




'''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     A      (	*lowercase						:	Tuple   ,				**lowercase						:	List[str]				):



              '''simple docstring'''

              pass


@is_pipeline_test
@require_torch
@require_vision
class 			_a  (	unittest.TestCase ):
       __a				:     int   				=					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
       def     A      (	self						:	Dict   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Tuple				):



           '''simple docstring'''

           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     A      (	self						:	Optional[Any]   ,				lowercase						:	List[str]   ,				lowercase						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		vqa_pipeline(lowercase_   ,				top_k=1				)
           self.assertEqual(
               lowercase_   ,				[
                   [{'''score''': ANY(lowercase_				), '''answer''': ANY(lowercase_				)}],
                   [{'''score''': ANY(lowercase_				), '''answer''': ANY(lowercase_				)}],
               ]   ,				)
       @require_torch
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           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=lowercase_   ,				question='''How many cats are there?'''   ,				top_k=2				)
           self.assertEqual(
               lowercase_   ,				[{'''score''': ANY(lowercase_				), '''answer''': ANY(lowercase_				)}, {'''score''': ANY(lowercase_				), '''answer''': ANY(lowercase_				)}]				)

           UpperCAmelCase			       =		vqa_pipeline({'''image''': image, '''question''': question}   ,				top_k=2				)
           self.assertEqual(
               lowercase_   ,				[{'''score''': ANY(lowercase_				), '''answer''': ANY(lowercase_				)}, {'''score''': ANY(lowercase_				), '''answer''': ANY(lowercase_				)}]				)
       @slow
       @require_torch
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           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=lowercase_   ,				question=lowercase_   ,				top_k=2				)
           self.assertEqual(
               nested_simplify(lowercase_   ,				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(lowercase_   ,				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(lowercase_   ,				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     A      (	self						:	str				):



           '''simple docstring'''

           pass







import os

# Precomputes a list of the 100 first triangular numbers
lowercase_       		=  [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def 						_snake_case(    )							->      int:





    '''simple docstring'''






    A__          =   os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__      )      )
    A__          =   os.path.join(SCREAMING_SNAKE_CASE__    ,			'words.txt'      )

    A__          =   ''
    with open(SCREAMING_SNAKE_CASE__      ) as f:
        A__          =   f.readline()

    A__          =   [word.strip('"'      ) for word in words.strip('\r\n'      ).split(','      )]
    A__          =   [
        word
        for word in [sum(ord(SCREAMING_SNAKE_CASE__      ) - 64 for x in word      ) for word in words]
        if word in TRIANGULAR_NUMBERS
    ]
    return len(SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     print(solution())

