import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:



    class  SCREAMING_SNAKE_CASE_ :
      '''simple docstring'''


      @staticmethod
      def 		SCREAMING_SNAKE_CASE_		(				*SCREAMING_SNAKE_CASE__    :					Tuple      ,	**SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				str:
             pass



@is_pipeline_test
@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     str            =				MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Dict:
         A						:     int         =pipeline('visual-question-answering'      ,	model='hf-internal-testing/tiny-vilt-random-vqa'     )
         A						:     List[Any]         =[
             {
                 'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     ),
                 'question': 'How many cats are there?',
             },
             {
                 'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                 'question': 'How many cats are there?',
             },
         ]
         return vqa_pipeline, examples







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				int:
         A						:     Any         =vqa_pipeline(SCREAMING_SNAKE_CASE__      ,	top_k=1     )
         self.assertEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 [{'score': ANY(SCREAMING_SNAKE_CASE__     ), 'answer': ANY(SCREAMING_SNAKE_CASE__     )}],
                 [{'score': ANY(SCREAMING_SNAKE_CASE__     ), 'answer': ANY(SCREAMING_SNAKE_CASE__     )}],
             ]      ,	)







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				List[Any]:
         A						:     Any         =pipeline('visual-question-answering'      ,	model='hf-internal-testing/tiny-vilt-random-vqa'     )
         A						:     str         ='./tests/fixtures/tests_samples/COCO/000000039769.png'
         A						:     Dict         ='How many cats are there?'

         A						:     Optional[Any]         =vqa_pipeline(image=SCREAMING_SNAKE_CASE__      ,	question='How many cats are there?'      ,	top_k=2     )
         self.assertEqual(
             SCREAMING_SNAKE_CASE__      ,	[{'score': ANY(SCREAMING_SNAKE_CASE__     ), 'answer': ANY(SCREAMING_SNAKE_CASE__     )}, {'score': ANY(SCREAMING_SNAKE_CASE__     ), 'answer': ANY(SCREAMING_SNAKE_CASE__     )}]     )

         A						:     List[Any]         =vqa_pipeline({'image': image, 'question': question}      ,	top_k=2     )
         self.assertEqual(
             SCREAMING_SNAKE_CASE__      ,	[{'score': ANY(SCREAMING_SNAKE_CASE__     ), 'answer': ANY(SCREAMING_SNAKE_CASE__     )}, {'score': ANY(SCREAMING_SNAKE_CASE__     ), 'answer': ANY(SCREAMING_SNAKE_CASE__     )}]     )







  @slow
  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				str:
         A						:     Union[str, Any]         =pipeline('visual-question-answering'      ,	model='dandelin/vilt-b32-finetuned-vqa'     )
         A						:     int         ='./tests/fixtures/tests_samples/COCO/000000039769.png'
         A						:     int         ='How many cats are there?'

         A						:     List[Any]         =vqa_pipeline(image=SCREAMING_SNAKE_CASE__      ,	question=SCREAMING_SNAKE_CASE__      ,	top_k=2     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]     )

         A						:     Optional[int]         =vqa_pipeline({'image': image, 'question': question}      ,	top_k=2     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]     )

         A						:     str         =vqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]      ,	top_k=2     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]] * 2      ,	)







  @require_tf
  @unittest.skip('Visual question answering not implemented in TF'     )
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Optional[Any]:
         pass







import heapq



def A__  (			lowercase:     dict					)				->		set[int]:
       A						:     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(lowercase,		[-1 * len(lowercase					), (key, value)]					)

       # chosen_vertices = set of chosen vertices
       A						:     Dict         =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
              A						:     List[str]         =heapq.heappop(lowercase					)[1][0]
              chosen_vertices.add(lowercase					)

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


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      List[Any]      							={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)}''')






