






'''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)











'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



