





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__UpperCamelCase										=  logging.get_logger(__name__)




class    _A   (				__lowercase		):
		lowercase__:      Dict							    =      ['''pixel_values''']

		def __init__(      self				:      Tuple     ,			__magic_name__				:      bool = True     ,			__magic_name__				:      Dict[str, int] = None     ,			__magic_name__				:      PILImageResampling = PIL.Image.NEAREST     ,			__magic_name__				:      bool = False     ,			__magic_name__				:      Dict[str, int] = None     ,			__magic_name__				:      Union[int, float] = 1 / 2_55     ,			__magic_name__				:      bool = False     ,			__magic_name__				:      bool = True     ,			__magic_name__				:      bool = True     ,			__magic_name__				:      Optional[Union[float, List[float]]] = None     ,			__magic_name__				:      Optional[Union[float, List[float]]] = None     ,			__magic_name__				:      bool = True     ,			**__magic_name__				:      List[str]     ,			)				->						None:


					"""simple docstring"""

					super().__init__(**__magic_name__ )
					__snake_case				:			Any       			=    size if size is not None else {"""height""": 3_46, """width""": 3_46}
					__snake_case				:			Tuple       			=    get_size_dict(__magic_name__ )
					__snake_case				:			List[Any]       			=    crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
					__snake_case				:			Optional[int]       			=    get_size_dict(__magic_name__     ,			param_name="""crop_size""" )

					__snake_case				:			List[str]       			=    do_resize
					__snake_case				:			Optional[Any]       			=    size
					__snake_case				:			Dict       			=    resample
					__snake_case				:			Dict       			=    do_center_crop
					__snake_case				:			List[Any]       			=    crop_size
					__snake_case				:			List[Any]       			=    do_rescale
					__snake_case				:			str       			=    rescale_factor
					__snake_case				:			Any       			=    rescale_offset
					__snake_case				:			Union[str, Any]       			=    do_normalize
					__snake_case				:			List[str]       			=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__snake_case				:			Any       			=    image_std if image_std is not None else IMAGENET_STANDARD_STD
					__snake_case				:			Optional[int]       			=    include_top

		def   lowercase__    (      self				:      Optional[Any]     ,			__magic_name__				:      np.ndarray     ,			__magic_name__				:      Dict[str, int]     ,			__magic_name__				:      PILImageResampling = PIL.Image.NEAREST     ,			__magic_name__				:      Optional[Union[str, ChannelDimension]] = None     ,			**__magic_name__				:      Any     ,			)				->						np.ndarray:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    get_size_dict(__magic_name__ )
					if "height" not in size or "width" not in size:
								raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''' )
					return resize(
					    __magic_name__     ,			size=(size["""height"""], size["""width"""])     ,			resample=__magic_name__     ,			data_format=__magic_name__     ,			**__magic_name__ )

		def   lowercase__    (      self				:      Any     ,			__magic_name__				:      np.ndarray     ,			__magic_name__				:      Dict[str, int]     ,			__magic_name__				:      Optional[Union[str, ChannelDimension]] = None     ,			**__magic_name__				:      Union[str, Any]     ,			)				->						np.ndarray:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    get_size_dict(__magic_name__ )
					if "height" not in size or "width" not in size:
								raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''' )
					return center_crop(__magic_name__     ,			size=(size["""height"""], size["""width"""])     ,			data_format=__magic_name__     ,			**__magic_name__ )

		def   lowercase__    (      self				:      Optional[Any]     ,			__magic_name__				:      np.ndarray     ,			__magic_name__				:      Union[int, float]     ,			__magic_name__				:      bool = True     ,			__magic_name__				:      Optional[Union[str, ChannelDimension]] = None     ,			**__magic_name__				:      Tuple     ,			)				->						int:


					"""simple docstring"""

					if offset:
								__snake_case				:			Optional[Any]       			=    (image - 127.5) * scale
								if data_format is not None:
											__snake_case				:			List[Any]       			=    to_channel_dimension_format(__magic_name__     ,			__magic_name__ )
								__snake_case				:			str       			=    rescaled_image.astype(np.floataa )
					else:
								__snake_case				:			int       			=    rescale(__magic_name__     ,			scale=__magic_name__     ,			data_format=__magic_name__     ,			**__magic_name__ )
					return rescaled_image

		def   lowercase__    (      self				:      str     ,			__magic_name__				:      np.ndarray     ,			__magic_name__				:      Union[float, List[float]]     ,			__magic_name__				:      Union[float, List[float]]     ,			__magic_name__				:      Optional[Union[str, ChannelDimension]] = None     ,			**__magic_name__				:      Tuple     ,			)				->						np.ndarray:


					"""simple docstring"""

					return normalize(__magic_name__     ,			mean=__magic_name__     ,			std=__magic_name__     ,			data_format=__magic_name__     ,			**__magic_name__ )

		def   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      ImageInput     ,			__magic_name__				:      bool = None     ,			__magic_name__				:      Dict[str, int] = None     ,			__magic_name__				:      int=None     ,			__magic_name__				:      bool = None     ,			__magic_name__				:      Dict[str, int] = None     ,			__magic_name__				:      bool = None     ,			__magic_name__				:      float = None     ,			__magic_name__				:      bool = None     ,			__magic_name__				:      bool = None     ,			__magic_name__				:      Optional[Union[float, List[float]]] = None     ,			__magic_name__				:      Optional[Union[float, List[float]]] = None     ,			__magic_name__				:      bool = None     ,			__magic_name__				:      Optional[Union[str, TensorType]] = None     ,			__magic_name__				:      ChannelDimension = ChannelDimension.FIRST     ,			**__magic_name__				:      Any     ,			)				->						PIL.Image.Image:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    do_resize if do_resize is not None else self.do_resize
					__snake_case				:			Tuple       			=    resample if resample is not None else self.resample
					__snake_case				:			List[Any]       			=    do_center_crop if do_center_crop is not None else self.do_center_crop
					__snake_case				:			Any       			=    do_rescale if do_rescale is not None else self.do_rescale
					__snake_case				:			int       			=    rescale_factor if rescale_factor is not None else self.rescale_factor
					__snake_case				:			Dict       			=    rescale_offset if rescale_offset is not None else self.rescale_offset
					__snake_case				:			str       			=    do_normalize if do_normalize is not None else self.do_normalize
					__snake_case				:			Optional[int]       			=    image_mean if image_mean is not None else self.image_mean
					__snake_case				:			Optional[int]       			=    image_std if image_std is not None else self.image_std
					__snake_case				:			Optional[Any]       			=    include_top if include_top is not None else self.include_top

					__snake_case				:			List[str]       			=    size if size is not None else self.size
					__snake_case				:			Optional[Any]       			=    get_size_dict(__magic_name__ )
					__snake_case				:			Optional[int]       			=    crop_size if crop_size is not None else self.crop_size
					__snake_case				:			str       			=    get_size_dict(__magic_name__     ,			param_name="""crop_size""" )

					__snake_case				:			List[str]       			=    make_list_of_images(__magic_name__ )

					if not valid_images(__magic_name__ ):
								raise ValueError(
								    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
								    """torch.Tensor, tf.Tensor or jax.ndarray.""" )

					if do_resize and size is None or resample is None:
								raise ValueError("""Size and resample must be specified if do_resize is True.""" )

					if do_center_crop and crop_size is None:
								raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

					if do_rescale and rescale_factor is None:
								raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

					if do_normalize and (image_mean is None or image_std is None):
								raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

					# All transformations expect numpy arrays.
					__snake_case				:			Any       			=    [to_numpy_array(__magic_name__ ) for image in images]

					if do_resize:
								__snake_case				:			Union[str, Any]       			=    [self.resize(image=__magic_name__     ,			size=__magic_name__     ,			resample=__magic_name__ ) for image in images]

					if do_center_crop:
								__snake_case				:			str       			=    [self.center_crop(image=__magic_name__     ,			size=__magic_name__ ) for image in images]

					if do_rescale:
								__snake_case				:			Dict       			=    [self.rescale(image=__magic_name__     ,			scale=__magic_name__     ,			offset=__magic_name__ ) for image in images]

					if do_normalize:
								__snake_case				:			Union[str, Any]       			=    [self.normalize(image=__magic_name__     ,			mean=__magic_name__     ,			std=__magic_name__ ) for image in images]

					if include_top:
								__snake_case				:			str       			=    [self.normalize(image=__magic_name__     ,			mean=[0, 0, 0]     ,			std=__magic_name__ ) for image in images]

					__snake_case				:			Dict       			=    [to_channel_dimension_format(__magic_name__     ,			__magic_name__ ) for image in images]

					__snake_case				:			Tuple       			=    {"""pixel_values""": images}
					return BatchFeature(data=__magic_name__     ,			tensor_type=__magic_name__ )









'''simple docstring'''






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

import numpy as np
import torch
from scipy import integrate

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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class    _A   (				__lowercase		):
		lowercase__:      torch.FloatTensor
		lowercase__:      Optional[torch.FloatTensor]							    =      None







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase=0.9_99						,  _lowerCamelCase="cosine"						,  )   ->							int:



			"""simple docstring"""


			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_lowerCamelCase   ):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2   ) ** 2

			elif alpha_transform_type == "exp":

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

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

			__snake_case				:			int       			=    []
			for i in range(_lowerCamelCase   ):
						__snake_case				:			int       			=    i / num_diffusion_timesteps
						__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    _A   (				__lowercase	,				__lowercase		):
		lowercase__:      Tuple							    =      [e.name for e in KarrasDiffusionSchedulers]
		lowercase__:      List[Any]							    =      1

		@register_to_config
		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      int = 10_00     ,			__magic_name__				:      float = 0.0001     ,			__magic_name__				:      float = 0.02     ,			__magic_name__				:      str = "linear"     ,			__magic_name__				:      Optional[Union[np.ndarray, List[float]]] = None     ,			__magic_name__				:      Optional[bool] = False     ,			__magic_name__				:      str = "epsilon"     ,			__magic_name__				:      str = "linspace"     ,			__magic_name__				:      int = 0     ,			)				->						List[Any]:


					"""simple docstring"""

					if trained_betas is not None:
								__snake_case				:			Union[str, Any]       			=    torch.tensor(__magic_name__     ,			dtype=torch.floataa )
					elif beta_schedule == "linear":
								__snake_case				:			List[str]       			=    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.
								__snake_case				:			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
								__snake_case				:			Optional[Any]       			=    betas_for_alpha_bar(__magic_name__ )
					else:
								raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

					__snake_case				:			Tuple       			=    1.0 - self.betas
					__snake_case				:			Optional[Any]       			=    torch.cumprod(self.alphas     ,			dim=0 )

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

					# setable values
					__snake_case				:			str       			=    None
					__snake_case				:			Union[str, Any]       			=    use_karras_sigmas
					self.set_timesteps(__magic_name__     ,			__magic_name__ )
					__snake_case				:			str       			=    []
					__snake_case				:			str       			=    False

		@property
		def   lowercase__    (      self				:      Optional[Any] )				->						Optional[int]:


					"""simple docstring"""

					if self.config.timestep_spacing in ["linspace", "trailing"]:
								return self.sigmas.max()

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

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


					"""simple docstring"""

					if isinstance(__magic_name__     ,			torch.Tensor ):
								__snake_case				:			int       			=    timestep.to(self.timesteps.device )
					__snake_case				:			str       			=    (self.timesteps == timestep).nonzero().item()
					__snake_case				:			str       			=    self.sigmas[step_index]
					__snake_case				:			Optional[int]       			=    sample / ((sigma**2 + 1) ** 0.5)
					__snake_case				:			List[Any]       			=    True
					return sample

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      int )				->						List[str]:


					"""simple docstring"""

					def lms_derivative(__magic_name__				:      Optional[int] ):
								__snake_case				:			Any       			=    1.0
								for k in range(__magic_name__ ):
											if current_order == k:
														continue
											prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
								return prod

					__snake_case				:			str       			=    integrate.quad(__magic_name__     ,			self.sigmas[t]     ,			self.sigmas[t + 1]     ,			epsrel=1E-4 )[0]

					return integrated_coeff

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


					"""simple docstring"""

					__snake_case				:			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":
								__snake_case				:			Any       			=    np.linspace(0     ,			self.config.num_train_timesteps - 1     ,			__magic_name__     ,			dtype=__magic_name__ )[
								    ::-1
								].copy()
					elif self.config.timestep_spacing == "leading":
								__snake_case				:			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
								__snake_case				:			List[Any]       			=    (np.arange(0     ,			__magic_name__ ) * step_ratio).round()[::-1].copy().astype(__magic_name__ )
								timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
								__snake_case				:			Dict       			=    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
								__snake_case				:			Any       			=    (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\'.''' )

					__snake_case				:			List[Any]       			=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					__snake_case				:			int       			=    np.log(__magic_name__ )
					__snake_case				:			Union[str, Any]       			=    np.interp(__magic_name__     ,			np.arange(0     ,			len(__magic_name__ ) )     ,			__magic_name__ )

					if self.use_karras_sigmas:
								__snake_case				:			Optional[Any]       			=    self._convert_to_karras(in_sigmas=__magic_name__ )
								__snake_case				:			Union[str, Any]       			=    np.array([self._sigma_to_t(__magic_name__     ,			__magic_name__ ) for sigma in sigmas] )

					__snake_case				:			Tuple       			=    np.concatenate([sigmas, [0.0]] ).astype(np.floataa )

					__snake_case				:			Optional[int]       			=    torch.from_numpy(__magic_name__ ).to(device=__magic_name__ )
					if str(__magic_name__ ).startswith("""mps""" ):
								# mps does not support float64
								__snake_case				:			int       			=    torch.from_numpy(__magic_name__ ).to(__magic_name__     ,			dtype=torch.floataa )
					else:
								__snake_case				:			Dict       			=    torch.from_numpy(__magic_name__ ).to(device=__magic_name__ )

					__snake_case				:			Optional[int]       			=    []

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      List[Any] )				->						List[Any]:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    np.log(__magic_name__ )

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

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

					__snake_case				:			Dict       			=    log_sigmas[low_idx]
					__snake_case				:			Any       			=    log_sigmas[high_idx]

					# interpolate sigmas
					__snake_case				:			List[Any]       			=    (low - log_sigma) / (low - high)
					__snake_case				:			Optional[int]       			=    np.clip(__magic_name__     ,			0     ,			1 )

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

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


					"""simple docstring"""

					__snake_case				:			float       			=    in_sigmas[-1].item()
					__snake_case				:			float       			=    in_sigmas[0].item()

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

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      Union[float, torch.FloatTensor]     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      int = 4     ,			__magic_name__				:      bool = True     ,			)				->						Union[LMSDiscreteSchedulerOutput, Tuple]:


					"""simple docstring"""

					if not self.is_scale_input_called:
								warnings.warn(
								    """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 ):
								__snake_case				:			Tuple       			=    timestep.to(self.timesteps.device )
					__snake_case				:			Any       			=    (self.timesteps == timestep).nonzero().item()
					__snake_case				:			Optional[Any]       			=    self.sigmas[step_index]

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
								__snake_case				:			int       			=    sample - sigma * model_output
					elif self.config.prediction_type == "v_prediction":
								# * c_out + input * c_skip
								__snake_case				:			str       			=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
					elif self.config.prediction_type == "sample":
								__snake_case				:			int       			=    model_output
					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
					__snake_case				:			Union[str, Any]       			=    (sample - pred_original_sample) / sigma
					self.derivatives.append(__magic_name__ )
					if len(self.derivatives ) > order:
								self.derivatives.pop(0 )

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

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

					if not return_dict:
								return (prev_sample,)

					return LMSDiscreteSchedulerOutput(prev_sample=__magic_name__     ,			pred_original_sample=__magic_name__ )

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      torch.FloatTensor     ,			)				->						torch.FloatTensor:


					"""simple docstring"""

					__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(__magic_name__ ):
								# mps does not support float64
								__snake_case				:			str       			=    self.timesteps.to(original_samples.device     ,			dtype=torch.floataa )
								__snake_case				:			List[Any]       			=    timesteps.to(original_samples.device     ,			dtype=torch.floataa )
					else:
								__snake_case				:			Tuple       			=    self.timesteps.to(original_samples.device )
								__snake_case				:			Union[str, Any]       			=    timesteps.to(original_samples.device )

					__snake_case				:			int       			=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

					__snake_case				:			List[str]       			=    original_samples + noise * sigma
					return noisy_samples

		def __len__(      self				:      Union[str, Any] )				->						Union[str, Any]:


					"""simple docstring"""

					return self.config.num_train_timesteps


