"""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  _lowerCamelCase			:







										@staticmethod
										def 							__UpperCAmelCase					( *lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       str						):

											"""simple docstring"""






											pass



@is_pipeline_test
@require_torch
@require_vision
class  _lowerCamelCase			(unittest.TestCase     ):
				_snake_case     =  MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    int							=							pipeline('visual-question-answering'			,					model='hf-internal-testing/tiny-vilt-random-vqa'						)
					_lowercase :    Tuple							=							[
					    {
					        '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 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							vqa_pipeline(lowerCamelCase_			,					top_k=1						)
					self.assertEqual(
					    lowerCamelCase_			,					[
					        [{'score': ANY(lowerCamelCase_						), 'answer': ANY(lowerCamelCase_						)}],
					        [{'score': ANY(lowerCamelCase_						), 'answer': ANY(lowerCamelCase_						)}],
					    ]			,					)







				@require_torch
				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






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

					_lowercase :    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_						)}]						)

					_lowercase :    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 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






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

					_lowercase :    int							=							vqa_pipeline(image=lowerCamelCase_			,					question=lowerCamelCase_			,					top_k=2						)
					self.assertEqual(
					    nested_simplify(lowerCamelCase_			,					decimals=4						)			,					[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]						)

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

					_lowercase :    Any							=							vqa_pipeline(
					    [{'image': image, 'question': question}, {'image': image, 'question': question}]			,					top_k=2						)
					self.assertEqual(
					    nested_simplify(lowerCamelCase_			,					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 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					pass




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    List[Any]							=							[[False for _ in range(m + 1			)] for _ in range(n + 1			)]
	_lowercase :    Union[str, Any]							=							True
	for i in range(__UpperCAmelCase			):
		for j in range(m + 1			):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					_lowercase :    Optional[int]							=							True
				if a[i].islower():
					_lowercase :    Union[str, Any]							=							True
	return dp[n][m]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



