§
    ‚Štj  ã                   óŽ   — 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	d
S )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                 ó  •— dgd„ t          d| j        dz   ¦  «        D ¦   «         z   | _        |                      |                     dd ¦  «        |                     dd ¦  «        ¬¦  «          t          ¦   «         j        di |¤Ž d S )NÚstemc                 ó   — g | ]}d |› �‘ŒS )Ústage© )Ú.0Úidxs     úk/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/pixio/configuration_pixio.pyú
<listcomp>z-PixioConfig.__post_init__.<locals>.<listcomp>N   s   € Ð&eÐ&eÐ&e¸ }¨s } }Ð&eÐ&eÐ&eó    é   Úout_indicesÚout_features)r0   r1   r)   )Úranger   Ústage_namesÚ"set_output_features_output_indicesÚpopÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s     €r,   r7   zPixioConfig.__post_init__M   s™   ø€ Ø"˜8Ð&eÐ&eÅÀaÈÔI_ÐbcÑIcÑ@dÔ@dÐ&eÑ&eÔ&eÑeˆÔØ×/Ò/ØŸ
š
 =°$Ñ7Ô7ÀfÇjÂjÐQ_ÐaeÑFfÔFfð 	0ñ 	
ô 	
ð 	
ð 	�‰ŒÔÐ'Ð' Ð'Ð'Ð'Ð'Ð'r.   )!Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚ__annotations__r   r   r   r   Ústrr   Úfloatr   r   r   r   ÚlistÚtupler   r   r   Úboolr   r   r    r!   r"   r$   r7   Ú__classcell__)r:   s   @r,   r
   r
      sÉ  ø€ € € € € € ðð ð2 €Jà€K�ÐÐÑØÐ�sÐÐÑØ!Ð˜Ð!Ð!Ñ!Ø€IˆsÐÐÑØ€J�ÐÐÑØ'*Ð˜ ™Ð*Ð*Ñ*Ø03Ð  %¨#¡+Ð3Ð3Ñ3Ø#Ð�uÐ#Ð#Ñ#Ø €N�EÐ Ð Ñ Ø47€J��d˜3”i‘ %¨¨S¨¤/Ñ1Ð7Ð7Ñ7Ø46€J��d˜3”i‘ %¨¨S¨¤/Ñ1Ð6Ð6Ñ6Ø€L�#ÐÐÑØ€HˆdÐÐÑØ"%€N�E˜C‘KÐ%Ð%Ñ%Ø&*€M�4˜”9˜tÑ#Ð*Ð*Ñ*Ø%)€L�$�s”)˜dÑ"Ð)Ð)Ñ)Ø €O�TÐ Ð Ñ Ø"&Ð˜4Ð&Ð&Ñ&Ø€L�#ÐÐÑð(ð (ð (ð (ð (ð (ð (ð (ð (r.   r
   N)
Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r
   Ú__all__r)   r.   r,   ú<module>rM      s°   ðð( /Ð .Ð .Ð .Ð .Ð .à 1Ð 1Ð 1Ð 1Ð 1Ð 1Ø 3Ð 3Ð 3Ð 3Ð 3Ð 3Ø #Ð #Ð #Ð #Ð #Ð #ð €Ð0Ð1Ñ1Ô1Øð5(ð 5(ð 5(ð 5(ð 5(Ð%Ð'7ñ 5(ô 5(ñ „ñ 2Ô1ð5(ðp ˆ/€€€r.   