code: 
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  class     __UpperCamelCase				:
  
  
  		def __init__(self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	int):
  							A    =      vertices
  							A    =      [[0] * vertices for _ in range(__SCREAMING_SNAKE_CASE)]
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	list[int]):
  							print("Vertex \t Distance from Source")
  							for vertex in range(self.vertices):
  												print(__SCREAMING_SNAKE_CASE  ,  "\t\t"  ,  distances_from_source[vertex])
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	list[int]  ,  __SCREAMING_SNAKE_CASE				:	list[bool]):
  							A    =      1E7
  							A    =      0
  
  							# Search not nearest vertex not in the shortest path tree
  							for vertex in range(self.vertices):
  												if distances_from_source[vertex] < minimum and visited[vertex] is False:
  																	A    =      distances_from_source[vertex]
  																	A    =      vertex
  							return min_index
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	int):
  							A    =      [int(1E7)] * self.vertices  # distances from the source
  							A    =      0
  							A    =      [False] * self.vertices
  
  							for _ in range(self.vertices):
  												A    =      self.minimum_distance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  												A    =      True
  
  												# Update dist value of the adjacent vertices
  												# of the picked vertex only if the current
  												# distance is greater than new distance and
  												# the vertex in not in the shortest path tree
  												for v in range(self.vertices):
  																	if (
  																	    self.graph[u][v] > 0
  																	    and visited[v] is False
  																	    and distances[v] > distances[u] + self.graph[u][v]
  																	):
  																						A    =      distances[u] + self.graph[u][v]
  
  							self.print_solution(__SCREAMING_SNAKE_CASE)
  
  
  if __name__ == "__main__":
  	__A  :					List[Any]												=	Graph(9)
  	__A  :					List[str]												=	[
  	    [0, 4, 0, 0, 0, 0, 0, 8, 0],
  	    [4, 0, 8, 0, 0, 0, 0, 11, 0],
  	    [0, 8, 0, 7, 0, 4, 0, 0, 2],
  	    [0, 0, 7, 0, 9, 14, 0, 0, 0],
  	    [0, 0, 0, 9, 0, 10, 0, 0, 0],
  	    [0, 0, 4, 14, 10, 0, 2, 0, 0],
  	    [0, 0, 0, 0, 0, 2, 0, 1, 6],
  	    [8, 11, 0, 0, 0, 0, 1, 0, 7],
  	    [0, 0, 2, 0, 0, 0, 6, 7, 0],
  	]
  	graph.dijkstra(0)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A  :					int												=	{
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    '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[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  