


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     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE		):
		A__=  1






		@register_to_config
		def __init__(					self      :					Optional[Any]		,						_lowercase      :					int = 10_00		,						_lowercase      :					Optional[Union[np.ndarray, List[float]]] = None ):




									"""simple docstring"""







									self.set_timesteps(_lowercase )

									# 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 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					int		,						_lowercase      :					Union[str, torch.device] = None ):




									"""simple docstring"""







									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(_lowercase )

									UpperCAmelCase__										=     []






		def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					torch.FloatTensor		,						_lowercase      :					int		,						_lowercase      :					torch.FloatTensor		,						_lowercase      :					bool = True		,						):




									"""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" )

									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(_lowercase )

									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__										=     (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
									else:
																UpperCAmelCase__										=     (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

									UpperCAmelCase__										=     self._get_prev_sample(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase )

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=_lowercase )






		def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					torch.FloatTensor		,						*_lowercase      :					List[str]		,						**_lowercase      :					Optional[Any] ):




									"""simple docstring"""







									return sample






		def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					List[Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[int]		,						_lowercase      :					Optional[Any] ):




									"""simple docstring"""







									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(_lowercase		,						1E-8 )
									UpperCAmelCase__										=     next_alpha * pred + ets * next_sigma

									return prev_sample






		def __len__(					self      :					List[str] ):




									"""simple docstring"""







									return self.config.num_train_timesteps









from __future__ import annotations





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						)						->					tuple:



       '''simple docstring'''





       if (electron_conc, hole_conc, intrinsic_conc).count(0			) != 1:
              raise ValueError("You cannot supply more or less than 2 values"			)
       elif electron_conc < 0:
              raise ValueError("Electron concentration cannot be negative in a semiconductor"			)
       elif hole_conc < 0:
              raise ValueError("Hole concentration cannot be negative in a semiconductor"			)
       elif intrinsic_conc < 0:
              raise ValueError(
                  "Intrinsic concentration cannot be negative in a semiconductor"			)
       elif electron_conc == 0:
              return (
                  "electron_conc",
                  intrinsic_conc**2 / hole_conc,
              )
       elif hole_conc == 0:
              return (
                  "hole_conc",
                  intrinsic_conc**2 / electron_conc,
              )
       elif intrinsic_conc == 0:
              return (
                  "intrinsic_conc",
                  (electron_conc * hole_conc) ** 0.5,
              )
       else:
              return (-1, -1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()





