
'''simple docstring'''






import copy

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


UpperCAmelCase_       :   int   =       logging.get_logger(__name__)

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

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'deta'
    lowerCAmelCase_				    =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }



    def __init__(	self      :       Optional[Any],__A      :       Union[str, Any]=None,__A      :       int=9_0_0,__A      :       Optional[int]=2_0_4_8,__A      :       Any=6,__A      :       str=2_0_4_8,__A      :       List[Any]=8,__A      :       Tuple=6,__A      :       Optional[Any]=1_0_2_4,__A      :       Optional[int]=8,__A      :       Union[str, Any]=0.0,__A      :       Optional[Any]=True,__A      :       Any="relu",__A      :       Dict=2_5_6,__A      :       int=0.1,__A      :       Dict=0.0,__A      :       Optional[int]=0.0,__A      :       Dict=0.02,__A      :       List[Any]=1.0,__A      :       Tuple=True,__A      :       int=False,__A      :       Optional[int]="sine",__A      :       Optional[int]=5,__A      :       List[Any]=4,__A      :       int=4,__A      :       Optional[Any]=True,__A      :       Optional[Any]=3_0_0,__A      :       Any=True,__A      :       Tuple=True,__A      :       Optional[Any]=1,__A      :       List[Any]=5,__A      :       List[str]=2,__A      :       Tuple=1,__A      :       int=1,__A      :       Tuple=5,__A      :       Optional[int]=2,__A      :       Optional[Any]=0.1,__A      :       Dict=0.25,**__A      :       List[str],):
          if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."     )
                _lowerCamelCase				:   Dict								=   CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]     )
          else:
                if isinstance(__A,__A     ):
                      _lowerCamelCase				:   Dict								=   backbone_config.pop("model_type"     )
                      _lowerCamelCase				:   List[Any]								=   CONFIG_MAPPING[backbone_model_type]
                      _lowerCamelCase				:   Optional[Any]								=   config_class.from_dict(__A     )

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



    @property
    def   lowerCamelCase_  (	self      :       str     ):
          return self.encoder_attention_heads



    @property
    def   lowerCamelCase_  (	self      :       str     ):
          return self.d_model



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   str								=   copy.deepcopy(self.__dict__     )
          _lowerCamelCase				:   List[Any]								=   self.backbone_config.to_dict()
          _lowerCamelCase				:   List[str]								=   self.__class__.model_type
          return output

'''simple docstring'''






def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'float' object cannot be interpreted as an integer"							)
      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'str' object cannot be interpreted as an integer"							)

      if num == 0:
            return "0b0"

      _lowerCamelCase				:   Tuple								=   False

      if num < 0:
            _lowerCamelCase				:   List[Any]								=   True
            _lowerCamelCase				:   int								=   -num

      _lowerCamelCase				:   list[int]								=   []
      while num > 0:
            binary.insert(0				,	num % 2							)
            num >>= 1

      if negative:
            return "-0b" + "".join(str(_lowerCAmelCase							) for e in binary							)

      return "0b" + "".join(str(_lowerCAmelCase							) for e in binary							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()