

# 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       snake_case_  (						__A      ,			__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							1







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







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Union[str, torch.device] = None				) ->List[Any]:
									snake_case_  =      torch.linspace(1				,  self.config.sampling_eps				,  _UpperCamelCase				,  device=_UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							int=None				) ->Optional[int]:
									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(_UpperCamelCase				)
									snake_case_  =      drift - diffusion**2 * score
									snake_case_  =      x + drift * dt

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

									return x, x_mean







			def __len__(	self					:							int				) ->Union[str, Any]:
									return self.config.num_train_timesteps



'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							value
            _lowerCAmelCase     :				Node | None       =							None
            _lowerCAmelCase     :				Node | None       =							None
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							tree







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left ) + self.depth_first_search(node.right )
            )







     def __iter__(			self ):

            '''simple docstring'''

            yield self.depth_first_search(self.tree )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

