


"""simple docstring"""
from statistics import mean

import numpy as np






def 		lowercase							(     _snake_case						:      list     ,   _snake_case						:      list     ,   _snake_case						:      list     ,   _snake_case						:      int     )     ->list:
	"""simple docstring"""





	__snake_case	:						List[str]						=				0
	# Number of processes finished
	__snake_case	:						List[str]						=				0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	__snake_case	:						Optional[int]						=				[0] * no_of_process
	# List to include calculation results
	__snake_case	:						Tuple						=				[0] * no_of_process

	# Sort by arrival time.
	__snake_case	:						int						=				[burst_time[i] for i in np.argsort(_snake_case     )]
	__snake_case	:						str						=				[process_name[i] for i in np.argsort(_snake_case     )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		__snake_case	:						List[str]						=				0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			__snake_case	:						List[Any]						=				arrival_time[i]

		__snake_case	:						Union[str, Any]						=				0
		# Index showing the location of the process being performed
		__snake_case	:						Dict						=				0
		# Saves the current response ratio.
		__snake_case	:						List[Any]						=				0
		for i in range(0     ,   _snake_case     ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				__snake_case	:						int						=				(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				__snake_case	:						List[str]						=				temp
				__snake_case	:						Any						=				i

        # Calculate the turn around time
		__snake_case	:						int						=				current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		__snake_case	:						Union[str, Any]						=				1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time






def 		lowercase							(     _snake_case						:      list     ,   _snake_case						:      list     ,   _snake_case						:      list     ,   _snake_case						:      int     )     ->list:
	"""simple docstring"""





	__snake_case	:						Optional[int]						=				[0] * no_of_process
	for i in range(0     ,   _snake_case     ):
		__snake_case	:						int						=				turn_around_time[i] - burst_time[i]
	return waiting_time


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

			SCREAMING_SNAKE_CASE :    List[Any]									=				calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			SCREAMING_SNAKE_CASE :    Union[str, Any]									=				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 unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



@slow
@require_torch_gpu
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''					)
			__snake_case	:						List[str]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						Tuple						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						str						=				torch.manual_seed(0					)
			__snake_case	:						int						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						List[str]						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Dict						=				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						str						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						List[str]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						Any						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Tuple						=				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-1



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_dpmpp_2m'''					)

			__snake_case	:						int						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						Optional[Any]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe(
			    [prompt]				,       generator=a_				,       guidance_scale=7.5				,       num_inference_steps=15				,       output_type='''np'''				,       use_karras_sigmas=a_				,       )

			__snake_case	:						Optional[int]						=				output.images

			__snake_case	:						Any						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Any						=				np.array(
			    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

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





