



"""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     (			snake_case    ):




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,):
			super().__init__()

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






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			with torch.no_grad():
					_lowercase							:      str			=					self.vae.encode(tfms.ToTensor()(UpperCAmelCase_					).unsqueeze(0					).to(self.device					) * 2 - 1					)
					_lowercase							:      Union[str, Any]			=					0.18215 * latent.latent_dist.sample()
			return latent






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			_lowercase							:      Dict			=					(1 / 0.18215) * latent
			with torch.no_grad():
					_lowercase							:      Tuple			=					self.vae.decode(UpperCAmelCase_					).sample
			_lowercase							:      Optional[Any]			=					(img / 2 + 0.5).clamp(0							,1					)
			_lowercase							:      List[Any]			=					img.detach().cpu().permute(0							,2							,3							,1					).numpy()
			_lowercase							:      List[Any]			=					(img * 2_55).round().astype("""uint8"""					)
			return Image.fromarray(img[0]					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			_lowercase							:      Optional[int]			=					self.tokenizer(
			    UpperCAmelCase_							,padding="""max_length"""							,max_length=self.tokenizer.model_max_length							,truncation=UpperCAmelCase_							,return_tensors="""pt"""							,)

			_lowercase							:      Dict			=					self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

			_lowercase							:      Tuple			=					self.tokenizer(
			    """"""							,padding="""max_length"""							,max_length=self.tokenizer.model_max_length							,truncation=UpperCAmelCase_							,return_tensors="""pt"""							,)

			_lowercase							:      Any			=					self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

			return torch.cat([uncond_embedding, text_embedding]					)






	def __call__(			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_ = 0.3							,UpperCAmelCase_ = 0.6							,UpperCAmelCase_ = 0.5							,UpperCAmelCase_ = 42							,UpperCAmelCase_ = 50							,UpperCAmelCase_ = 7.5							,):
			_lowercase							:      List[str]			=					steps - int(kmin * steps					)
			_lowercase							:      str			=					steps - int(kmax * steps					)

			_lowercase							:      List[Any]			=					self.prep_text(UpperCAmelCase_					)

			self.scheduler.set_timesteps(UpperCAmelCase_					)

			_lowercase			,   _lowercase							:      Optional[Any]			=					img.size
			_lowercase							:      Any			=					self.encode(UpperCAmelCase_					)

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

			_lowercase							:      Any			=					self.scheduler.add_noise(
			    UpperCAmelCase_							,UpperCAmelCase_							,timesteps=self.scheduler.timesteps[tmax]							,)

			_lowercase							:      int			=					torch.cat([latents] * 2					)

			_lowercase							:      int			=					self.scheduler.scale_model_input(UpperCAmelCase_							,self.scheduler.timesteps[tmax]					)

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

			_lowercase			,   _lowercase							:      int			=					pred.chunk(2					)
			_lowercase							:      str			=					pred_uncond + guidance_scale * (pred_text - pred_uncond)

			_lowercase							:      Dict			=					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
									_lowercase							:      Tuple			=					self.scheduler.add_noise(
									    UpperCAmelCase_							,UpperCAmelCase_							,timesteps=UpperCAmelCase_							,)

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

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

							_lowercase							:      Union[str, Any]			=					self.scheduler.scale_model_input(UpperCAmelCase_							,UpperCAmelCase_					)

							with torch.no_grad():
									_lowercase							:      Optional[Any]			=					self.unet(
									    UpperCAmelCase_							,UpperCAmelCase_							,encoder_hidden_states=UpperCAmelCase_							,).sample

							_lowercase			,   _lowercase							:      Dict			=					pred.chunk(2					)
							_lowercase							:      Any			=					pred_uncond + guidance_scale * (pred_text - pred_uncond)

							_lowercase							:      Tuple			=					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)



