





"""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 							lowercase__			:

					"""simple docstring"""







					@staticmethod
					def   _a      (      *_A						,   **_A    ):






											'''simple docstring'''







											pass






@is_pipeline_test
@require_torch
@require_vision
class 							lowercase__			(   unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[str]										=  MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			pipeline("""visual-question-answering"""						,   model="""hf-internal-testing/tiny-vilt-random-vqa"""    )
									UpperCamelCase				:		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   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			vqa_pipeline(_A						,   top_k=1    )
									self.assertEqual(
									    _A						,   [
									        [{"""score""": ANY(_A    ), """answer""": ANY(_A    )}],
									        [{"""score""": ANY(_A    ), """answer""": ANY(_A    )}],
									    ]						,   )







			@require_torch
			def   _a      (      self    ):






									'''simple docstring'''







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

									UpperCamelCase				:		Tuple				   =			vqa_pipeline(image=_A						,   question="""How many cats are there?"""						,   top_k=2    )
									self.assertEqual(
									    _A						,   [{"""score""": ANY(_A    ), """answer""": ANY(_A    )}, {"""score""": ANY(_A    ), """answer""": ANY(_A    )}]    )

									UpperCamelCase				:		Optional[int]				   =			vqa_pipeline({"""image""": image, """question""": question}						,   top_k=2    )
									self.assertEqual(
									    _A						,   [{"""score""": ANY(_A    ), """answer""": ANY(_A    )}, {"""score""": ANY(_A    ), """answer""": ANY(_A    )}]    )







			@slow
			@require_torch
			def   _a      (      self    ):






									'''simple docstring'''







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

									UpperCamelCase				:		List[Any]				   =			vqa_pipeline(image=_A						,   question=_A						,   top_k=2    )
									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]    )

									UpperCamelCase				:		Optional[Any]				   =			vqa_pipeline({"""image""": image, """question""": question}						,   top_k=2    )
									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]    )

									UpperCamelCase				:		int				   =			vqa_pipeline(
									    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]						,   top_k=2    )
									self.assertEqual(
									    nested_simplify(_A						,   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   _a      (      self    ):






									'''simple docstring'''







									pass





def  __lowerCamelCase					( _lowercase			)       ->      list:
     UpperCamelCase						    =					len(_lowercase			)
     for _ in range(_lowercase			):
          for i in range(_ % 2						,     arr_size - 1						,     2			):
               if arr[i + 1] < arr[i]:
                    UpperCamelCase    ,		UpperCamelCase						    =					arr[i + 1], arr[i]

     return arr


if __name__ == "__main__":
 _snake_case      = list(range(10, 0, -1))
 print(F"Original: {arr}. Sorted: {odd_even_transposition(arr)}")


