






import copy

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


A_		:		str		     =			logging.get_logger(__name__)

A_		:		Optional[Any]		     =			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Optional[int]									='''deta'''
       a    :		Optional[Any]									={
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }





       def __init__( self  ,      _lowerCamelCase=None  ,      _lowerCamelCase=9_0_0  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=6  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=8  ,      _lowerCamelCase=6  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=8  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=True  ,      _lowerCamelCase="relu"  ,      _lowerCamelCase=2_5_6  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1.0  ,      _lowerCamelCase=True  ,      _lowerCamelCase=False  ,      _lowerCamelCase="sine"  ,      _lowerCamelCase=5  ,      _lowerCamelCase=4  ,      _lowerCamelCase=4  ,      _lowerCamelCase=True  ,      _lowerCamelCase=3_0_0  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=1  ,      _lowerCamelCase=5  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=1  ,      _lowerCamelCase=5  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.2_5  ,      **_lowerCamelCase  ,      ):
         if backbone_config is None:
           logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'							)
           UpperCamelCase_: Union[str, Any]   				=    CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4']							)
         else:
           if isinstance(_lowerCamelCase  ,      _lowerCamelCase							):
             UpperCamelCase_: Dict   				=    backbone_config.pop('model_type'							)
             UpperCamelCase_: Optional[int]   				=    CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_: Dict   				=    config_class.from_dict(_lowerCamelCase							)

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





       @property
       def       _a   ( self							):
         return self.encoder_attention_heads





       @property
       def       _a   ( self							):
         return self.d_model





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    copy.deepcopy(self.__dict__							)
         UpperCamelCase_: str   				=    self.backbone_config.to_dict()
         UpperCamelCase_: Any   				=    self.__class__.model_type
         return output







def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  while b:
    UpperCamelCase_				,UpperCamelCase_: int   				=    b, a % b
  return a

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase__     ,   a % b  )

def   snake_case ()	->    int:
  print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3     ,   5  )}'''  )
  print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5     ,   3  )}'''  )
  print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1     ,   3  )}'''  )
  print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3     ,   6  )}'''  )
  print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6     ,   3  )}'''  )

  print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3     ,   5  )}'''  )
  print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5     ,   3  )}'''  )
  print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1     ,   3  )}'''  )
  print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3     ,   6  )}'''  )
  print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6     ,   3  )}'''  )


if __name__ == "__main__":
   main()