


from __future__ import annotations

import pandas as pd

def       lowercase_		(		_A			:    list[int]    ,					_A			:    list[int]    ,					_A			:    int						):

      """simple docstring"""




      lowerCamelCase__		:				int    =  [0] * no_of_processes
      lowerCamelCase__		:				Any    =  [0] * no_of_processes
      # Copy the burst time into remaining_time[]
      for i in range(_A						):
            lowerCamelCase__		:				Optional[int]    =  burst_time[i]

      lowerCamelCase__		:				Optional[Any]    =  0
      lowerCamelCase__		:				Optional[int]    =  0
      lowerCamelCase__		:				List[str]    =  999999999
      lowerCamelCase__		:				Union[str, Any]    =  0
      lowerCamelCase__		:				Optional[int]    =  False

      # Process until all processes are completed
      while complete != no_of_processes:
            for j in range(_A						):
                  if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                        if remaining_time[j] < minm:
                              lowerCamelCase__		:				List[Any]    =  remaining_time[j]
                              lowerCamelCase__		:				int    =  j
                              lowerCamelCase__		:				str    =  True

            if not check:
                  increment_time += 1
                  continue
            remaining_time[short] -= 1

            lowerCamelCase__		:				List[Any]    =  remaining_time[short]
            if minm == 0:
                  lowerCamelCase__		:				int    =  999999999

            if remaining_time[short] == 0:
                  complete += 1
                  lowerCamelCase__		:				List[Any]    =  False

                  # Find finish time of current process
                  lowerCamelCase__		:				Tuple    =  increment_time + 1

                  # Calculate waiting time
                  lowerCamelCase__		:				Union[str, Any]    =  finish_time - arrival_time[short]
                  lowerCamelCase__		:				List[Any]    =  finar - burst_time[short]

                  if waiting_time[short] < 0:
                        lowerCamelCase__		:				str    =  0

        # Increment time
            increment_time += 1
      return waiting_time

def       lowercase_		(		_A			:    list[int]    ,					_A			:    int    ,					_A			:    list[int]						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  [0] * no_of_processes
      for i in range(_A						):
            lowerCamelCase__		:				List[str]    =  burst_time[i] + waiting_time[i]
      return turn_around_time

def       lowercase_		(		_A			:    list[int]    ,					_A			:    list[int]    ,					_A			:    int						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  0
      lowerCamelCase__		:				Dict    =  0
      for i in range(_A						):
            lowerCamelCase__		:				Optional[Any]    =  total_waiting_time + waiting_time[i]
            lowerCamelCase__		:				Any    =  total_turn_around_time + turn_around_time[i]
      print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}"						)
      print("Average turn around time ="    ,					total_turn_around_time / no_of_processes						)


if __name__ == "__main__":
    print("Enter how many process you want to analyze")
    A      :    Tuple		=					int(input())
    A      :    int		=					[0] * no_of_processes
    A      :    Dict		=					[0] * no_of_processes
    A      :    int		=					list(range(1, no_of_processes + 1))

    for i in range(no_of_processes):
        print("Enter the arrival time and burst time for process:--" + str(i + 1))
        A,  A      :    Any		=					map(int, input().split())

    A      :    List[Any]		=					calculate_waitingtime(arrival_time, burst_time, no_of_processes)

    A      :    Optional[Any]		=					burst_time
    A      :    List[Any]		=					no_of_processes
    A      :    Optional[int]		=					waiting_time
    A      :    Optional[int]		=					calculate_turnaroundtime(bt, n, wt)

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    A      :    str		=					pd.DataFrame(
        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
        columns=[
            "Process",
            "BurstTime",
            "ArrivalTime",
            "WaitingTime",
            "TurnAroundTime",
        ],
    )

    # Printing the dataFrame
    pd.set_option("display.max_rows", fcfs.shape[0] + 1)
    print(fcfs)






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A      :    List[str]		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def __init__(   self							:			Optional[int]					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



             warnings.warn(
                 "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                 " use BeitImageProcessor instead."					,      __lowerCamelCase					,      )
             super().__init__(*__lowerCamelCase					,      **__lowerCamelCase       )


