§
    ‚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LeViT model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringzfacebook/levit-128S)Ú
checkpointc                   ó  ‡ — e Zd ZU dZdZdZeee         z  eee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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         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         eedf         z  e	d<   dZee         eedf         z  e	d<   dZee	d<   ˆ fd„Zˆ xZS )ÚLevitConfigaÀ  
    stride (`int`, *optional*, defaults to 2):
        The stride size for the initial convolution layers of patch embedding.
    padding (`int`, *optional*, defaults to 1):
        The padding size for the initial convolution layers of patch embedding.
    key_dim (`list[int]`, *optional*, defaults to `[16, 16, 16]`):
        The size of key in each of the encoder blocks.
    attention_ratio (`list[int]`, *optional*, defaults to `[2, 2, 2]`):
        Ratio of the size of the output dimension compared to input dimension of attention layers.

    Example:

    ```python
    >>> from transformers import LevitConfig, LevitModel

    >>> # Initializing a LeViT levit-128S style configuration
    >>> configuration = LevitConfig()

    >>> # Initializing a model (with random weights) from the levit-128S style configuration
    >>> model = LevitModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úlevitéà   Ú
image_sizer   Únum_channelsÚkernel_sizeé   Ústrideé   Úpaddingé   Ú
patch_size)é€   é   i€  .Úhidden_sizes)é   é   é   Únum_attention_heads)r   r   r   Údepths)r   r   r   Úkey_dimr   Údrop_path_rate)r   r   r   Ú	mlp_ratioÚattention_ratiog{®Gáz”?Úinitializer_rangec                 óô   •— d| j         d         | j        d         | j         d         z  dddgd| j         d         | j        d         | j         d         z  dddgg| _         t          ¦   «         j        di |¤Ž d S )NÚ	Subsampler   r   r   r   © )r   r   Údown_opsÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s     €úk/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/levit/configuration_levit.pyr'   zLevitConfig.__post_init__C   s‹   ø€ à˜$œ, qœ/¨4Ô+<¸QÔ+?À4Ä<ÐPQÄ?Ñ+RÐTUÐWXÐZ[Ð\Ø˜$œ, qœ/¨4Ô+<¸QÔ+?À4Ä<ÐPQÄ?Ñ+RÐTUÐWXÐZ[Ð\ð
ˆŒð 	�‰ŒÔÐ'Ð' Ð'Ð'Ð'Ð'Ð'ó    )Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚlistÚtupleÚ__annotations__r   r   r   r   r   r   r   r   r   r   r   r    r!   Úfloatr'   Ú__classcell__)r*   s   @r+   r	   r	      sº  ø€ € € € € € ðð ð2 €Jà47€J��d˜3”i‘ %¨¨S¨¤/Ñ1Ð7Ð7Ñ7Ø€L�#ÐÐÑØ€K�ÐÐÑØ€FˆC€O€O�OØ€GˆSÐÐÑØ46€J��d˜3”i‘ %¨¨S¨¤/Ñ1Ð6Ð6Ñ6Ø0?€L�$�s”)˜e C¨ HœoÑ-Ð?Ð?Ñ?Ø7AÐ˜˜cœ U¨3°¨8¤_Ñ4ÐAÐAÑAØ*3€FˆD�ŒI˜˜c 3˜hœÑ'Ð3Ð3Ñ3Ø+7€GˆT�#ŒY˜˜s C˜xœÑ(Ð7Ð7Ñ7Ø€N�CÐÐÑØ-6€Iˆt�CŒy˜5  c œ?Ñ*Ð6Ð6Ñ6Ø3<€O�T˜#”Y  s¨C x¤Ñ0Ð<Ð<Ñ<Ø#Ð�uÐ#Ð#Ñ#ð(ð (ð (ð (ð (ð (ð (ð (ð (r,   r	   N)	r0   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r$   r,   r+   ú<module>r<      s¡   ðð  Ð à .Ð .Ð .Ð .Ð .Ð .à 3Ð 3Ð 3Ð 3Ð 3Ð 3Ø #Ð #Ð #Ð #Ð #Ð #ð €Ð0Ð1Ñ1Ô1Øð0(ð 0(ð 0(ð 0(ð 0(Ð"ñ 0(ô 0(ñ „ñ 2Ô1ð0(ðf ˆ/€€€r,   