'''simple docstring'''


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin






class 						A   (   SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_   ):
							a_        =      1





							@register_to_config
							def __init__(      self				:							List[str]     ,		__a				:							str=2_0_0_0     ,		__a				:							List[Any]=0.1     ,		__a				:							Any=2_0     ,		__a				:							str=1e-3	)							->	Optional[int]:
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None





							def       snake_case__						(      self				:							int     ,		__a				:							int     ,		__a				:							Dict = None	)							->	Optional[int]:
								__UpperCAmelCase							=			torch.linspace(1     ,		self.config.sampling_eps     ,		UpperCamelCase__     ,		device=UpperCamelCase__	)





							def       snake_case__						(      self				:							Any     ,		__a				:							Tuple     ,		__a				:							Union[str, Any]     ,		__a				:							Any     ,		__a				:							List[Any]=None	)							->	str:
								if self.timesteps is None:
									raise ValueError(
									    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''	)

								# TODO(Patrick) better comments + non-PyTorch
								# postprocess model score
								__UpperCAmelCase							=			(
								    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								__UpperCAmelCase							=			torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff	)	)
								__UpperCAmelCase							=			std.flatten()
								while len(std.shape	) < len(score.shape	):
									__UpperCAmelCase							=			std.unsqueeze(-1	)
								__UpperCAmelCase							=			-score / std

								# compute
								__UpperCAmelCase							=			-1.0 / len(self.timesteps	)

								__UpperCAmelCase							=			self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								__UpperCAmelCase							=			beta_t.flatten()
								while len(beta_t.shape	) < len(x.shape	):
									__UpperCAmelCase							=			beta_t.unsqueeze(-1	)
								__UpperCAmelCase							=			-0.5 * beta_t * x

								__UpperCAmelCase							=			torch.sqrt(UpperCamelCase__	)
								__UpperCAmelCase							=			drift - diffusion**2 * score
								__UpperCAmelCase							=			x + drift * dt

								# add noise
								__UpperCAmelCase							=			randn_tensor(x.shape     ,		layout=x.layout     ,		generator=UpperCamelCase__     ,		device=x.device     ,		dtype=x.dtype	)
								__UpperCAmelCase							=			x_mean + diffusion * math.sqrt(-dt	) * noise

								return x, x_mean






							def __len__(      self				:							Dict	)							->	str:
								return self.config.num_train_timesteps

'''simple docstring'''


from __future__ import annotations

from statistics import mean





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	__UpperCAmelCase							=			[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i]
	__UpperCAmelCase							=			[]

	__UpperCAmelCase							=			0
	__UpperCAmelCase							=			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							=			[]
		__UpperCAmelCase							=			-1

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

		if len(UpperCamelCase__       ) > 0:
			__UpperCAmelCase							=			ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					__UpperCAmelCase							=			i
			total_time += burst_time[target_process]
			completed += 1
			__UpperCAmelCase							=			0
			__UpperCAmelCase							=			(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   list[int]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i] + waiting_time[i]
	return turn_around_time


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

					__lowerCAmelCase	:       List[Any]						       =	4
					__lowerCAmelCase	:       List[Any]						       =	[2, 5, 3, 7]
					__lowerCAmelCase	:       Tuple						       =	[0, 0, 0, 0]
					__lowerCAmelCase	:       Optional[int]						       =	calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					__lowerCAmelCase	:       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}""")
