import copy

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


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

SCREAMING_SNAKE_CASE__		:					Optional[int]						=					{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'deta'
  lowercase_			=     {
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }




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




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

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




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       int:




       """simple docstring"""
       return self.encoder_attention_heads




  @property
  def 	__lowercase(      self     :	str				)->       int:




       """simple docstring"""
       return self.d_model




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		copy.deepcopy(self.__dict__				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.backbone_config.to_dict()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.__class__.model_type
       return output




def     _a				(      lowercase__  :			int = 1_00_00_00 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[i - 1 for i in range(limit + 1 )]

     for i in range(2     ,  limit + 1 ):
          if phi[i] == i - 1:
               for j in range(2 * i     ,  limit + 1     ,  lowercase__ ):
                    phi[j] -= phi[j] // i

     return sum(phi[2 : limit + 1] )


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



