


import heapq






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							Optional[Any]					,					__magic_name__  :							Optional[int]  )     ->						List[Any]:
		"""simple docstring"""




		lowercase__      =	[(0, start)]  # cost from start node,end node
		lowercase__      =	set()
		while heap:
				((lowercase__)    ,			(lowercase__))      =	heapq.heappop(__magic_name__  )
				if u in visited:
						continue
				visited.add(__magic_name__  )
				if u == end:
						return cost
				for v, c in graph[u]:
						if v in visited:
								continue
						lowercase__      =	cost + c
						heapq.heappush(__magic_name__					,					(next_item, v)  )
		return -1


A :	int												=     {
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
A :	Tuple												=     {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
A :	int												=     {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

A :	List[Any]												=     dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A :	Dict												=     dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

A :	str												=     dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
	import doctest

	doctest.testmod()





from typing import TYPE_CHECKING

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


A :	str												=     {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	Optional[int]												=     [
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

