






'''simple docstring'''

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






class        a_		(    UpperCamelCase__       ):
  lowercase  	=   """deta"""
  lowercase  	=   {
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=900    ,	_SCREAMING_SNAKE_CASE=2048    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=2048    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE="relu"    ,	_SCREAMING_SNAKE_CASE=256    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=1.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE="sine"    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=300    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.2_5    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[Any]:



        """simple docstring"""






        if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
              UpperCamelCase       						=      CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]   )
        else:
              if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                    UpperCamelCase       						=      backbone_config.pop("""model_type"""   )
                    UpperCamelCase       						=      CONFIG_MAPPING[backbone_model_type]
                    UpperCamelCase       						=      config_class.from_dict(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      backbone_config
        UpperCamelCase       						=      num_queries
        UpperCamelCase       						=      max_position_embeddings
        UpperCamelCase       						=      d_model
        UpperCamelCase       						=      encoder_ffn_dim
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      encoder_attention_heads
        UpperCamelCase       						=      decoder_ffn_dim
        UpperCamelCase       						=      decoder_layers
        UpperCamelCase       						=      decoder_attention_heads
        UpperCamelCase       						=      dropout
        UpperCamelCase       						=      attention_dropout
        UpperCamelCase       						=      activation_dropout
        UpperCamelCase       						=      activation_function
        UpperCamelCase       						=      init_std
        UpperCamelCase       						=      init_xavier_std
        UpperCamelCase       						=      encoder_layerdrop
        UpperCamelCase       						=      auxiliary_loss
        UpperCamelCase       						=      position_embedding_type
        # deformable attributes
        UpperCamelCase       						=      num_feature_levels
        UpperCamelCase       						=      encoder_n_points
        UpperCamelCase       						=      decoder_n_points
        UpperCamelCase       						=      two_stage
        UpperCamelCase       						=      two_stage_num_proposals
        UpperCamelCase       						=      with_box_refine
        UpperCamelCase       						=      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       						=      class_cost
        UpperCamelCase       						=      bbox_cost
        UpperCamelCase       						=      giou_cost
        # Loss coefficients
        UpperCamelCase       						=      mask_loss_coefficient
        UpperCamelCase       						=      dice_loss_coefficient
        UpperCamelCase       						=      bbox_loss_coefficient
        UpperCamelCase       						=      giou_loss_coefficient
        UpperCamelCase       						=      eos_coefficient
        UpperCamelCase       						=      focal_alpha
        super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

  @property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return self.encoder_attention_heads

  @property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return self.d_model


  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      copy.deepcopy(self.__dict__   )
        UpperCamelCase       						=      self.backbone_config.to_dict()
        UpperCamelCase       						=      self.__class__.model_type
        return output








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase					)->				str:
      if not all(char in """01""" for char in bin_string					):
            raise ValueError("""Non-binary value was passed to the function"""					)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""					)
      UpperCamelCase       						=      """"""
      while len(__UpperCamelCase					) % 3 != 0:
            UpperCamelCase       						=      """0""" + bin_string
      UpperCamelCase       						=      [
          bin_string[index : index + 3]
          for index in range(len(__UpperCamelCase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            UpperCamelCase       						=      0
            for index, val in enumerate(__UpperCamelCase					):
                  oct_val += int(2 ** (2 - index) * int(__UpperCamelCase					)					)
            oct_string += str(__UpperCamelCase					)
      return oct_string


if __name__ == "__main__":
     from doctest import testmod

     testmod()
