

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     :





       '''simple docstring'''



       @staticmethod
       def    snake_case_       (  *SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ):

           '''simple docstring'''
           pass



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





  '''simple docstring'''



  __lowerCamelCase			:      Optional[Any]					=   MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		pipeline("""visual-question-answering"""   ,model="""hf-internal-testing/tiny-vilt-random-vqa"""    )
      snake_case							:		str        =		[
          {
              """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    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		vqa_pipeline(SCREAMING_SNAKE_CASE_   ,top_k=1    )
      self.assertEqual(
          SCREAMING_SNAKE_CASE_   ,[
              [{"""score""": ANY(SCREAMING_SNAKE_CASE_    ), """answer""": ANY(SCREAMING_SNAKE_CASE_    )}],
              [{"""score""": ANY(SCREAMING_SNAKE_CASE_    ), """answer""": ANY(SCREAMING_SNAKE_CASE_    )}],
          ]   ,)





  @require_torch
  def    snake_case_       (  self    ):

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

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

      snake_case							:		Dict        =		vqa_pipeline({"""image""": image, """question""": question}   ,top_k=2    )
      self.assertEqual(
          SCREAMING_SNAKE_CASE_   ,[{"""score""": ANY(SCREAMING_SNAKE_CASE_    ), """answer""": ANY(SCREAMING_SNAKE_CASE_    )}, {"""score""": ANY(SCREAMING_SNAKE_CASE_    ), """answer""": ANY(SCREAMING_SNAKE_CASE_    )}]    )





  @slow
  @require_torch
  def    snake_case_       (  self    ):

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

      snake_case							:		str        =		vqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,question=SCREAMING_SNAKE_CASE_   ,top_k=2    )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE_   ,decimals=4    )   ,[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]    )

      snake_case							:		Union[str, Any]        =		vqa_pipeline({"""image""": image, """question""": question}   ,top_k=2    )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE_   ,decimals=4    )   ,[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]    )

      snake_case							:		List[str]        =		vqa_pipeline(
          [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]   ,top_k=2    )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE_   ,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    snake_case_       (  self    ):

      '''simple docstring'''
      pass






def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							0
	while b > 0:
		if b & 1:
			res += a

		a += a
		b >>= 1

	return res

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							0
	while b > 0:
		if b & 1:
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							((res % c) + (a % c)) % c

		a += a
		b >>= 1

	return res