






"""simple docstring"""







a					:							List[Any]    =						{
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}





def 		lowercase__(A   ,						A   ,						A		)							->list[str]:




						"""simple docstring"""



						lowercase__							: Optional[int]=			set()
						# keep track of all the paths to be checked
						lowercase__							: Union[str, Any]=			[[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
												lowercase__							: Dict=			queue.pop(0		)
												# get the last node from the path
												lowercase__							: Dict=			path[-1]
												if node not in explored:
																		lowercase__							: List[Any]=			graph[node]
																		# go through all neighbour nodes, construct a new path and
																		# push it into the queue
																		for neighbour in neighbours:
																								lowercase__							: List[str]=			list(snake_case__		)
																								new_path.append(snake_case__		)
																								queue.append(snake_case__		)
																								# return path if neighbour is goal
																								if neighbour == goal:
																														return new_path

            # mark node as explored
																		explored.add(snake_case__		)

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





def 		lowercase__(A   ,						A   ,						A		)							->int:




						"""simple docstring"""



						if not graph or start not in graph or target not in graph:
												return -1
						if start == target:
												return 0
						lowercase__							: str=			[start]
						lowercase__							: Any=			set(snake_case__		)
						# Keep tab on distances from `start` node.
						lowercase__							: List[str]=			{start: 0, target: -1}
						while queue:
												lowercase__							: Optional[int]=			queue.pop(0		)
												if node == target:
																		lowercase__							: Optional[Any]=			(
																		    dist[node] if dist[target] == -1 else min(dist[target]   ,						dist[node]		)
																		)
												for adjacent in graph[node]:
																		if adjacent not in visited:
																								visited.add(snake_case__		)
																								queue.append(snake_case__		)
																								lowercase__							: Any=			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










"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__				:Union[str, Any]			   =	logging.get_logger(__name__)

UpperCamelCase__				:Union[str, Any]			   =	{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"mobilenet_v2"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=2_24					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__="relu6"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0.8					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0_0_1					,				SCREAMING_SNAKE_CASE__=2_55					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )

					if depth_multiplier <= 0:
								raise ValueError('''depth_multiplier must be greater than zero.'''   )

					_UpperCamelCase					:Optional[int]					     =	num_channels
					_UpperCamelCase					:Tuple					     =	image_size
					_UpperCamelCase					:Union[str, Any]					     =	depth_multiplier
					_UpperCamelCase					:Any					     =	depth_divisible_by
					_UpperCamelCase					:Optional[int]					     =	min_depth
					_UpperCamelCase					:Union[str, Any]					     =	expand_ratio
					_UpperCamelCase					:str					     =	output_stride
					_UpperCamelCase					:Tuple					     =	first_layer_is_expansion
					_UpperCamelCase					:Any					     =	finegrained_output
					_UpperCamelCase					:Union[str, Any]					     =	hidden_act
					_UpperCamelCase					:Any					     =	tf_padding
					_UpperCamelCase					:str					     =	classifier_dropout_prob
					_UpperCamelCase					:Optional[Any]					     =	initializer_range
					_UpperCamelCase					:int					     =	layer_norm_eps
					_UpperCamelCase					:Union[str, Any]					     =	semantic_loss_ignore_index






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							version.parse("1.11"		)
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					if self.task == "image-classification":
								return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
								return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )





		@property
		def   _UpperCamelCase( self   )      ->	float:

					"""simple docstring"""



					return 1E-4

