



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__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''mobilenet_v1'''



			def __init__(						self       :  Dict			,		A       :  Union[str, Any]=3			,		A       :  List[Any]=2_24			,		A       :  List[Any]=1.0			,		A       :  Tuple=8			,		A       :  str="relu6"			,		A       :  str=True			,		A       :  Any=0.9_9_9			,		A       :  Tuple=0.0_2			,		A       :  int=0.0_0_1			,		**A       :  int			,		)       ->   List[str]:



						"""simple docstring"""






						super().__init__(**A)

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

						_UpperCAmelCase			=       num_channels
						_UpperCAmelCase			=       image_size
						_UpperCAmelCase			=       depth_multiplier
						_UpperCAmelCase			=       min_depth
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       tf_padding
						_UpperCAmelCase			=       classifier_dropout_prob
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       layer_norm_eps







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					version.parse('''1.11'''						)



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   Mapping[str, Mapping[int, str]]:



						"""simple docstring"""






						return OrderedDict([('pixel_values', {0: 'batch'})])



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   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    _lowerCamelCase	(						self       :  str)       ->   float:



						"""simple docstring"""






						return 1E-4





UpperCAmelCase__       =	{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







			'''simple docstring'''
			_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(_UpperCAmelCase   )
												new_path.append(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(_UpperCAmelCase   )

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





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







			'''simple docstring'''
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			_UpperCAmelCase			=       [start]
			_UpperCAmelCase			=       set(_UpperCAmelCase   )
			# 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(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												_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
