






from __future__ import annotations

import pandas as pd







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							list[int]					,  snake_case_ :							list[int]					,  snake_case_ :							int       ):
	snake_case__			:				Dict       =  [0] * no_of_processes
	snake_case__			:				List[str]       =  [0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(_A       ):
		snake_case__			:				Any       =  burst_time[i]

	snake_case__			:				List[Any]       =  0
	snake_case__			:				Tuple       =  0
	snake_case__			:				Any       =  999999999
	snake_case__			:				Tuple       =  0
	snake_case__			:				Union[str, Any]       =  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:
					snake_case__			:				Optional[Any]       =  remaining_time[j]
					snake_case__			:				Any       =  j
					snake_case__			:				Any       =  True

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

		snake_case__			:				str       =  remaining_time[short]
		if minm == 0:
			snake_case__			:				List[str]       =  999999999

		if remaining_time[short] == 0:
			complete += 1
			snake_case__			:				Union[str, Any]       =  False

			# Find finish time of current process
			snake_case__			:				Dict       =  increment_time + 1

			# Calculate waiting time
			snake_case__			:				Optional[Any]       =  finish_time - arrival_time[short]
			snake_case__			:				Optional[Any]       =  finar - burst_time[short]

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

        # Increment time
		increment_time += 1
	return waiting_time







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							list[int]					,  snake_case_ :							int					,  snake_case_ :							list[int]       ):
	snake_case__			:				List[Any]       =  [0] * no_of_processes
	for i in range(_A       ):
		snake_case__			:				str       =  burst_time[i] + waiting_time[i]
	return turn_around_time







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							list[int]					,  snake_case_ :							list[int]					,  snake_case_ :							int       ):
	snake_case__			:				Optional[int]       =  0
	snake_case__			:				Dict       =  0
	for i in range(_A       ):
		snake_case__			:				int       =  total_waiting_time + waiting_time[i]
		snake_case__			:				List[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""")
	__lowerCamelCase :      Optional[Any]										=				int(input())
	__lowerCamelCase :      str										=				[0] * no_of_processes
	__lowerCamelCase :      Tuple										=				[0] * no_of_processes
	__lowerCamelCase :      Dict										=				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))
		__lowerCamelCase :      Any										=				map(int, input().split())

	__lowerCamelCase :      List[str]										=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

	calculate_average_times(waiting_time, turn_around_time, no_of_processes)

	__lowerCamelCase :      List[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)











# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)



