

'''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  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Union[str, Any]  ,  SCREAMING_SNAKE_CASE_					:     List[str]=0.999  ,  SCREAMING_SNAKE_CASE_					:     List[Any]="cosine"  ,  )							->       Dict:
      """simple docstring"""

      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE_					:     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(SCREAMING_SNAKE_CASE_   ):
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE_				:      List[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      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase,_UpperCAmelCase				):
    _A         =							[e.name for e in KarrasDiffusionSchedulers]
    _A         =							2




    @register_to_config
    def __init__( self    ,					lowercase__ = 1000    ,					lowercase__ = 0.00085    ,					lowercase__ = 0.012    ,					lowercase__ = "linear"    ,					lowercase__ = None    ,					lowercase__ = "epsilon"    ,					lowercase__ = "linspace"    ,					lowercase__ = 0    ,					):






          """simple docstring"""






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

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

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




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__=None      ):






          """simple docstring"""






          if schedule_timesteps is None:
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.timesteps

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(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:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	1 if len(lowercase__      ) > 1 else 0
          else:
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	timestep.cpu().item() if torch.is_tensor(lowercase__      ) else timestep
                SCREAMING_SNAKE_CASE_				:      Dict					=	self._index_counter[timestep_int]

          return indices[pos].item()




    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

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




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.index_for_timestep(lowercase__      )

          if self.state_in_first_order:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	self.sigmas[step_index]
          else:
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.sigmas_interpol[step_index - 1]

          SCREAMING_SNAKE_CASE_				:      str					=	sample / ((sigma**2 + 1) ** 0.5)
          return sample




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None    ,					lowercase__ = None    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	num_inference_steps

          SCREAMING_SNAKE_CASE_				:      str					=	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":
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	np.linspace(0    ,					num_train_timesteps - 1    ,					lowercase__    ,					dtype=lowercase__      )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
                SCREAMING_SNAKE_CASE_				:      Union[str, 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
                SCREAMING_SNAKE_CASE_				:      List[str]					=	(np.arange(0    ,					lowercase__      ) * step_ratio).round()[::-1].copy().astype(lowercase__      )
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                SCREAMING_SNAKE_CASE_				:      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
                SCREAMING_SNAKE_CASE_				:      int					=	(np.arange(lowercase__    ,					0    ,					-step_ratio      )).round().copy().astype(lowercase__      )
                timesteps -= 1
          else:
                raise ValueError(
                    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

          SCREAMING_SNAKE_CASE_				:      Dict					=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	torch.from_numpy(np.log(lowercase__      )      ).to(lowercase__      )

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

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

          # compute interpolated sigmas
          SCREAMING_SNAKE_CASE_				:      int					=	sigmas.log().lerp(sigmas_down.log()    ,					0.5      ).exp()
          SCREAMING_SNAKE_CASE_				:      Any					=	0.0

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

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

          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.sigma_to_t(lowercase__      ).to(lowercase__    ,					dtype=timesteps.dtype      )
          SCREAMING_SNAKE_CASE_				:      Any					=	torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])    ,					dim=-1      ).flatten()

          SCREAMING_SNAKE_CASE_				:      Dict					=	torch.cat([timesteps[:1], interleaved_timesteps]      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	None

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




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	sigma.log()

          # get distribution
          SCREAMING_SNAKE_CASE_				:      int					=	log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
          SCREAMING_SNAKE_CASE_				:      int					=	low_idx + 1

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.log_sigmas[low_idx]
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.log_sigmas[high_idx]

          # interpolate sigmas
          SCREAMING_SNAKE_CASE_				:      Any					=	(low - log_sigma) / (low - high)
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	w.clamp(0    ,					1      )

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




    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return self.sample is None




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__ = None    ,					lowercase__ = True    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	self.index_for_timestep(lowercase__      )

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

          if self.state_in_first_order:
                SCREAMING_SNAKE_CASE_				:      Any					=	self.sigmas[step_index]
                SCREAMING_SNAKE_CASE_				:      Dict					=	self.sigmas_interpol[step_index]
                SCREAMING_SNAKE_CASE_				:      List[str]					=	self.sigmas_up[step_index]
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.sigmas_down[step_index - 1]
          else:
                # 2nd order / KPDM2's method
                SCREAMING_SNAKE_CASE_				:      List[str]					=	self.sigmas[step_index - 1]
                SCREAMING_SNAKE_CASE_				:      Tuple					=	self.sigmas_interpol[step_index - 1]
                SCREAMING_SNAKE_CASE_				:      Any					=	self.sigmas_up[step_index - 1]
                SCREAMING_SNAKE_CASE_				:      Dict					=	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
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	0
          SCREAMING_SNAKE_CASE_				:      Tuple					=	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          SCREAMING_SNAKE_CASE_				:      str					=	model_output.device
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	randn_tensor(model_output.shape    ,					dtype=model_output.dtype    ,					device=lowercase__    ,					generator=lowercase__      )

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                SCREAMING_SNAKE_CASE_				:      Dict					=	sigma_hat if self.state_in_first_order else sigma_interpol
                SCREAMING_SNAKE_CASE_				:      Any					=	sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	sigma_hat if self.state_in_first_order else sigma_interpol
                SCREAMING_SNAKE_CASE_				:      int					=	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
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(sample - pred_original_sample) / sigma_hat
                # 3. delta timestep
                SCREAMING_SNAKE_CASE_				:      int					=	sigma_interpol - sigma_hat

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

                SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.sample
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	None

                SCREAMING_SNAKE_CASE_				:      Any					=	sample + derivative * dt
                SCREAMING_SNAKE_CASE_				:      Dict					=	prev_sample + noise * sigma_up

          if not return_dict:
                return (prev_sample,)

          return SchedulerOutput(prev_sample=lowercase__      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.sigmas.to(device=original_samples.device    ,					dtype=original_samples.dtype      )
          if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__      ):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_				:      str					=	self.timesteps.to(original_samples.device    ,					dtype=torch.floataa      )
                SCREAMING_SNAKE_CASE_				:      Dict					=	timesteps.to(original_samples.device    ,					dtype=torch.floataa      )
          else:
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.timesteps.to(original_samples.device      )
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	timesteps.to(original_samples.device      )

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[self.index_for_timestep(lowercase__    ,					lowercase__      ) for t in timesteps]

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

          SCREAMING_SNAKE_CASE_				:      Any					=	original_samples + noise * sigma
          return noisy_samples







    def __len__( self      ):






          """simple docstring"""






          return self.config.num_train_timesteps




'''simple docstring'''






from sklearn.metrics import roc_auc_score

import datasets


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

snake_case_					  =	'\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:\n    - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `\'weighted\'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- 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`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `\'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.\n    - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric("roc_auc")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results[\'roc_auc\']])\n        [0.83, 0.38, 0.94]\n'

snake_case_					  =	'\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={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.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,_KWARGS_DESCRIPTION				)
class      SCREAMING_SNAKE_CASE__      (    datasets.Metric				):




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__="macro"    ,					lowercase__=None    ,					lowercase__=None    ,					lowercase__="raise"    ,					lowercase__=None    ,					):






          """simple docstring"""






          return {
              "roc_auc": roc_auc_score(
                  lowercase__    ,					lowercase__    ,					average=lowercase__    ,					sample_weight=lowercase__    ,					max_fpr=lowercase__    ,					multi_class=lowercase__    ,					labels=lowercase__    ,					)
          }

