






_UpperCAmelCase			:		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 	lowerCAmelCase_		(lowercase__						:       dict     ,					lowercase__						:       str     ,					lowercase__						:       List[Any]       )		->    list[str]:


				'''simple docstring'''




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

            # mark node as explored
												explored.add(lowercase__       )

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





def 	lowerCAmelCase_		(lowercase__						:       dict     ,					lowercase__						:       Dict     ,					lowercase__						:       Dict       )		->    int:


				'''simple docstring'''




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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





