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					:




        @staticmethod
        def 	__lowercase(      *a_     :	List[str]     , **a_     :	str				)->       str:




             """simple docstring"""
             pass

@is_pipeline_test
@require_torch
@require_vision
class       snake_case					(      unittest.TestCase						):
  lowercase_			=     MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




  def 	__lowercase(      self     :	Dict     , a_     :	Dict     , a_     :	Optional[int]     , a_     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		pipeline('visual-question-answering'     , model='hf-internal-testing/tiny-vilt-random-vqa'				)
       SCREAMING_SNAKE_CASE__				:				List[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 	__lowercase(      self     :	List[Any]     , a_     :	Any     , a_     :	Tuple				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		vqa_pipeline(a_     , top_k=1				)
       self.assertEqual(
           a_     , [
               [{'score': ANY(a_				), 'answer': ANY(a_				)}],
               [{'score': ANY(a_				), 'answer': ANY(a_				)}],
           ]     , )




  @require_torch
  def 	__lowercase(      self     :	List[Any]				)->       Optional[Any]:




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

       SCREAMING_SNAKE_CASE__				:				str			=		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_				)}]				)

       SCREAMING_SNAKE_CASE__				:				List[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 	__lowercase(      self     :	Union[str, Any]				)->       Any:




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

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		vqa_pipeline(image=a_     , question=a_     , top_k=2				)
       self.assertEqual(
           nested_simplify(a_     , decimals=4				)     , [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		vqa_pipeline({'image': image, 'question': question}     , top_k=2				)
       self.assertEqual(
           nested_simplify(a_     , decimals=4				)     , [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]				)

       SCREAMING_SNAKE_CASE__				:				int			=		vqa_pipeline(
           [{'image': image, 'question': question}, {'image': image, 'question': question}]     , top_k=2				)
       self.assertEqual(
           nested_simplify(a_     , 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 	__lowercase(      self     :	List[str]				)->       Optional[int]:




       """simple docstring"""
       pass




import requests

SCREAMING_SNAKE_CASE__		:					int						=					"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		requests.get(_NEWS_API + bbc_news_api_key ).json()
     # each article in the list is a dict
     for i, article in enumerate(bbc_news_page['articles']     ,  1 ):
          print(f'''{i}.) {article['title']}''' )


if __name__ == "__main__":
      fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")



