
import copy

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


UpperCamelCase_						=						logging.get_logger(__name__)

UpperCamelCase_						=						{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}

class      _SCREAMING_SNAKE_CASE				(		_UpperCAmelCase				):
				lowerCamelCase_	  =  'deta'
				lowerCamelCase_	  =  {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}





				def __init__(		self			:		Optional[Any]       ,   snake_case_			:		Optional[Any]=None       ,   snake_case_			:		int=900       ,   snake_case_			:		List[Any]=2048       ,   snake_case_			:		int=6       ,   snake_case_			:		Dict=2048       ,   snake_case_			:		str=8       ,   snake_case_			:		Optional[Any]=6       ,   snake_case_			:		str=1024       ,   snake_case_			:		int=8       ,   snake_case_			:		Optional[int]=0.0       ,   snake_case_			:		List[Any]=True       ,   snake_case_			:		Union[str, Any]="relu"       ,   snake_case_			:		Dict=256       ,   snake_case_			:		Tuple=0.1       ,   snake_case_			:		Union[str, Any]=0.0       ,   snake_case_			:		Tuple=0.0       ,   snake_case_			:		str=0.02       ,   snake_case_			:		Union[str, Any]=1.0       ,   snake_case_			:		Tuple=True       ,   snake_case_			:		str=False       ,   snake_case_			:		Union[str, Any]="sine"       ,   snake_case_			:		str=5       ,   snake_case_			:		Tuple=4       ,   snake_case_			:		Optional[Any]=4       ,   snake_case_			:		int=True       ,   snake_case_			:		List[Any]=300       ,   snake_case_			:		Optional[int]=True       ,   snake_case_			:		List[str]=True       ,   snake_case_			:		str=1       ,   snake_case_			:		List[Any]=5       ,   snake_case_			:		Optional[Any]=2       ,   snake_case_			:		Optional[Any]=1       ,   snake_case_			:		Optional[Any]=1       ,   snake_case_			:		List[Any]=5       ,   snake_case_			:		Optional[Any]=2       ,   snake_case_			:		int=0.1       ,   snake_case_			:		Optional[Any]=0.25       ,   **snake_case_			:		Any       ,   ):


								"""simple docstring"""

								if backbone_config is None:
												logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''				)
												A  :	Any				=					CONFIG_MAPPING['resnet'](out_features=['''stage2''', '''stage3''', '''stage4''']				)
								else:
												if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_				):
																A  :	Dict				=					backbone_config.pop('''model_type'''				)
																A  :	List[str]				=					CONFIG_MAPPING[backbone_model_type]
																A  :	int				=					config_class.from_dict(SCREAMING_SNAKE_CASE_				)

								A  :	str				=					backbone_config
								A  :	Dict				=					num_queries
								A  :	Dict				=					max_position_embeddings
								A  :	Tuple				=					d_model
								A  :	Optional[int]				=					encoder_ffn_dim
								A  :	Dict				=					encoder_layers
								A  :	Optional[int]				=					encoder_attention_heads
								A  :	str				=					decoder_ffn_dim
								A  :	int				=					decoder_layers
								A  :	int				=					decoder_attention_heads
								A  :	List[str]				=					dropout
								A  :	List[Any]				=					attention_dropout
								A  :	Union[str, Any]				=					activation_dropout
								A  :	str				=					activation_function
								A  :	Optional[Any]				=					init_std
								A  :	Optional[Any]				=					init_xavier_std
								A  :	Optional[int]				=					encoder_layerdrop
								A  :	str				=					auxiliary_loss
								A  :	Union[str, Any]				=					position_embedding_type
								# deformable attributes
								A  :	int				=					num_feature_levels
								A  :	List[str]				=					encoder_n_points
								A  :	Optional[Any]				=					decoder_n_points
								A  :	Tuple				=					two_stage
								A  :	List[str]				=					two_stage_num_proposals
								A  :	Optional[Any]				=					with_box_refine
								A  :	str				=					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
								A  :	List[Any]				=					class_cost
								A  :	str				=					bbox_cost
								A  :	Optional[Any]				=					giou_cost
								# Loss coefficients
								A  :	Optional[int]				=					mask_loss_coefficient
								A  :	Any				=					dice_loss_coefficient
								A  :	Optional[int]				=					bbox_loss_coefficient
								A  :	str				=					giou_loss_coefficient
								A  :	Tuple				=					eos_coefficient
								A  :	str				=					focal_alpha
								super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_				)





				@property
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								return self.encoder_attention_heads





				@property
				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								return self.d_model






				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A  :	List[str]				=					copy.deepcopy(self.__dict__				)
								A  :	Optional[Any]				=					self.backbone_config.to_dict()
								A  :	Optional[int]				=					self.__class__.model_type
								return output



'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 10_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						3
     __lowerCamelCase  :      Dict														=						0
     while a < n:
          if a % 3 == 0 or a % 5 == 0:
               result += a
          elif a % 15 == 0:
               result -= a
          a += 1
     return result


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






