


"""simple docstring"""






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_lowerCamelCase  =	logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class       lowerCamelCase_		(      UpperCAmelCase_ ):





				"""simple docstring"""







				_lowerCAmelCase   :   torch.FloatTensor
				_lowerCAmelCase   :   Optional[torch.FloatTensor]   				=       None


def __lowercase  (   lowerCamelCase_	:   Tuple    ,       lowerCamelCase_	:   Optional[int]=0.999    ,       lowerCamelCase_	:   Dict="cosine"    ,       ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase_	:   Optional[int]	):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase_	:   Optional[Any]	):
												return math.exp(t * -12.0	)

				else:
								raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

				SCREAMING_SNAKE_CASE__  				=					[]
				for i in range(lowerCamelCase_	):
								SCREAMING_SNAKE_CASE__  				=					i / num_diffusion_timesteps
								SCREAMING_SNAKE_CASE__  				=					(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase_	) / alpha_bar_fn(lowerCamelCase_	)    ,       lowerCamelCase_	)	)
				return torch.tensor(lowerCamelCase_    ,       dtype=torch.floataa	)


class       lowerCamelCase_		(      UpperCAmelCase_							,	UpperCAmelCase_ ):





				"""simple docstring"""







				_lowerCAmelCase   :   List[Any]   				=       [e.name for e in KarrasDiffusionSchedulers]
				_lowerCAmelCase   :   List[str]   				=       1



				@register_to_config
				def __init__(    self							,		UpperCAmelCase__ = 1000							,		UpperCAmelCase__ = 0.0_001							,		UpperCAmelCase__ = 0.02							,		UpperCAmelCase__ = "linear"							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = "epsilon"							,		UpperCAmelCase__ = "linear"							,		UpperCAmelCase__ = False							,		UpperCAmelCase__ = "linspace"							,		UpperCAmelCase__ = 0							,		):
								if trained_betas is not None:
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(_lowercase							,		dtype=torch.floataa  )
								elif beta_schedule == "linear":
												SCREAMING_SNAKE_CASE__  				=					torch.linspace(_lowercase							,		_lowercase							,		_lowercase							,		dtype=torch.floataa  )
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												SCREAMING_SNAKE_CASE__  				=					(
												    torch.linspace(beta_start**0.5							,		beta_end**0.5							,		_lowercase							,		dtype=torch.floataa  ) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												SCREAMING_SNAKE_CASE__  				=					betas_for_alpha_bar(_lowercase  )
								else:
												raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''  )

								SCREAMING_SNAKE_CASE__  				=					1.0 - self.betas
								SCREAMING_SNAKE_CASE__  				=					torch.cumprod(self.alphas							,		dim=0  )

								SCREAMING_SNAKE_CASE__  				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
								SCREAMING_SNAKE_CASE__  				=					np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
								SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_lowercase  )

								# setable values
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					np.linspace(0							,		num_train_timesteps - 1							,		_lowercase							,		dtype=_lowercase  )[::-1].copy()
								SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					False
								SCREAMING_SNAKE_CASE__  				=					use_karras_sigmas



				@property
				def 	lowerCAmelCase__		(    self  ):
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								if isinstance(_lowercase							,		torch.Tensor  ):
												SCREAMING_SNAKE_CASE__  				=					timestep.to(self.timesteps.device  )
								SCREAMING_SNAKE_CASE__  				=					(self.timesteps == timestep).nonzero().item()
								SCREAMING_SNAKE_CASE__  				=					self.sigmas[step_index]

								SCREAMING_SNAKE_CASE__  				=					sample / ((sigma**2 + 1) ** 0.5)

								SCREAMING_SNAKE_CASE__  				=					True
								return sample



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__ = None  ):
								SCREAMING_SNAKE_CASE__  				=					num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
												SCREAMING_SNAKE_CASE__  				=					np.linspace(0							,		self.config.num_train_timesteps - 1							,		_lowercase							,		dtype=_lowercase  )[
												    ::-1
												].copy()
								elif self.config.timestep_spacing == "leading":
												SCREAMING_SNAKE_CASE__  				=					self.config.num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												SCREAMING_SNAKE_CASE__  				=					(np.arange(0							,		_lowercase  ) * step_ratio).round()[::-1].copy().astype(_lowercase  )
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												SCREAMING_SNAKE_CASE__  				=					self.config.num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												SCREAMING_SNAKE_CASE__  				=					(np.arange(self.config.num_train_timesteps							,		0							,		-step_ratio  )).round().copy().astype(_lowercase  )
												timesteps -= 1
								else:
												raise ValueError(
												    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''  )

								SCREAMING_SNAKE_CASE__  				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
								SCREAMING_SNAKE_CASE__  				=					np.log(_lowercase  )

								if self.config.interpolation_type == "linear":
												SCREAMING_SNAKE_CASE__  				=					np.interp(_lowercase							,		np.arange(0							,		len(_lowercase  )  )							,		_lowercase  )
								elif self.config.interpolation_type == "log_linear":
												SCREAMING_SNAKE_CASE__  				=					torch.linspace(np.log(sigmas[-1]  )							,		np.log(sigmas[0]  )							,		num_inference_steps + 1  ).exp()
								else:
												raise ValueError(
												    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
												    " \'linear\' or \'log_linear\'"  )

								if self.use_karras_sigmas:
												SCREAMING_SNAKE_CASE__  				=					self._convert_to_karras(in_sigmas=_lowercase							,		num_inference_steps=self.num_inference_steps  )
												SCREAMING_SNAKE_CASE__  				=					np.array([self._sigma_to_t(_lowercase							,		_lowercase  ) for sigma in sigmas]  )

								SCREAMING_SNAKE_CASE__  				=					np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
								SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_lowercase  ).to(device=_lowercase  )
								if str(_lowercase  ).startswith("mps"  ):
												# mps does not support float64
												SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_lowercase  ).to(_lowercase							,		dtype=torch.floataa  )
								else:
												SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_lowercase  ).to(device=_lowercase  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								# get log sigma
								SCREAMING_SNAKE_CASE__  				=					np.log(_lowercase  )

								# get distribution
								SCREAMING_SNAKE_CASE__  				=					log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								SCREAMING_SNAKE_CASE__  				=					np.cumsum((dists >= 0)							,		axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
								SCREAMING_SNAKE_CASE__  				=					low_idx + 1

								SCREAMING_SNAKE_CASE__  				=					log_sigmas[low_idx]
								SCREAMING_SNAKE_CASE__  				=					log_sigmas[high_idx]

								# interpolate sigmas
								SCREAMING_SNAKE_CASE__  				=					(low - log_sigma) / (low - high)
								SCREAMING_SNAKE_CASE__  				=					np.clip(_lowercase							,		0							,		1  )

								# transform interpolation to time range
								SCREAMING_SNAKE_CASE__  				=					(1 - w) * low_idx + w * high_idx
								SCREAMING_SNAKE_CASE__  				=					t.reshape(sigma.shape  )
								return t



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					in_sigmas[-1].item()
								SCREAMING_SNAKE_CASE__  				=					in_sigmas[0].item()

								SCREAMING_SNAKE_CASE__  				=					7.0  # 7.0 is the value used in the paper
								SCREAMING_SNAKE_CASE__  				=					np.linspace(0							,		1							,		_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					sigma_min ** (1 / rho)
								SCREAMING_SNAKE_CASE__  				=					sigma_max ** (1 / rho)
								SCREAMING_SNAKE_CASE__  				=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = 0.0							,		UpperCAmelCase__ = 0.0							,		UpperCAmelCase__ = float("inf"  )							,		UpperCAmelCase__ = 1.0							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = True							,		):

								if (
								    isinstance(_lowercase							,		_lowercase  )
								    or isinstance(_lowercase							,		torch.IntTensor  )
								    or isinstance(_lowercase							,		torch.LongTensor  )
								):
												raise ValueError(
												    (
												        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
												        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
												        " one of the `scheduler.timesteps` as a timestep."
												    )							,		)

								if not self.is_scale_input_called:
												logger.warning(
												    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
												    "See `StableDiffusionPipeline` for a usage example."  )

								if isinstance(_lowercase							,		torch.Tensor  ):
												SCREAMING_SNAKE_CASE__  				=					timestep.to(self.timesteps.device  )

								SCREAMING_SNAKE_CASE__  				=					(self.timesteps == timestep).nonzero().item()
								SCREAMING_SNAKE_CASE__  				=					self.sigmas[step_index]

								SCREAMING_SNAKE_CASE__  				=					min(s_churn / (len(self.sigmas  ) - 1)							,		2**0.5 - 1  ) if s_tmin <= sigma <= s_tmax else 0.0

								SCREAMING_SNAKE_CASE__  				=					randn_tensor(
								    model_output.shape							,		dtype=model_output.dtype							,		device=model_output.device							,		generator=_lowercase  )

								SCREAMING_SNAKE_CASE__  				=					noise * s_noise
								SCREAMING_SNAKE_CASE__  				=					sigma * (gamma + 1)

								if gamma > 0:
												SCREAMING_SNAKE_CASE__  				=					sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								# NOTE: "original_sample" should not be an expected prediction_type but is left in for
								# backwards compatibility
								if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
												SCREAMING_SNAKE_CASE__  				=					model_output
								elif self.config.prediction_type == "epsilon":
												SCREAMING_SNAKE_CASE__  				=					sample - sigma_hat * model_output
								elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												SCREAMING_SNAKE_CASE__  				=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
												raise ValueError(
												    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

								# 2. Convert to an ODE derivative
								SCREAMING_SNAKE_CASE__  				=					(sample - pred_original_sample) / sigma_hat

								SCREAMING_SNAKE_CASE__  				=					self.sigmas[step_index + 1] - sigma_hat

								SCREAMING_SNAKE_CASE__  				=					sample + derivative * dt

								if not return_dict:
												return (prev_sample,)

								return EulerDiscreteSchedulerOutput(prev_sample=_lowercase							,		pred_original_sample=_lowercase  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								SCREAMING_SNAKE_CASE__  				=					self.sigmas.to(device=original_samples.device							,		dtype=original_samples.dtype  )
								if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase  ):
												# mps does not support float64
												SCREAMING_SNAKE_CASE__  				=					self.timesteps.to(original_samples.device							,		dtype=torch.floataa  )
												SCREAMING_SNAKE_CASE__  				=					timesteps.to(original_samples.device							,		dtype=torch.floataa  )
								else:
												SCREAMING_SNAKE_CASE__  				=					self.timesteps.to(original_samples.device  )
												SCREAMING_SNAKE_CASE__  				=					timesteps.to(original_samples.device  )

								SCREAMING_SNAKE_CASE__  				=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								SCREAMING_SNAKE_CASE__  				=					sigmas[step_indices].flatten()
								while len(sigma.shape  ) < len(original_samples.shape  ):
												SCREAMING_SNAKE_CASE__  				=					sigma.unsqueeze(-1  )

								SCREAMING_SNAKE_CASE__  				=					original_samples + noise * sigma
								return noisy_samples






				def __len__(    self  ):
								return self.config.num_train_timesteps








'''simple docstring'''

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


A:   int						=					logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 				__magic_name__  (						UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   torch.FloatTensor
	SCREAMING_SNAKE_CASE_       :   Optional[torch.FloatTensor]   =	None






def 						_UpperCAmelCase     (	a		:						Any    ,      a		:						Dict=0.9_99    ,      a		:						Optional[int]="cosine"    ,      )      ->      Tuple:





						"""simple docstring"""
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(a		:						Optional[int]				):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(a		:						Optional[Any]				):
																		return math.exp(t * -12.0				)

						else:
												raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"				)

						lowercase_   : Optional[int]   	=      []
						for i in range(a				):
												lowercase_   : List[Any]   	=      i / num_diffusion_timesteps
												lowercase_   : Union[str, Any]   	=      (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(a				) / alpha_bar_fn(a				)    ,      a				)				)
						return torch.tensor(a    ,      dtype=torch.floataa				)






class 				__magic_name__  (						UpperCAmelCase_,    UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	[e.name for e in KarrasDiffusionSchedulers]
	SCREAMING_SNAKE_CASE_       :   Tuple   =	1
	@register_to_config
	def __init__(				self	,   _lowercase = 1000	,   _lowercase = 0.00_01	,   _lowercase = 0.02	,   _lowercase = "linear"	,   _lowercase = None	,   _lowercase = "epsilon"	,   _lowercase = "linspace"	,   _lowercase = 0	,   )    ->	List[str]:
							if trained_betas is not None:
													lowercase_   : Any   	=      torch.tensor(_lowercase	,   dtype=torch.floataa							)
							elif beta_schedule == "linear":
													lowercase_   : Optional[int]   	=      torch.linspace(_lowercase	,   _lowercase	,   _lowercase	,   dtype=torch.floataa							)
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													lowercase_   : Dict   	=      (
													    torch.linspace(beta_start**0.5	,   beta_end**0.5	,   _lowercase	,   dtype=torch.floataa							) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													lowercase_   : Any   	=      betas_for_alpha_bar(_lowercase							)
							else:
													raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"							)

							lowercase_   : Dict   	=      1.0 - self.betas
							lowercase_   : int   	=      torch.cumprod(self.alphas	,   dim=0							)

							lowercase_   : Optional[int]   	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
							lowercase_   : str   	=      np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
							lowercase_   : Tuple   	=      torch.from_numpy(_lowercase							)

							# setable values
							lowercase_   : Union[str, Any]   	=      None
							lowercase_   : Optional[Any]   	=      np.linspace(0	,   num_train_timesteps - 1	,   _lowercase	,   dtype=_lowercase							)[::-1].copy()
							lowercase_   : Dict   	=      torch.from_numpy(_lowercase							)
							lowercase_   : int   	=      False
	@property
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase							)    ->	torch.FloatTensor:
							if isinstance(_lowercase	,   torch.Tensor							):
													lowercase_   : List[str]   	=      timestep.to(self.timesteps.device							)
							lowercase_   : List[str]   	=      (self.timesteps == timestep).nonzero().item()
							lowercase_   : Any   	=      self.sigmas[step_index]
							lowercase_   : Tuple   	=      sample / ((sigma**2 + 1) ** 0.5)
							lowercase_   : List[str]   	=      True
							return sample
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	Tuple:
							lowercase_   : Any   	=      num_inference_steps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
													lowercase_   : Tuple   	=      np.linspace(0	,   self.config.num_train_timesteps - 1	,   _lowercase	,   dtype=_lowercase							)[
													    ::-1
													].copy()
							elif self.config.timestep_spacing == "leading":
													lowercase_   : int   	=      self.config.num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													lowercase_   : Optional[int]   	=      (np.arange(0	,   _lowercase							) * step_ratio).round()[::-1].copy().astype(_lowercase							)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													lowercase_   : Optional[Any]   	=      self.config.num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													lowercase_   : Dict   	=      (np.arange(self.config.num_train_timesteps	,   0	,   -step_ratio							)).round().copy().astype(_lowercase							)
													timesteps -= 1
							else:
													raise ValueError(
													    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."							)

							lowercase_   : str   	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
							lowercase_   : Dict   	=      np.interp(_lowercase	,   np.arange(0	,   len(_lowercase							)							)	,   _lowercase							)
							lowercase_   : Any   	=      np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
							lowercase_   : Dict   	=      torch.from_numpy(_lowercase							).to(device=_lowercase							)
							if str(_lowercase							).startswith('mps'							):
													# mps does not support float64
													lowercase_   : Union[str, Any]   	=      torch.from_numpy(_lowercase							).to(_lowercase	,   dtype=torch.floataa							)
							else:
													lowercase_   : Optional[int]   	=      torch.from_numpy(_lowercase							).to(device=_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase	,   _lowercase = None	,   _lowercase = True	,   )    ->	Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

							if (
							    isinstance(_lowercase	,   _lowercase							)
							    or isinstance(_lowercase	,   torch.IntTensor							)
							    or isinstance(_lowercase	,   torch.LongTensor							)
							):
													raise ValueError(
													    (
													        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
													        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
													        ' one of the `scheduler.timesteps` as a timestep.'
													    )	,   )

							if not self.is_scale_input_called:
													logger.warning(
													    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
													    'See `StableDiffusionPipeline` for a usage example.'							)

							if isinstance(_lowercase	,   torch.Tensor							):
													lowercase_   : Optional[int]   	=      timestep.to(self.timesteps.device							)

							lowercase_   : List[Any]   	=      (self.timesteps == timestep).nonzero().item()
							lowercase_   : List[str]   	=      self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													lowercase_   : List[str]   	=      sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													lowercase_   : int   	=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							elif self.config.prediction_type == "sample":
													raise NotImplementedError('prediction_type not implemented yet: sample'							)
							else:
													raise ValueError(
													    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"							)

							lowercase_   : Tuple   	=      self.sigmas[step_index]
							lowercase_   : str   	=      self.sigmas[step_index + 1]
							lowercase_   : Dict   	=      (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							lowercase_   : Optional[Any]   	=      (sigma_to**2 - sigma_up**2) ** 0.5

							# 2. Convert to an ODE derivative
							lowercase_   : Any   	=      (sample - pred_original_sample) / sigma

							lowercase_   : Tuple   	=      sigma_down - sigma

							lowercase_   : str   	=      sample + derivative * dt

							lowercase_   : Optional[int]   	=      model_output.device
							lowercase_   : Union[str, Any]   	=      randn_tensor(model_output.shape	,   dtype=model_output.dtype	,   device=_lowercase	,   generator=_lowercase							)

							lowercase_   : int   	=      prev_sample + noise * sigma_up

							if not return_dict:
													return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=_lowercase	,   pred_original_sample=_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase	,   )    ->	torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							lowercase_   : List[str]   	=      self.sigmas.to(device=original_samples.device	,   dtype=original_samples.dtype							)
							if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase							):
													# mps does not support float64
													lowercase_   : Dict   	=      self.timesteps.to(original_samples.device	,   dtype=torch.floataa							)
													lowercase_   : List[str]   	=      timesteps.to(original_samples.device	,   dtype=torch.floataa							)
							else:
													lowercase_   : List[Any]   	=      self.timesteps.to(original_samples.device							)
													lowercase_   : List[Any]   	=      timesteps.to(original_samples.device							)

							lowercase_   : Optional[Any]   	=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

							lowercase_   : Any   	=      sigmas[step_indices].flatten()
							while len(sigma.shape							) < len(original_samples.shape							):
													lowercase_   : Optional[int]   	=      sigma.unsqueeze(-1							)

							lowercase_   : str   	=      original_samples + noise * sigma
							return noisy_samples







	def __len__(				self							)    ->	Dict:
							return self.config.num_train_timesteps
