+Ñ3ñQ ñR Ñ#ñS ñT Ñ!ñU ñV ÑñW ñX ˜d�
Ñ)ñY ñZ Ñò[ r   )r   )r   é   r1   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   r   r   r   r   r   r   r   r    r!   r"   r#   r$   r%   r   r&   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r.   s   @@r   r   r      su   ù‡ € ñð2 €Jà47€JØ€JØ0;€LØ€LØ€KØÐØ!ÐØ!ÐØ!€JØ'*ÐØ03Ð Ø#ÐØ!€NØ€HØ%)ÐØ€Jõ(÷_ … r   r   N)	r6   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r   r   Ú<module>r@      sG   ðñ  å .å 3Ý #ñ Ð<Ô=Øô/(Ð"ó /(ó ó >ð/(ðd ˆ/‚r   