





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 			UpperCamelCase_		:




          @staticmethod
          def 	lowerCAmelCase			(		*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Optional[Any]:
               pass
@is_pipeline_test
@require_torch
@require_vision
class 			UpperCamelCase_		(  unittest.TestCase					):
     lowerCAmelCase_							=	MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




     def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Tuple:
          _snake_case									=				pipeline('visual-question-answering'				,						model='hf-internal-testing/tiny-vilt-random-vqa'  )
          _snake_case									=				[
              {
                  '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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							str:
          _snake_case									=				vqa_pipeline(lowerCAmelCase_				,						top_k=1  )
          self.assertEqual(
              lowerCAmelCase_				,						[
                  [{'score': ANY(lowerCAmelCase_  ), 'answer': ANY(lowerCAmelCase_  )}],
                  [{'score': ANY(lowerCAmelCase_  ), 'answer': ANY(lowerCAmelCase_  )}],
              ]				,						)




     @require_torch
     def 	lowerCAmelCase			(		self  )		->							str:
          _snake_case									=				pipeline('visual-question-answering'				,						model='hf-internal-testing/tiny-vilt-random-vqa'  )
          _snake_case									=				'./tests/fixtures/tests_samples/COCO/000000039769.png'
          _snake_case									=				'How many cats are there?'

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

          _snake_case									=				vqa_pipeline({'image': image, 'question': question}				,						top_k=2  )
          self.assertEqual(
              lowerCAmelCase_				,						[{'score': ANY(lowerCAmelCase_  ), 'answer': ANY(lowerCAmelCase_  )}, {'score': ANY(lowerCAmelCase_  ), 'answer': ANY(lowerCAmelCase_  )}]  )




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

          _snake_case									=				vqa_pipeline(image=lowerCAmelCase_				,						question=lowerCAmelCase_				,						top_k=2  )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_				,						decimals=4  )				,						[{'score': 0.87_99, 'answer': '2'}, {'score': 0.2_96, 'answer': '1'}]  )

          _snake_case									=				vqa_pipeline({'image': image, 'question': question}				,						top_k=2  )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_				,						decimals=4  )				,						[{'score': 0.87_99, 'answer': '2'}, {'score': 0.2_96, 'answer': '1'}]  )

          _snake_case									=				vqa_pipeline(
              [{'image': image, 'question': question}, {'image': image, 'question': question}]				,						top_k=2  )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_				,						decimals=4  )				,						[[{'score': 0.87_99, 'answer': '2'}, {'score': 0.2_96, 'answer': '1'}]] * 2				,						)






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










import random





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       List[Any]						)			-> tuple:



					'''simple docstring'''




					_snake_case							,     _snake_case							,     _snake_case									=				[], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase__						)
										elif element > pivot:
															greater.append(UpperCamelCase__						)
										else:
															equal.append(UpperCamelCase__						)
					return less, equal, greater





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       int						)			-> Dict:



					'''simple docstring'''




					if index >= len(UpperCamelCase__						) or index < 0:
										return None

					_snake_case									=				items[random.randint(0       ,    len(UpperCamelCase__						) - 1						)]
					_snake_case									=				0
					_snake_case							,     _snake_case							,     _snake_case									=				_partition(UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase__       ,    UpperCamelCase__						)
					# must be in larger
					else:
										return quick_select(UpperCamelCase__       ,    index - (m + count)						)



