


'''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   __lowerCAmelCase						(lowercase_  ,     lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							1



      @register_to_config
      def __init__(self    :    Tuple   ,						UpperCamelCase    :    int = 1000   ,						UpperCamelCase    :    Optional[Union[np.ndarray, List[float]]] = None ):

             '''simple docstring'''







             self.set_timesteps(_lowerCAmelCase )

             # 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       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Union[str, torch.device] = None ):

             '''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(_lowerCAmelCase )

             lowercase__      			= []



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    torch.FloatTensor   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    torch.FloatTensor   ,						UpperCamelCase    :    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''' )

             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(_lowerCAmelCase )

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

             lowercase__      			= self._get_prev_sample(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase )

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=_lowerCAmelCase )



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    torch.FloatTensor   ,						*UpperCamelCase    :    int   ,						**UpperCamelCase    :    Dict ):

             '''simple docstring'''







             return sample



      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    List[str] ):

             '''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(_lowerCAmelCase   ,						1E-8 )
             lowercase__      			= next_alpha * pred + ets * next_sigma

             return prev_sample






      def __len__(self    :    int ):

             '''simple docstring'''







             return self.config.num_train_timesteps










def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[str]							=  ""
  for word_or_phrase in separated:
    if not isinstance(lowerCamelCase,	lowerCamelCase      ):
      raise Exception("join() accepts only strings to be joined"      )
    joined += word_or_phrase + separator
  return joined.strip(lowerCamelCase      )


if __name__ == "__main__":
     from doctest import testmod

     testmod()



