




'''simple docstring'''


import copy

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


_snake_case : Dict  				=						logging.get_logger(__name__)

_snake_case : int  				=						{
    '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      A       (						_a       ):
							lowercase_							     =							'dpt'







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


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )

														_a      	=							hidden_size
														_a      	=							is_hybrid

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

																					_a      	=							backbone_featmap_shape
																					_a      	=							neck_ignore_stages

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

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







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							copy.deepcopy(self.__dict__   )

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

														_a      	=							self.__class__.model_type
														return output



'''simple docstring'''
lowerCAmelCase					:    Optional[Any]           = {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Optional[Any]):
							UpperCamelCase   			=     set()
							# keep track of all the paths to be checked
							UpperCamelCase   			=     [[start]]

							# return path if start is goal
							if start == goal:
														return [start]

							# keeps looping until all possible paths have been checked
							while queue:
														# pop the first path from the queue
														UpperCamelCase   			=     queue.pop(0)
														# get the last node from the path
														UpperCamelCase   			=     path[-1]
														if node not in explored:
																					UpperCamelCase   			=     graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												UpperCamelCase   			=     list(A)
																												new_path.append(A)
																												queue.append(A)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(A)

    # in case there's no path between the 2 nodes
							return []







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Tuple):
							if not graph or start not in graph or target not in graph:
														return -1
							if start == target:
														return 0
							UpperCamelCase   			=     [start]
							UpperCamelCase   			=     set(A)
							# Keep tab on distances from `start` node.
							UpperCamelCase   			=     {start: 0, target: -1}
							while queue:
														UpperCamelCase   			=     queue.pop(0)
														if node == target:
																					UpperCamelCase   			=     (
																					    dist[node] if dist[target] == -1 else min(dist[target]		,     dist[node])
																					)
														for adjacent in graph[node]:
																					if adjacent not in visited:
																												visited.add(A)
																												queue.append(A)
																												UpperCamelCase   			=     dist[node] + 1
							return dist[target]


if __name__ == "__main__":
						print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
						print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4




