



"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCAmelCase__              =							logging.get_logger(__name__)

lowerCAmelCase__              =							{
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}






class 			__snake_case			(      _lowercase):
  snake_case__							:  Optional[int]         =		"dpt"




  def __init__(					self	:		Optional[int]		,   __lowerCAmelCase	:		List[Any]=7_6_8		,   __lowerCAmelCase	:		Optional[int]=1_2		,   __lowerCAmelCase	:		Tuple=1_2		,   __lowerCAmelCase	:		List[Any]=3_0_7_2		,   __lowerCAmelCase	:		Optional[int]="gelu"		,   __lowerCAmelCase	:		Union[str, Any]=0.0		,   __lowerCAmelCase	:		int=0.0		,   __lowerCAmelCase	:		Any=0.02		,   __lowerCAmelCase	:		Optional[Any]=1E-12		,   __lowerCAmelCase	:		Dict=3_8_4		,   __lowerCAmelCase	:		Any=1_6		,   __lowerCAmelCase	:		Optional[int]=3		,   __lowerCAmelCase	:		Tuple=False		,   __lowerCAmelCase	:		List[str]=True		,   __lowerCAmelCase	:		int=[2, 5, 8, 1_1]		,   __lowerCAmelCase	:		Optional[Any]="project"		,   __lowerCAmelCase	:		Optional[Any]=[4, 2, 1, 0.5]		,   __lowerCAmelCase	:		Dict=[9_6, 1_9_2, 3_8_4, 7_6_8]		,   __lowerCAmelCase	:		str=2_5_6		,   __lowerCAmelCase	:		Union[str, Any]=-1		,   __lowerCAmelCase	:		Dict=False		,   __lowerCAmelCase	:		Tuple=True		,   __lowerCAmelCase	:		List[str]=0.4		,   __lowerCAmelCase	:		List[Any]=2_5_5		,   __lowerCAmelCase	:		Optional[int]=0.1		,   __lowerCAmelCase	:		Any=[1, 1_0_2_4, 2_4, 2_4]		,   __lowerCAmelCase	:		List[Any]=[0, 1]		,   __lowerCAmelCase	:		int=None		,   **__lowerCAmelCase	:		List[str]		,   ):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase						)

         _lowerCamelCase		:							str            =					hidden_size
         _lowerCamelCase		:							str            =					is_hybrid

         if self.is_hybrid:
                if backbone_config is None:
                       logger.info('''Initializing the config with a `BiT` backbone.'''						)
                       _lowerCamelCase		:							Any            =					{
                           '''global_padding''': '''same''',
                           '''layer_type''': '''bottleneck''',
                           '''depths''': [3, 4, 9],
                           '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
                           '''embedding_dynamic_padding''': True,
                       }
                       _lowerCamelCase		:							Any            =					BitConfig(**__lowerCAmelCase						)
                elif isinstance(__lowerCAmelCase		,   __lowerCAmelCase						):
                       logger.info('''Initializing the config with a `BiT` backbone.'''						)
                       _lowerCamelCase		:							Tuple            =					BitConfig(**__lowerCAmelCase						)
                elif isinstance(__lowerCAmelCase		,   __lowerCAmelCase						):
                       _lowerCamelCase		:							str            =					backbone_config
                else:
                       raise ValueError(
                           f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''						)

                _lowerCamelCase		:							List[Any]            =					backbone_featmap_shape
                _lowerCamelCase		:							str            =					neck_ignore_stages

                if readout_type != "project":
                       raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''						)
         else:
                _lowerCamelCase		:							Dict            =					None
                _lowerCamelCase		:							List[Any]            =					None
                _lowerCamelCase		:							List[Any]            =					[]

         _lowerCamelCase		:							str            =					num_hidden_layers
         _lowerCamelCase		:							int            =					num_attention_heads
         _lowerCamelCase		:							Optional[Any]            =					intermediate_size
         _lowerCamelCase		:							Any            =					hidden_act
         _lowerCamelCase		:							List[str]            =					hidden_dropout_prob
         _lowerCamelCase		:							int            =					attention_probs_dropout_prob
         _lowerCamelCase		:							str            =					initializer_range
         _lowerCamelCase		:							Dict            =					layer_norm_eps
         _lowerCamelCase		:							int            =					image_size
         _lowerCamelCase		:							List[str]            =					patch_size
         _lowerCamelCase		:							List[Any]            =					num_channels
         _lowerCamelCase		:							Dict            =					qkv_bias
         _lowerCamelCase		:							Dict            =					backbone_out_indices
         if readout_type not in ["ignore", "add", "project"]:
                raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''						)
         _lowerCamelCase		:							List[Any]            =					readout_type
         _lowerCamelCase		:							Optional[int]            =					reassemble_factors
         _lowerCamelCase		:							Optional[int]            =					neck_hidden_sizes
         _lowerCamelCase		:							Optional[Any]            =					fusion_hidden_size
         _lowerCamelCase		:							Tuple            =					head_in_index
         _lowerCamelCase		:							Tuple            =					use_batch_norm_in_fusion_residual
         # auxiliary head attributes (semantic segmentation)
         _lowerCamelCase		:							str            =					use_auxiliary_head
         _lowerCamelCase		:							Optional[int]            =					auxiliary_loss_weight
         _lowerCamelCase		:							List[str]            =					semantic_loss_ignore_index
         _lowerCamelCase		:							Dict            =					semantic_classifier_dropout




  def 		SCREAMING_SNAKE_CASE							(					self	:		str						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					copy.deepcopy(self.__dict__						)

         if output["backbone_config"] is not None:
                _lowerCamelCase		:							Any            =					self.backbone_config.to_dict()

         _lowerCamelCase		:							str            =					self.__class__.model_type
         return output






'''simple docstring'''






from __future__ import annotations







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





  lowerCAmelCase_     : int     =  []
  lowerCAmelCase_,				lowerCAmelCase_     : Any     =  input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0   )   )
  lowerCAmelCase_     : Dict     =  result + left + right
  return input_list







def 							UpperCamelCase_     (       A__   :  list   ):
  '''simple docstring'''





  if len(A__   ) <= 1:
    return input_list
  lowerCAmelCase_     : str     =  list(A__   )

  # iteration for two-way merging
  lowerCAmelCase_     : Dict     =  2
  while p <= len(A__   ):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0				,       len(A__   )				,       A__   ):
      lowerCAmelCase_     : Tuple     =  i
      lowerCAmelCase_     : List[str]     =  i + p - 1
      lowerCAmelCase_     : Optional[Any]     =  (low + high + 1) // 2
      lowerCAmelCase_     : List[str]     =  merge(A__				,       A__				,       A__				,       A__   )
    # final merge of last two parts
    if p * 2 >= len(A__   ):
      lowerCAmelCase_     : Union[str, Any]     =  i
      lowerCAmelCase_     : Dict     =  merge(A__				,       0				,       A__				,       len(A__   ) - 1   )
      break
    p *= 2

  return input_list


if __name__ == "__main__":
  __A   :    int											= input("Enter numbers separated by a comma:\n").strip()
  if user_input == "":
    __A   :    str											= []
  else:
    __A   :    Optional[Any]											= [int(item.strip()) for item in user_input.split(",")]
  print(iter_merge_sort(unsorted))



