


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


a					  =     {
    '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
}

a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   'maskformer'
					__SCREAMING_SNAKE_CASE       :  Union[str, Any]						=   {'hidden_size': 'mask_feature_size'}
					__SCREAMING_SNAKE_CASE       :  str						=   ['resnet', 'swin']
					__SCREAMING_SNAKE_CASE       :  Dict						=   ['detr']

					def __init__(     self       :   Dict							,					UpperCamelCase__       :   int = 256							,					UpperCamelCase__       :   int = 256							,					UpperCamelCase__       :   float = 0.1							,					UpperCamelCase__       :   bool = False							,					UpperCamelCase__       :   Optional[Dict] = None							,					UpperCamelCase__       :   Optional[Dict] = None							,					UpperCamelCase__       :   float = 0.02							,					UpperCamelCase__       :   float = 1.0							,					UpperCamelCase__       :   float = 1.0							,					UpperCamelCase__       :   float = 1.0							,					UpperCamelCase__       :   float = 20.0							,					UpperCamelCase__       :   Optional[bool] = None							,					**UpperCamelCase__       :   Dict							,					):






												'''simple docstring'''






												if backbone_config is None:
																			# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																			lowercase_  							=			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(UpperCamelCase__							,					UpperCamelCase__       ):
																			lowercase_  							=			backbone_config.pop("""model_type"""       )
																			lowercase_  							=			CONFIG_MAPPING[backbone_model_type]
																			lowercase_  							=			config_class.from_dict(UpperCamelCase__       )

												# 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
																			lowercase_  							=			DetrConfig()
												else:
																			# verify that the decoder is supported
																			lowercase_  							=			(
																			    decoder_config.pop("""model_type"""       ) if isinstance(UpperCamelCase__							,					UpperCamelCase__       ) 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(UpperCamelCase__							,					UpperCamelCase__       ):
																										lowercase_  							=			CONFIG_MAPPING[decoder_type]
																										lowercase_  							=			config_class.from_dict(UpperCamelCase__       )

												lowercase_  							=			backbone_config
												lowercase_  							=			decoder_config
												# main feature dimension for the model
												lowercase_  							=			fpn_feature_size
												lowercase_  							=			mask_feature_size
												# initializer
												lowercase_  							=			init_std
												lowercase_  							=			init_xavier_std
												# Hungarian matcher && loss
												lowercase_  							=			cross_entropy_weight
												lowercase_  							=			dice_weight
												lowercase_  							=			mask_weight
												lowercase_  							=			use_auxiliary_loss
												lowercase_  							=			no_object_weight
												lowercase_  							=			output_auxiliary_logits

												lowercase_  							=			self.decoder_config.encoder_attention_heads
												lowercase_  							=			self.decoder_config.num_hidden_layers
												super().__init__(**UpperCamelCase__       )

					@classmethod
					def   UpperCAmelCase__       (     cls       :   Union[str, Any]							,					UpperCamelCase__       :   PretrainedConfig							,					UpperCamelCase__       :   PretrainedConfig							,					**UpperCamelCase__       :   Any       ):






												'''simple docstring'''






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







					def   UpperCAmelCase__       (     self       :   List[str]       ):






												'''simple docstring'''






												lowercase_  							=			copy.deepcopy(self.__dict__       )
												lowercase_  							=			self.backbone_config.to_dict()
												lowercase_  							=			self.decoder_config.to_dict()
												lowercase_  							=			self.__class__.model_type
												return output






def 			UpperCAmelCase_				(      UpperCAmelCase__=2_8_1_2_3  ):
					lowercase_  							=			[1] * (limit + 1)

					for i in range(2						,							int(limit**0.5  ) + 1  ):
										sum_divs[i * i] += i
										for k in range(i + 1						,							limit // i + 1  ):
															sum_divs[k * i] += k + i

					lowercase_  							=			set()
					lowercase_  							=			0

					for n in range(1						,							limit + 1  ):
										if sum_divs[n] > n:
															abundants.add(UpperCAmelCase__  )

										if not any((n - a in abundants) for a in abundants  ):
															res += n

					return res


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


