





import copy

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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class SCREAMING_SNAKE_CASE_  (   _a   ):
						__magic_name__:     int	 =    "deformable_detr"
						__magic_name__:     Union[str, Any]	 =    {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}






						def __init__( self : int						,				_A : int=True						,				_A : Dict=None						,				_A : Optional[int]=3						,				_A : Dict=300						,				_A : Optional[int]=1024						,				_A : Optional[Any]=6						,				_A : Dict=1024						,				_A : Union[str, Any]=8						,				_A : int=6						,				_A : Optional[int]=1024						,				_A : Any=8						,				_A : Optional[Any]=0.0						,				_A : Optional[int]=True						,				_A : Tuple="relu"						,				_A : Dict=256						,				_A : Union[str, Any]=0.1						,				_A : Optional[int]=0.0						,				_A : List[Any]=0.0						,				_A : List[str]=0.0_2						,				_A : List[Any]=1.0						,				_A : int=True						,				_A : str=False						,				_A : List[str]="sine"						,				_A : Dict="resnet50"						,				_A : Any=True						,				_A : List[str]=False						,				_A : Any=4						,				_A : List[str]=4						,				_A : Union[str, Any]=4						,				_A : int=False						,				_A : Optional[int]=300						,				_A : str=False						,				_A : Optional[Any]=1						,				_A : str=5						,				_A : Union[str, Any]=2						,				_A : Optional[int]=1						,				_A : Optional[Any]=1						,				_A : Tuple=5						,				_A : Any=2						,				_A : int=0.1						,				_A : List[Any]=0.2_5						,				_A : Union[str, Any]=False						,				**_A : Any						,				)							->      Union[str, Any]:







							"""simple docstring"""


							if backbone_config is not None and use_timm_backbone:
								raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'		)

							if not use_timm_backbone:
								if backbone_config is None:
									logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
									snake_case_						:     Optional[int]     =		CONFIG_MAPPING["""resnet"""](out_features=['stage4']		)
								elif isinstance(snake_case_						,				snake_case_		):
									snake_case_						:     Union[str, Any]     =		backbone_config.get('model_type'		)
									snake_case_						:     str     =		CONFIG_MAPPING[backbone_model_type]
									snake_case_						:     Union[str, Any]     =		config_class.from_dict(snake_case_		)
							snake_case_						:     Any     =		use_timm_backbone
							snake_case_						:     str     =		backbone_config
							snake_case_						:     Tuple     =		num_channels
							snake_case_						:     Any     =		num_queries
							snake_case_						:     List[str]     =		max_position_embeddings
							snake_case_						:     str     =		d_model
							snake_case_						:     Tuple     =		encoder_ffn_dim
							snake_case_						:     int     =		encoder_layers
							snake_case_						:     Union[str, Any]     =		encoder_attention_heads
							snake_case_						:     Optional[Any]     =		decoder_ffn_dim
							snake_case_						:     Dict     =		decoder_layers
							snake_case_						:     int     =		decoder_attention_heads
							snake_case_						:     Union[str, Any]     =		dropout
							snake_case_						:     Tuple     =		attention_dropout
							snake_case_						:     Optional[Any]     =		activation_dropout
							snake_case_						:     Optional[int]     =		activation_function
							snake_case_						:     Optional[Any]     =		init_std
							snake_case_						:     List[Any]     =		init_xavier_std
							snake_case_						:     Tuple     =		encoder_layerdrop
							snake_case_						:     Optional[Any]     =		auxiliary_loss
							snake_case_						:     int     =		position_embedding_type
							snake_case_						:     Optional[Any]     =		backbone
							snake_case_						:     Tuple     =		use_pretrained_backbone
							snake_case_						:     Dict     =		dilation
							# deformable attributes
							snake_case_						:     Optional[Any]     =		num_feature_levels
							snake_case_						:     int     =		encoder_n_points
							snake_case_						:     List[str]     =		decoder_n_points
							snake_case_						:     int     =		two_stage
							snake_case_						:     List[Any]     =		two_stage_num_proposals
							snake_case_						:     int     =		with_box_refine
							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
							snake_case_						:     Optional[int]     =		class_cost
							snake_case_						:     Dict     =		bbox_cost
							snake_case_						:     Union[str, Any]     =		giou_cost
							# Loss coefficients
							snake_case_						:     str     =		mask_loss_coefficient
							snake_case_						:     List[str]     =		dice_loss_coefficient
							snake_case_						:     List[str]     =		bbox_loss_coefficient
							snake_case_						:     str     =		giou_loss_coefficient
							snake_case_						:     Optional[int]     =		eos_coefficient
							snake_case_						:     Any     =		focal_alpha
							snake_case_						:     str     =		disable_custom_kernels
							super().__init__(is_encoder_decoder=snake_case_						,				**snake_case_		)






						@property
						def UpperCAmelCase_			( self : Tuple		)							->      Union[str, Any]:







							"""simple docstring"""


							return self.encoder_attention_heads






						@property
						def UpperCAmelCase_			( self : Any		)							->      List[str]:







							"""simple docstring"""


							return self.d_model






						def UpperCAmelCase_			( self : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Dict     =		copy.deepcopy(self.__dict__		)
							if self.backbone_config is not None:
								snake_case_						:     Tuple     =		self.backbone_config.to_dict()
							snake_case_						:     List[str]     =		self.__class__.model_type
							return output








def 							SCREAMING_SNAKE_CASE__  (      __a = 60_08_51_47_51_43				):

	try:
		snake_case_						:     Optional[Any]     =		int(__a				)
	except (TypeError, ValueError):
		raise TypeError('Parameter n must be int or castable to int.'				)
	if n <= 0:
		raise ValueError('Parameter n must be greater than or equal to one.'				)
	snake_case_						:     Any     =		2
	snake_case_						:     Any     =		0
	if n == 2:
		return 2
	while n > 2:
		while n % i != 0:
			i += 1
		snake_case_						:     Tuple     =		i
		while n % i == 0:
			snake_case_						:     List[str]     =		n // i
		i += 1
	return int(__a				)


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

