code: 
  
  
  from __future__ import annotations
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     Tuple							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       vertices
  								SCREAMING_SNAKE_CASE_	      =       [[0] * vertices for _ in range(lowerCAmelCase_					)]
  
  			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     list[int]					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								print('''Vertex \t Distance from Source'''					)
  								for vertex in range(self.vertices					):
  													print(lowerCAmelCase_							,							'''\t\t'''							,							distances_from_source[vertex]					)
  
  			def _lowercase (	self     :     List[str]							,							lowerCAmelCase_     :     list[int]							,							lowerCAmelCase_     :     list[bool]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       1e7
  								SCREAMING_SNAKE_CASE_	      =       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:
  																		SCREAMING_SNAKE_CASE_	      =       distances_from_source[vertex]
  																		SCREAMING_SNAKE_CASE_	      =       vertex
  								return min_index
  
  
  
  
  			def _lowercase (	self     :     int							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       [int(1e7					)] * self.vertices  # distances from the source
  								SCREAMING_SNAKE_CASE_	      =       0
  								SCREAMING_SNAKE_CASE_	      =       [False] * self.vertices
  
  								for _ in range(self.vertices					):
  													SCREAMING_SNAKE_CASE_	      =       self.minimum_distance(lowerCAmelCase_							,							lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       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]
  																		):
  																							SCREAMING_SNAKE_CASE_	      =       distances[u] + self.graph[u][v]
  
  								self.print_solution(lowerCAmelCase_					)
  
  
  if __name__ == "__main__":
  		A_														=      Graph(9)
  		A_														=      [
  		    [0, 4, 0, 0, 0, 0, 0, 8, 0],
  		    [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
  		    [0, 8, 0, 7, 0, 4, 0, 0, 2],
  		    [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
  		    [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
  		    [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
  		    [0, 0, 0, 0, 0, 2, 0, 1, 6],
  		    [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
  		    [0, 0, 2, 0, 0, 0, 6, 7, 0],
  		]
  		graph.dijkstra(0)
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_														=      {
      "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["MarianTokenizer"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "MarianForCausalLM",
  		    "MarianModel",
  		    "MarianMTModel",
  		    "MarianPreTrainedModel",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  