


'''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 		lowerCAmelCase__	(	__lowerCamelCase		):
			lowerCAmelCase       :		Optional[int]				      =   "dpt"




			def __init__(						self     :   int		,     lowerCamelCase__     :   Optional[int]=7_68		,     lowerCamelCase__     :   Any=12		,     lowerCamelCase__     :   Tuple=12		,     lowerCamelCase__     :   Optional[Any]=30_72		,     lowerCamelCase__     :   List[Any]="gelu"		,     lowerCamelCase__     :   Tuple=0.0		,     lowerCamelCase__     :   List[Any]=0.0		,     lowerCamelCase__     :   List[Any]=0.0_2		,     lowerCamelCase__     :   Dict=1E-12		,     lowerCamelCase__     :   List[Any]=3_84		,     lowerCamelCase__     :   List[str]=16		,     lowerCamelCase__     :   Union[str, Any]=3		,     lowerCamelCase__     :   Optional[int]=False		,     lowerCamelCase__     :   str=True		,     lowerCamelCase__     :   Union[str, Any]=[2, 5, 8, 11]		,     lowerCamelCase__     :   Any="project"		,     lowerCamelCase__     :   List[str]=[4, 2, 1, 0.5]		,     lowerCamelCase__     :   Union[str, Any]=[96, 1_92, 3_84, 7_68]		,     lowerCamelCase__     :   List[str]=2_56		,     lowerCamelCase__     :   str=-1		,     lowerCamelCase__     :   Dict=False		,     lowerCamelCase__     :   Tuple=True		,     lowerCamelCase__     :   Union[str, Any]=0.4		,     lowerCamelCase__     :   int=2_55		,     lowerCamelCase__     :   Tuple=0.1		,     lowerCamelCase__     :   Optional[Any]=[1, 10_24, 24, 24]		,     lowerCamelCase__     :   List[str]=[0, 1]		,     lowerCamelCase__     :   Optional[Any]=None		,     **lowerCamelCase__     :   Tuple		,     )      ->Optional[int]:



					'''simple docstring'''







					super().__init__(**__A       )

					_UpperCAmelCase						:				Dict	    =					hidden_size
					_UpperCAmelCase						:				Optional[Any]	    =					is_hybrid

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

							_UpperCAmelCase						:				Tuple	    =					backbone_featmap_shape
							_UpperCAmelCase						:				Optional[Any]	    =					neck_ignore_stages

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

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






			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					copy.deepcopy(self.__dict__       )

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

					_UpperCAmelCase						:				str	    =					self.__class__.model_type
					return output










'''simple docstring'''
import random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )
