



'''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 	__magic_name__						:







														@staticmethod
														def 							__lowercase			(							*_UpperCAmelCase					:       Union[str, Any]	,**_UpperCAmelCase					:       Tuple		):
																pass

@is_pipeline_test
@require_torch
@require_vision
class 	__magic_name__						(    unittest.TestCase   ):
							lowerCAmelCase       :    Union[str, Any]   				=					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       int		):
									_a     :      int        =							pipeline('visual-question-answering'	,model='hf-internal-testing/tiny-vilt-random-vqa'		)
									_a     :      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 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       List[Any]		):
									_a     :      int        =							vqa_pipeline(__lowerCAmelCase	,top_k=1		)
									self.assertEqual(
									    __lowerCAmelCase	,[
									        [{'score': ANY(__lowerCAmelCase		), 'answer': ANY(__lowerCAmelCase		)}],
									        [{'score': ANY(__lowerCAmelCase		), 'answer': ANY(__lowerCAmelCase		)}],
									    ]	,)







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

									_a     :      Optional[int]        =							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		)}]		)

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

									_a     :      List[Any]        =							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'}]		)

									_a     :      Optional[int]        =							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'}]		)

									_a     :      Union[str, Any]        =							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 							__lowercase			(							self					:       Any		):
									pass





"""simple docstring"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	['torch', 'torchsde']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """torchsde"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)



