





import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_SCREAMING_SNAKE_CASE 		=						False
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def UpperCAmelCase_			( self : Dict						,				_A : Any=32		)							->      int:







							"""simple docstring"""


							set_seed(0		)
							snake_case_						:     Optional[int]     =		UNetaDModel(sample_size=_A						,				in_channels=3						,				out_channels=3		)
							snake_case_						:     Union[str, Any]     =		torch.optim.SGD(model.parameters()						,				lr=0.0_0_0_1		)
							return model, optimizer






						@slow
						def UpperCAmelCase_			( self : Any		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							snake_case_						:     int     =		DDPMScheduler(
							    num_train_timesteps=1000						,				beta_start=0.0_0_0_1						,				beta_end=0.0_2						,				beta_schedule='linear'						,				clip_sample=_A						,				)
							snake_case_						:     Union[str, Any]     =		DDIMScheduler(
							    num_train_timesteps=1000						,				beta_start=0.0_0_0_1						,				beta_end=0.0_2						,				beta_schedule='linear'						,				clip_sample=_A						,				)

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0		)
							snake_case_						:     Any     =		[torch.randn((4, 3, 32, 32)		).clip(-1						,				1		).to(_A		) for _ in range(4		)]
							snake_case_						:     Optional[int]     =		[torch.randn((4, 3, 32, 32)		).to(_A		) for _ in range(4		)]
							snake_case_						:     List[str]     =		[torch.randint(0						,				1000						,				(4,)		).long().to(_A		) for _ in range(4		)]

							# train with a DDPM scheduler
							snake_case_						:     Dict     =		self.get_model_optimizer(resolution=32		)
							model.train().to(_A		)
							for i in range(4		):
								optimizer.zero_grad()
								snake_case_						:     List[Any]     =		ddpm_scheduler.add_noise(clean_images[i]						,				noise[i]						,				timesteps[i]		)
								snake_case_						:     str     =		model(_A						,				timesteps[i]		).sample
								snake_case_						:     Dict     =		torch.nn.functional.mse_loss(_A						,				noise[i]		)
								loss.backward()
								optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							snake_case_						:     List[str]     =		self.get_model_optimizer(resolution=32		)
							model.train().to(_A		)
							for i in range(4		):
								optimizer.zero_grad()
								snake_case_						:     Any     =		ddim_scheduler.add_noise(clean_images[i]						,				noise[i]						,				timesteps[i]		)
								snake_case_						:     int     =		model(_A						,				timesteps[i]		).sample
								snake_case_						:     Tuple     =		torch.nn.functional.mse_loss(_A						,				noise[i]		)
								loss.backward()
								optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(_A						,				_A						,				atol=1E-5		)		)
							self.assertTrue(torch.allclose(_A						,				_A						,				atol=1E-5		)		)








from __future__ import annotations

import pandas as pd


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Optional[Any]     =		[0] * no_of_processes
	snake_case_						:     Tuple     =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		burst_time[i]

	snake_case_						:     Optional[Any]     =		0
	snake_case_						:     Dict     =		0
	snake_case_						:     Any     =		9_99_99_99_99
	snake_case_						:     Tuple     =		0
	snake_case_						:     List[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_						:     str     =		remaining_time[j]
					snake_case_						:     Any     =		j
					snake_case_						:     List[str]     =		True

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

		snake_case_						:     Any     =		remaining_time[short]
		if minm == 0:
			snake_case_						:     Dict     =		9_99_99_99_99

		if remaining_time[short] == 0:
			complete += 1
			snake_case_						:     List[str]     =		False

			# Find finish time of current process
			snake_case_						:     List[str]     =		increment_time + 1

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

			if waiting_time[short] < 0:
				snake_case_						:     Optional[int]     =		0

        # Increment time
		increment_time += 1
	return waiting_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Tuple     =		[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__  (      __a				,	__a				,	__a				):
	snake_case_						:     int     =		0
	snake_case_						:     Optional[Any]     =		0
	for i in range(__a				):
		snake_case_						:     int     =		total_waiting_time + waiting_time[i]
		snake_case_						:     Optional[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""")
						_SCREAMING_SNAKE_CASE 		=						int(input())
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						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		,							_SCREAMING_SNAKE_CASE 		=						map(int, input().split())

						_SCREAMING_SNAKE_CASE 		=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

						_SCREAMING_SNAKE_CASE 		=						burst_time
						_SCREAMING_SNAKE_CASE 		=						no_of_processes
						_SCREAMING_SNAKE_CASE 		=						waiting_time
						_SCREAMING_SNAKE_CASE 		=						calculate_turnaroundtime(bt, n, wt)

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						_SCREAMING_SNAKE_CASE 		=						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)

