

"""simple docstring"""





import copy

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


__UpperCamelCase  :      str							      =		logging.get_logger(__name__)

__UpperCamelCase  :      Union[str, Any]							      =		{
    '''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 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "dpt"







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

						lowerCAmelCase__	:						List[str]  				=	hidden_size
						lowerCAmelCase__	:						Any  				=	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__	:						Optional[Any]  				=	BitConfig(**lowercase_		)
							elif isinstance(lowercase_   ,lowercase_		):
								logger.info('''Initializing the config with a `BiT` backbone.'''		)
								lowerCAmelCase__	:						Tuple  				=	BitConfig(**lowercase_		)
							elif isinstance(lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						Union[str, Any]  				=	backbone_config
							else:
								raise ValueError(
								    F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'		)

							lowerCAmelCase__	:						Optional[Any]  				=	backbone_featmap_shape
							lowerCAmelCase__	:						Union[str, Any]  				=	neck_ignore_stages

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

						lowerCAmelCase__	:						Dict  				=	num_hidden_layers
						lowerCAmelCase__	:						Optional[int]  				=	num_attention_heads
						lowerCAmelCase__	:						Tuple  				=	intermediate_size
						lowerCAmelCase__	:						Any  				=	hidden_act
						lowerCAmelCase__	:						str  				=	hidden_dropout_prob
						lowerCAmelCase__	:						Optional[int]  				=	attention_probs_dropout_prob
						lowerCAmelCase__	:						List[str]  				=	initializer_range
						lowerCAmelCase__	:						List[Any]  				=	layer_norm_eps
						lowerCAmelCase__	:						Any  				=	image_size
						lowerCAmelCase__	:						Optional[Any]  				=	patch_size
						lowerCAmelCase__	:						Tuple  				=	num_channels
						lowerCAmelCase__	:						Union[str, Any]  				=	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__	:						str  				=	readout_type
						lowerCAmelCase__	:						Dict  				=	reassemble_factors
						lowerCAmelCase__	:						Optional[int]  				=	neck_hidden_sizes
						lowerCAmelCase__	:						Union[str, Any]  				=	fusion_hidden_size
						lowerCAmelCase__	:						str  				=	head_in_index
						lowerCAmelCase__	:						Optional[int]  				=	use_batch_norm_in_fusion_residual
						# auxiliary head attributes (semantic segmentation)
						lowerCAmelCase__	:						Tuple  				=	use_auxiliary_head
						lowerCAmelCase__	:						str  				=	auxiliary_loss_weight
						lowerCAmelCase__	:						Any  				=	semantic_loss_ignore_index
						lowerCAmelCase__	:						List[Any]  				=	semantic_classifier_dropout







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Union[str, Any]  				=	copy.deepcopy(self.__dict__		)

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

						lowerCAmelCase__	:						List[Any]  				=	self.__class__.model_type
						return output







"""simple docstring"""





import random

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	[], [], []
	for element in data:
		if element < pivot:
			less.append(A_ )
		elif element > pivot:
			greater.append(A_ )
		else:
			equal.append(A_ )
	return less, equal, greater





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# index = len(items) // 2 when trying to find the median
	#   (value of index when items is sorted)

	# invalid input
	if index >= len(A_ ) or index < 0:
		return None

	lowerCAmelCase__	:						str  				=	items[random.randint(0						,     len(A_ ) - 1 )]
	lowerCAmelCase__	:						Optional[Any]  				=	0
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	_partition(A_						,     A_ )
	lowerCAmelCase__	:						str  				=	len(A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	len(A_ )

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




