


'''simple docstring'''







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__					:



            """simple docstring"""






            @staticmethod
            def a	( *snake_case__      ,	**snake_case__   ):

                   '''simple docstring'''

                   pass
@is_pipeline_test
@require_torch
@require_vision
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       pipeline('visual-question-answering'      ,	model='hf-internal-testing/tiny-vilt-random-vqa'   )
            _lowerCAmelCase    :							str   			=       [
                {
                    "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 a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       vqa_pipeline(snake_case__      ,	top_k=1   )
            self.assertEqual(
                snake_case__      ,	[
                    [{'score': ANY(snake_case__   ), 'answer': ANY(snake_case__   )}],
                    [{'score': ANY(snake_case__   ), 'answer': ANY(snake_case__   )}],
                ]      ,	)







     @require_torch
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       pipeline('visual-question-answering'      ,	model='hf-internal-testing/tiny-vilt-random-vqa'   )
            _lowerCAmelCase    :							Optional[int]   			=       "./tests/fixtures/tests_samples/COCO/000000039769.png"
            _lowerCAmelCase    :							Optional[int]   			=       "How many cats are there?"

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

            _lowerCAmelCase    :							Optional[Any]   			=       vqa_pipeline({'image': image, 'question': question}      ,	top_k=2   )
            self.assertEqual(
                snake_case__      ,	[{'score': ANY(snake_case__   ), 'answer': ANY(snake_case__   )}, {'score': ANY(snake_case__   ), 'answer': ANY(snake_case__   )}]   )







     @slow
     @require_torch
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       pipeline('visual-question-answering'      ,	model='dandelin/vilt-b32-finetuned-vqa'   )
            _lowerCAmelCase    :							Tuple   			=       "./tests/fixtures/tests_samples/COCO/000000039769.png"
            _lowerCAmelCase    :							Union[str, Any]   			=       "How many cats are there?"

            _lowerCAmelCase    :							List[Any]   			=       vqa_pipeline(image=snake_case__      ,	question=snake_case__      ,	top_k=2   )
            self.assertEqual(
                nested_simplify(snake_case__      ,	decimals=4   )      ,	[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]   )

            _lowerCAmelCase    :							Optional[Any]   			=       vqa_pipeline({'image': image, 'question': question}      ,	top_k=2   )
            self.assertEqual(
                nested_simplify(snake_case__      ,	decimals=4   )      ,	[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]   )

            _lowerCAmelCase    :							Tuple   			=       vqa_pipeline(
                [{'image': image, 'question': question}, {'image': image, 'question': question}]      ,	top_k=2   )
            self.assertEqual(
                nested_simplify(snake_case__      ,	decimals=4   )      ,	[[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2      ,	)







     @require_tf
     @unittest.skip('Visual question answering not implemented in TF'   )
     def a	( self   ):

            '''simple docstring'''

            pass






'''simple docstring'''







def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Dict   			=       len(_A					)
       while cur > 1:
              # Find the maximum number in arr
              _lowerCAmelCase    :							int   			=       arr.index(max(arr[0:cur]					)					)
              # Reverse from 0 to mi
              _lowerCAmelCase    :							Dict   			=       arr[mi::-1] + arr[mi + 1 : len(_A					)]
              # Reverse whole list
              _lowerCAmelCase    :							Optional[int]   			=       arr[cur - 1 :: -1] + arr[cur : len(_A					)]
              cur -= 1
       return arr


if __name__ == "__main__":
       lowerCAmelCase    :     Tuple					=						input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase    :     Tuple					=						[int(item) for item in user_input.split(""",""")]
       print(pancake_sort(unsorted))

