code: 
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Tuple:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			len(SCREAMING_SNAKE_CASE      )
      # Create a stack and push index of fist element to it
      __UpperCAmelCase									=			[]
      st.append(0      )
  
      # Span value of first element is always 1
      __UpperCAmelCase									=			1
  
      # Calculate span values for rest of the elements
      for i in range(1       ,   SCREAMING_SNAKE_CASE      ):
          # Pop elements from stack while stack is not
          # empty and top of stack is smaller than price[i]
          while len(SCREAMING_SNAKE_CASE      ) > 0 and price[st[0]] <= price[i]:
              st.pop()
  
          # If stack becomes empty, then price[i] is greater
          # than all elements on left of it, i.e. price[0],
          # price[1], ..price[i-1]. Else the price[i]  is
          # greater than elements after top of stack
          __UpperCAmelCase									=			i + 1 if len(SCREAMING_SNAKE_CASE      ) <= 0 else (i - st[0])
  
          # Push this element to stack
          st.append(SCREAMING_SNAKE_CASE      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Tuple:
  
  
  
  
  
  
      '''simple docstring'''
      for i in range(0       ,   SCREAMING_SNAKE_CASE      ):
          print(arr[i]       ,   end=''' '''      )
  
  
  # Driver program to test above function
  A_		:			List[Any]										=   [10, 4, 5, 90, 120, 80]
  A_		:			Union[str, Any]										=   [0 for i in range(len(price) + 1)]
  
  # Fill the span values in array S[]
  calculation_span(price, S)
  print_array(S, len(price))
  
  
  
  
  
style_context: 
  
  import numpy as np
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Tuple:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase					,     __UpperCAmelCase									=			a.shape
      __UpperCAmelCase									=			min(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			np.eye(SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			a.copy()
  
      for k in range(t - 1      ):
          # select a column of modified matrix A':
          __UpperCAmelCase									=			r[k:, [k]]
          # construct first basis vector
          __UpperCAmelCase									=			np.zeros_like(SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			1.0
          # determine scaling factor
          __UpperCAmelCase									=			np.linalg.norm(SCREAMING_SNAKE_CASE      )
          # construct vector v for Householder reflection
          __UpperCAmelCase									=			x + np.sign(x[0]      ) * alpha * ea
          v /= np.linalg.norm(SCREAMING_SNAKE_CASE      )
  
          # construct the Householder matrix
          __UpperCAmelCase									=			np.eye(m - k      ) - 2.0 * v @ v.T
          # pad with ones and zeros as necessary
          __UpperCAmelCase									=			np.block([[np.eye(SCREAMING_SNAKE_CASE      ), np.zeros((k, m - k)      )], [np.zeros((m - k, k)      ), q_k]]      )
  
          __UpperCAmelCase									=			q @ q_k.T
          __UpperCAmelCase									=			q_k @ r
  
      return q, r
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
  
  
  
  
  