






from __future__ import annotations

from statistics import mean

def _UpperCAmelCase   (	a						:	list[int]  ,					a						:	list[int]  ,					a						:	int ):
       snake_case__	  =       [0] * no_of_processes
       snake_case__	  =       [0] * no_of_processes

       # Initialize remaining_time to waiting_time.

       for i in range(a ):
              snake_case__	  =       burst_time[i]
       snake_case__	  =       []

       snake_case__	  =       0
       snake_case__	  =       0

       # When processes are not completed,
       # A process whose arrival time has passed \
       # and has remaining execution time is put into the ready_process.
       # The shortest process in the ready_process, target_process is executed.

       while completed != no_of_processes:
              snake_case__	  =       []
              snake_case__	  =       -1

              for i in range(a ):
                     if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                            ready_process.append(a )

              if len(a ) > 0:
                     snake_case__	  =       ready_process[0]
                     for i in ready_process:
                            if remaining_time[i] < remaining_time[target_process]:
                                   snake_case__	  =       i
                     total_time += burst_time[target_process]
                     completed += 1
                     snake_case__	  =       0
                     snake_case__	  =       (
                         total_time - arrival_time[target_process] - burst_time[target_process]
                     )
              else:
                     total_time += 1

       return waiting_time

def _UpperCAmelCase   (	a						:	list[int]  ,					a						:	int  ,					a						:	list[int] ):
       snake_case__	  =       [0] * no_of_processes
       for i in range(a ):
              snake_case__	  =       burst_time[i] + waiting_time[i]
       return turn_around_time


if __name__ == "__main__":
 print("""[TEST CASE 01]""")

 a__       =     4
 a__       =     [2, 5, 3, 7]
 a__       =     [0, 0, 0, 0]
 a__       =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 a__       =     calculate_turnaroundtime(
     burst_time, no_of_processes, waiting_time
 )

 # Printing the Result
 print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
 for i, process_id in enumerate(list(range(1, 5))):
  print(
      F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
      F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
  )
 print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
 print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')








import requests
from bsa import BeautifulSoup

def _UpperCAmelCase   (	a						:	str  ,					a						:	dict ):
							snake_case__	  =       BeautifulSoup(requests.get(a  ,					params=a ).content  ,					"""html.parser""" )
							snake_case__	  =       soup.find("""div"""  ,					attrs={"""class""": """gs_ri"""} )
							snake_case__	  =       div.find("""div"""  ,					attrs={"""class""": """gs_fl"""} ).find_all("""a""" )
							return anchors[2].get_text()


if __name__ == "__main__":
	a__       =     {
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))
