






"""simple docstring"""



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

__A		:       int  				=		{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				int							    =   "deta"
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   {
					    "hidden_size": "d_model",
					    "num_attention_heads": "encoder_attention_heads",
					}




					def __init__(				self     :						Union[str, Any]    ,						A     :						Optional[int]=None    ,						A     :						Union[str, Any]=9_00    ,						A     :						Tuple=20_48    ,						A     :						int=6    ,						A     :						str=20_48    ,						A     :						Any=8    ,						A     :						Optional[int]=6    ,						A     :						Dict=10_24    ,						A     :						str=8    ,						A     :						Dict=0.0    ,						A     :						Union[str, Any]=True    ,						A     :						List[Any]="relu"    ,						A     :						Tuple=2_56    ,						A     :						Optional[int]=0.1    ,						A     :						int=0.0    ,						A     :						str=0.0    ,						A     :						List[Any]=0.02    ,						A     :						Union[str, Any]=1.0    ,						A     :						str=True    ,						A     :						str=False    ,						A     :						Optional[int]="sine"    ,						A     :						Optional[Any]=5    ,						A     :						str=4    ,						A     :						Union[str, Any]=4    ,						A     :						Tuple=True    ,						A     :						Union[str, Any]=3_00    ,						A     :						Optional[Any]=True    ,						A     :						int=True    ,						A     :						Dict=1    ,						A     :						Tuple=5    ,						A     :						Optional[Any]=2    ,						A     :						Optional[Any]=1    ,						A     :						Any=1    ,						A     :						int=5    ,						A     :						Optional[Any]=2    ,						A     :						List[str]=0.1    ,						A     :						Dict=0.25    ,						**A     :						Tuple    ,						)  ->  Dict:
							if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
									lowercase_ : Optional[int]							       =   CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']   )
							else:
									if isinstance(A    ,						A   ):
											lowercase_ : List[str]							       =   backbone_config.pop('''model_type'''   )
											lowercase_ : List[str]							       =   CONFIG_MAPPING[backbone_model_type]
											lowercase_ : Union[str, Any]							       =   config_class.from_dict(A   )

							lowercase_ : List[str]							       =   backbone_config
							lowercase_ : Optional[int]							       =   num_queries
							lowercase_ : str							       =   max_position_embeddings
							lowercase_ : Any							       =   d_model
							lowercase_ : Optional[Any]							       =   encoder_ffn_dim
							lowercase_ : List[str]							       =   encoder_layers
							lowercase_ : Dict							       =   encoder_attention_heads
							lowercase_ : int							       =   decoder_ffn_dim
							lowercase_ : List[Any]							       =   decoder_layers
							lowercase_ : int							       =   decoder_attention_heads
							lowercase_ : Optional[Any]							       =   dropout
							lowercase_ : Tuple							       =   attention_dropout
							lowercase_ : str							       =   activation_dropout
							lowercase_ : List[str]							       =   activation_function
							lowercase_ : int							       =   init_std
							lowercase_ : Dict							       =   init_xavier_std
							lowercase_ : List[Any]							       =   encoder_layerdrop
							lowercase_ : str							       =   auxiliary_loss
							lowercase_ : Dict							       =   position_embedding_type
							# deformable attributes
							lowercase_ : Union[str, Any]							       =   num_feature_levels
							lowercase_ : Optional[int]							       =   encoder_n_points
							lowercase_ : Dict							       =   decoder_n_points
							lowercase_ : Tuple							       =   two_stage
							lowercase_ : Union[str, Any]							       =   two_stage_num_proposals
							lowercase_ : Tuple							       =   with_box_refine
							lowercase_ : Optional[int]							       =   assign_first_stage
							if two_stage is True and with_box_refine is False:
									raise ValueError('''If two_stage is True, with_box_refine must be True.'''   )
							# Hungarian matcher
							lowercase_ : Optional[Any]							       =   class_cost
							lowercase_ : Dict							       =   bbox_cost
							lowercase_ : Optional[int]							       =   giou_cost
							# Loss coefficients
							lowercase_ : Optional[int]							       =   mask_loss_coefficient
							lowercase_ : Optional[Any]							       =   dice_loss_coefficient
							lowercase_ : Dict							       =   bbox_loss_coefficient
							lowercase_ : int							       =   giou_loss_coefficient
							lowercase_ : Union[str, Any]							       =   eos_coefficient
							lowercase_ : Dict							       =   focal_alpha
							super().__init__(is_encoder_decoder=A    ,						**A   )




					@property
					def   A  (				self     :						Any   )  ->  int:
							return self.encoder_attention_heads




					@property
					def   A  (				self     :						Optional[int]   )  ->  int:
							return self.d_model






					def   A  (				self     :						List[Any]   )  ->  Dict:
							lowercase_ : str							       =   copy.deepcopy(self.__dict__   )
							lowercase_ : Union[str, Any]							       =   self.backbone_config.to_dict()
							lowercase_ : List[Any]							       =   self.__class__.model_type
							return output











"""simple docstring"""



def 						lowercase    (				__snake_case				: list[int]	):
		lowercase_ : List[Any]							       =   len(__snake_case	)
		for i in range(__snake_case	):
				for j in range(i + 1		,					__snake_case	):
						if numbers[j] < numbers[i]:
								lowercase_       ,					lowercase_ : Optional[int]							       =   numbers[j], numbers[i]
		return numbers


if __name__ == "__main__":
						__A		:       int  				=		input('''Enter numbers separated by a comma:\n''').strip()
						__A		:       Any  				=		[int(item) for item in user_input.split(''',''')]
						print(exchange_sort(unsorted))



