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«      e G d„ de«      «       «       ZdgZy)zUperNet model configurationé    )Ústricté   )Ú%consolidate_backbone_kwargs_to_config)ÚPreTrainedConfig)Úauto_docstringé   )Ú
AutoConfigzopenmmlab/upernet-convnext-tiny)Ú
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    pool_scales (`tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
        Pooling scales used in Pooling Pyramid Module applied on the last feature map.
    use_auxiliary_head (`bool`, *optional*, defaults to `True`):
        Whether to use an auxiliary head during training.
    auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
        Weight of the cross-entropy loss of the auxiliary head.
    auxiliary_in_channels (`int`, *optional*, defaults to 256):
        Number of input channels in the auxiliary head.
    auxiliary_channels (`int`, *optional*, defaults to 256):
        Number of channels to use in the auxiliary head.
    auxiliary_num_convs (`int`, *optional*, defaults to 1):
        Number of convolutional layers to use in the auxiliary head.
    auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
        Whether to concatenate the output of the auxiliary head with the input before the classification layer.
    loss_ignore_index (`int`, *optional*, defaults to 255):
        The index that is ignored by the loss function.

    Examples:

    ```python
    >>> from transformers import UperNetConfig, UperNetForSemanticSegmentation

    >>> # Initializing a configuration
    >>> configuration = UperNetConfig()

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

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```ÚupernetÚbackbone_configNi   Úhidden_sizeg{®Gáz”?Úinitializer_range)é   r   r   é   .Úpool_scalesTÚuse_auxiliary_headgš™™™™™Ù?Úauxiliary_loss_weightÚauxiliary_in_channelsé   Úauxiliary_channelsr   Úauxiliary_num_convsFÚauxiliary_concat_inputéÿ   Úloss_ignore_indexc                 ón   •— t        d| j                  ddg d¢idœ|¤Ž\  | _        }t        ‰| �  di |¤Ž y )NÚresnetÚout_features)Ústage1Ústage2Ústage3Ústage4)r   Údefault_config_typeÚdefault_config_kwargs© )r   r   ÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s     €ú|/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/upernet/configuration_upernet.pyr(   zUperNetConfig.__post_init__K   sM   ø€ Ü'Lð (
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model_typer	   Úsub_configsr   Údictr   Ú__annotations__r   Úintr   Úfloatr   ÚlistÚtupler   Úboolr   r   r   r   r   r   r(   Ú__classcell__)r+   s   @r,   r   r      sº   ø… ñðB €JØ$ jÐ1€Kà6:€O�TÐ,Ñ,¨tÑ3Ó:Ø€K�ÓØ#Ð�uÓ#Ø/;€K��c‘˜U 3¨ 8™_Ñ,Ó;Ø#Ð˜Ó#Ø#&Ð˜5Ó&Ø(,Ð˜3 ™:Ó,Ø!Ð˜Ó!Ø Ð˜Ó Ø#(Ð˜DÓ(Ø Ð�sÓ ÷	(ð 	(r-   r   N)r1   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   Úauto.configuration_autor	   r   Ú__all__r&   r-   r,   ú<module>rB      sN   ðñ "å .å CÝ 3Ý #Ý 0ñ Ð<Ô=Øô:(Ð$ó :(ó ó >ð:(ðz Ð
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