
    sg                     r    d Z ddlmZ ddlmZ ddlmZ ddlmZ  ej                  e
      Z G d de      Zy	)
zUperNet model configuration   )PretrainedConfig)logging) verify_backbone_config_arguments   )CONFIG_MAPPINGc                   J     e Zd ZdZdZdddddddg ddd	d
ddddf fd	Z xZS )UperNetConfiga  
    This is the configuration class to store the configuration of an [`UperNetForSemanticSegmentation`]. It is used to
    instantiate an UperNet model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the UperNet
    [openmmlab/upernet-convnext-tiny](https://huggingface.co/openmmlab/upernet-convnext-tiny) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`):
            The configuration of the backbone model.
        backbone (`str`, *optional*):
            Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
            will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
            is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
        use_pretrained_backbone (`bool`, *optional*, `False`):
            Whether to use pretrained weights for the backbone.
        use_timm_backbone (`bool`, *optional*, `False`):
            Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
            library.
        backbone_kwargs (`dict`, *optional*):
            Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
            e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
        hidden_size (`int`, *optional*, defaults to 512):
            The number of hidden units in the convolutional layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        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_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
    ```upernetNFi   g{Gz?)   r   r      Tg?i     r      c                    t        |   di | |)|'t        j                  d       t	        d   g d      }n;t        |t              r+|j                  d      }t        |   }|j                  |      }t        |||||       || _
        || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _        || _        || _        || _        || _        || _        y )NzX`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.resnet)stage1stage2stage3stage4)out_features
model_type)use_timm_backboneuse_pretrained_backbonebackbonebackbone_configbackbone_kwargs )super__init__loggerinfor   
isinstancedictget	from_dictr   r   r   r   r   r   hidden_sizeinitializer_rangepool_scalesuse_auxiliary_headauxiliary_loss_weightauxiliary_in_channelsauxiliary_channelsauxiliary_num_convsauxiliary_concat_inputloss_ignore_index)selfr   r   r   r   r   r%   r&   r'   r(   r)   r*   r+   r,   r-   r.   kwargsbackbone_model_typeconfig_class	__class__s                      d/var/www/html/venv/lib/python3.12/site-packages/transformers/models/upernet/configuration_upernet.pyr   zUperNetConfig.__init__W   s   & 	"6""x'7KKrs,X6DlmO."1"5"5l"C)*=>L*44_EO(/$;++	
  / '>$!2.&!2&"4%:"%:""4#6 &<#!2    )__name__
__module____qualname____doc__r   r   __classcell__)r3   s   @r4   r	   r	      sI    8t J  % !!$!23 23r5   r	   N)r9   configuration_utilsr   utilsr   utils.backbone_utilsr   auto.configuration_autor   
get_loggerr6   r   r	   r   r5   r4   <module>r@      s9    " 3  D 4 
		H	%o3$ o3r5   