'''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				(				_UpperCamelCase     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     )		->     Any:
           super().__init__()

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

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     List[str]:
           with torch.no_grad():
               A_     =   self.vae.encode(tfms.ToTensor()(UpperCAmelCase_ ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
               A_     =   0.18_215 * latent.latent_dist.sample()
           return latent

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

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     int:
           A_     =   self.tokenizer(
               UpperCAmelCase_    ,     padding='''max_length'''    ,     max_length=self.tokenizer.model_max_length    ,     truncation=UpperCAmelCase_    ,     return_tensors='''pt'''    ,     )

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

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

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

           return torch.cat([uncond_embedding, text_embedding] )

       def __call__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE = 0.3    ,     _SCREAMING_SNAKE_CASE = 0.6    ,     _SCREAMING_SNAKE_CASE = 0.5    ,     _SCREAMING_SNAKE_CASE = 42    ,     _SCREAMING_SNAKE_CASE = 50    ,     _SCREAMING_SNAKE_CASE = 7.5    ,     )		->     Tuple:
           A_     =   steps - int(kmin * steps )
           A_     =   steps - int(kmax * steps )

           A_     =   self.prep_text(UpperCAmelCase_ )

           self.scheduler.set_timesteps(UpperCAmelCase_ )

           A_     =   img.size
           A_     =   self.encode(UpperCAmelCase_ )

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

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

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

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

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

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

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

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

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

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

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

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

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

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

           return self.decode(UpperCAmelCase_ )











"""simple docstring"""







import requests
from bsa import BeautifulSoup


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase	):
		_lowercase							:      Dict			=					(
		    """https://www.horoscope.com/us/horoscopes/general/"""
		    F"""horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"""
		)
		_lowercase							:      Any			=					BeautifulSoup(requests.get(__UpperCAmelCase	).content					,							"""html.parser"""	)
		return soup.find("""div"""					,							class_="""main-horoscope"""	).p.text


if __name__ == "__main__":
		print("""Daily Horoscope. \n""")
		print(
		    """enter your Zodiac sign number:\n""",
		    """1. Aries\n""",
		    """2. Taurus\n""",
		    """3. Gemini\n""",
		    """4. Cancer\n""",
		    """5. Leo\n""",
		    """6. Virgo\n""",
		    """7. Libra\n""",
		    """8. Scorpio\n""",
		    """9. Sagittarius\n""",
		    """10. Capricorn\n""",
		    """11. Aquarius\n""",
		    """12. Pisces\n""",
		)
		UpperCAmelCase:   Union[str, Any]       			=							int(input("""number> """).strip())
		print("""choose some day:\n""", """yesterday\n""", """today\n""", """tomorrow\n""")
		UpperCAmelCase:   List[Any]       			=							input("""enter the day> """)
		UpperCAmelCase:   List[Any]       			=							horoscope(zodiac_sign, day)
		print(horoscope_text)



