



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->EulerAncestralDiscrete
class    _SCREAMING_SNAKE_CASE						(a_			):
     lowerCAmelCase       =      42
     lowerCAmelCase       =      None






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]		,						__lowerCamelCase :  Optional[Any]=0.999		,						__lowerCamelCase :  Optional[int]="cosine"		,						):



 """simple docstring"""





 if alpha_transform_type == "cosine":

  def alpha_bar_fn(__lowerCamelCase :  List[Any]  ):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(__lowerCamelCase :  Any  ):
   return math.exp(t * -12.0  )

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

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[]
 for i in range(__lowerCamelCase  ):
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						i / num_diffusion_timesteps
  __SCREAMING_SNAKE_CASE							:     Any							=						(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    _SCREAMING_SNAKE_CASE						(a_      ,     a_			):
     lowerCAmelCase       =      [e.name for e in KarrasDiffusionSchedulers]
     lowerCAmelCase       =      1





     @register_to_config
     def __init__(							self			:				Tuple  ,	UpperCamelCase			:				str = 1_0_0_0  ,	UpperCamelCase			:				int = 0.0_0_0_1  ,	UpperCamelCase			:				Dict = 0.0_2  ,	UpperCamelCase			:				List[Any] = "linear"  ,	UpperCamelCase			:				Dict = None  ,	UpperCamelCase			:				int = "epsilon"  ,	UpperCamelCase			:				Any = "linspace"  ,	UpperCamelCase			:				int = 0  ,	)->List[str]:
      if trained_betas is not None:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.tensor(_snake_case  ,	dtype=torch.floataa					)
      elif beta_schedule == "linear":
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.linspace(_snake_case  ,	_snake_case  ,	_snake_case  ,	dtype=torch.floataa					)
      elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						(
           torch.linspace(beta_start**0.5  ,	beta_end**0.5  ,	_snake_case  ,	dtype=torch.floataa					) ** 2
       )
      elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __SCREAMING_SNAKE_CASE							:     int							=						betas_for_alpha_bar(_snake_case					)
      else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						1.0 - self.betas
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.cumprod(self.alphas  ,	dim=0					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
      __SCREAMING_SNAKE_CASE							:     int							=						np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
      __SCREAMING_SNAKE_CASE							:     int							=						torch.from_numpy(_snake_case					)

      # setable values
      __SCREAMING_SNAKE_CASE							:     List[str]							=						None
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						np.linspace(0  ,	num_train_timesteps - 1  ,	_snake_case  ,	dtype=_snake_case					)[::-1].copy()
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.from_numpy(_snake_case					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						False





     @property
     def 						__snake_case     (							self			:				int					)->Optional[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 						__snake_case     (							self			:				Tuple  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				int					)->torch.FloatTensor:
      if isinstance(_snake_case  ,	torch.Tensor					):
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						timestep.to(self.timesteps.device					)
      __SCREAMING_SNAKE_CASE							:     int							=						(self.timesteps == timestep).nonzero().item()
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.sigmas[step_index]
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sample / ((sigma**2 + 1) ** 0.5)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						True
      return sample





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				List[str] = None					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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							:     List[Any]							=						np.linspace(0  ,	self.config.num_train_timesteps - 1  ,	_snake_case  ,	dtype=_snake_case					)[
           ::-1
       ].copy()
      elif self.config.timestep_spacing == "leading":
       __SCREAMING_SNAKE_CASE							:     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
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						(np.arange(0  ,	_snake_case					) * step_ratio).round()[::-1].copy().astype(_snake_case					)
       timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
       __SCREAMING_SNAKE_CASE							:     List[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
       __SCREAMING_SNAKE_CASE							:     Any							=						(np.arange(self.config.num_train_timesteps  ,	0  ,	-step_ratio					)).round().copy().astype(_snake_case					)
       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							:     Any							=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						np.interp(_snake_case  ,	np.arange(0  ,	len(_snake_case					)					)  ,	_snake_case					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
      __SCREAMING_SNAKE_CASE							:     int							=						torch.from_numpy(_snake_case					).to(device=_snake_case					)
      if str(_snake_case					).startswith("mps"					):
       # mps does not support float64
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.from_numpy(_snake_case					).to(_snake_case  ,	dtype=torch.floataa					)
      else:
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.from_numpy(_snake_case					).to(device=_snake_case					)





     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Dict = None  ,	UpperCamelCase			:				Union[str, Any] = True  ,	)->Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

      if (
          isinstance(_snake_case  ,	_snake_case					)
          or isinstance(_snake_case  ,	torch.IntTensor					)
          or isinstance(_snake_case  ,	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(_snake_case  ,	torch.Tensor					):
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						timestep.to(self.timesteps.device					)

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

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
       __SCREAMING_SNAKE_CASE							:     Tuple							=						sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       __SCREAMING_SNAKE_CASE							:     str							=						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`"""					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						self.sigmas[step_index]
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.sigmas[step_index + 1]
      __SCREAMING_SNAKE_CASE							:     str							=						(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      __SCREAMING_SNAKE_CASE							:     Any							=						(sigma_to**2 - sigma_up**2) ** 0.5

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

      __SCREAMING_SNAKE_CASE							:     Tuple							=						sigma_down - sigma

      __SCREAMING_SNAKE_CASE							:     List[str]							=						sample + derivative * dt

      __SCREAMING_SNAKE_CASE							:     Any							=						model_output.device
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						randn_tensor(model_output.shape  ,	dtype=model_output.dtype  ,	device=_snake_case  ,	generator=_snake_case					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prev_sample + noise * sigma_up

      if not return_dict:
       return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=_snake_case  ,	pred_original_sample=_snake_case					)





     def 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Optional[int]  ,	)->torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.sigmas.to(device=original_samples.device  ,	dtype=original_samples.dtype					)
      if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case					):
       # mps does not support float64
       __SCREAMING_SNAKE_CASE							:     str							=						self.timesteps.to(original_samples.device  ,	dtype=torch.floataa					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						timesteps.to(original_samples.device  ,	dtype=torch.floataa					)
      else:
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.timesteps.to(original_samples.device					)
       __SCREAMING_SNAKE_CASE							:     str							=						timesteps.to(original_samples.device					)

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

      __SCREAMING_SNAKE_CASE							:     Any							=						sigmas[step_indices].flatten()
      while len(sigma.shape					) < len(original_samples.shape					):
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sigma.unsqueeze(-1					)

      __SCREAMING_SNAKE_CASE							:     int							=						original_samples + noise * sigma
      return noisy_samples






     def __len__(							self			:				Optional[Any]					)->Optional[Any]:
      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


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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        lowercase    (	a_			):
							_lowerCamelCase		:   torch.FloatTensor
							_lowerCamelCase		:   Optional[torch.FloatTensor]= None







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=0.999 ,UpperCamelCase="cosine" ,)     ->     List[str]:
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase	):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCamelCase	):
			return math.exp(t * -12.0	)

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

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







class        lowercase    (	a_,       a_			):
							_lowerCamelCase		:   List[str]= [e.name for e in KarrasDiffusionSchedulers]
							_lowerCamelCase		:   Optional[int]= 1


							@register_to_config
							def __init__(      self     ,      _snake_case = 1000     ,      _snake_case = 0.0_001     ,      _snake_case = 0.02     ,      _snake_case = "linear"     ,      _snake_case = None     ,      _snake_case = "epsilon"     ,      _snake_case = "linspace"     ,      _snake_case = 0     ,      )				->					List[str]:
								if trained_betas is not None:
									UpperCAmelCase_		:   List[str]													=       torch.tensor(_snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "linear":
									UpperCAmelCase_		:   List[Any]													=       torch.linspace(_snake_case     ,      _snake_case     ,      _snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									UpperCAmelCase_		:   List[Any]													=       (
									    torch.linspace(beta_start**0.5     ,      beta_end**0.5     ,      _snake_case     ,      dtype=torch.floataa) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									UpperCAmelCase_		:   int													=       betas_for_alpha_bar(_snake_case)
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

								UpperCAmelCase_		:   List[Any]													=       1.0 - self.betas
								UpperCAmelCase_		:   Union[str, Any]													=       torch.cumprod(self.alphas     ,      dim=0)

								UpperCAmelCase_		:   Union[str, Any]													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   int													=       np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case)

								# setable values
								UpperCAmelCase_		:   List[str]													=       None
								UpperCAmelCase_		:   Union[str, Any]													=       np.linspace(0     ,      num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[::-1].copy()
								UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       False


							@property
							def     _snake_case				(      self)				->					Optional[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     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					torch.FloatTensor:
								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   List[Any]													=       timestep.to(self.timesteps.device)
								UpperCAmelCase_		:   int													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       sample / ((sigma**2 + 1) ** 0.5)
								UpperCAmelCase_		:   Optional[Any]													=       True
								return sample


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Optional[Any]:
								UpperCAmelCase_		:   Optional[int]													=       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":
									UpperCAmelCase_		:   List[Any]													=       np.linspace(0     ,      self.config.num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[
									    ::-1
									].copy()
								elif self.config.timestep_spacing == "leading":
									UpperCAmelCase_		:   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
									UpperCAmelCase_		:   List[Any]													=       (np.arange(0     ,      _snake_case) * step_ratio).round()[::-1].copy().astype(_snake_case)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									UpperCAmelCase_		:   List[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
									UpperCAmelCase_		:   Any													=       (np.arange(self.config.num_train_timesteps     ,      0     ,      -step_ratio)).round().copy().astype(_snake_case)
									timesteps -= 1
								else:
									raise ValueError(
									    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

								UpperCAmelCase_		:   Any													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   Dict													=       np.interp(_snake_case     ,      np.arange(0     ,      len(_snake_case))     ,      _snake_case)
								UpperCAmelCase_		:   Optional[int]													=       np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case).to(device=_snake_case)
								if str(_snake_case).startswith('mps'):
									# mps does not support float64
									UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case).to(_snake_case     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       torch.from_numpy(_snake_case).to(device=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = True     ,      )				->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

								if (
								    isinstance(_snake_case     ,      _snake_case)
								    or isinstance(_snake_case     ,      torch.IntTensor)
								    or isinstance(_snake_case     ,      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(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   Optional[int]													=       timestep.to(self.timesteps.device)

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

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									UpperCAmelCase_		:   Tuple													=       sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									UpperCAmelCase_		:   str													=       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`""")

								UpperCAmelCase_		:   Dict													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index + 1]
								UpperCAmelCase_		:   str													=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								UpperCAmelCase_		:   Any													=       (sigma_to**2 - sigma_up**2) ** 0.5

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

								UpperCAmelCase_		:   Tuple													=       sigma_down - sigma

								UpperCAmelCase_		:   List[str]													=       sample + derivative * dt

								UpperCAmelCase_		:   Any													=       model_output.device
								UpperCAmelCase_		:   List[Any]													=       randn_tensor(model_output.shape     ,      dtype=model_output.dtype     ,      device=_snake_case     ,      generator=_snake_case)

								UpperCAmelCase_		:   Union[str, Any]													=       prev_sample + noise * sigma_up

								if not return_dict:
									return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=_snake_case     ,      pred_original_sample=_snake_case)


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

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

								UpperCAmelCase_		:   Any													=       sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
									UpperCAmelCase_		:   Union[str, Any]													=       sigma.unsqueeze(-1)

								UpperCAmelCase_		:   int													=       original_samples + noise * sigma
								return noisy_samples





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






