





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str = ""			)   ->							dict[str, float]:




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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str = "IMDb_Top_250_Movies.csv"			)   ->							None:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		get_imdb_top_aaa_movies()
		with open(_lowercase   ,    'w'   ,    newline=''			) as out_file:
				lowercase__					:      Optional[Any]      =		csv.writer(_lowercase			)
				writer.writerow(['Movie title', 'IMDb rating']			)
				for title, rating in movies.items():
						writer.writerow([title, rating]			)


if __name__ == "__main__":
			write_movies()









from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42
	_A													=				42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


