

"""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 			lowercase    (	lowercase_     ,  lowercase_      ):
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      1




				@register_to_config
				def __init__( self				,       snake_case=2000				,       snake_case=0.1				,       snake_case=20				,       snake_case=1e-3	):
											snake_case_          =   None
											snake_case_          =   None
											snake_case_          =   None




				def    a							( self				,       snake_case				,       snake_case = None	):
											snake_case_          =   torch.linspace(1				,       self.config.sampling_eps				,       snake_case				,       device=snake_case	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case=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
											snake_case_          =   (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											snake_case_          =   torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff	)	)
											snake_case_          =   std.flatten()
											while len(std.shape	) < len(score.shape	):
																		snake_case_          =   std.unsqueeze(-1	)
											snake_case_          =   -score / std

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

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

											snake_case_          =   torch.sqrt(snake_case	)
											snake_case_          =   drift - diffusion**2 * score
											snake_case_          =   x + drift * dt

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

											return x, x_mean




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







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = None				):







							'''simple docstring'''






							if start is None:
														snake_case_          =   0

							if end is None:
														snake_case_          =   len(UpperCamelCase__				) - 1

							if start >= end:
														return

							snake_case_          =   (start + end) // 2

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							slowsort(UpperCamelCase__       ,							mid + 1       ,							UpperCamelCase__				)

							if sequence[end] < sequence[mid]:
														snake_case_    ,   snake_case_          =   sequence[mid], sequence[end]

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							end - 1				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()




