



'''simple docstring'''



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 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):
					a      :     List[str]        =							1



					@register_to_config
					def __init__(    self			,				UpperCamelCase=2000			,				UpperCamelCase=0.1			,				UpperCamelCase=20			,				UpperCamelCase=1E-3     )     ->			Any:
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  None



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Optional[int]:
						__lowerCAmelCase												=  torch.linspace(1			,				self.config.sampling_eps			,				UpperCamelCase			,				device=UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			Tuple:
						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
						__lowerCAmelCase												=  (
						    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
						)
						__lowerCAmelCase												=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
						__lowerCAmelCase												=  std.flatten()
						while len(std.shape     ) < len(score.shape     ):
							__lowerCAmelCase												=  std.unsqueeze(-1     )
						__lowerCAmelCase												=  -score / std

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

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

						__lowerCAmelCase												=  torch.sqrt(UpperCamelCase     )
						__lowerCAmelCase												=  drift - diffusion**2 * score
						__lowerCAmelCase												=  x + drift * dt

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

						return x, x_mean



					def __len__(    self     )     ->			List[Any]:
						return self.config.num_train_timesteps




'''simple docstring'''



from __future__ import annotations



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




	if not nums:
		raise ValueError("List is empty"					)
	return sum(lowerCamelCase					) / len(lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()