


'''simple docstring'''
from __future__ import annotations


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



					'''simple docstring'''






					print(f'''Vertex\tShortest Distance from vertex {src}'''     )
					for i, d in enumerate(__a     ):
										print(f'''{i}\t\t{d}'''     )


def     a   (    __a				,      __a				,      __a     )						->			Tuple:



					'''simple docstring'''






					for j in range(__a     ):
										UpperCamelCase__     :Optional[Any]							      =				(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
										if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
															return True
					return False


def     a   (    __a				,      __a				,      __a				,      __a     )						->			list[float]:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				[float('''inf'''     )] * vertex_count
					UpperCamelCase__     :Any							      =				0.0

					for _ in range(vertex_count - 1     ):
										for j in range(__a     ):
															UpperCamelCase__     :str							      =				(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

															if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
																				UpperCamelCase__     :Union[str, Any]							      =				distance[u] + w

					UpperCamelCase__     :Optional[Any]							      =				check_negative_cycle(__a				,      __a				,      __a     )
					if negative_cycle_exists:
										raise Exception('''Negative cycle found'''     )

					return distance


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__snake_case							   =  int(input('''Enter number of vertices: ''').strip())
		__snake_case							   =  int(input('''Enter number of edges: ''').strip())

		__snake_case							   =  [{} for _ in range(E)]

		for i in range(E):
				print('''Edge ''', i + 1)
				__snake_case   ,      __snake_case   ,      __snake_case							   =  (
				    int(x)
				    for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
				)
				__snake_case							   =  {'''src''': src, '''dst''': dest, '''weight''': weight}

		__snake_case							   =  int(input('''\nEnter shortest path source:''').strip())
		__snake_case							   =  bellman_ford(graph, V, E, source)
		print_distance(shortest_distance, 0)



'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

__snake_case							   =  logging.getLogger()


def     a   (    __a     )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :str							      =				{}
					UpperCamelCase__     :Dict							      =				os.path.join(__a				,      '''all_results.json'''     )
					if os.path.exists(__a     ):
										with open(__a				,      '''r'''     ) as f:
															UpperCamelCase__     :int							      =				json.load(__a     )
					else:
										raise ValueError(f'''can\'t find {path}'''     )
					return results


__snake_case							   =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class      lowercase			(							A__      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							import xla_spawn

							UpperCamelCase__     :Any							      =				self.get_auto_remove_tmp_dir()
							UpperCamelCase__     :List[str]							      =				F'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

							with patch.object(UpperCamelCase_					,     '''argv'''					,     UpperCamelCase_							):
												UpperCamelCase__     :List[Any]							      =				time()
												xla_spawn.main()
												UpperCamelCase__     :List[Any]							      =				time()

												UpperCamelCase__     :Tuple							      =				get_results(UpperCamelCase_							)
												self.assertGreaterEqual(result['''eval_accuracy''']					,     0.75							)

												# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
												self.assertLess(end - start					,     500							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							import xla_spawn

							UpperCamelCase__     :Tuple							      =				'''
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            '''.split()
							with patch.object(UpperCamelCase_					,     '''argv'''					,     UpperCamelCase_							):
												xla_spawn.main()