






'''simple docstring'''





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):



		def __init__(    self:    Optional[int]				,   a:    AutoencoderKL				,   a:    CLIPTextModel				,   a:    CLIPTokenizer				,   a:    UNetaDConditionModel				,   a:    DDIMScheduler				,   a:    float = 0.93				,   a:    bool = True				,   )  ->int:






								'''simple docstring'''


								a_				     =   mixing_coeff
								a_				     =   leapfrog_steps

								super().__init__()
								self.register_modules(
								    vae=a				,   text_encoder=a				,   tokenizer=a				,   unet=a				,   scheduler=a				,   )

								a_				     =   2 ** (len(self.vae.config.block_out_channels) - 1)
								a_				     =   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)



		def _lowerCAmelCase     (    self:    int				,   a:    str				,   a:    Optional[str] = None				,   a:    bool = False)  ->Optional[int]:






								'''simple docstring'''


								a_				     =   self.tokenizer(
								    a				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=a				,   return_tensors="pt"				,   )

								a_				     =   self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state

								a_				     =   prompt_embeds.to(dtype=self.text_encoder.dtype				,   device=self.device)

								if do_classifier_free_guidance:
														a_				     =   "" if negative_prompt is None else negative_prompt

														a_				     =   self.tokenizer(
														    a				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=a				,   return_tensors="pt"				,   )

														a_				     =   self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state

														a_				     =   torch.cat([negative_prompt_embeds, prompt_embeds])

								return prompt_embeds



		def _lowerCAmelCase     (    self:    Union[str, Any]				,   a:    torch.Tensor				,   a:    torch.Tensor)  ->Union[str, Any]:






								'''simple docstring'''


								a_				     =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								a_				     =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]



		def _lowerCAmelCase     (    self:    Tuple				,   a:    torch.Tensor				,   a:    torch.Tensor)  ->Optional[Any]:






								'''simple docstring'''


								a_				     =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								a_				     =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]



		def _lowerCAmelCase     (    self:    str				,   a:    torch.Tensor)  ->Tuple:






								'''simple docstring'''


								a_				     =   int(a)

								a_				     =   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

								return alpha_prod, 1 - alpha_prod



		def _lowerCAmelCase     (    self:    List[Any]				,   a:    torch.Tensor				,   a:    torch.Tensor				,   a:    torch.Tensor				,   a:    torch.Tensor				,   )  ->str:






								'''simple docstring'''


								a_				     =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								a_     ,       a_				     =   self._get_alpha_and_beta(a)
								a_     ,       a_				     =   self._get_alpha_and_beta(a)

								a_				     =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								a_				     =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

								a_				     =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype)



		def _lowerCAmelCase     (    self:    List[Any]				,   a:    torch.Tensor				,   a:    torch.Tensor				,   a:    torch.Tensor				,   a:    torch.Tensor				,   )  ->Dict:






								'''simple docstring'''


								a_				     =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								a_     ,       a_				     =   self._get_alpha_and_beta(a)
								a_     ,       a_				     =   self._get_alpha_and_beta(a)

								a_				     =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								a_				     =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
								a_				     =   a_t * base + b_t * model_output

								return model_input, next_model_input.to(base.dtype)



		@torch.no_grad()
		def _lowerCAmelCase     (    self:    List[str]				,   a:    torch.Tensor)  ->int:






								'''simple docstring'''


								a_				     =   1 / self.vae.config.scaling_factor * latents
								a_				     =   self.vae.decode(a).sample
								a_				     =   (image / 2 + 0.5).clamp(0				,   1)
								return image



		@torch.no_grad()
		def _lowerCAmelCase     (    self:    int				,   a:    Image.Image				,   a:    torch.Tensor				,   a:    torch.Tensor				,   a:    float				,   a:    Optional[torch.Generator] = None				,   )  ->Any:






								'''simple docstring'''


								a_				     =   guidance_scale > 1.0

								a_				     =   image.to(device=self.device				,   dtype=text_embeds.dtype)
								a_				     =   self.vae.encode(a).latent_dist.sample(a)

								a_				     =   self.vae.config.scaling_factor * latent

								a_				     =   [latent.clone(), latent.clone()]

								for i, t in tqdm(enumerate(a)				,   total=len(a)):
														a_				     =   self.noise_mixing_layer(x=coupled_latents[0]				,   y=coupled_latents[1])

														# j - model_input index, k - base index
														for j in range(2):
																				a_				     =   j ^ 1

																				if self.leapfrog_steps:
																										if i % 2 == 0:
																																a_     ,       a_				     =   j, k

																				a_				     =   coupled_latents[j]
																				a_				     =   coupled_latents[k]

																				a_				     =   torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

																				a_				     =   self.unet(a				,   a				,   encoder_hidden_states=a).sample

																				if do_classifier_free_guidance:
																										a_     ,       a_				     =   noise_pred.chunk(2)
																										a_				     =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																				a_     ,       a_				     =   self.noise_step(
																				    base=a				,   model_input=a				,   model_output=a				,   timestep=a				,   )

																				a_				     =   model_input

								return coupled_latents




		@torch.no_grad()
		def __call__(    self:    Optional[int]				,   a:    str				,   a:    str				,   a:    Image.Image				,   a:    float = 3.0				,   a:    int = 50				,   a:    float = 0.8				,   a:    Optional[str] = None				,   a:    Optional[torch.Generator] = None				,   a:    Optional[str] = "pil"				,   )  ->List[str]:






								'''simple docstring'''


								a_				     =   guidance_scale > 1.0

								a_				     =   self.image_processor.preprocess(a)

								a_				     =   self._encode_prompt(a				,   a				,   a)
								a_				     =   self._encode_prompt(a				,   a				,   a)

								self.scheduler.set_timesteps(a				,   self.device)

								a_				     =   num_inference_steps - int(num_inference_steps * strength)
								a_				     =   self.scheduler.timesteps[t_limit:]
								a_				     =   fwd_timesteps.flip(0)

								a_				     =   self.prepare_latents(a				,   a				,   a				,   a				,   a)

								for i, t in tqdm(enumerate(a)				,   total=len(a)):
														# j - model_input index, k - base index
														for k in range(2):
																				a_				     =   k ^ 1

																				if self.leapfrog_steps:
																										if i % 2 == 1:
																																a_     ,       a_				     =   j, k

																				a_				     =   coupled_latents[j]
																				a_				     =   coupled_latents[k]

																				a_				     =   torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

																				a_				     =   self.unet(a				,   a				,   encoder_hidden_states=a).sample

																				if do_classifier_free_guidance:
																										a_     ,       a_				     =   noise_pred.chunk(2)
																										a_				     =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																				a_     ,       a_				     =   self.denoise_step(
																				    base=a				,   model_input=a				,   model_output=a				,   timestep=a				,   )

																				a_				     =   model_input

														a_				     =   self.denoise_mixing_layer(x=coupled_latents[0]				,   y=coupled_latents[1])

								# either one is fine
								a_				     =   coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
														a_				     =   (
														    f"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
														    "`pil`, `np`, `pt`, `latent`"
														)
														deprecate("Unsupported output_type"				,   "1.0.0"				,   a				,   standard_warn=a)
														a_				     =   "np"

								if output_type == "latent":
														a_				     =   final_latent
								else:
														a_				     =   self.decode_latents(a)
														a_				     =   self.image_processor.postprocess(a				,   output_type=a)

								return image











'''simple docstring'''





import requests
from bsa import BeautifulSoup



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				str:

						'''simple docstring'''


						a_				     =   (
						    "https://www.horoscope.com/us/horoscopes/general/"
						    F"""horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"""
						)
						a_				     =   BeautifulSoup(requests.get(lowercase__				).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',
							)
							a_      = int(input('number> ').strip())
							print('choose some day:\n', 'yesterday\n', 'today\n', 'tomorrow\n')
							a_      = input('enter the day> ')
							a_      = horoscope(zodiac_sign, day)
							print(horoscope_text)



