class_docstr: This is the configuration class to store the configuration of a [`MaskFormerModel`]. It is used to instantiate a
  MaskFormer 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 MaskFormer
  [facebook/maskformer-swin-base-ade](https://huggingface.co/facebook/maskformer-swin-base-ade) architecture trained
  on [ADE20k-150](https://huggingface.co/datasets/scene_parse_150).
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Currently, MaskFormer only supports the [Swin Transformer](swin) as backbone.
  
  Args:
      mask_feature_size (`int`, *optional*, defaults to 256):
          The masks' features size, this value will also be used to specify the Feature Pyramid Network features'
          size.
      no_object_weight (`float`, *optional*, defaults to 0.1):
          Weight to apply to the null (no object) class.
      use_auxiliary_loss(`bool`, *optional*, defaults to `False`):
          If `True` [`MaskFormerForInstanceSegmentationOutput`] will contain the auxiliary losses computed using the
          logits from each decoder's stage.
      backbone_config (`Dict`, *optional*):
          The configuration passed to the backbone, if unset, the configuration corresponding to
          `swin-base-patch4-window12-384` will be used.
      decoder_config (`Dict`, *optional*):
          The configuration passed to the transformer decoder model, if unset the base config for `detr-resnet-50`
          will be used.
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      init_xavier_std (`float`, *optional*, defaults to 1):
          The scaling factor used for the Xavier initialization gain in the HM Attention map module.
      dice_weight (`float`, *optional*, defaults to 1.0):
          The weight for the dice loss.
      cross_entropy_weight (`float`, *optional*, defaults to 1.0):
          The weight for the cross entropy loss.
      mask_weight (`float`, *optional*, defaults to 20.0):
          The weight for the mask loss.
      output_auxiliary_logits (`bool`, *optional*):
          Should the model output its `auxiliary_logits` or not.
  
  Raises:
      `ValueError`:
          Raised if the backbone model type selected is not in `["swin"]` or the decoder model type selected is not
          in `["detr"]`
  
  Examples:
  
  ```python
  >>> from transformers import MaskFormerConfig, MaskFormerModel
  
  >>> # Initializing a MaskFormer facebook/maskformer-swin-base-ade configuration
  >>> configuration = MaskFormerConfig()
  
  >>> # Initializing a model (with random weights) from the facebook/maskformer-swin-base-ade style configuration
  >>> model = MaskFormerModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
docstr: Instantiate a [`MaskFormerConfig`] (or a derived class) from a pre-trained backbone model configuration and DETR model
  configuration.
  
      Args:
          backbone_config ([`PretrainedConfig`]):
              The backbone configuration.
          decoder_config ([`PretrainedConfig`]):
              The transformer decoder configuration to use.
  
      Returns:
          [`MaskFormerConfig`]: An instance of a configuration object
code: def from_backbone_and_decoder_configs(
      cls,
      backbone_config: PretrainedConfig,
      decoder_config: PretrainedConfig,
      **kwargs,
  ):
      """Instantiate a [`MaskFormerConfig`] (or a derived class) from a pre-trained backbone model configuration and DETR model
      configuration.
  
          Args:
              backbone_config ([`PretrainedConfig`]):
                  The backbone configuration.
              decoder_config ([`PretrainedConfig`]):
                  The transformer decoder configuration to use.
  
          Returns:
              [`MaskFormerConfig`]: An instance of a configuration object
      """
      return cls(
          backbone_config=backbone_config,
          decoder_config=decoder_config,
          **kwargs,
      )
  
file_path: transformers/src/transformers/models/maskformer/configuration_maskformer.py
imports: from typing import Dict, Optional
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  from ..detr import DetrConfig
  from ..swin import SwinConfig
label_desc: Use image node_id 2 for calling the MaskFormerConfig obj's underlying member method code with example usage: obj.from_backbone_and_decoder_configs(cls, backbone_config, decoder_config) and returns: cls