

"""simple docstring"""





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      __a (   snake_case__     ):

			"""simple docstring"""

			def __init__(				self  :       Dict    ,	lowercase_  :       Dict    ,	lowercase_  :       int    ):
								super().__init__()
								self.register_modules(unet=lowercase_    ,	scheduler=lowercase_    )






			@torch.no_grad()
			def __call__(				self  :       Any    ,	lowercase_  :       int = 1    ,	lowercase_  :       Optional[torch.Generator] = None    ,	lowercase_  :       int = 50    ,	lowercase_  :       Optional[str] = "pil"    ,	lowercase_  :       bool = True    ,	**lowercase_  :       Optional[int]    ,	):
								UpperCamelCase__		:     Any								=torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,	generator=lowercase_    ,	)
								UpperCamelCase__		:     Union[str, Any]								=image.to(self.device    )

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

								for t in self.progress_bar(self.scheduler.timesteps    ):
													# 1. predict noise model_output
													UpperCamelCase__		:     Optional[int]								=self.unet(lowercase_    ,	lowercase_    ).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
													UpperCamelCase__		:     Tuple								=self.scheduler.step(lowercase_    ,	lowercase_    ,	lowercase_    ).prev_sample

								UpperCamelCase__		:     str								=(image / 2 + 0.5).clamp(0    ,	1    )
								UpperCamelCase__		:     List[str]								=image.cpu().permute(0    ,	2    ,	3    ,	1    ).numpy()
								if output_type == "pil":
													UpperCamelCase__		:     List[str]								=self.numpy_to_pil(lowercase_    )

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

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





"""simple docstring"""





import requests
from bsa import BeautifulSoup



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				str					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=(
					    '''https://www.horoscope.com/us/horoscopes/general/'''
					    F'''horoscope-general-daily-{day}.aspx?sign={zodiac_sign}'''
					)
					UpperCamelCase__		:     List[str]								=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""",
				)
				_SCREAMING_SNAKE_CASE							:	Any										=			int(input("""number> """).strip())
				print("""choose some day:\n""", """yesterday\n""", """today\n""", """tomorrow\n""")
				_SCREAMING_SNAKE_CASE							:	List[str]										=			input("""enter the day> """)
				_SCREAMING_SNAKE_CASE							:	Dict										=			horoscope(zodiac_sign, day)
				print(horoscope_text)


