




"""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 				snake_case_			(					snake_case_	, snake_case_			):







   """simple docstring"""




   A_				      =       1







   @register_to_config
   def __init__(		self       ,					lowerCamelCase_ = 1_0_0_0       ,					lowerCamelCase_ = None)				->   Dict:
       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       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Union[str, Any]:
       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       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = True       ,					)				->   Optional[int]:

       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              =	(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(lowercase__       ,					lowercase__       ,					lowercase__       ,					lowercase__)

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=lowercase__)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					*lowerCamelCase_       ,					**lowerCamelCase_)				->   Dict:
       return sample







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   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(lowercase__       ,					1e-8)
       UpperCamelCase              =	next_alpha * pred + ets * next_sigma

       return prev_sample







   def __len__(		self)				->   Optional[int]:
       return self.config.num_train_timesteps





'''simple docstring'''


from __future__ import annotations



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					list[float]				):




     """simple docstring"""


     if len(UpperCamelCase__				) < 2:
          raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""				)
     if any(i <= 0 for i in nums				):
          raise ValueError("""All values must be greater than 0"""				)
     a_     :							int     						=  nums.copy()
     copy_nums.sort()
     return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
