
"""simple docstring"""



from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str = ""						):






							"""simple docstring"""



							_lowerCAmelCase  	=			url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
							_lowerCAmelCase  	=			BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE						).text     ,  'html.parser'						)
							_lowerCAmelCase  	=			soup.find_all('td'     ,  attrs='titleColumn'						)
							_lowerCAmelCase  	=			soup.find_all('td'     ,  class_='ratingColumn imdbRating'						)
							return {
							    title.a.text: float(rating.strong.text						)
							    for title, rating in zip(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							}






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str = "IMDb_Top_250_Movies.csv"						):






							"""simple docstring"""



							_lowerCAmelCase  	=			get_imdb_top_aaa_movies()
							with open(SCREAMING_SNAKE_CASE     ,  'w'     ,  newline=''						) as out_file:
														_lowerCAmelCase  	=			csv.writer(SCREAMING_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"""



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case          =						argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
_snake_case          =						parser.parse_args()


_snake_case          =						'''cpu'''
_snake_case          =						'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

_snake_case          =						'''path-to-your-trained-model'''
_snake_case          =						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
	_snake_case          =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_snake_case          =						pipe.to(device)

# to channels last
_snake_case          =						pipe.unet.to(memory_format=torch.channels_last)
_snake_case          =						pipe.vae.to(memory_format=torch.channels_last)
_snake_case          =						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
	_snake_case          =						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_snake_case          =						torch.randn(2, 4, 6_4, 6_4)
_snake_case          =						torch.rand(1) * 9_9_9
_snake_case          =						torch.randn(2, 7_7, 7_6_8)
_snake_case          =						(sample, timestep, encoder_hidden_status)
try:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
	_snake_case          =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case          =						6_6_6
_snake_case          =						torch.Generator(device).manual_seed(seed)
_snake_case          =						{'''generator''': generator}
if args.steps is not None:
	_snake_case          =						args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
	_snake_case          =						pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('''generated.png''')






