




from statistics import mean

import numpy as np







def      _a				(						SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Any					):
					__lowerCAmelCase           =	0
					# Number of processes finished
					__lowerCAmelCase           =	0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					__lowerCAmelCase           =	[0] * no_of_process
					# List to include calculation results
					__lowerCAmelCase           =	[0] * no_of_process

					# Sort by arrival time.
					__lowerCAmelCase           =	[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE_					)]
					__lowerCAmelCase           =	[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE_					)]
					arrival_time.sort()

					while no_of_process > finished_process_count:
										__lowerCAmelCase           =	0
										while finished_process[i] == 1:
															i += 1
										if current_time < arrival_time[i]:
															__lowerCAmelCase           =	arrival_time[i]

										__lowerCAmelCase           =	0
										# Index showing the location of the process being performed
										__lowerCAmelCase           =	0
										# Saves the current response ratio.
										__lowerCAmelCase           =	0
										for i in range(0				,				SCREAMING_SNAKE_CASE_					):
															if finished_process[i] == 0 and arrival_time[i] <= current_time:
																				__lowerCAmelCase           =	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																				    i
																				]
															if response_ratio < temp:
																				__lowerCAmelCase           =	temp
																				__lowerCAmelCase           =	i

        # Calculate the turn around time
										__lowerCAmelCase           =	current_time + burst_time[loc] - arrival_time[loc]
										current_time += burst_time[loc]
										# Indicates that the process has been performed.
										__lowerCAmelCase           =	1
										# Increase finished_process_count by 1
										finished_process_count += 1

					return turn_around_time







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Any					):
					__lowerCAmelCase           =	[0] * no_of_process
					for i in range(0				,				SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	turn_around_time[i] - burst_time[i]
					return waiting_time


if __name__ == "__main__":
					UpperCamelCase__         =      5
					UpperCamelCase__         =      ["""A""", """B""", """C""", """D""", """E"""]
					UpperCamelCase__         =      [1, 2, 3, 4, 5]
					UpperCamelCase__         =      [1, 2, 3, 4, 5]

					UpperCamelCase__         =      calculate_turn_around_time(
					    process_name, arrival_time, burst_time, no_of_process
					)
					UpperCamelCase__         =      calculate_waiting_time(
					    process_name, turn_around_time, burst_time, no_of_process
					)

					print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
					for i in range(0, no_of_process):
										print(
										    f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
										    f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
										)

					print(f'''average waiting time : {mean(waiting_time):.5f}''')
					print(f'''average turn around time : {mean(turn_around_time):.5f}''')










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       a__   ( unittest.TestCase				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	UNetaDModel(
											    block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=3		,      out_channels=3		,      down_block_types=("DownBlock2D", "AttnDownBlock2D")		,      up_block_types=("AttnUpBlock2D", "UpBlock2D")		,      )
											return model






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.dummy_uncond_unet
											__lowerCAmelCase           =	ScoreSdeVeScheduler()

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A		,      return_dict=_A      )[
											    0
											]

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]
											__lowerCAmelCase           =	image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase           =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






@slow
@require_torch
class       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"google/ncsnpp-church-256"
											__lowerCAmelCase           =	UNetaDModel.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVeScheduler.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=1_0		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_5_6, 2_5_6, 3)

											__lowerCAmelCase           =	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




