
'''simple docstring'''
from __future__ import annotations

import pandas as pd






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						[0] * no_of_processes
	a_      				=						[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(A__						):
		a_      				=						burst_time[i]

	a_      				=						0
	a_      				=						0
	a_      				=						9_99_99_99_99
	a_      				=						0
	a_      				=						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:
					a_      				=						remaining_time[j]
					a_      				=						j
					a_      				=						True

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

		a_      				=						remaining_time[short]
		if minm == 0:
			a_      				=						9_99_99_99_99

		if remaining_time[short] == 0:
			complete += 1
			a_      				=						False

			# Find finish time of current process
			a_      				=						increment_time + 1

			# Calculate waiting time
			a_      				=						finish_time - arrival_time[short]
			a_      				=						finar - burst_time[short]

			if waiting_time[short] < 0:
				a_      				=						0

        # Increment time
		increment_time += 1
	return waiting_time






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						[0] * no_of_processes
	for i in range(A__						):
		a_      				=						burst_time[i] + waiting_time[i]
	return turn_around_time






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						0
	a_      				=						0
	for i in range(A__						):
		a_      				=						total_waiting_time + waiting_time[i]
		a_      				=						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')
		lowercase__											=int(input())
		lowercase__											=[0] * no_of_processes
		lowercase__											=[0] * no_of_processes
		lowercase__											=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))
				lowercase__				, lowercase__											=map(int, input().split())

		lowercase__											=calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

		calculate_average_times(waiting_time, turn_around_time, no_of_processes)

		lowercase__											=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)


'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=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__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=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)
