


'''simple docstring'''



import heapq


def    lowerCamelCase__   ( A     :    dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]

						# 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(A   ,     [-1 * len(A  ), (key, value)]  )

						# chosen_vertices = set of chosen vertices
						UpperCAmelCase         =			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         =			heapq.heappop(A  )[1][0]
												chosen_vertices.add(A  )

												# 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         =			elem[1][1].index(A  )
																								del elem[1][1][index]
																								elem[0] += 1
        # re-order the queue
												heapq.heapify(A  )
						return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowercase    :    Optional[int]			     =  {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)}""")









'''simple docstring'''



import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
	import torch

	from transformers.generation import (
	    MaxLengthCriteria,
	    MaxNewTokensCriteria,
	    MaxTimeCriteria,
	    StoppingCriteriaList,
	    validate_stopping_criteria,
	)






@require_torch
class  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Optional[Any]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			3
										UpperCAmelCase         =			250

										UpperCAmelCase         =			ids_tensor((batch_size, length)   ,					lowerCAmelCase   )
										UpperCAmelCase         =			torch.ones((batch_size, length)   ,					device=lowerCAmelCase   ,					dtype=torch.float   ) / length
										return input_ids, scores




				def   a__(	self    :				Dict   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )

										UpperCAmelCase         =			StoppingCriteriaList(
										    [
										        MaxLengthCriteria(max_length=10   ),
										        MaxTimeCriteria(max_time=0.1   ),
										    ]   )

										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )




				def   a__(	self    :				Union[str, Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			MaxLengthCriteria(max_length=10   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )




				def   a__(	self    :				Optional[Any]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			MaxNewTokensCriteria(start_length=5   ,					max_new_tokens=5   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase         =			StoppingCriteriaList([criteria]   )
										self.assertEqual(criteria_list.max_length   ,					10   )




				def   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )

										UpperCAmelCase         =			MaxTimeCriteria(max_time=0.1   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase         =			MaxTimeCriteria(max_time=0.1   ,					initial_timestamp=time.time() - 0.2   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )





				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




										validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,					10   )

										with self.assertWarns(lowerCAmelCase   ):
																validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,					11   )

										UpperCAmelCase         =			validate_stopping_criteria(StoppingCriteriaList()   ,					11   )

										self.assertEqual(len(lowerCAmelCase   )   ,					1   )





