Ë
    ÜÓ^j„  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&Swinv2 Transformer model configurationé    )Ústricté   )ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringz(microsoft/swinv2-tiny-patch4-window8-256)Ú
checkpointc                   óî  ‡ — 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e   z  e	eef   z  e
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Zee
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d+<   ˆ fd,„Z!ˆ xZ"S )-ÚSwinv2ConfigaF  
    window_size (`int`, *optional*, defaults to 7):
        Size of windows.
    pretrained_window_sizes (`list(int)`, *optional*, defaults to `[0, 0, 0, 0]`):
        Size of windows during pretraining.
    encoder_stride (`int`, *optional*, defaults to 32):
        Factor to increase the spatial resolution by in the decoder head for masked image modeling.

    Example:

    ```python
    >>> from transformers import Swinv2Config, Swinv2Model

    >>> # Initializing a Swinv2 microsoft/swinv2-tiny-patch4-window8-256 style configuration
    >>> configuration = Swinv2Config()

    >>> # Initializing a model (with random weights) from the microsoft/swinv2-tiny-patch4-window8-256 style configuration
    >>> model = Swinv2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úswinv2Ú	num_headsÚ
num_layers)Únum_attention_headsÚnum_hidden_layerséà   Ú
image_sizeé   Ú
patch_sizer   Únum_channelsé`   Ú	embed_dim)é   r   é   r   .Údepths)r   r   é   é   é   Úwindow_size)r   r   r   r   Úpretrained_window_sizesg      @Ú	mlp_ratioTÚqkv_biasg        Úhidden_dropout_probÚattention_probs_dropout_probgš™™™™™¹?Údrop_path_rateÚgeluÚ
hidden_actFÚuse_absolute_embeddingsg{®Gáz”?Úinitializer_rangegñhãˆµøä>Úlayer_norm_epsé    Úencoder_strideNÚ_out_featuresÚ_out_indicesc                 ó¬  •— t        | j                  «      | _        dgt        dt        | j                  «      dz   «      D �cg c]  }d|› �‘Œ	 c}z   | _        | j                  |j                  dd «      |j                  dd «      ¬«       t        | j                  dt        | j                  «      dz
  z  z  «      | _	        t        ‰| �,  di |¤Ž y c c}w )	NÚstemé   ÚstageÚout_indicesÚout_features)r1   r2   r   © )Úlenr   r   ÚrangeÚstage_namesÚ"set_output_features_output_indicesÚpopÚintr   Úhidden_sizeÚ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/swinv2/configuration_swinv2.pyr<   zSwinv2Config.__post_init__M   sº   ø€ Ü˜dŸk™kÓ*ˆŒØ"˜8ÄÀaÌÈTÏ[É[ÓIYÐ\]ÑI]Ó@^Ö&_¸¨¨s¨e¢}Ò&_Ñ_ˆÔØ×/Ñ/ØŸ
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 ˜tŸ~™~°´c¸$¿+¹+Ó6FÈÑ6JÑ0KÑKÓLˆÔÜ‰ÑÑ' Ó'ùò '`s   ÁC)#Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typeÚattribute_mapr   r9   ÚlistÚtupleÚ__annotations__r   r   r   r   r   r   r   r   Úfloatr    Úboolr!   r"   r#   r%   Ústrr&   r'   r(   r*   r+   r,   r<   Ú__classcell__)r@   s   @rA   r
   r
      sr  ø… ñð. €Jð  +Ø)ñ€Mð
 58€J��d˜3‘i‘ %¨¨S¨¡/Ñ1Ó7Ø45€J��d˜3‘i‘ %¨¨S¨¡/Ñ1Ó5Ø€L�#ÓØ€IˆsÓØ*6€FˆD�‰I˜˜c 3˜h™Ñ'Ó6Ø-;€Iˆt�C‰y˜5  c ™?Ñ*Ó;Ø€K�ÓØ;GÐ˜T #™Y¨¨s°C¨x©Ñ8ÓGØ€IˆuÓØ€HˆdÓØ'*Ð˜ ™Ó*Ø03Ð  %¨#¡+Ó3Ø"%€N�E˜C‘KÓ%Ø€J�ÓØ$)Ð˜TÓ)Ø#Ð�uÓ#Ø €N�EÓ Ø€N�CÓØ&*€M�4˜‘9˜tÑ#Ó*Ø%)€L�$�s‘)˜dÑ"Ó)÷	(ð 	(ó    r
   N)rE   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   r
   Ú__all__r3   rO   rA   ú<module>rU      sO   ðñ -å .å 1Ý 3Ý #ñ ÐEÔFØô=(Ð&Ð(8ó =(ó ó Gð=(ð@ Ð
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