


import requests
from bsa import BeautifulSoup







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =(
				    '''https://www.horoscope.com/us/horoscopes/general/'''
				    f"horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"
				)
				_SCREAMING_SNAKE_CASE	     =BeautifulSoup(requests.get(a__).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''',
							)
							snake_case_  :	Union[str, Any]							  =							int(input('''number> ''').strip())
							print('''choose some day:\n''', '''yesterday\n''', '''today\n''', '''tomorrow\n''')
							snake_case_  :	Dict							  =							input('''enter the day> ''')
							snake_case_  :	Any							  =							horoscope(zodiac_sign, day)
							print(horoscope_text)



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[str]     )							->   Tuple:


										"""simple docstring"""





										super().__init__()
										self.register_modules(unet=_a ,			scheduler=_a     )




						@torch.no_grad()
						def __call__(				self    :					Any ,			_a    :					int = 1 ,			_a    :					Optional[torch.Generator] = None ,			_a    :					int = 50 ,			_a    :					Optional[str] = "pil" ,			_a    :					bool = True ,			**_a    :					Dict ,			)							->   Union[ImagePipelineOutput, Tuple]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=_a ,			)
										_SCREAMING_SNAKE_CASE	     =image.to(self.device     )

										# set step values
										self.scheduler.set_timesteps(_a     )

										for t in self.progress_bar(self.scheduler.timesteps     ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE	     =self.unet(_a ,			_a     ).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														_SCREAMING_SNAKE_CASE	     =self.scheduler.step(_a ,			_a ,			_a     ).prev_sample

										_SCREAMING_SNAKE_CASE	     =(image / 2 + 0.5).clamp(0 ,			1     )
										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     ).numpy()
										if output_type == "pil":
														_SCREAMING_SNAKE_CASE	     =self.numpy_to_pil(_a     )

										if not return_dict:
														return (image,), "This is a local test"

										return ImagePipelineOutput(images=_a     ), "This is a local test"