"""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 					UpperCamelCase   (							lowerCAmelCase__  ):







       def __init__(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Tuple:
            super().__init__()

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







       def 						a_		(					self,				lowerCAmelCase__) -> Dict:
            with torch.no_grad():
                 snake_case_   			=   self.vae.encode(tfms.ToTensor()(lowerCAmelCase__).unsqueeze(0).to(self.device) * 2 - 1)
                 snake_case_   			=   0.18215 * latent.latent_dist.sample()
            return latent







       def 						a_		(					self,				lowerCAmelCase__) -> Any:
            snake_case_   			=   (1 / 0.18215) * latent
            with torch.no_grad():
                 snake_case_   			=   self.vae.decode(lowerCAmelCase__).sample
            snake_case_   			=   (img / 2 + 0.5).clamp(0,				1)
            snake_case_   			=   img.detach().cpu().permute(0,				2,				3,				1).numpy()
            snake_case_   			=   (img * 255).round().astype('uint8')
            return Image.fromarray(img[0])







       def 						a_		(					self,				lowerCAmelCase__) -> List[str]:
            snake_case_   			=   self.tokenizer(
                lowerCAmelCase__,				padding='max_length',				max_length=self.tokenizer.model_max_length,				truncation=lowerCAmelCase__,				return_tensors='pt',				)

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

            snake_case_   			=   self.tokenizer(
                '',				padding='max_length',				max_length=self.tokenizer.model_max_length,				truncation=lowerCAmelCase__,				return_tensors='pt',				)

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

            return torch.cat([uncond_embedding, text_embedding])







       def __call__(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = 0.3,				lowerCAmelCase__ = 0.6,				lowerCAmelCase__ = 0.5,				lowerCAmelCase__ = 42,				lowerCAmelCase__ = 50,				lowerCAmelCase__ = 7.5,				) -> Image.Image:
            snake_case_   			=   steps - int(kmin * steps)
            snake_case_   			=   steps - int(kmax * steps)

            snake_case_   			=   self.prep_text(lowerCAmelCase__)

            self.scheduler.set_timesteps(lowerCAmelCase__)

            snake_case_			,      snake_case_   			=   img.size
            snake_case_   			=   self.encode(lowerCAmelCase__)

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

            snake_case_   			=   self.scheduler.add_noise(
                lowerCAmelCase__,				lowerCAmelCase__,				timesteps=self.scheduler.timesteps[tmax],				)

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

            snake_case_   			=   self.scheduler.scale_model_input(lowerCAmelCase__,				self.scheduler.timesteps[tmax])

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

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

            snake_case_   			=   self.scheduler.step(lowerCAmelCase__,				self.scheduler.timesteps[tmax],				lowerCAmelCase__).prev_sample

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

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

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

                      snake_case_   			=   self.scheduler.scale_model_input(lowerCAmelCase__,				lowerCAmelCase__)

                      with torch.no_grad():
                           snake_case_   			=   self.unet(
                               lowerCAmelCase__,				lowerCAmelCase__,				encoder_hidden_states=lowerCAmelCase__,				).sample

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

                      snake_case_   			=   self.scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__).prev_sample

            return self.decode(lowerCAmelCase__)


"""simple docstring"""


import bsa
import requests







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


if __name__ == "__main__":
    import json

    __UpperCamelCase										=     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)
        )
    )

