




"""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 				snake_case_			:







     """simple docstring"""




     @staticmethod
     def   UpperCAmelCase__			(		*lowerCamelCase_       ,					**lowerCamelCase_)				->   Optional[int]:
         pass

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







   """simple docstring"""




   A_				      =       MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







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

       UpperCamelCase              =	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_)}])

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

       UpperCamelCase              =	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'''}])

       UpperCamelCase              =	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'''}])

       UpperCamelCase              =	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)				->   Optional[int]:
       pass




import itertools
import math

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5	,   int(math.sqrt(_snake_case				) + 1				)	,   6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Dict:
   _A      =       2
   while True:
      if is_prime(_snake_case				):
         yield num
      num += 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 10_001				)  ->     int:
   return next(itertools.islice(prime_generator()	,   nth - 1	,   _snake_case				)				)


if __name__ == "__main__":
   print(f'{solution() = }')



