

import bsa
import requests



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							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    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int = 5 )     -> tuple:




				'''simple docstring'''





				A__            =     int(float(SCREAMING_SNAKE_CASE_ ) )
				if num_movies < 1:
								return ()
				A__            =     (
				    "https://www.imdb.com/search/title?title_type="
				    F'feature&sort=num_votes,desc&count={num_movies}'
				)
				A__            =     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__						=	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)
	    )
	)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__						=	{"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__						=	[
	    """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """NatForImageClassification""",
	    """NatModel""",
	    """NatPreTrainedModel""",
	    """NatBackbone""",
	]

if TYPE_CHECKING:
	from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nat import (
		    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NatBackbone,
		    NatForImageClassification,
		    NatModel,
		    NatPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase__						=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

