






"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Any    =				1





	@register_to_config
	def __init__(self     ,     lowerCAmelCase_ = 1000     ,     lowerCAmelCase_ = None							):
		# set `betas`, `alphas`, `timesteps`
		self.set_timesteps(lowerCAmelCase_							)

		# standard deviation of the initial noise distribution
		A_		:						str	    =   1.0

		# For now we only support F-PNDM, i.e. the runge-kutta method
		# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
		# mainly at formula (9), (12), (13) and the Algorithm 2.
		A_		:						Dict	    =   4

		# running values
		A_		:						Optional[Any]	    =   []





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						Union[str, Any]	    =   num_inference_steps
		A_		:						List[str]	    =   torch.linspace(1     ,     0     ,     num_inference_steps + 1							)[:-1]
		A_		:						Union[str, Any]	    =   torch.cat([steps, torch.tensor([0.0]							)]							)

		if self.config.trained_betas is not None:
			A_		:						int	    =   torch.tensor(self.config.trained_betas     ,     dtype=torch.floataa							)
		else:
			A_		:						Dict	    =   torch.sin(steps * math.pi / 2							) ** 2

		A_		:						Any	    =   (1.0 - self.betas**2) ** 0.5

		A_		:						Optional[int]	    =   (torch.atana(self.betas     ,     self.alphas							) / math.pi * 2)[:-1]
		A_		:						Union[str, Any]	    =   timesteps.to(lowerCAmelCase_							)

		A_		:						Tuple	    =   []





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = True     ,     ):
		if self.num_inference_steps is None:
			raise ValueError(
			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""							)

		A_		:						Union[str, Any]	    =   (self.timesteps == timestep).nonzero().item()
		A_		:						Dict	    =   timestep_index + 1

		A_		:						List[str]	    =   sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
		self.ets.append(lowerCAmelCase_							)

		if len(self.ets							) == 1:
			A_		:						List[str]	    =   self.ets[-1]
		elif len(self.ets							) == 2:
			A_		:						int	    =   (3 * self.ets[-1] - self.ets[-2]) / 2
		elif len(self.ets							) == 3:
			A_		:						Optional[Any]	    =   (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
		else:
			A_		:						str	    =   (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

		A_		:						Tuple	    =   self._get_prev_sample(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)

		if not return_dict:
			return (prev_sample,)

		return SchedulerOutput(prev_sample=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return sample





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						int	    =   self.alphas[timestep_index]
		A_		:						Optional[int]	    =   self.betas[timestep_index]

		A_		:						Tuple	    =   self.alphas[prev_timestep_index]
		A_		:						str	    =   self.betas[prev_timestep_index]

		A_		:						Union[str, Any]	    =   (sample - sigma * ets) / max(lowerCAmelCase_     ,     1e-8							)
		A_		:						str	    =   next_alpha * pred + ets * next_sigma

		return prev_sample





	def __len__(self							):
		return self.config.num_train_timesteps









"""simple docstring"""
_lowerCAmelCase         =							{"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowerCAmelCase         =							["a", "b", "c", "d", "e"]






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						int	    =   start
	# add current to visited
	visited.append(snake_case__					)
	A_		:						Any	    =   edges[current]
	for neighbor in neighbors:
		# if neighbor not in visited, visit
		if neighbor not in visited:
			A_		:						Optional[Any]	    =   topological_sort(snake_case__   ,   snake_case__   ,   snake_case__					)
    # if all neighbors visited add current to sort
	sort.append(snake_case__					)
	# if all vertices haven't been visited select a new one to visit
	if len(snake_case__					) != len(snake_case__					):
		for vertice in vertices:
			if vertice not in visited:
				A_		:						Optional[Any]	    =   topological_sort(snake_case__   ,   snake_case__   ,   snake_case__					)
    # return sort
	return sort


if __name__ == "__main__":
							_lowerCAmelCase         =							topological_sort("a", [], [])
							print(sort)

