from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments


def SCREAMING_SNAKE_CASE					(  ):
       _UpperCamelCase  :							int	    =	HfArgumentParser(lowercase_					)
       try:
              _UpperCamelCase  :							Dict	    =	parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              _UpperCamelCase  :							Tuple	    =	'Arg --no_{0} is no longer used, please use --no-{0} instead.'
              _UpperCamelCase  :							List[Any]	    =	' '.join(str(lowercase_					).split(' '					)[:-1]					)
              _UpperCamelCase  :							List[Any]	    =	''
              _UpperCamelCase  :							Dict	    =	eval(str(lowercase_					).split(' '					)[-1]					)
              _UpperCamelCase  :							Optional[Any]	    =	[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]					)
                     else:
                            wrong_args.append(lowercase_					)
              if len(lowercase_					) > 0:
                     _UpperCamelCase  :							Optional[int]	    =	full_error_msg + begin_error_msg + str(lowercase_					)
              raise ValueError(lowercase_					)

       _UpperCamelCase  :							List[str]	    =	PyTorchBenchmark(args=lowercase_					)
       benchmark.run()


if __name__ == "__main__":
 main()








import bsa
import requests







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''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  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__ = 5  ):


						'''simple docstring'''




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


if __name__ == "__main__":
		import json

		lowerCAmelCase					:				Union[str, Any]   	=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)
		    )
		)

