





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_						(					_lowerCamelCase							,					_lowerCamelCase   ):
     lowerCAmelCase_							=		1




     @register_to_config
     def __init__(						self			,  lowerCAmelCase_ = 1000			,  lowerCAmelCase_ = None					)						->							Optional[int]:
          # set `betas`, `alphas`, `timesteps`
          self.set_timesteps(lowerCAmelCase_					)

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

          # running values
          _snake_case													=			[]




     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							List[str]:
          _snake_case													=			num_inference_steps
          _snake_case													=			torch.linspace(1			,  0			,  num_inference_steps + 1					)[:-1]
          _snake_case													=			torch.cat([steps, torch.tensor([0.0]					)]					)

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

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

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

          _snake_case													=			[]




     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 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'					)

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

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

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

          _snake_case													=			self._get_prev_sample(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCAmelCase_					)




     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							torch.FloatTensor:
          return sample




     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
          _snake_case													=			self.alphas[timestep_index]
          _snake_case													=			self.betas[timestep_index]

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

          _snake_case													=			(sample - sigma * ets) / max(lowerCAmelCase_			,  1E-8					)
          _snake_case													=			next_alpha * pred + ets * next_sigma

          return prev_sample






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










import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCAmelCase_ 							=       logging.get_logger(__name__)
class      UpperCamelCase_						(					_lowerCamelCase   ):




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



