



"""simple docstring"""






import requests
from bsa import BeautifulSoup






def 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Any      ):
			"""simple docstring"""
			lowerCamelCase__						:						str						      =(
			    '''https://www.horoscope.com/us/horoscopes/general/'''
			    f'''horoscope-general-daily-{day}.aspx?sign={zodiac_sign}'''
			)
			lowerCamelCase__						:						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",
		)
		_lowercase					:					int 		=		int(input("number> ").strip())
		print("choose some day:\n", "yesterday\n", "today\n", "tomorrow\n")
		_lowercase					:					Optional[int] 		=		input("enter the day> ")
		_lowercase					:					List[Any] 		=		horoscope(zodiac_sign, day)
		print(horoscope_text)








"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     ArgumentParser(
						    description=(
						        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
						    )			)

						# Optional arguments for the launch helper
						parser.add_argument('''--num_cores'''						,      type=_UpperCamelCase						,      default=1						,      help='''Number of TPU cores to use (1 or 8).'''			)

						# positional
						parser.add_argument(
						    '''training_script'''						,      type=_UpperCamelCase						,      help=(
						        '''The full path to the single TPU training '''
						        '''program/script to be launched in parallel, '''
						        '''followed by all the arguments for the '''
						        '''training script'''
						    )						,      )

						# rest from the training program
						parser.add_argument('''training_script_args'''						,      nargs=_UpperCamelCase			)

						return parser.parse_args()






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     parse_args()

						# Import training_script as a module.
						UpperCAmelCase__												=     Path(args.training_script			)
						sys.path.append(str(script_fpath.parent.resolve()			)			)
						UpperCAmelCase__												=     script_fpath.stem
						UpperCAmelCase__												=     importlib.import_module(_UpperCamelCase			)

						# Patch sys.argv
						UpperCAmelCase__												=     [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores			)]

						xmp.spawn(mod._mp_fn						,      args=()						,      nprocs=args.num_cores			)


if __name__ == "__main__":
						main()