


import copy

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


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

a_ :List[str]  							=			{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}

class 							lowercase  (						_SCREAMING_SNAKE_CASE			):
     lowerCamelCase			:			Optional[int]      = '''deta'''
     lowerCamelCase			:			Optional[int]      = {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }







     def __init__(						self :						Dict   ,   _lowercase :						str=None   ,   _lowercase :						int=9_00   ,   _lowercase :						Optional[Any]=20_48   ,   _lowercase :						str=6   ,   _lowercase :						str=20_48   ,   _lowercase :						int=8   ,   _lowercase :						Dict=6   ,   _lowercase :						Dict=10_24   ,   _lowercase :						Optional[int]=8   ,   _lowercase :						str=0.0   ,   _lowercase :						int=True   ,   _lowercase :						Dict="relu"   ,   _lowercase :						List[Any]=2_56   ,   _lowercase :						Optional[Any]=0.1   ,   _lowercase :						Dict=0.0   ,   _lowercase :						Optional[Any]=0.0   ,   _lowercase :						Tuple=0.02   ,   _lowercase :						Tuple=1.0   ,   _lowercase :						str=True   ,   _lowercase :						Dict=False   ,   _lowercase :						Optional[int]="sine"   ,   _lowercase :						List[Any]=5   ,   _lowercase :						Any=4   ,   _lowercase :						List[Any]=4   ,   _lowercase :						Optional[int]=True   ,   _lowercase :						Any=3_00   ,   _lowercase :						Optional[Any]=True   ,   _lowercase :						List[Any]=True   ,   _lowercase :						List[Any]=1   ,   _lowercase :						Dict=5   ,   _lowercase :						Any=2   ,   _lowercase :						Dict=1   ,   _lowercase :						List[str]=1   ,   _lowercase :						Tuple=5   ,   _lowercase :						List[str]=2   ,   _lowercase :						str=0.1   ,   _lowercase :						Optional[int]=0.25   ,   **_lowercase :						Tuple   ,   ):
           if backbone_config is None:
                 logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
                 SCREAMING_SNAKE_CASE__       :  Any						     =		CONFIG_MAPPING["""resnet"""](out_features=['''stage2''', '''stage3''', '''stage4''']      )
           else:
                 if isinstance(_lowercase   ,   _lowercase      ):
                       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		backbone_config.pop('''model_type'''      )
                       SCREAMING_SNAKE_CASE__       :  Any						     =		CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		config_class.from_dict(_lowercase      )

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







     @property
     def 							lowercase__       (						self :						Any      ):
           return self.encoder_attention_heads







     @property
     def 							lowercase__       (						self :						List[Any]      ):
           return self.d_model







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		copy.deepcopy(self.__dict__      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.backbone_config.to_dict()
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.__class__.model_type
           return output










'''simple docstring'''






from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__A   :    Tuple											= 10







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  list[int]				,       A__   :  int   ):
  '''simple docstring'''





  for i in range(A__				,       A__   ):
    if array[i] == target:
      return i
  return -1







def 							UpperCamelCase_     (       A__   :  list[int]				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  0
  lowerCAmelCase_     : Union[str, Any]     =  len(A__   )
  while left <= right:
    if right - left < precision:
      return lin_search(A__				,       A__				,       A__				,       A__   )

    lowerCAmelCase_     : Dict     =  (left + right) // 3 + 1
    lowerCAmelCase_     : List[Any]     =  2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      lowerCAmelCase_     : List[str]     =  one_third - 1
    elif array[two_third] < target:
      lowerCAmelCase_     : Tuple     =  two_third + 1

    else:
      lowerCAmelCase_     : Tuple     =  one_third + 1
      lowerCAmelCase_     : Any     =  two_third - 1
  else:
    return -1







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  list[int]				,       A__   :  int   ):
  '''simple docstring'''





  if left < right:
    if right - left < precision:
      return lin_search(A__				,       A__				,       A__				,       A__   )
    lowerCAmelCase_     : Dict     =  (left + right) // 3 + 1
    lowerCAmelCase_     : Dict     =  2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      return rec_ternary_search(A__				,       one_third - 1				,       A__				,       A__   )
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1				,       A__				,       A__				,       A__   )
    else:
      return rec_ternary_search(one_third + 1				,       two_third - 1				,       A__				,       A__   )
  else:
    return -1


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A   :    List[Any]											= input("Enter numbers separated by comma:\n").strip()
  __A   :    Union[str, Any]											= [int(item.strip()) for item in user_input.split(",")]
  assert collection == sorted(collection), F"List must be ordered.\n{collection}."
  __A   :    List[Any]											= int(input("Enter the number to be found in the list:\n").strip())
  __A   :    str											= ite_ternary_search(collection, target)
  __A   :    Dict											= rec_ternary_search(0, len(collection) - 1, collection, target)
  if resulta != -1:
    print(F'''Iterative search: {target} found at positions: {resulta}''')
    print(F'''Recursive search: {target} found at positions: {resulta}''')
  else:
    print("Not found")



