
'''simple docstring'''
import requests
from bsa import BeautifulSoup




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       str:


		'''simple docstring'''


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




'''simple docstring'''
import numpy as np




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:


		'''simple docstring'''


		snake_case ,snake_case     :					str     =	a.shape
		snake_case     :					Union[str, Any]     =	min(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
		snake_case     :					List[Any]     =	np.eye(SCREAMING_SNAKE_CASE__ )
		snake_case     :					Union[str, Any]     =	a.copy()

		for k in range(t - 1 ):
				# select a column of modified matrix A':
				snake_case     :					Union[str, Any]     =	r[k:, [k]]
				# construct first basis vector
				snake_case     :					str     =	np.zeros_like(SCREAMING_SNAKE_CASE__ )
				snake_case     :					List[str]     =	1.0
				# determine scaling factor
				snake_case     :					int     =	np.linalg.norm(SCREAMING_SNAKE_CASE__ )
				# construct vector v for Householder reflection
				snake_case     :					Optional[int]     =	x + np.sign(x[0] ) * alpha * ea
				v /= np.linalg.norm(SCREAMING_SNAKE_CASE__ )

				# construct the Householder matrix
				snake_case     :					Tuple     =	np.eye(m - k ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				snake_case     :					int     =	np.block([[np.eye(SCREAMING_SNAKE_CASE__ ), np.zeros((k, m - k) )], [np.zeros((m - k, k) ), q_k]] )

				snake_case     :					int     =	q @ q_k.T
				snake_case     :					Dict     =	q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()


