





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    A__  :





          @staticmethod
          def 			__UpperCamelCase(					*A_			,				**A_  ):
           '''simple docstring'''




           pass



@is_pipeline_test
@require_torch
@require_vision
class    A__  (			unittest.TestCase     ):
      _UpperCAmelCase      :Optional[Any]		       =  MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  pipeline("visual-question-answering"			,				model="hf-internal-testing/tiny-vilt-random-vqa"  )
       UpperCamelCase      :   Optional[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 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




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





      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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

       UpperCamelCase      :   Optional[Any]			      =  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      :   str			      =  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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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

       UpperCamelCase      :   str			      =  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      :   List[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      :   Optional[Any]			      =  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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase			:	Optional[Any]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):





      def __init__(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       warnings.warn(
           "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use BeitImageProcessor instead."			,				A_			,				)
       super().__init__(*A_			,				**A_  )
