


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 .imports import is_tqdm_available


if is_tqdm_available():
    from tqdm.auto import tqdm as _tqdm

from ..state import PartialState





def __UpperCAmelCase       (		__A = True ,						*__A ,						**__A			)						->					Any:



       '''simple docstring'''





       if not is_tqdm_available():
              raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."			)
       UpperCAmelCase__										=     False
       if main_process_only:
              UpperCAmelCase__										=     PartialState().local_process_index == 0
       return _tqdm(*__A ,						**__A ,						disable=__A			)





