
'''simple docstring'''

from sklearn.metrics import roc_auc_score

import datasets


lowerCAmelCase_			:			Tuple			   =     '''
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model\'s predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
'''

lowerCAmelCase_			:			List[str]			   =     '''
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:
    - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
    - `\'weighted\'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `\'ovr\'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == \'macro\'`, because class imbalance affects the composition of each of the \'rest\' groupings.
    - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric("roc_auc")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results[\'roc_auc\'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')
        >>> print(round(results[\'roc_auc\'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results[\'roc_auc\']])
        [0.83, 0.38, 0.94]
'''

lowerCAmelCase_			:			Dict			   =     '''\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,					_KWARGS_DESCRIPTION				)
class 	__lowerCAmelCase    (  datasets.Metric				):



     def     snake_case_ (self	):
          return datasets.MetricInfo(
              description=_DESCRIPTION						,	citation=_CITATION						,	inputs_description=_KWARGS_DESCRIPTION						,	features=datasets.Features(
                  {
                      """prediction_scores""": datasets.Sequence(datasets.Value("""float"""	)	),
                      """references""": datasets.Value("""int32"""	),
                  }
                  if self.config_name == """multiclass"""
                  else {
                      """references""": datasets.Sequence(datasets.Value("""int32"""	)	),
                      """prediction_scores""": datasets.Sequence(datasets.Value("""float"""	)	),
                  }
                  if self.config_name == """multilabel"""
                  else {
                      """references""": datasets.Value("""int32"""	),
                      """prediction_scores""": datasets.Value("""float"""	),
                  }	)						,	reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"""]						,	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__="macro"						,	lowerCAmelCase__=None						,	lowerCAmelCase__=None						,	lowerCAmelCase__="raise"						,	lowerCAmelCase__=None						,	):
          return {
              "roc_auc": roc_auc_score(
                  lowerCAmelCase__						,	lowerCAmelCase__						,	average=lowerCAmelCase__						,	sample_weight=lowerCAmelCase__						,	max_fpr=lowerCAmelCase__						,	multi_class=lowerCAmelCase__						,	labels=lowerCAmelCase__						,	)
          }




'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_=0.999 ,       lowerCAmelCase_="cosine" ,       ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCAmelCase_       ):
               return math.cos((t + 0.008) / 1.008 * 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}"       )

     _UpperCAmelCase					:  str         =    []
     for i in range(lowerCAmelCase_       ):
          _UpperCAmelCase					:  List[str]         =    i / num_diffusion_timesteps
          _UpperCAmelCase					:  str         =    (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    (  __a       ,					__a				):
     snake_case   :    Any							      =      [e.name for e in KarrasDiffusionSchedulers]
     snake_case   :    List[Any]							      =      2



     @register_to_config
     def __init__(self						,	lowerCAmelCase__ = 1_0_0_0						,	lowerCAmelCase__ = 0.0_0_0_8_5						,	lowerCAmelCase__ = 0.0_1_2						,	lowerCAmelCase__ = "linear"						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = "epsilon"						,	lowerCAmelCase__ = "linspace"						,	lowerCAmelCase__ = 0						,	):
          if trained_betas is not None:
               _UpperCAmelCase					:  Optional[int]         =    torch.tensor(lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "linear":
               _UpperCAmelCase					:  Optional[int]         =    torch.linspace(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase					:  Tuple         =    (
                   torch.linspace(beta_start**0.5						,	beta_end**0.5						,	lowerCAmelCase__						,	dtype=torch.floataa	) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase					:  Union[str, Any]         =    betas_for_alpha_bar(lowerCAmelCase__	)
          else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

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

          #  set all values
          self.set_timesteps(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__=None	):
          if schedule_timesteps is None:
               _UpperCAmelCase					:  Optional[Any]         =    self.timesteps

          _UpperCAmelCase					:  Optional[Any]         =    (schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter	) == 0:
               _UpperCAmelCase					:  Dict         =    1 if len(lowerCAmelCase__	) > 1 else 0
          else:
               _UpperCAmelCase					:  str         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
               _UpperCAmelCase					:  Any         =    self._index_counter[timestep_int]

          return indices[pos].item()



     @property
     def     snake_case_ (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     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Dict         =    self.index_for_timestep(lowerCAmelCase__	)

          if self.state_in_first_order:
               _UpperCAmelCase					:  Any         =    self.sigmas[step_index]
          else:
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]

          _UpperCAmelCase					:  Dict         =    sample / ((sigma**2 + 1) ** 0.5)
          return sample



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	):
          _UpperCAmelCase					:  Dict         =    num_inference_steps

          _UpperCAmelCase					:  int         =    num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
               _UpperCAmelCase					:  Dict         =    np.linspace(0						,	num_train_timesteps - 1						,	lowerCAmelCase__						,	dtype=lowerCAmelCase__	)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase					:  List[str]         =    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					:  Optional[Any]         =    (np.arange(0						,	lowerCAmelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__	)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase					:  Any         =    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(lowerCAmelCase__						,	0						,	-step_ratio	)).round().copy().astype(lowerCAmelCase__	)
               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					:  List[str]         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          _UpperCAmelCase					:  Dict         =    torch.from_numpy(np.log(lowerCAmelCase__	)	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    np.interp(lowerCAmelCase__						,	np.arange(0						,	len(lowerCAmelCase__	)	)						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Dict         =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
          _UpperCAmelCase					:  Any         =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)

          # compute up and down sigmas
          _UpperCAmelCase					:  str         =    sigmas.roll(-1	)
          _UpperCAmelCase					:  List[Any]         =    0.0
          _UpperCAmelCase					:  Optional[int]         =    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          _UpperCAmelCase					:  Any         =    (sigmas_next**2 - sigmas_up**2) ** 0.5
          _UpperCAmelCase					:  Tuple         =    0.0

          # compute interpolated sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    sigmas.log().lerp(sigmas_down.log()						,	0.5	).exp()
          _UpperCAmelCase					:  int         =    0.0

          # set sigmas
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
          _UpperCAmelCase					:  str         =    torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
          _UpperCAmelCase					:  Optional[int]         =    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

          if str(lowerCAmelCase__	).startswith("""mps"""	):
               # mps does not support float64
               _UpperCAmelCase					:  List[str]         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  int         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  int         =    self.sigma_to_t(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=timesteps.dtype	)
          _UpperCAmelCase					:  Optional[int]         =    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,	dim=-1	).flatten()

          _UpperCAmelCase					:  Any         =    torch.cat([timesteps[:1], interleaved_timesteps]	)

          _UpperCAmelCase					:  Dict         =    None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          _UpperCAmelCase					:  Dict         =    defaultdict(lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__	):
          # get log sigma
          _UpperCAmelCase					:  Union[str, Any]         =    sigma.log()

          # get distribution
          _UpperCAmelCase					:  str         =    log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          _UpperCAmelCase					:  str         =    dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
          _UpperCAmelCase					:  List[Any]         =    low_idx + 1

          _UpperCAmelCase					:  Optional[int]         =    self.log_sigmas[low_idx]
          _UpperCAmelCase					:  Dict         =    self.log_sigmas[high_idx]

          # interpolate sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    (low - log_sigma) / (low - high)
          _UpperCAmelCase					:  Union[str, Any]         =    w.clamp(0						,	1	)

          # transform interpolation to time range
          _UpperCAmelCase					:  Union[str, Any]         =    (1 - w) * low_idx + w * high_idx
          _UpperCAmelCase					:  str         =    t.view(sigma.shape	)
          return t



     @property
     def     snake_case_ (self	):
          return self.sample is None



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = True						,	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.index_for_timestep(lowerCAmelCase__	)

          # advance index counter by 1
          _UpperCAmelCase					:  Optional[Any]         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               _UpperCAmelCase					:  Tuple         =    self.sigmas[step_index]
               _UpperCAmelCase					:  List[str]         =    self.sigmas_interpol[step_index]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_up[step_index]
               _UpperCAmelCase					:  Optional[int]         =    self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               _UpperCAmelCase					:  List[Any]         =    self.sigmas[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]
               _UpperCAmelCase					:  List[Any]         =    self.sigmas_up[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_down[step_index - 1]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          _UpperCAmelCase					:  int         =    0
          _UpperCAmelCase					:  str         =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          _UpperCAmelCase					:  Optional[int]         =    model_output.device
          _UpperCAmelCase					:  Tuple         =    randn_tensor(model_output.shape						,	dtype=model_output.dtype						,	device=lowerCAmelCase__						,	generator=lowerCAmelCase__	)

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               _UpperCAmelCase					:  int         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  List[Any]         =    sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               _UpperCAmelCase					:  Tuple         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  Dict         =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**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`"	)

          if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               _UpperCAmelCase					:  Any         =    (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               _UpperCAmelCase					:  Any         =    sigma_interpol - sigma_hat

               # store for 2nd order step
               _UpperCAmelCase					:  List[str]         =    sample
               _UpperCAmelCase					:  Optional[Any]         =    dt
               _UpperCAmelCase					:  Optional[int]         =    sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               _UpperCAmelCase					:  Optional[int]         =    (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               _UpperCAmelCase					:  List[Any]         =    sigma_down - sigma_hat

               _UpperCAmelCase					:  int         =    self.sample
               _UpperCAmelCase					:  Union[str, Any]         =    None

               _UpperCAmelCase					:  str         =    sample + derivative * dt
               _UpperCAmelCase					:  List[str]         =    prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _UpperCAmelCase					:  List[Any]         =    self.sigmas.to(device=original_samples.device						,	dtype=original_samples.dtype	)
          if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__	):
               # mps does not support float64
               _UpperCAmelCase					:  Any         =    self.timesteps.to(original_samples.device						,	dtype=torch.floataa	)
               _UpperCAmelCase					:  Dict         =    timesteps.to(original_samples.device						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  Dict         =    self.timesteps.to(original_samples.device	)
               _UpperCAmelCase					:  Tuple         =    timesteps.to(original_samples.device	)

          _UpperCAmelCase					:  Any         =    [self.index_for_timestep(lowerCAmelCase__						,	lowerCAmelCase__	) for t in timesteps]

          _UpperCAmelCase					:  List[Any]         =    sigmas[step_indices].flatten()
          while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase					:  Dict         =    sigma.unsqueeze(-1	)

          _UpperCAmelCase					:  str         =    original_samples + noise * sigma
          return noisy_samples



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


