code: 
  '''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)
  
  
  
style_context: 
  '''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()
  
  
  