

'''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        a__  :


          @staticmethod
          def 						_lowerCamelCase       (			*_UpperCamelCase      ,    **_UpperCamelCase  ):





             """simple docstring"""





             pass


@is_pipeline_test
@require_torch
@require_vision
class        a__  (							unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       pipeline("visual-question-answering"      ,    model="hf-internal-testing/tiny-vilt-random-vqa"  )
      _lowercase      :				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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   @require_torch
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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

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

      _lowercase      :				Tuple      					=       vqa_pipeline({"image": image, "question": question}      ,    top_k=2  )
      self.assertEqual(
          _UpperCAmelCase      ,    [{"score": ANY(_UpperCAmelCase  ), "answer": ANY(_UpperCAmelCase  )}, {"score": ANY(_UpperCAmelCase  ), "answer": ANY(_UpperCAmelCase  )}]  )


   @slow
   @require_torch
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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

      _lowercase      :				List[str]      					=       vqa_pipeline(image=_UpperCAmelCase      ,    question=_UpperCAmelCase      ,    top_k=2  )
      self.assertEqual(
          nested_simplify(_UpperCAmelCase      ,    decimals=4  )      ,    [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]  )

      _lowercase      :				Any      					=       vqa_pipeline({"image": image, "question": question}      ,    top_k=2  )
      self.assertEqual(
          nested_simplify(_UpperCAmelCase      ,    decimals=4  )      ,    [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]  )

      _lowercase      :				int      					=       vqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]      ,    top_k=2  )
      self.assertEqual(
          nested_simplify(_UpperCAmelCase      ,    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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      pass









'''simple docstring'''
from __future__ import annotations







def  _A		(     snake_case		,							snake_case = None		,							snake_case = None					)      ->     None:
   if start is None:
      _lowercase      :				Dict      					=       0

   if end is None:
      _lowercase      :				List[Any]      					=       len(snake_case					) - 1

   if start >= end:
      return

   _lowercase      :				int      					=       (start + end) // 2

   slowsort(snake_case		,							snake_case		,							snake_case					)
   slowsort(snake_case		,							mid + 1		,							snake_case					)

   if sequence[end] < sequence[mid]:
      _lowercase					,				_lowercase      :				Any      					=       sequence[mid], sequence[end]

   slowsort(snake_case		,							snake_case		,							end - 1					)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






