





def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Tuple											=	[[float('inf'   ) for _ in range(__a   )] for _ in range(__a   )]

	for i in range(__a   ):
		for j in range(__a   ):
			snake_case_		:			Optional[Any]											=	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]
				):
					snake_case_		:			int											=	dist[i][k] + dist[k][j]

	_print_dist(__a   , __a   )
	return dist, v


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

						_SCREAMING_SNAKE_CASE      	=		[[float("""inf""") for i in range(v)] for j in range(v)]

						for i in range(v):
												_SCREAMING_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)
												_SCREAMING_SNAKE_CASE      	=		int(input("""Enter source:"""))
												_SCREAMING_SNAKE_CASE      	=		int(input("""Enter destination:"""))
												_SCREAMING_SNAKE_CASE      	=		float(input("""Enter weight:"""))
												_SCREAMING_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








import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_SCREAMING_SNAKE_CASE      	=		get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
						_SCREAMING_SNAKE_CASE      	=		json.load(f)
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

							snake_case_		:			Optional[int]											=	tokenizer(_A		,   return_tensors='pt'		,   truncation=_A		,   padding='longest'	).to(_A	)
							snake_case_		:			List[Any]											=	model.generate(
							    input_ids=batch.input_ids		,   num_beams=8		,   )
							snake_case_		:			Dict											=	tokenizer.batch_decode(
							    _A		,   skip_special_tokens=_A		,   clean_up_tokenization_spaces=_A	)
							snake_case_		:			Tuple											=	calculate_bleu(_A		,   _A	)
							print(_A	)
							self.assertGreaterEqual(scores['bleu']		,   _A	)

