






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






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			[[float('inf') for _ in range(_UpperCAmelCase)] for _ in range(_UpperCAmelCase)]

			for i in range(_UpperCAmelCase):
						for j in range(_UpperCAmelCase):
									SCREAMING_SNAKE_CASE							=			graph[i][j]

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

			_print_dist(_UpperCAmelCase				,							_UpperCAmelCase)
			return dist, v


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

				a_		:    Tuple   							=     [[float('inf') for i in range(v)] for j in range(v)]

				for i in range(v):
								a_		:    Dict   							=     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)
								a_		:    str   							=     int(input('Enter source:'))
								a_		:    Any   							=     int(input('Enter destination:'))
								a_		:    List[Any]   							=     float(input('Enter weight:'))
								a_		:    str   							=     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 os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_							:					Optional[Any]		 =   [
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str=True       )					->				List[Any]:
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]






@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase	)	)
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   str     					=				None




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     Optional[Any]							):
		with TemporaryDirectory() as tmp_dir:
			__A :			List[Any]		    =			dataset_module_factory(__A					,      cache_dir=__A							)

			__A :			Tuple		    =			import_main_class(dataset_module.module_path					,      dataset=__A							)

			__A :			DatasetBuilder		    =			builder_cls(
			    cache_dir=__A					,      config_name=__A					,      hash=dataset_module.hash					,      )

			__A :			List[Any]		    =			"""/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=__A							).replace(os.sep					,      """/"""							),
			        config.DATASET_INFO_FILENAME,
			    ]							)
			__A :			Union[str, Any]		    =			cached_path(__A					,      cache_dir=__A							)
			self.assertTrue(os.path.exists(__A							)							)






@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict       )					->				Optional[Any]:
	__A :			Optional[Any]		    =			tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
	__A :			Union[str, Any]		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			List[Any]		    =			import_main_class(dataset_module.module_path       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	__A :			Any		    =			None
	builder_instance.download_and_prepare()
	__A :			Union[str, Any]		    =			builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				List[str]:
	__A :			Tuple		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			str		    =			import_main_class(dataset_module.module_path			,dataset=a__       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	__A :			Optional[int]		    =			builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(a__			,a__       )
	assert "train" in ds
	assert isinstance(ds["""train"""]			,a__       )
	assert next(iter(ds["""train"""]       )       )





