





"""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


lowercase__				:Optional[Any]				   =       {
    '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
}

lowercase__				:Any				   =       logging.get_logger(__name__)




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       int         =       'maskformer'
      _A     :       Dict         =       {'hidden_size': 'mask_feature_size'}
      _A     :       int         =       ['resnet', 'swin']
      _A     :       Tuple         =       ['detr']

      def __init__(							self       :      Any						,				__lowercase       :      int = 256						,				__lowercase       :      int = 256						,				__lowercase       :      float = 0.1						,				__lowercase       :      bool = False						,				__lowercase       :      Optional[Dict] = None						,				__lowercase       :      Optional[Dict] = None						,				__lowercase       :      float = 0.0_2						,				__lowercase       :      float = 1.0						,				__lowercase       :      float = 1.0						,				__lowercase       :      float = 1.0						,				__lowercase       :      float = 2_0.0						,				__lowercase       :      Optional[bool] = None						,				**__lowercase       :      Dict						,				):


        '''simple docstring'''




        if backbone_config is None:
          # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
          __UpperCAmelCase    :						Optional[int]       =						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 ):
          __UpperCAmelCase    :						List[str]       =						backbone_config.pop('''model_type''' )
          __UpperCAmelCase    :						List[Any]       =						CONFIG_MAPPING[backbone_model_type]
          __UpperCAmelCase    :						str       =						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
          __UpperCAmelCase    :						Tuple       =						DetrConfig()
        else:
          # verify that the decoder is supported
          __UpperCAmelCase    :						List[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 ):
            __UpperCAmelCase    :						Dict       =						CONFIG_MAPPING[decoder_type]
            __UpperCAmelCase    :						Tuple       =						config_class.from_dict(__lowercase )

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

        __UpperCAmelCase    :						Tuple       =						self.decoder_config.encoder_attention_heads
        __UpperCAmelCase    :						Optional[Any]       =						self.decoder_config.num_hidden_layers
        super().__init__(**__lowercase )

      @classmethod
      def       A_					(							cls       :      List[Any]						,				__lowercase       :      PretrainedConfig						,				__lowercase       :      PretrainedConfig						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




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







      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						copy.deepcopy(self.__dict__ )
        __UpperCAmelCase    :						List[str]       =						self.backbone_config.to_dict()
        __UpperCAmelCase    :						Dict       =						self.decoder_config.to_dict()
        __UpperCAmelCase    :						List[Any]       =						self.__class__.model_type
        return output






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_ = 10_00  )						->int:





  """simple docstring"""


  return sum(2 * a * ((a - 1) // 2) for a in range(3  ,			n + 1  )  )


if __name__ == "__main__":
  print(solution())