






"""simple docstring"""
from __future__ import annotations

import pandas as pd






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   [0] * no_of_processes
	A_		:						str	    =   [0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(snake_case__					):
		A_		:						List[Any]	    =   burst_time[i]

	A_		:						int	    =   0
	A_		:						str	    =   0
	A_		:						Dict	    =   999_999_999
	A_		:						Union[str, Any]	    =   0
	A_		:						Union[str, Any]	    =   False

	# Process until all processes are completed
	while complete != no_of_processes:
		for j in range(snake_case__					):
			if arrival_time[j] <= increment_time and remaining_time[j] > 0:
				if remaining_time[j] < minm:
					A_		:						Optional[int]	    =   remaining_time[j]
					A_		:						List[Any]	    =   j
					A_		:						int	    =   True

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

		A_		:						Dict	    =   remaining_time[short]
		if minm == 0:
			A_		:						str	    =   999_999_999

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

			# Find finish time of current process
			A_		:						Tuple	    =   increment_time + 1

			# Calculate waiting time
			A_		:						List[Any]	    =   finish_time - arrival_time[short]
			A_		:						Tuple	    =   finar - burst_time[short]

			if waiting_time[short] < 0:
				A_		:						Optional[Any]	    =   0

        # Increment time
		increment_time += 1
	return waiting_time






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						int	    =   [0] * no_of_processes
	for i in range(snake_case__					):
		A_		:						Union[str, Any]	    =   burst_time[i] + waiting_time[i]
	return turn_around_time






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Optional[Any]	    =   0
	A_		:						List[Any]	    =   0
	for i in range(snake_case__					):
		A_		:						Dict	    =   total_waiting_time + waiting_time[i]
		A_		:						List[Any]	    =   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")
							_lowerCAmelCase         =							int(input())
							_lowerCAmelCase         =							[0] * no_of_processes
							_lowerCAmelCase         =							[0] * no_of_processes
							_lowerCAmelCase         =							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))
														_lowerCAmelCase     ,    _lowerCAmelCase         =							map(int, input().split())

							_lowerCAmelCase         =							calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

							calculate_average_times(waiting_time, turn_around_time, no_of_processes)

							_lowerCAmelCase         =							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

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

    # extract state_dict for UNetLDM
	A_		:						Tuple	    =   {}
	A_		:						Dict	    =   """model.diffusion_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						Optional[Any]	    =   state_dict[key]

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						List[str]	    =   DDIMScheduler(
	    timesteps=config.model.params.timesteps   ,   beta_schedule="""scaled_linear"""   ,   beta_start=config.model.params.linear_start   ,   beta_end=config.model.params.linear_end   ,   clip_sample=snake_case__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							parser.add_argument("--checkpoint_path", type=str, required=True)
							parser.add_argument("--config_path", type=str, required=True)
							parser.add_argument("--output_path", type=str, required=True)
							_lowerCAmelCase         =							parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

