

from __future__ import annotations

from statistics import mean




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list[int]		,						_lowerCamelCase				:    list[int]		,						_lowerCamelCase				:    int)      ->					Dict:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      [0] * no_of_processes
							__UpperCamelCase			:     List[Any]         =      [0] * no_of_processes

							# Initialize remaining_time to waiting_time.

							for i in range(_lowerCamelCase):
														__UpperCamelCase			:     Any         =      burst_time[i]
							__UpperCamelCase			:     Dict         =      []

							__UpperCamelCase			:     Any         =      0
							__UpperCamelCase			:     List[str]         =      0

							# When processes are not completed,
							# A process whose arrival time has passed \
							# and has remaining execution time is put into the ready_process.
							# The shortest process in the ready_process, target_process is executed.

							while completed != no_of_processes:
														__UpperCamelCase			:     List[Any]         =      []
														__UpperCamelCase			:     List[str]         =      -1

														for i in range(_lowerCamelCase):
																					if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
																												ready_process.append(_lowerCamelCase)

														if len(_lowerCamelCase) > 0:
																					__UpperCamelCase			:     Union[str, Any]         =      ready_process[0]
																					for i in ready_process:
																												if remaining_time[i] < remaining_time[target_process]:
																																			__UpperCamelCase			:     int         =      i
																					total_time += burst_time[target_process]
																					completed += 1
																					__UpperCamelCase			:     Any         =      0
																					__UpperCamelCase			:     Tuple         =      (
																					    total_time - arrival_time[target_process] - burst_time[target_process]
																					)
														else:
																					total_time += 1

							return waiting_time




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list[int]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    list[int])      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      [0] * no_of_processes
							for i in range(_lowerCamelCase):
														__UpperCamelCase			:     int         =      burst_time[i] + waiting_time[i]
							return turn_around_time


if __name__ == "__main__":
		print('[TEST CASE 01]')

		lowercase						:      List[Any]									= 4
		lowercase						:      List[str]									= [2, 5, 3, 7]
		lowercase						:      str									= [0, 0, 0, 0]
		lowercase						:      Union[str, Any]									= calculate_waitingtime(arrival_time, burst_time, no_of_processes)
		lowercase						:      Dict									= calculate_turnaroundtime(
		    burst_time, no_of_processes, waiting_time
		)

		# Printing the Result
		print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
		for i, process_id in enumerate(list(range(1, 5))):
				print(
				    f"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
				    f"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
				)
		print(f"\nAverage waiting time = {mean(waiting_time):.5f}")
		print(f"Average turnaround time = {mean(turn_around_time):.5f}")

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase						:      List[str]									= argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase						:      int									= parser.parse_args()

		lowercase						:      str									= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase						:      int									= CLIPImageProcessor()
		lowercase						:      Optional[int]									= CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase						:      int									= UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)