



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     (   *_A				,							**_A		):







																'''simple docstring'''

																pass

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

						'''simple docstring'''




						UpperCamelCase__      :			Dict      =  MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING






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







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= pipeline('visual-question-answering'				,							model='hf-internal-testing/tiny-vilt-random-vqa'		)
													__SCREAMING_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 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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'''

													__SCREAMING_SNAKE_CASE									= pipeline('visual-question-answering'				,							model='hf-internal-testing/tiny-vilt-random-vqa'		)
													__SCREAMING_SNAKE_CASE									= './tests/fixtures/tests_samples/COCO/000000039769.png'
													__SCREAMING_SNAKE_CASE									= 'How many cats are there?'

													__SCREAMING_SNAKE_CASE									= 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		)}]		)

													__SCREAMING_SNAKE_CASE									= 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'''

													__SCREAMING_SNAKE_CASE									= pipeline('visual-question-answering'				,							model='dandelin/vilt-b32-finetuned-vqa'		)
													__SCREAMING_SNAKE_CASE									= './tests/fixtures/tests_samples/COCO/000000039769.png'
													__SCREAMING_SNAKE_CASE									= 'How many cats are there?'

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

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

													__SCREAMING_SNAKE_CASE									= vqa_pipeline(
													    [{'image': image, 'question': question}, {'image': image, 'question': question}]				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							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 	_A     (   self		):







													'''simple docstring'''

													pass











lowerCAmelCase__		:					Optional[int]							=9.80_665






def      __lowercase       (			a__							,       a__							,       a__ = g       )    ->			float:

							if fluid_density <= 0:
														raise ValueError('Impossible fluid density'       )
							if volume < 0:
														raise ValueError('Impossible Object volume'       )
							if gravity <= 0:
														raise ValueError('Impossible Gravity'       )

							return fluid_density * gravity * volume


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()






