





'''simple docstring'''







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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class    __a :
       __a   :				CommonSchedulerState

       # setable values
       __a   :				jnp.ndarray
       __a   :				jnp.ndarray
       __a   :				jnp.ndarray
       __a   :				Optional[int]       =    None

       # running values
       __a   :				Optional[jnp.ndarray]       =    None

       @classmethod
       def       UpperCAmelCase__		(							cls							:       int    ,       __magic_name__							:       CommonSchedulerState    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       jnp.ndarray				)   ->    List[Any]:







             """simple docstring"""
             return cls(common=__magic_name__    ,       init_noise_sigma=__magic_name__    ,       timesteps=__magic_name__    ,       sigmas=__magic_name__				)





@dataclass
class    __a (lowerCamelCase       ):
       __a   :				LMSDiscreteSchedulerState





class    __a (lowerCamelCase    ,						lowerCamelCase       ):
       __a   :				Tuple       =    [e.name for e in FlaxKarrasDiffusionSchedulers]

       __a   :				jnp.dtype

       @property
       def       UpperCAmelCase__		(							self							:       Tuple				)   ->    int:







             """simple docstring"""
             return True

       @register_to_config
       def __init__(							self							:       Tuple    ,       __magic_name__							:       int = 10_00    ,       __magic_name__							:       float = 0.0_0_0_1    ,       __magic_name__							:       float = 0.0_2    ,       __magic_name__							:       str = "linear"    ,       __magic_name__							:       Optional[jnp.ndarray] = None    ,       __magic_name__							:       str = "epsilon"    ,       __magic_name__							:       jnp.dtype = jnp.floataa    ,       )   ->    Union[str, Any]:







             """simple docstring"""
             UpperCAmelCase_     : str         =					dtype

       def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       Optional[CommonSchedulerState] = None				)   ->    LMSDiscreteSchedulerState:







             """simple docstring"""
             if common is None:
                   UpperCAmelCase_     : Optional[int]         =					CommonSchedulerState.create(self				)

             UpperCAmelCase_     : Union[str, Any]         =					jnp.arange(0    ,       self.config.num_train_timesteps				).round()[::-1]
             UpperCAmelCase_     : Dict         =					((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

             # standard deviation of the initial noise distribution
             UpperCAmelCase_     : Dict         =					sigmas.max()

             return LMSDiscreteSchedulerState.create(
                 common=__magic_name__    ,       init_noise_sigma=__magic_name__    ,       timesteps=__magic_name__    ,       sigmas=__magic_name__    ,       )

       def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       int				)   ->    jnp.ndarray:







             """simple docstring"""
             (UpperCAmelCase_    )     : Dict         =					jnp.where(state.timesteps == timestep    ,       size=1				)
             UpperCAmelCase_     : List[Any]         =					step_index[0]

             UpperCAmelCase_     : Optional[int]         =					state.sigmas[step_index]
             UpperCAmelCase_     : Dict         =					sample / ((sigma**2 + 1) ** 0.5)
             return sample

       def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       Optional[int]    ,       __magic_name__							:       Tuple    ,       __magic_name__							:       int				)   ->    Dict:







             """simple docstring"""

             def lms_derivative(__magic_name__							:       int				):
                   UpperCAmelCase_     : Union[str, Any]         =					1.0
                   for k in range(__magic_name__				):
                         if current_order == k:
                               continue
                         prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
                   return prod

             UpperCAmelCase_     : Optional[Any]         =					integrate.quad(__magic_name__    ,       state.sigmas[t]    ,       state.sigmas[t + 1]    ,       epsrel=1E-4				)[0]

             return integrated_coeff

       def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       int    ,       __magic_name__							:       Tuple = ()				)   ->    LMSDiscreteSchedulerState:







             """simple docstring"""
             UpperCAmelCase_     : List[str]         =					jnp.linspace(self.config.num_train_timesteps - 1    ,       0    ,       __magic_name__    ,       dtype=self.dtype				)

             UpperCAmelCase_     : Dict         =					jnp.floor(__magic_name__				).astype(jnp.intaa				)
             UpperCAmelCase_     : List[str]         =					jnp.ceil(__magic_name__				).astype(jnp.intaa				)

             UpperCAmelCase_     : List[Any]         =					jnp.mod(__magic_name__    ,       1.0				)

             UpperCAmelCase_     : List[str]         =					((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
             UpperCAmelCase_     : str         =					(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
             UpperCAmelCase_     : List[str]         =					jnp.concatenate([sigmas, jnp.array([0.0]    ,       dtype=self.dtype				)]				)

             UpperCAmelCase_     : Optional[Any]         =					timesteps.astype(jnp.intaa				)

             # initial running values
             UpperCAmelCase_     : List[Any]         =					jnp.zeros((0,) + shape    ,       dtype=self.dtype				)

             return state.replace(
                 timesteps=__magic_name__    ,       sigmas=__magic_name__    ,       num_inference_steps=__magic_name__    ,       derivatives=__magic_name__    ,       )

       def       UpperCAmelCase__		(							self							:       Any    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       int    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       int = 4    ,       __magic_name__							:       bool = True    ,       )   ->    Union[FlaxLMSSchedulerOutput, Tuple]:







             """simple docstring"""
             if state.num_inference_steps is None:
                   raise ValueError(
                       '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''				)

             UpperCAmelCase_     : Optional[int]         =					state.sigmas[timestep]

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                   UpperCAmelCase_     : int         =					sample - sigma * model_output
             elif self.config.prediction_type == "v_prediction":
                   # * c_out + input * c_skip
                   UpperCAmelCase_     : int         =					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
             UpperCAmelCase_     : Optional[Any]         =					(sample - pred_original_sample) / sigma
             UpperCAmelCase_     : Tuple         =					state.replace(derivatives=jnp.append(state.derivatives    ,       __magic_name__				)				)
             if len(state.derivatives				) > order:
                   UpperCAmelCase_     : Optional[int]         =					state.replace(derivatives=jnp.delete(state.derivatives    ,       0				)				)

             # 3. Compute linear multistep coefficients
             UpperCAmelCase_     : Any         =					min(timestep + 1    ,       __magic_name__				)
             UpperCAmelCase_     : List[Any]         =					[self.get_lms_coefficient(__magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__				) for curr_order in range(__magic_name__				)]

             # 4. Compute previous sample based on the derivatives path
             UpperCAmelCase_     : Dict         =					sample + sum(
                 coeff * derivative for coeff, derivative in zip(__magic_name__    ,       reversed(state.derivatives				)				)				)

             if not return_dict:
                   return (prev_sample, state)

             return FlaxLMSSchedulerOutput(prev_sample=__magic_name__    ,       state=__magic_name__				)

       def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       jnp.ndarray    ,       )   ->    jnp.ndarray:







             """simple docstring"""
             UpperCAmelCase_     : int         =					state.sigmas[timesteps].flatten()
             UpperCAmelCase_     : Dict         =					broadcast_to_shape_from_left(__magic_name__    ,       noise.shape				)

             UpperCAmelCase_     : List[Any]         =					original_samples + noise * sigma

             return noisy_samples



       def __len__(							self							:       str				)   ->    int:







             """simple docstring"""
             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


snake_case_       :    Union[str, Any]          =					logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class    __a (lowerCamelCase       ):
							__a   :				torch.FloatTensor
							__a   :				Optional[torch.FloatTensor]       =    None





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     str,     SCREAMING_SNAKE_CASE__ :     Optional[Any]=0.9_99,     SCREAMING_SNAKE_CASE__ :     Tuple="cosine",     )	->		Optional[Any]:
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(SCREAMING_SNAKE_CASE__ :     Union[str, Any]				):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

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

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

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


class    __a (lowerCamelCase    ,						lowerCamelCase       ):
							__a   :				str       =    [e.name for e in KarrasDiffusionSchedulers]
							__a   :				Tuple       =    1

							@register_to_config
							def __init__(							self							:       Union[str, Any]    ,       __magic_name__							:       int = 10_00    ,       __magic_name__							:       float = 0.0_0_0_1    ,       __magic_name__							:       float = 0.0_2    ,       __magic_name__							:       str = "linear"    ,       __magic_name__							:       Optional[Union[np.ndarray, List[float]]] = None    ,       __magic_name__							:       str = "epsilon"    ,       __magic_name__							:       str = "linear"    ,       __magic_name__							:       Optional[bool] = False    ,       __magic_name__							:       str = "linspace"    ,       __magic_name__							:       int = 0    ,       )   ->    Any:







													"""simple docstring"""
													if trained_betas is not None:
																			UpperCAmelCase_     : Optional[Any]         =					torch.tensor(__magic_name__    ,       dtype=torch.floataa				)
													elif beta_schedule == "linear":
																			UpperCAmelCase_     : Tuple         =					torch.linspace(__magic_name__    ,       __magic_name__    ,       __magic_name__    ,       dtype=torch.floataa				)
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			UpperCAmelCase_     : Optional[Any]         =					(
																			    torch.linspace(beta_start**0.5    ,       beta_end**0.5    ,       __magic_name__    ,       dtype=torch.floataa				) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			UpperCAmelCase_     : Tuple         =					betas_for_alpha_bar(__magic_name__				)
													else:
																			raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""				)

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

													UpperCAmelCase_     : Dict         =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													UpperCAmelCase_     : Optional[Any]         =					np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
													UpperCAmelCase_     : Optional[Any]         =					torch.from_numpy(__magic_name__				)

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

							@property
							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[Any]:







													"""simple docstring"""
													# 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       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       Union[float, torch.FloatTensor]				)   ->    torch.FloatTensor:







													"""simple docstring"""
													if isinstance(__magic_name__    ,       torch.Tensor				):
																			UpperCAmelCase_     : Tuple         =					timestep.to(self.timesteps.device				)
													UpperCAmelCase_     : Tuple         =					(self.timesteps == timestep).nonzero().item()
													UpperCAmelCase_     : List[str]         =					self.sigmas[step_index]

													UpperCAmelCase_     : List[str]         =					sample / ((sigma**2 + 1) ** 0.5)

													UpperCAmelCase_     : Tuple         =					True
													return sample

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       int    ,       __magic_name__							:       Union[str, torch.device] = None				)   ->    Any:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[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":
																			UpperCAmelCase_     : int         =					np.linspace(0    ,       self.config.num_train_timesteps - 1    ,       __magic_name__    ,       dtype=__magic_name__				)[
																			    ::-1
																			].copy()
													elif self.config.timestep_spacing == "leading":
																			UpperCAmelCase_     : List[str]         =					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[str]         =					(np.arange(0    ,       __magic_name__				) * step_ratio).round()[::-1].copy().astype(__magic_name__				)
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			UpperCAmelCase_     : Union[str, 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_     : int         =					(np.arange(self.config.num_train_timesteps    ,       0    ,       -step_ratio				)).round().copy().astype(__magic_name__				)
																			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_     : Optional[Any]         =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													UpperCAmelCase_     : Union[str, Any]         =					np.log(__magic_name__				)

													if self.config.interpolation_type == "linear":
																			UpperCAmelCase_     : List[Any]         =					np.interp(__magic_name__    ,       np.arange(0    ,       len(__magic_name__				)				)    ,       __magic_name__				)
													elif self.config.interpolation_type == "log_linear":
																			UpperCAmelCase_     : str         =					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:
																			UpperCAmelCase_     : List[Any]         =					self._convert_to_karras(in_sigmas=__magic_name__    ,       num_inference_steps=self.num_inference_steps				)
																			UpperCAmelCase_     : Dict         =					np.array([self._sigma_to_t(__magic_name__    ,       __magic_name__				) for sigma in sigmas]				)

													UpperCAmelCase_     : Tuple         =					np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
													UpperCAmelCase_     : Any         =					torch.from_numpy(__magic_name__				).to(device=__magic_name__				)
													if str(__magic_name__				).startswith('''mps'''				):
																			# mps does not support float64
																			UpperCAmelCase_     : List[str]         =					torch.from_numpy(__magic_name__				).to(__magic_name__    ,       dtype=torch.floataa				)
													else:
																			UpperCAmelCase_     : int         =					torch.from_numpy(__magic_name__				).to(device=__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       __magic_name__							:       Union[str, Any]    ,       __magic_name__							:       Optional[Any]				)   ->    Optional[Any]:







													"""simple docstring"""
													# get log sigma
													UpperCAmelCase_     : List[Any]         =					np.log(__magic_name__				)

													# get distribution
													UpperCAmelCase_     : Optional[int]         =					log_sigma - log_sigmas[:, np.newaxis]

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

													UpperCAmelCase_     : Optional[int]         =					log_sigmas[low_idx]
													UpperCAmelCase_     : Union[str, Any]         =					log_sigmas[high_idx]

													# interpolate sigmas
													UpperCAmelCase_     : Union[str, Any]         =					(low - log_sigma) / (low - high)
													UpperCAmelCase_     : Dict         =					np.clip(__magic_name__    ,       0    ,       1				)

													# transform interpolation to time range
													UpperCAmelCase_     : Optional[Any]         =					(1 - w) * low_idx + w * high_idx
													UpperCAmelCase_     : List[Any]         =					t.reshape(sigma.shape				)
													return t

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       Union[str, Any]				)   ->    torch.FloatTensor:







													"""simple docstring"""
													UpperCAmelCase_     : float         =					in_sigmas[-1].item()
													UpperCAmelCase_     : float         =					in_sigmas[0].item()

													UpperCAmelCase_     : List[str]         =					7.0  # 7.0 is the value used in the paper
													UpperCAmelCase_     : int         =					np.linspace(0    ,       1    ,       __magic_name__				)
													UpperCAmelCase_     : int         =					sigma_min ** (1 / rho)
													UpperCAmelCase_     : str         =					sigma_max ** (1 / rho)
													UpperCAmelCase_     : Optional[Any]         =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
													return sigmas

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       Union[float, torch.FloatTensor]    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       float = 0.0    ,       __magic_name__							:       float = 0.0    ,       __magic_name__							:       float = float('''inf'''				)    ,       __magic_name__							:       float = 1.0    ,       __magic_name__							:       Optional[torch.Generator] = None    ,       __magic_name__							:       bool = True    ,       )   ->    Union[EulerDiscreteSchedulerOutput, Tuple]:







													"""simple docstring"""

													if (
													    isinstance(__magic_name__    ,       __magic_name__				)
													    or isinstance(__magic_name__    ,       torch.IntTensor				)
													    or isinstance(__magic_name__    ,       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(__magic_name__    ,       torch.Tensor				):
																			UpperCAmelCase_     : Tuple         =					timestep.to(self.timesteps.device				)

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

													UpperCAmelCase_     : List[str]         =					min(s_churn / (len(self.sigmas				) - 1)    ,       2**0.5 - 1				) if s_tmin <= sigma <= s_tmax else 0.0

													UpperCAmelCase_     : Dict         =					randn_tensor(
													    model_output.shape    ,       dtype=model_output.dtype    ,       device=model_output.device    ,       generator=__magic_name__				)

													UpperCAmelCase_     : Tuple         =					noise * s_noise
													UpperCAmelCase_     : Tuple         =					sigma * (gamma + 1)

													if gamma > 0:
																			UpperCAmelCase_     : Any         =					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":
																			UpperCAmelCase_     : str         =					model_output
													elif self.config.prediction_type == "epsilon":
																			UpperCAmelCase_     : Any         =					sample - sigma_hat * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			UpperCAmelCase_     : Optional[Any]         =					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
													UpperCAmelCase_     : int         =					(sample - pred_original_sample) / sigma_hat

													UpperCAmelCase_     : List[str]         =					self.sigmas[step_index + 1] - sigma_hat

													UpperCAmelCase_     : str         =					sample + derivative * dt

													if not return_dict:
																			return (prev_sample,)

													return EulerDiscreteSchedulerOutput(prev_sample=__magic_name__    ,       pred_original_sample=__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       torch.FloatTensor    ,       )   ->    torch.FloatTensor:







													"""simple docstring"""
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													UpperCAmelCase_     : Union[str, Any]         =					self.sigmas.to(device=original_samples.device    ,       dtype=original_samples.dtype				)
													if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__				):
																			# mps does not support float64
																			UpperCAmelCase_     : str         =					self.timesteps.to(original_samples.device    ,       dtype=torch.floataa				)
																			UpperCAmelCase_     : Dict         =					timesteps.to(original_samples.device    ,       dtype=torch.floataa				)
													else:
																			UpperCAmelCase_     : Optional[int]         =					self.timesteps.to(original_samples.device				)
																			UpperCAmelCase_     : Dict         =					timesteps.to(original_samples.device				)

													UpperCAmelCase_     : List[str]         =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

													UpperCAmelCase_     : str         =					original_samples + noise * sigma
													return noisy_samples



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







													"""simple docstring"""
													return self.config.num_train_timesteps





