






"""simple docstring"""






from __future__ import annotations

import pandas as pd



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       [0] * no_of_processes
				A__							 =       [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(_SCREAMING_SNAKE_CASE				):
								A__							 =       burst_time[i]

				A__							 =       0
				A__							 =       0
				A__							 =       9_9999_9999
				A__							 =       0
				A__							 =       False

				# Process until all processes are completed
				while complete != no_of_processes:
								for j in range(_SCREAMING_SNAKE_CASE				):
												if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																if remaining_time[j] < minm:
																				A__							 =       remaining_time[j]
																				A__							 =       j
																				A__							 =       True

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

								A__							 =       remaining_time[short]
								if minm == 0:
												A__							 =       9_9999_9999

								if remaining_time[short] == 0:
												complete += 1
												A__							 =       False

												# Find finish time of current process
												A__							 =       increment_time + 1

												# Calculate waiting time
												A__							 =       finish_time - arrival_time[short]
												A__							 =       finar - burst_time[short]

												if waiting_time[short] < 0:
																A__							 =       0

        # Increment time
								increment_time += 1
				return waiting_time



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       [0] * no_of_processes
				for i in range(_SCREAMING_SNAKE_CASE				):
								A__							 =       burst_time[i] + waiting_time[i]
				return turn_around_time



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       0
				A__							 =       0
				for i in range(_SCREAMING_SNAKE_CASE				):
								A__							 =       total_waiting_time + waiting_time[i]
								A__							 =       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''')
					SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       int(input())
					SCREAMING_SNAKE_CASE						:						Optional[int]					   =       [0] * no_of_processes
					SCREAMING_SNAKE_CASE						:						List[str]					   =       [0] * no_of_processes
					SCREAMING_SNAKE_CASE						:						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))
										SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       map(int, input().split())

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

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

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					SCREAMING_SNAKE_CASE						:						Any					   =       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)










"""simple docstring"""






from math import ceil



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(0       ,lowerCAmelCase__				)				)

				A__							 =       [item for sublist in list(device_map.values()				) for item in sublist]

				# Duplicate check
				A__							 =       []
				for i in device_map_blocks:
								if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
				A__							 =       [i for i in blocks if i not in device_map_blocks]
				A__							 =       [i for i in device_map_blocks if i not in blocks]

				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
								    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
								    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
								    + str(lowerCAmelCase__				)				)







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(lowerCAmelCase__				)				)
				A__							 =       int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
				A__							 =       [layers[i : i + n_blocks] for i in range(0       ,lowerCAmelCase__       ,lowerCAmelCase__				)]

				return dict(zip(lowerCAmelCase__       ,lowerCAmelCase__				)				)


