

import heapq

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  dict )   ->       set[int]:
				_UpperCAmelCase       :		list[list] 		=    []

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCAmelCase  ,      [-1 * len(lowerCAmelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				_UpperCAmelCase       :		int 		=    set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								_UpperCAmelCase       :		Optional[Any] 		=    heapq.heappop(lowerCAmelCase )[1][0]
								chosen_vertices.add(lowerCAmelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																_UpperCAmelCase       :		Dict 		=    elem[1][1].index(lowerCAmelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCAmelCase )
				return chosen_vertices


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE_        =      {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
					print(F'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE_        =      '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'

class 				a  (	unittest.TestCase							, UpperCAmelCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    load_tool("text-question-answering"	)
										self.tool.setup()
										_UpperCAmelCase       :		str 		=    load_tool("text-question-answering"						,					remote=A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.remote_tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.remote_tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


