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    ‚Štj$  ã                   ó„   — d Z ddlmZ ddlmZ ddlmZ  ed¬¦  «        e G d„ d	e¦  «        ¦   «         ¦   «         Zd	gZd
S )zMobileViT model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringzgoogle/mobilenet_v2_1.0_224)Ú
checkpointc                   ó  — e Zd ZU dZdZdZeed<   dZee	e         z  e
eef         z  ed<   dZee	e         z  e
eef         z  ed<   d	Ze	e         e
ed
f         z  ed<   dZe	e         e
ed
f         z  ed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeez  ed<   dZeez  ed<   dZeez  ed<   dZeed<   d Zeed!<   d"Zeed#<   dZeed$<   d%Ze	e         e
ed
f         z  ed&<   dZeez  ed'<   d(Z eed)<   d*S )+ÚMobileViTConfigañ  
    neck_hidden_sizes (`list[int]`, *optional*, defaults to `[16, 32, 64, 96, 128, 160, 640]`):
        The number of channels for the feature maps of the backbone.
    aspp_out_channels (`int`, *optional*, defaults to 256):
        Number of output channels used in the ASPP layer for semantic segmentation.
    atrous_rates (`list[int]`, *optional*, defaults to `[6, 12, 18]`):
        Dilation (atrous) factors used in the ASPP layer for semantic segmentation.
    aspp_dropout_prob (`float`, *optional*, defaults to 0.1):
        The dropout ratio for the ASPP layer for semantic segmentation.

    Example:

    ```python
    >>> from transformers import MobileViTConfig, MobileViTModel

    >>> # Initializing a mobilevit-small style configuration
    >>> configuration = MobileViTConfig()

    >>> # Initializing a model from the mobilevit-small style configuration
    >>> model = MobileViTModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Ú	mobilevitr   Únum_channelsé   Ú
image_sizeé   Ú
patch_size)é�   éÀ   éð   .Úhidden_sizes)é   é    é@   é`   é€   é    i€  Úneck_hidden_sizesé   Únum_attention_headsg       @Ú	mlp_ratiog      @Úexpand_ratioÚsiluÚ
hidden_actÚconv_kernel_sizer   Úoutput_stridegš™™™™™¹?Úhidden_dropout_probg        Úattention_probs_dropout_probÚclassifier_dropout_probg{®Gáz”?Úinitializer_rangegñhãˆµøä>Úlayer_norm_epsTÚqkv_biasÚaspp_out_channels)é   é   é   Úatrous_ratesÚaspp_dropout_probéÿ   Úsemantic_loss_ignore_indexN)!Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚ__annotations__r   ÚlistÚtupler   r   r   r   r   Úfloatr   r    Ústrr!   r"   r#   r$   r%   r&   r'   r(   Úboolr)   r-   r.   r0   © ó    ús/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/mobilevit/configuration_mobilevit.pyr	   r	      sê  € € € € € € ðð ð2 €Jà€L�#ÐÐÑØ47€J��d˜3”i‘ %¨¨S¨¤/Ñ1Ð7Ð7Ñ7Ø45€J��d˜3”i‘ %¨¨S¨¤/Ñ1Ð5Ð5Ñ5Ø0?€L�$�s”)˜e C¨ HœoÑ-Ð?Ð?Ñ?Ø5TÐ�t˜C”y 5¨¨c¨¤?Ñ2ÐTÐTÑTØ Ð˜Ð Ð Ñ Ø€IˆuÐÐÑØ€L�%ÐÐÑØ€J�ÐÐÑØÐ�cÐÐÑØ€M�3ÐÐÑØ'*Ð˜ ™Ð*Ð*Ñ*Ø03Ð  %¨#¡+Ð3Ð3Ñ3Ø+.Ð˜U S™[Ð.Ð.Ñ.Ø#Ð�uÐ#Ð#Ñ#Ø €N�EÐ Ð Ñ Ø€HˆdÐÐÑØ Ð�sÐ Ð Ñ Ø0;€L�$�s”)˜e C¨ HœoÑ-Ð;Ð;Ñ;Ø%(Ð�u˜s‘{Ð(Ð(Ñ(Ø&)Ð Ð)Ð)Ñ)Ð)Ð)r>   r	   N)	r4   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r=   r>   r?   ú<module>rD      s¢   ðð $Ð #à .Ð .Ð .Ð .Ð .Ð .à 3Ð 3Ð 3Ð 3Ð 3Ð 3Ø #Ð #Ð #Ð #Ð #Ð #ð €Ð8Ð9Ñ9Ô9Øð0*ð 0*ð 0*ð 0*ð 0*Ð&ñ 0*ô 0*ñ „ñ :Ô9ð0*ðf Ð
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