"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:
   lowercase__:   Optional[Any]					=      0
   lowercase__:   List[Any]					=      len(__UpperCAmelCase ) - 1

   while left <= right:
      # avoid divided by 0 during interpolation
      if sorted_collection[left] == sorted_collection[right]:
         if sorted_collection[left] == item:
            return left
         else:
            return None

      lowercase__:   Tuple					=      left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(__UpperCAmelCase ):
         return None

      lowercase__:   Optional[int]					=      sorted_collection[point]
      if current_item == item:
         return point
      else:
         if point < left:
            lowercase__:   List[Any]					=      left
            lowercase__:   int					=      point
         elif point > right:
            lowercase__:   Dict					=      right
            lowercase__:   List[str]					=      point
         else:
            if item < current_item:
               lowercase__:   int					=      point - 1
            else:
               lowercase__:   int					=      point + 1
   return None



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:

   # avoid divided by 0 during interpolation
   if sorted_collection[left] == sorted_collection[right]:
      if sorted_collection[left] == item:
         return left
      else:
         return None

   lowercase__:   Optional[int]					=      left + ((item - sorted_collection[left]) * (right - left)) // (
       sorted_collection[right] - sorted_collection[left]
   )

   # out of range check
   if point < 0 or point >= len(__UpperCAmelCase ):
      return None

   if sorted_collection[point] == item:
      return point
   elif point < left:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   elif point > right:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   else:
      if sorted_collection[point] > item:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    point - 1 )
      else:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    point + 1       ,    __UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[Any]:
   if collection != sorted(__UpperCAmelCase ):
      raise ValueError('''Collection must be ascending sorted''' )
   return True


if __name__ == "__main__":
       import sys
       __A			   = 0
       if debug == 1:
              __A			   = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
              try:
                     __assert_sorted(collection)
              except ValueError:
                     sys.exit("Sequence must be ascending sorted to apply interpolation search")
              __A			   = 6_7

       __A			   = interpolation_search(collection, target)
       if result is not None:
              print(f'''{target} found at positions: {result}''')
       else:
              print("Not found")

"""simple docstring"""






import copy

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


__A			   = logging.get_logger(__name__)

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


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = "deta"
    _UpperCAmelCase	:Any						    = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=900    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="relu"    ,			_UpperCAmelCase=256    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=1.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase="sine"    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=300    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.25    ,			**_UpperCAmelCase    ,			):
       if backbone_config is None:
          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
          lowercase__:   str					=      CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']							)
       else:
          if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      backbone_config.pop('''model_type'''							)
             lowercase__:   str					=      CONFIG_MAPPING[backbone_model_type]
             lowercase__:   Optional[int]					=      config_class.from_dict(_UpperCAmelCase							)

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






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






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






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      copy.deepcopy(self.__dict__							)
       lowercase__:   Dict					=      self.backbone_config.to_dict()
       lowercase__:   Union[str, Any]					=      self.__class__.model_type
       return output
