






'''simple docstring'''






import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class a_     (     unittest.TestCase		,   UpperCamelCase_				):







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			load_tool('document-question-answering')
      self.tool.setup()
      __snake_case					:   List[str]  				=			load_tool('document-question-answering'       , remote=__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      __snake_case					:   int  				=			load_dataset('hf-internal-testing/example-documents'       , split='test')
      __snake_case					:   Optional[int]  				=			dataset[0]['image']

      __snake_case					:   Any  				=			self.tool(__a       , 'When is the coffee break?')
      self.assertEqual(__a       , '11-14 to 11:39 a.m.')







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   Dict  				=			load_dataset('hf-internal-testing/example-documents'       , split='test')
      __snake_case					:   int  				=			dataset[0]['image']

      __snake_case					:   Optional[Any]  				=			self.remote_tool(__a       , 'When is the coffee break?')
      self.assertEqual(__a       , '11-14 to 11:39 a.m.')







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   List[str]  				=			load_dataset('hf-internal-testing/example-documents'       , split='test')
      __snake_case					:   Optional[Any]  				=			dataset[0]['image']

      self.tool(document=__a       , question='When is the coffee break?')







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   List[str]  				=			load_dataset('hf-internal-testing/example-documents'       , split='test')
      __snake_case					:   Any  				=			dataset[0]['image']

      __snake_case					:   Optional[Any]  				=			self.remote_tool(document=__a       , question='When is the coffee break?')
      self.assertEqual(__a       , '11-14 to 11:39 a.m.')







'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			dict[str, str]:







   """simple docstring"""
   return {
       "name": soup.ha.a.text,
       "genre": soup.find('span'	,		class_='genre'	).text.strip(),
       "rating": soup.strong.text,
       "page_link": F"""https://www.imdb.com{soup.a.get("href"	)}""",
   }




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       for movie in source.find_all('div'	,		class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
      import json

      __A     						= int(input('''How many movies would you like to see? '''))
      print(
          ''', '''.join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )