

"""simple docstring"""




from __future__ import annotations





class      __UpperCamelCase					:


		def __init__(self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						int):
							A     =   vertices
							A     =   [[0] * vertices for _ in range(_lowerCAmelCase)]


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						list[int]):
							print("Vertex \t Distance from Source")
							for vertex in range(self.vertices):
												print(_lowerCAmelCase	,	"\t\t"	,	distances_from_source[vertex])


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	__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					:						int	,	__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(_lowerCAmelCase	,	_lowerCAmelCase)
												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(_lowerCAmelCase)


if __name__ == "__main__":
	__A	:       Tuple        =						Graph(9)
	__A	:       Optional[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)












'''simple docstring'''


import sys

from transformers import AutoTokenizer


_lowerCamelCase					:						Any					     =							sys.argv[1]
_lowerCamelCase					:						Union[str, Any]					     =							sys.argv[2]
_lowerCamelCase					:						Optional[Any]					     =							int(sys.argv[3])

_lowerCamelCase					:						Union[str, Any]					     =							0

_lowerCamelCase					:						Tuple					     =							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:
								_lowerCamelCase					:						Optional[int]					     =							line.rstrip()

								if not line:
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							0
												continue

								_lowerCamelCase					:						int					     =							line.split()[0]

								_lowerCamelCase					:						Union[str, Any]					     =							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)
												_lowerCamelCase					:						Optional[int]					     =							current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

