

"""simple docstring"""




def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 return numa ^ numa < 0


if __name__ == "__main__":
      import doctest

      doctest.testmod()






'''simple docstring'''


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class     A ( UpperCAmelCase							,						UpperCAmelCase ):
							a_										=   1





							@register_to_config
							def __init__(   self					:				Tuple  ,		__a					:				int = 1_0_0_0  ,		__a					:				Optional[Union[np.ndarray, List[float]]] = None						)				->						int:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(__a						)

								# standard deviation of the initial noise distribution
								__UpperCAmelCase      					=      1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								__UpperCAmelCase      					=      4

								# running values
								__UpperCAmelCase      					=      []





							def    snake_case__    (   self					:				str  ,		__a					:				int  ,		__a					:				Union[str, torch.device] = None						)				->						str:
								__UpperCAmelCase      					=      num_inference_steps
								__UpperCAmelCase      					=      torch.linspace(1  ,		0  ,		num_inference_steps + 1						)[:-1]
								__UpperCAmelCase      					=      torch.cat([steps, torch.tensor([0.0]						)]						)

								if self.config.trained_betas is not None:
									__UpperCAmelCase      					=      torch.tensor(self.config.trained_betas  ,		dtype=torch.floataa						)
								else:
									__UpperCAmelCase      					=      torch.sin(steps * math.pi / 2						) ** 2

								__UpperCAmelCase      					=      (1.0 - self.betas**2) ** 0.5

								__UpperCAmelCase      					=      (torch.atana(self.betas  ,		self.alphas						) / math.pi * 2)[:-1]
								__UpperCAmelCase      					=      timesteps.to(__a						)

								__UpperCAmelCase      					=      []





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				torch.FloatTensor  ,		__a					:				int  ,		__a					:				torch.FloatTensor  ,		__a					:				bool = True  ,		)				->						Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
									raise ValueError(
									    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''						)

								__UpperCAmelCase      					=      (self.timesteps == timestep).nonzero().item()
								__UpperCAmelCase      					=      timestep_index + 1

								__UpperCAmelCase      					=      sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(__a						)

								if len(self.ets						) == 1:
									__UpperCAmelCase      					=      self.ets[-1]
								elif len(self.ets						) == 2:
									__UpperCAmelCase      					=      (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets						) == 3:
									__UpperCAmelCase      					=      (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
								else:
									__UpperCAmelCase      					=      (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

								__UpperCAmelCase      					=      self._get_prev_sample(__a  ,		__a  ,		__a  ,		__a						)

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=__a						)





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				torch.FloatTensor  ,		*__a					:				Any  ,		**__a					:				Optional[int]						)				->						torch.FloatTensor:
								return sample





							def    snake_case__    (   self					:				int  ,		__a					:				List[Any]  ,		__a					:				Any  ,		__a					:				int  ,		__a					:				Union[str, Any]						)				->						Optional[Any]:
								__UpperCAmelCase      					=      self.alphas[timestep_index]
								__UpperCAmelCase      					=      self.betas[timestep_index]

								__UpperCAmelCase      					=      self.alphas[prev_timestep_index]
								__UpperCAmelCase      					=      self.betas[prev_timestep_index]

								__UpperCAmelCase      					=      (sample - sigma * ets) / max(__a  ,		1e-8						)
								__UpperCAmelCase      					=      next_alpha * pred + ets * next_sigma

								return prev_sample






							def __len__(   self					:				Dict						)				->						str:
								return self.config.num_train_timesteps
