"""simple docstring"""






from statistics import mean

import numpy as np



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

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

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

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

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

				return turn_around_time



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							[0] * no_of_process
				for i in range(0      ,    lowerCamelCase ):
								UpperCAmelCase__    =							turn_around_time[i] - burst_time[i]
				return waiting_time


if __name__ == "__main__":
						lowerCAmelCase__						:						Union[str, Any]		  =			5
						lowerCAmelCase__						:						Dict		  =			['A', 'B', 'C', 'D', 'E']
						lowerCAmelCase__						:						Dict		  =			[1, 2, 3, 4, 5]
						lowerCAmelCase__						:						str		  =			[1, 2, 3, 4, 5]

						lowerCAmelCase__						:						int		  =			calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						lowerCAmelCase__						:						Tuple		  =			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}""")




"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__						:						Union[str, Any]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






@nightly
@require_torch_gpu
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Any    ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )

									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowerCamelCase__    )
													UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained(lowerCamelCase__					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							generator.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									assert np.abs(image - new_image    ).sum() < 1e-5, "Models don't have the same forward pass"







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							'cyberpunk 2077'
									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt=lowerCamelCase__					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]    )

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

									UpperCAmelCase__    =							'A painting of a squirrel eating a burger '
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.text_to_image(
									    prompt=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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

									UpperCAmelCase__    =							pipe.image_variation(lowerCamelCase__					,generator=lowerCamelCase__					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]    )

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



