


"""simple docstring"""
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 		lowercase							(     _snake_case						:      str = ""     )     ->dict[str, float]:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
	__snake_case	:						Dict						=				BeautifulSoup(requests.get(_snake_case     ).text     ,   '''html.parser'''     )
	__snake_case	:						Union[str, Any]						=				soup.find_all('''td'''     ,   attrs='''titleColumn'''     )
	__snake_case	:						int						=				soup.find_all('''td'''     ,   class_='''ratingColumn imdbRating'''     )
	return {
	    title.a.text: float(rating.strong.text     )
	    for title, rating in zip(_snake_case     ,   _snake_case     )
	}






def 		lowercase							(     _snake_case						:      str = "IMDb_Top_250_Movies.csv"     )     ->None:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				get_imdb_top_aaa_movies()
	with open(_snake_case     ,   '''w'''     ,   newline=''''''     ) as out_file:
		__snake_case	:						Dict						=				csv.writer(_snake_case     )
		writer.writerow(['''Movie title''', '''IMDb rating''']     )
		for title, rating in movies.items():
			writer.writerow([title, rating]     )


if __name__ == "__main__":
			write_movies()









"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline





