
'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class        SCREAMING_SNAKE_CASE__	(			snake_case_):







				def __init__(    self		,							A_		,							A_		,							A_		,							A_		,							A_		,							)->						str:







											'''simple docstring'''






											super().__init__()

											self.register_modules(vae=A_		,							text_encoder=A_		,							tokenizer=A_		,							unet=A_		,							scheduler=A_			)







				def 		UpperCAmelCase_  (    self		,							A_			)->						str:







											'''simple docstring'''






											with torch.no_grad():
																		UpperCamelCase					       =			self.vae.encode(tfms.ToTensor()(A_			).unsqueeze(0			).to(self.device			) * 2 - 1			)
																		UpperCamelCase					       =			0.18_215 * latent.latent_dist.sample()
											return latent







				def 		UpperCAmelCase_  (    self		,							A_			)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			(1 / 0.18_215) * latent
											with torch.no_grad():
																		UpperCamelCase					       =			self.vae.decode(A_			).sample
											UpperCamelCase					       =			(img / 2 + 0.5).clamp(0		,							1			)
											UpperCamelCase					       =			img.detach().cpu().permute(0		,							2		,							3		,							1			).numpy()
											UpperCamelCase					       =			(img * 255).round().astype('uint8'			)
											return Image.fromarray(img[0]			)







				def 		UpperCAmelCase_  (    self		,							A_			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			self.tokenizer(
											    A_		,							padding='max_length'		,							max_length=self.tokenizer.model_max_length		,							truncation=A_		,							return_tensors='pt'		,							)

											UpperCamelCase					       =			self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

											UpperCamelCase					       =			self.tokenizer(
											    ''		,							padding='max_length'		,							max_length=self.tokenizer.model_max_length		,							truncation=A_		,							return_tensors='pt'		,							)

											UpperCamelCase					       =			self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

											return torch.cat([uncond_embedding, text_embedding]			)







				def __call__(    self		,							A_		,							A_		,							A_ = 0.3		,							A_ = 0.6		,							A_ = 0.5		,							A_ = 42		,							A_ = 50		,							A_ = 7.5		,							)->						Image.Image:







											'''simple docstring'''






											UpperCamelCase					       =			steps - int(kmin * steps			)
											UpperCamelCase					       =			steps - int(kmax * steps			)

											UpperCamelCase					       =			self.prep_text(A_			)

											self.scheduler.set_timesteps(A_			)

											UpperCamelCase       ,     UpperCamelCase					       =			img.size
											UpperCamelCase					       =			self.encode(A_			)

											torch.manual_seed(A_			)
											UpperCamelCase					       =			torch.randn(
											    (1, self.unet.config.in_channels, height // 8, width // 8)		,							).to(self.device			)

											UpperCamelCase					       =			self.scheduler.add_noise(
											    A_		,							A_		,							timesteps=self.scheduler.timesteps[tmax]		,							)

											UpperCamelCase					       =			torch.cat([latents] * 2			)

											UpperCamelCase					       =			self.scheduler.scale_model_input(A_		,							self.scheduler.timesteps[tmax]			)

											with torch.no_grad():
																		UpperCamelCase					       =			self.unet(
																		    A_		,							self.scheduler.timesteps[tmax]		,							encoder_hidden_states=A_		,							).sample

											UpperCamelCase       ,     UpperCamelCase					       =			pred.chunk(2			)
											UpperCamelCase					       =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

											UpperCamelCase					       =			self.scheduler.step(A_		,							self.scheduler.timesteps[tmax]		,							A_			).prev_sample

											for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
																		if i > tmax:
																									if i < tmin:  # layout generation phase
																																UpperCamelCase					       =			self.scheduler.add_noise(
																																    A_		,							A_		,							timesteps=A_		,							)

																																UpperCamelCase					       =			(mix_factor * latents) + (
																																    1 - mix_factor
																																) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																																UpperCamelCase					       =			torch.cat([input] * 2			)

																									else:  # content generation phase
																																UpperCamelCase					       =			torch.cat([latents] * 2			)

																									UpperCamelCase					       =			self.scheduler.scale_model_input(A_		,							A_			)

																									with torch.no_grad():
																																UpperCamelCase					       =			self.unet(
																																    A_		,							A_		,							encoder_hidden_states=A_		,							).sample

																									UpperCamelCase       ,     UpperCamelCase					       =			pred.chunk(2			)
																									UpperCamelCase					       =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

																									UpperCamelCase					       =			self.scheduler.step(A_		,							A_		,							A_			).prev_sample

											return self.decode(A_			)












"""simple docstring"""
import bsa
import requests






def        lowerCamelCase_  (						_lowerCamelCase				):
 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_  (						_lowerCamelCase = 5				):
 lowerCamelCase__     :			Optional[int]     				=						int(float(_lowerCamelCase				)				)
 if num_movies < 1:
  return ()
 lowerCamelCase__     :			Any     				=						(
     'https://www.imdb.com/search/title?title_type='
     f'''feature&sort=num_votes,desc&count={num_movies}'''
 )
 lowerCamelCase__     :			Union[str, Any]     				=						bsa.BeautifulSoup(requests.get(_lowerCamelCase				).content			,   'html.parser'				)
 return tuple(
     get_movie_data_from_soup(_lowerCamelCase				)
     for movie in source.find_all('div'			,   class_='lister-item mode-advanced'				)				)


if __name__ == "__main__":
      import json

      A_			: str				=			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)
          )
      )

