Ë
    ÛÓ^j½  ã                   óx   — d Z ddlmZ ddlmZ ddlmZ ddlmZ  ed¬«      e G d	„ d
ee«      «       «       Z	d
gZ
y)z>Dilated Neighborhood Attention Transformer model configurationé    )Ústricté   )ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringzshi-labs/dinat-mini-in1k-224)Ú
checkpointc                   ó¬  ‡ — e Zd ZU dZdZdddœ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   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	z  dz  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   dz  e
d$<   dZee   dz  e
d%<   ˆ fd&„Zˆ xZ S )'ÚDinatConfiga‚  
    dilations (`list[list[int]]`, *optional*, defaults to `[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]`):
        Dilation value of each NA layer in the Transformer encoder.

    Example:

    ```python
    >>> from transformers import DinatConfig, DinatModel

    >>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration
    >>> configuration = DinatConfig()

    >>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration
    >>> model = DinatModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```ÚdinatÚ	num_headsÚ
num_layers)Únum_attention_headsÚnum_hidden_layersé   Ú
patch_sizer   Únum_channelsé@   Ú	embed_dim)r   r   é   é   .Údepths)é   r   é   é   é   Úkernel_sizeNÚ	dilationsg      @Ú	mlp_ratioTÚqkv_biasg        Úhidden_dropout_probÚattention_probs_dropout_probgš™™™™™¹?Údrop_path_rateÚgeluÚ
hidden_actg{®Gáz”?Úinitializer_rangegñhãˆµøä>Úlayer_norm_epsÚlayer_scale_init_valueÚ_out_featuresÚ_out_indicesc                 óî  •— t        | j                  «      | _        | j                  xs g d¢g d¢g d¢g d¢g| _        t	        | j
                  dt        | j                  «      dz
  z  z  «      | _        dgt        dt        | j                  «      dz   «      D �cg c]  }d|› �‘Œ	 c}z   | _        | j                  |j                  d	d «      |j                  d
d «      ¬«       t        ‰| �0  di |¤Ž y c c}w )N)é   r   r+   )r+   r   r+   r   )r+   r   r+   r   r+   r   )r+   r+   r+   r+   r+   r   r+   ÚstemÚstageÚout_indicesÚout_features)r.   r/   © )Úlenr   r   r   Úintr   Úhidden_sizeÚrangeÚstage_namesÚ"set_output_features_output_indicesÚpopÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s      €úx/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/dinat/configuration_dinat.pyr9   zDinatConfig.__post_init__G   s×   ø€ Ü˜dŸk™kÓ*ˆŒØŸ™ÒiªI²|ÒEWÒYhÐ+iˆŒô ˜tŸ~™~°´c¸$¿+¹+Ó6FÈÑ6JÑ0KÑKÓLˆÔØ"˜8ÄÀaÌÈTÏ[É[ÓIYÐ\]ÑI]Ó@^Ö&_¸¨¨s¨e¢}Ò&_Ñ_ˆÔØ×/Ñ/ØŸ
™
 =°$Ó7ÀfÇjÁjÐQ_ÐaeÓFfð 	0ô 	
ô 	‰ÑÑ' Ó'ùò	 '`s   ÂC2)!Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typeÚattribute_mapr   r2   ÚlistÚtupleÚ__annotations__r   r   r   r   r   r   r   Úfloatr   Úboolr    r!   r"   r$   Ústrr%   r&   r'   r(   r)   r9   Ú__classcell__)r=   s   @r>   r
   r
      s@  ø… ñð& €Jð  +Ø)ñ€Mð
 56€J��d˜3‘i‘ %¨¨S¨¡/Ñ1Ó5Ø€L�#ÓØ€IˆsÓØ*6€FˆD�‰I˜˜c 3˜h™Ñ'Ó6Ø-:€Iˆt�C‰y˜5  c ™?Ñ*Ó:Ø€K�ÓØ%)€Iˆt�e‰|˜dÑ"Ó)Ø€IˆuÓØ€HˆdÓØ'*Ð˜ ™Ó*Ø03Ð  %¨#¡+Ó3Ø"%€N�E˜C‘KÓ%Ø€J�ÓØ#Ð�uÓ#Ø €N�EÓ Ø$'Ð˜EÓ'Ø&*€M�4˜‘9˜tÑ#Ó*Ø%)€L�$�s‘)˜dÑ"Ó)÷(ð (ó    r
   N)rB   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r
   Ú__all__r0   rL   r>   ú<module>rR      sN   ðñ Eå .å 1Ý 3Ý #ñ Ð9Ô:Øô9(Ð%Ð'7ó 9(ó ó ;ð9(ðx ˆ/�rL   