



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 							lowerCAmelCase__  (	UpperCAmelCase__					,							UpperCAmelCase__						):
					__A     :    Tuple           =						1







					@register_to_config
					def __init__(       self      :     Tuple			,		_A      :     List[str]=2000			,		_A      :     List[Any]=0.1			,		_A      :     List[str]=20			,		_A      :     Any=1e-3):
											A__	:							List[str]						    = None
											A__	:							Tuple						    = None
											A__	:							Union[str, Any]						    = None







					def  _lowercase			(       self      :     Dict			,		_A      :     Optional[int]			,		_A      :     Union[str, torch.device] = None):
											A__	:							List[str]						    = torch.linspace(1			,		self.config.sampling_eps			,		_A			,		device=_A)







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     Union[str, Any]			,		_A      :     Dict			,		_A      :     List[str]			,		_A      :     Union[str, Any]=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__	:							str						    = (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											A__	:							Optional[int]						    = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											A__	:							int						    = std.flatten()
											while len(std.shape) < len(score.shape):
																	A__	:							Optional[int]						    = std.unsqueeze(-1)
											A__	:							Dict						    = -score / std

											# compute
											A__	:							Union[str, Any]						    = -1.0 / len(self.timesteps)

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

											A__	:							Tuple						    = torch.sqrt(_A)
											A__	:							str						    = drift - diffusion**2 * score
											A__	:							Tuple						    = x + drift * dt

											# add noise
											A__	:							Tuple						    = randn_tensor(x.shape			,		layout=x.layout			,		generator=_A			,		device=x.device			,		dtype=x.dtype)
											A__	:							Tuple						    = x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean







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




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase = 0							,     __lowercase = 0       )		->		int:







						"""simple docstring"""

						A__	:							Tuple						    = right or len(__lowercase       ) - 1
						if left > right:
												return -1
						elif list_data[left] == key:
												return left
						elif list_data[right] == key:
												return right
						else:
												return search(__lowercase							,     __lowercase							,     left + 1							,     right - 1       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()