"""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     UpperCamelCase__(			__A		,						__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		1





						@register_to_config
						def __init__(       self		,__UpperCAmelCase = 10_00		,__UpperCAmelCase = None     )					->				Tuple:
													# set `betas`, `alphas`, `timesteps`
													self.set_timesteps(__UpperCAmelCase     )

													# standard deviation of the initial noise distribution
													A__	     =							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.
													A__	     =							4

													# running values
													A__	     =							[]





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				Union[str, Any]:
													A__	     =							num_inference_steps
													A__	     =							torch.linspace(1		,0		,num_inference_steps + 1     )[:-1]
													A__	     =							torch.cat([steps, torch.tensor([0.0]     )]     )

													if self.config.trained_betas is not None:
																				A__	     =							torch.tensor(self.config.trained_betas		,dtype=torch.floataa     )
													else:
																				A__	     =							torch.sin(steps * math.pi / 2     ) ** 2

													A__	     =							(1.0 - self.betas**2) ** 0.5

													A__	     =							(torch.atana(self.betas		,self.alphas     ) / math.pi * 2)[:-1]
													A__	     =							timesteps.to(__UpperCAmelCase     )

													A__	     =							[]





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = 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'     )

													A__	     =							(self.timesteps == timestep).nonzero().item()
													A__	     =							timestep_index + 1

													A__	     =							sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
													self.ets.append(__UpperCAmelCase     )

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

													A__	     =							self._get_prev_sample(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )

													if not return_dict:
																				return (prev_sample,)

													return SchedulerOutput(prev_sample=__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				torch.FloatTensor:
													return sample





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Any:
													A__	     =							self.alphas[timestep_index]
													A__	     =							self.betas[timestep_index]

													A__	     =							self.alphas[prev_timestep_index]
													A__	     =							self.betas[prev_timestep_index]

													A__	     =							(sample - sigma * ets) / max(__UpperCAmelCase		,1e-8     )
													A__	     =							next_alpha * pred + ets * next_sigma

													return prev_sample





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






"""simple docstring"""




import warnings

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


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			__A ):





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





