


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 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					1






					@register_to_config
					def __init__(					self			,							snake_case_ = 1_0_0_0			,							snake_case_ = None    ) ->      List[str]:







								'''simple docstring'''

								self.set_timesteps(snake_case_    )

								# standard deviation of the initial noise distribution
								__lowercase			       = 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.
								__lowercase			       = 4

								# running values
								__lowercase			       = []






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      int:







								'''simple docstring'''

								__lowercase			       = num_inference_steps
								__lowercase			       = torch.linspace(1			,							0			,							num_inference_steps + 1    )[:-1]
								__lowercase			       = torch.cat([steps, torch.tensor([0.0]    )]    )

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

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

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

								__lowercase			       = []






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = True			,							) ->      Union[SchedulerOutput, Tuple]:







								'''simple docstring'''

								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'''    )

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

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

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

								__lowercase			       = self._get_prev_sample(snake_case_			,							snake_case_			,							snake_case_			,							snake_case_    )

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=snake_case_    )






					def A   (					self			,							snake_case_			,							*snake_case_			,							**snake_case_    ) ->      torch.FloatTensor:







								'''simple docstring'''

								return sample






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.alphas[timestep_index]
								__lowercase			       = self.betas[timestep_index]

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

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

								return prev_sample






					def __len__(					self    ) ->      Optional[Any]:







								'''simple docstring'''

								return self.config.num_train_timesteps






from maths.prime_factors import prime_factors


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase							,      _UpperCamelCase	):
						__lowercase			       = F'Input value of [number={number}] must be an integer'
						raise TypeError(_UpperCamelCase	)
			if number < 1:
						raise ValueError('''Input must be a positive integer'''	)
			return -1 if len(prime_factors(_UpperCamelCase	)	) % 2 else 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()


