Ë
    ÜÓ^j  ã                   ót   — 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
)é    )Ústricté   )ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringzfacebook/pixio-huge)Ú
checkpointc                   ó”  ‡ — 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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d<   dZeed<   dZeez  ed<   dZee   dz  ed<   dZee   dz  ed<   dZeed<   dZeed <   d!Zeed"<   ˆ fd#„Zˆ xZ S )$ÚPixioConfigaû  
    apply_layernorm (`bool`, *optional*, defaults to `True`):
        Whether to apply layer normalization to the feature maps in case the model is used as backbone.
    reshape_hidden_states (`bool`, *optional*, defaults to `True`):
        Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in
        case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size,
        seq_len, hidden_size)`.
    n_cls_tokens (`int`, *optional*, defaults to 8):
        Number of class tokens in the Transformer encoder.

    Example:

    ```python
    >>> from transformers import PixioConfig, PixioModel

    >>> # Initializing a Pixio pixio-huge style configuration
    >>> configuration = PixioConfig()

    >>> # Initializing a model (with random weights) from the pixio-huge style configuration
    >>> model = PixioModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úpixioi   Úhidden_sizeé    Únum_hidden_layersé   Únum_attention_headsé   Ú	mlp_ratioÚgeluÚ
hidden_actg        Úhidden_dropout_probÚattention_probs_dropout_probg{®Gáz”?Úinitializer_rangeg�íµ ÷Æ°>Úlayer_norm_epsé   Ú
image_sizeÚ
patch_sizer   Únum_channelsTÚqkv_biasÚdrop_path_rateNÚ_out_featuresÚ_out_indicesÚapply_layernormÚreshape_hidden_statesé   Ún_cls_tokensc                 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*   © )Úranger   Ú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/pixio/configuration_pixio.pyr1   zPixioConfig.__post_init__M   s~   ø€ Ø"˜8ÄÀaÈ×I_ÑI_ÐbcÑIcÓ@dÖ&e¸¨¨s¨e¢}Ò&eÑeˆÔØ×/Ñ/ØŸ
™
 =°$Ó7ÀfÇjÁjÐQ_ÐaeÓFfð 	0ô 	
ô 	‰ÑÑ' Ó'ùò	 'fs   ŸA8)!Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚ__annotations__r   r   r   r   Ústrr   Úfloatr   r   r   r   ÚlistÚtupler   r   r   Úboolr   r   r    r!   r"   r$   r1   Ú__classcell__)r5   s   @r6   r
   r
      s%  ø… ñð2 €Jà€K�ÓØÐ�sÓØ!Ð˜Ó!Ø€IˆsÓØ€J�ÓØ'*Ð˜ ™Ó*Ø03Ð  %¨#¡+Ó3Ø#Ð�uÓ#Ø €N�EÓ Ø47€J��d˜3‘i‘ %¨¨S¨¡/Ñ1Ó7Ø46€J��d˜3‘i‘ %¨¨S¨¡/Ñ1Ó6Ø€L�#ÓØ€HˆdÓØ"%€N�E˜C‘KÓ%Ø&*€M�4˜‘9˜tÑ#Ó*Ø%)€L�$�s‘)˜dÑ"Ó)Ø €O�TÓ Ø"&Ð˜4Ó&Ø€L�#Ó÷(ð (ó    r
   N)
Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r
   Ú__all__r+   rD   r6   ú<module>rJ      sJ   ðõ( /å 1Ý 3Ý #ñ Ð0Ô1Øô5(Ð%Ð'7ó 5(ó ó 2ð5(ðp ˆ/�rD   