






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
   import torch

   from transformers.generation import DisjunctiveConstraint



@require_torch
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    def _a							(				self      :     Tuple      )      ->							List[Any]:
           # For consistency across different places the DisjunctiveConstraint is called,
           # dc.token_ids is a list of integers. It is also initialized only by integers.

           __UpperCAmelCase     				=[[1, 2, 4], [1, 2, 3, 4]]
           __UpperCAmelCase     				=DisjunctiveConstraint(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(isinstance(dc.token_ids ,     __SCREAMING_SNAKE_CASE      )      )

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]      )      )

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  DisjunctiveConstraint([torch.LongTensor([1, 2, 4]      ), torch.LongTensor([1, 2, 3, 4, 5]      )]      )



    def _a							(				self      :     Union[str, Any]      )      ->							Optional[Any]:
           # We can't have constraints that are complete subsets of another. This leads to a preverse
           # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
           # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
           # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
           # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
           __UpperCAmelCase     				=[[1, 2], [1, 2, 3, 4]]

           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  DisjunctiveConstraint(__SCREAMING_SNAKE_CASE      )  # fails here



    def _a							(				self      :     int      )      ->							Tuple:
           __UpperCAmelCase     				=[[1, 2, 3], [1, 2, 4]]

           __UpperCAmelCase     				=DisjunctiveConstraint(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(1      )
           __UpperCAmelCase     				=stepped is True and completed is False and reset is False
           self.assertTrue(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(not dc.completed      )
           self.assertTrue(dc.current_seq == [1]      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(2      )
           __UpperCAmelCase     				=stepped is True and completed is False and reset is False
           self.assertTrue(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(not dc.completed      )
           self.assertTrue(dc.current_seq == [1, 2]      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(3      )
           __UpperCAmelCase     				=stepped is True and completed is True and reset is False
           self.assertTrue(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(dc.completed      )  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 3]      )



    def _a							(				self      :     Optional[Any]      )      ->							Optional[int]:
           __UpperCAmelCase     				=[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

           __UpperCAmelCase     				=DisjunctiveConstraint(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(1      )
           self.assertTrue(not dc.completed      )
           self.assertTrue(dc.current_seq == [1]      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(2      )
           self.assertTrue(not dc.completed      )
           self.assertTrue(dc.current_seq == [1, 2]      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(4      )
           self.assertTrue(not dc.completed      )
           self.assertTrue(dc.current_seq == [1, 2, 4]      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(5      )
           self.assertTrue(dc.completed      )  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 4, 5]      )

           dc.reset()

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(1      )
           self.assertTrue(not dc.completed      )
           self.assertTrue(dc.remaining() == 3      )
           self.assertTrue(dc.current_seq == [1]      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(2      )
           self.assertTrue(not dc.completed      )
           self.assertTrue(dc.remaining() == 2      )
           self.assertTrue(dc.current_seq == [1, 2]      )

           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=dc.update(5      )
           self.assertTrue(dc.completed      )  # Completed!
           self.assertTrue(dc.remaining() == 0      )
           self.assertTrue(dc.current_seq == [1, 2, 5]      )






"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""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(__snake_case,	[-1 * len(__snake_case      ), (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(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

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


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{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)}""")






