


'''simple docstring'''
def      a (     __a					,   __a  )    ->      List[Any]:



					'''simple docstring'''






					print('''\nThe shortest path matrix using Floyd Warshall algorithm\n'''  )
					for i in range(__a  ):
										for j in range(__a  ):
															if dist[i][j] != float('''inf'''  ):
																				print(int(dist[i][j]  )					,   end='''\t'''  )
															else:
																				print('''INF'''					,   end='''\t'''  )
										print()


def      a (     __a					,   __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					[[float('''inf'''  ) for _ in range(__a  )] for _ in range(__a  )]

					for i in range(__a  ):
										for j in range(__a  ):
															UpperCamelCase__  :List[str]				   =					graph[i][j]

															# check vertex k against all other vertices (i, j)
					for k in range(__a  ):
										# looping through rows of graph array
										for i in range(__a  ):
															# looping through columns of graph array
															for j in range(__a  ):
																				if (
																				    dist[i][k] != float('''inf'''  )
																				    and dist[k][j] != float('''inf'''  )
																				    and dist[i][k] + dist[k][j] < dist[i][j]
																				):
																									UpperCamelCase__  :Dict				   =					dist[i][k] + dist[k][j]

					_print_dist(__a					,   __a  )
					return dist, v


if __name__ == "__main__":
		__snake_case				 =	int(input('''Enter number of vertices: '''))
		__snake_case				 =	int(input('''Enter number of edges: '''))

		__snake_case				 =	[[float('''inf''') for i in range(v)] for j in range(v)]

		for i in range(v):
				__snake_case				 =	0.0

				# src and dst are indices that must be within the array size graph[e][v]
				# failure to follow this will result in an error
		for i in range(e):
				print('''\nEdge ''', i + 1)
				__snake_case				 =	int(input('''Enter source:'''))
				__snake_case				 =	int(input('''Enter destination:'''))
				__snake_case				 =	float(input('''Enter weight:'''))
				__snake_case				 =	weight

		floyd_warshall(graph, v)

		# Example Input
		# Enter number of vertices: 3
		# Enter number of edges: 2

		# # generated graph from vertex and edge inputs
		# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
		# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

		# specify source, destination and weight for edge #1
		# Edge  1
		# Enter source:1
		# Enter destination:2
		# Enter weight:2

		# specify source, destination and weight for edge #2
		# Edge  2
		# Enter source:2
		# Enter destination:1
		# Enter weight:1

		# # Expected Output from the vertice, edge and src, dst, weight inputs!!
		# 0		INF	INF
		# INF	0	2
		# INF	1	0



'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							field(
		    default=0.0      ,						metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Whether to SortishSamler or not.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'whether to use adafactor'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Attention dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default='linear'      ,						metadata={'help': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}'''}      ,						)