




'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						List[str]      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						str)    ->							None:
								warnings.warn(
								    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
								    ' use YolosImageProcessor instead.'      ,       _lowercase      ,       )
								super().__init__(*_lowercase      ,       **_lowercase)










'''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				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

								# 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     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							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(_lowercase)

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = 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(_lowercase)

								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(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, 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(_lowercase      ,       1E-8)
								A_									= next_alpha * pred + ets * next_sigma

								return prev_sample



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




