






"""simple docstring"""






import copy
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


UpperCAmelCase_     :				Optional[int]         =				{
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"maskformer"
      __UpperCamelCase							=						{"hidden_size": "mask_feature_size"}
      __UpperCamelCase							=						["resnet", "swin"]
      __UpperCamelCase							=						["detr"]
      def __init__(		self  :						Optional[Any]		,			lowercase_  :						int = 256		,			lowercase_  :						int = 256		,			lowercase_  :						float = 0.1		,			lowercase_  :						bool = False		,			lowercase_  :						Optional[Dict] = None		,			lowercase_  :						Optional[Dict] = None		,			lowercase_  :						float = 0.02		,			lowercase_  :						float = 1.0		,			lowercase_  :						float = 1.0		,			lowercase_  :						float = 1.0		,			lowercase_  :						float = 20.0		,			lowercase_  :						Optional[bool] = None		,			**lowercase_  :						Optional[Any]		,			):




          '''simple docstring'''


          if backbone_config is None:
              # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
              SCREAMING_SNAKE_CASE_   :						Any							=	SwinConfig(
                  image_size=384		,			in_channels=3		,			patch_size=4		,			embed_dim=128		,			depths=[2, 2, 18, 2]		,			num_heads=[4, 8, 16, 32]		,			window_size=12		,			drop_path_rate=0.3		,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		,			)

          if isinstance(lowercase_		,			lowercase_):
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	backbone_config.pop('''model_type''')
              SCREAMING_SNAKE_CASE_   :						Any							=	CONFIG_MAPPING[backbone_model_type]
              SCREAMING_SNAKE_CASE_   :						int							=	config_class.from_dict(lowercase_)

          # verify that the backbone is supported
          if backbone_config.model_type not in self.backbones_supported:
              logger.warning_once(
                  F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
                  F'Supported model types: {",".join(self.backbones_supported)}')

          if decoder_config is None:
              # fall back to https://huggingface.co/facebook/detr-resnet-50
              SCREAMING_SNAKE_CASE_   :						Any							=	DetrConfig()
          else:
              # verify that the decoder is supported
              SCREAMING_SNAKE_CASE_   :						str							=	(
                  decoder_config.pop('''model_type''') if isinstance(lowercase_		,			lowercase_) else decoder_config.model_type
              )
              if decoder_type not in self.decoders_supported:
                  raise ValueError(
                      F'Transformer Decoder {decoder_type} not supported, please use one of'
                      F' {",".join(self.decoders_supported)}')
              if isinstance(lowercase_		,			lowercase_):
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	CONFIG_MAPPING[decoder_type]
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	config_class.from_dict(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	backbone_config
          SCREAMING_SNAKE_CASE_   :						str							=	decoder_config
          # main feature dimension for the model
          SCREAMING_SNAKE_CASE_   :						Tuple							=	fpn_feature_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	mask_feature_size
          # initializer
          SCREAMING_SNAKE_CASE_   :						Any							=	init_std
          SCREAMING_SNAKE_CASE_   :						Any							=	init_xavier_std
          # Hungarian matcher && loss
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	cross_entropy_weight
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	dice_weight
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	mask_weight
          SCREAMING_SNAKE_CASE_   :						Dict							=	use_auxiliary_loss
          SCREAMING_SNAKE_CASE_   :						str							=	no_object_weight
          SCREAMING_SNAKE_CASE_   :						Any							=	output_auxiliary_logits

          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.decoder_config.encoder_attention_heads
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.decoder_config.num_hidden_layers
          super().__init__(**lowercase_)
      @classmethod
      def _SCREAMING_SNAKE_CASE				(		cls  :						Tuple		,			lowercase_  :						PretrainedConfig		,			lowercase_  :						PretrainedConfig		,			**lowercase_  :						int):




          '''simple docstring'''


          return cls(
              backbone_config=lowercase_		,			decoder_config=lowercase_		,			**lowercase_		,			)



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	copy.deepcopy(self.__dict__)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.backbone_config.to_dict()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.decoder_config.to_dict()
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.__class__.model_type
          return output










"""simple docstring"""






def       _A    (__a = 1_00							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	(n * (n + 1) // 2) ** 2
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	n * (n + 1) * (2 * n + 1) // 6
    return sum_cubes - sum_squares


if __name__ == "__main__":
  print(f'''{solution() = }''')


