


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    UpperCAmelCase__	( A_       ,  A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]              =  1






    @register_to_config
    def __init__(      self ,       A_ = 1000 ,       A_ = None   )  ->	Union[str, Any]:
         # set `betas`, `alphas`, `timesteps`
         self.set_timesteps(A_   )

         # 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 _a (      self ,       A_ ,       A_ = None   )  ->	List[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(A_   )

         __UpperCamelCase   							=[]






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = True ,       )  ->	Union[SchedulerOutput, Tuple]:
         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(A_   )

         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(A_ ,       A_ ,       A_ ,       A_   )

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=A_   )






    def _a (      self ,       A_ ,       *A_ ,       **A_   )  ->	torch.FloatTensor:
         return sample






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_   )  ->	Union[str, 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(A_ ,       1E-8   )
         __UpperCamelCase   							=next_alpha * pred + ets * next_sigma

         return prev_sample






    def __len__(      self   )  ->	Union[str, Any]:
         return self.config.num_train_timesteps









import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       *A_ ,       **A_   )  ->	None:
         warnings.warn(
             'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
             ' Please use ImageGPTImageProcessor instead.' ,       A_ ,       )
         super().__init__(*A_ ,       **A_   )





