






"""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      SCREAMING_SNAKE_CASE  (				lowercase__       ,       lowercase__						):





	"""simple docstring"""




	_A       :				str    =				1





	@register_to_config
	def __init__(self     ,     lowerCAmelCase_=2000     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=20     ,     lowerCAmelCase_=1e-3							):
		A_		:						str	    =   None
		A_		:						int	    =   None
		A_		:						Optional[Any]	    =   None





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						Dict	    =   torch.linspace(1     ,     self.config.sampling_eps     ,     __lowercase     ,     device=__lowercase							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_=None							):
		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
		A_		:						Tuple	    =   (
		    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
		)
		A_		:						int	    =   torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
		A_		:						Dict	    =   std.flatten()
		while len(std.shape							) < len(score.shape							):
			A_		:						Dict	    =   std.unsqueeze(-1							)
		A_		:						Union[str, Any]	    =   -score / std

		# compute
		A_		:						int	    =   -1.0 / len(self.timesteps							)

		A_		:						Optional[Any]	    =   self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
		A_		:						str	    =   beta_t.flatten()
		while len(beta_t.shape							) < len(x.shape							):
			A_		:						Tuple	    =   beta_t.unsqueeze(-1							)
		A_		:						int	    =   -0.5 * beta_t * x

		A_		:						str	    =   torch.sqrt(__lowercase							)
		A_		:						str	    =   drift - diffusion**2 * score
		A_		:						Dict	    =   x + drift * dt

		# add noise
		A_		:						Optional[int]	    =   randn_tensor(x.shape     ,     layout=x.layout     ,     generator=__lowercase     ,     device=x.device     ,     dtype=x.dtype							)
		A_		:						Any	    =   x_mean + diffusion * math.sqrt(-dt							) * noise

		return x, x_mean





	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)

