Ë
    ÜÓ^jú  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VitDet model configurationé    )Ústricté   )ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringzgoogle/vitdet-base-patch16-224)Ú
checkpointc                   óò  ‡ — e Zd ZU dZdZ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d<   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   z  eeef   z  ed<   dZeed<   dZeed<   dZee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%z  ed&<   d%Z ee   d%z  ed'<   ˆ fd(„Z!ˆ xZ"S ))ÚVitDetConfigaM  
    pretrain_image_size (`int`, *optional*, defaults to 224):
        The size (resolution) of each image during pretraining.
    window_block_indices (`list[int]`, *optional*, defaults to `[]`):
        List of indices of blocks that should have window attention instead of regular global self-attention.
    residual_block_indices (`list[int]`, *optional*, defaults to `[]`):
        List of indices of blocks that should have an extra residual block after the MLP.
    use_relative_position_embeddings (`bool`, *optional*, defaults to `False`):
        Whether to add relative position embeddings to the attention maps.
    window_size (`int`, *optional*, defaults to 0):
        The size of the attention window.

    Example:

    ```python
    >>> from transformers import VitDetConfig, VitDetModel

    >>> # Initializing a VitDet configuration
    >>> configuration = VitDetConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = VitDetModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úvitdeti   Úhidden_sizeé   Únum_hidden_layersÚnum_attention_headsé   Ú	mlp_ratioÚgeluÚ
hidden_actg        Údropout_probg{®Gáz”?Úinitializer_rangeg�íµ ÷Æ°>Úlayer_norm_epséà   Ú
image_sizeÚpretrain_image_sizeé   Ú
patch_sizer   Únum_channelsTÚqkv_biasÚdrop_path_rate© .Úwindow_block_indicesÚresidual_block_indicesÚ use_absolute_position_embeddingsFÚ use_relative_position_embeddingsr   Úwindow_sizeNÚ_out_featuresÚ_out_indicesc                 óú   •— dgt        d| j                  dz   «      D �cg c]  }d|› �‘Œ	 c}z   | _        | j                  |j	                  dd «      |j	                  dd «      ¬«       t        ‰| �  di |¤Ž y c c}w )NÚstemé   ÚstageÚout_indicesÚout_features)r+   r,   r   )Úranger   Ústage_namesÚ"set_output_features_output_indicesÚpopÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s      €úz/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/vitdet/configuration_vitdet.pyr2   zVitDetConfig.__post_init__M   s~   ø€ Ø"˜8ÄÀaÈ×I_ÑI_ÐbcÑIcÓ@dÖ&e¸¨¨s¨e¢}Ò&eÑeˆÔØ×/Ñ/ØŸ
™
 =°$Ó7ÀfÇjÁjÐQ_ÐaeÓFfð 	0ô 	
ô 	‰ÑÑ' Ó'ùò	 'fs   ŸA8)#Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚ__annotations__r   r   r   r   Ústrr   Úfloatr   r   r   ÚlistÚtupler   r   r   r   Úboolr   r    r!   r"   r#   r$   r%   r&   r2   Ú__classcell__)r6   s   @r7   r
   r
      sn  ø… ñð6 €Jà€K�ÓØÐ�sÓØ!Ð˜Ó!Ø€IˆsÓØ€J�ÓØ #€L�%˜#‘+Ó#Ø#Ð�uÓ#Ø €N�EÓ Ø47€J��d˜3‘i‘ %¨¨S¨¡/Ñ1Ó7Ø=@Ð˜˜t C™y™¨5°°c°©?Ñ:Ó@Ø46€J��d˜3‘i‘ %¨¨S¨¡/Ñ1Ó6Ø€L�#ÓØ€HˆdÓØ"%€N�E˜C‘KÓ%Ø8:Ð˜$˜s™) e¨C°¨H¡oÑ5Ó:Ø:<Ð˜D ™I¨¨c°3¨h©Ñ7Ó<Ø-1Ð$ dÓ1Ø-2Ð$ dÓ2Ø€K�ÓØ&*€M�4˜‘9˜tÑ#Ó*Ø%)€L�$�s‘)˜dÑ"Ó)÷(ð (ó    r
   N)r;   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r
   Ú__all__r   rE   r7   ú<module>rK      sN   ðñ !å .å 1Ý 3Ý #ñ Ð;Ô<Øô9(Ð&Ð(8ó 9(ó ó =ð9(ðx Ð
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