code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  class 							__snake_case    :
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_		)			->		None:
  		snake_case__  					=      vertices
  		snake_case__  					=      [[0] * vertices for _ in range(UpperCamelCase_		)]
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		None:
  		print('Vertex \t Distance from Source'		)
  		for vertex in range(self.vertices		):
  			print(UpperCamelCase_			,					'\t\t'			,					distances_from_source[vertex]		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_		)			->		int:
  		snake_case__  					=      1E7
  		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:
  				snake_case__  					=      distances_from_source[vertex]
  				snake_case__  					=      vertex
  		return min_index
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		None:
  		snake_case__  					=      [int(1E7		)] * self.vertices  # distances from the source
  		snake_case__  					=      0
  		snake_case__  					=      [False] * self.vertices
  
  		for _ in range(self.vertices		):
  			snake_case__  					=      self.minimum_distance(UpperCamelCase_			,					UpperCamelCase_		)
  			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]
  				):
  					snake_case__  					=      distances[u] + self.graph[u][v]
  
  		self.print_solution(UpperCamelCase_		)
  
  
  if __name__ == "__main__":
  			a__				: str									=    Graph(9)
  			a__				: int									=    [
  			    [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'''
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  a__				: Optional[int]									=    sys.argv[1]
  a__				: Any									=    sys.argv[2]
  a__				: str									=    int(sys.argv[3])
  
  a__				: str									=    0
  
  a__				: int									=    AutoTokenizer.from_pretrained(model_name_or_path)
  max_len -= tokenizer.num_special_tokens_to_add()
  
  with open(dataset, '''rt''') as f_p:
  			for line in f_p:
  						a__				: Tuple									=    line.rstrip()
  
  						if not line:
  									print(line)
  									a__				: List[str]									=    0
  									continue
  
  						a__				: Optional[int]									=    line.split()[0]
  
  						a__				: Tuple									=    len(tokenizer.tokenize(token))
  
  						# Token contains strange control characters like \x96 or \x95
  						# Just filter out the complete line
  						if current_subwords_len == 0:
  									continue
  
  						if (subword_len_counter + current_subwords_len) > max_len:
  									print('''''')
  									print(line)
  									a__				: List[str]									=    current_subwords_len
  									continue
  
  						subword_len_counter += current_subwords_len
  
  						print(line)
  
  
  
  