

'''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


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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      torch.FloatTensor
     UpperCAmelCase_		:      Optional[torch.FloatTensor]    						=    None




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_=0.999    ,			UpperCAmelCase_="cosine"    ,			):
 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

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




class 			A_					(							_snake_case			,  _snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    [e.name for e in KarrasDiffusionSchedulers]
     UpperCAmelCase_		:      Tuple    						=    1


     @register_to_config
     def __init__(						self					:	Union[str, Any]	,     lowercase_					:	int = 1_000	,     lowercase_					:	float = 0.0001	,     lowercase_					:	float = 0.02	,     lowercase_					:	str = "linear"	,     lowercase_					:	Optional[Union[np.ndarray, List[float]]] = None	,     lowercase_					:	str = "epsilon"	,     lowercase_					:	str = "linear"	,     lowercase_					:	Optional[bool] = False	,     lowercase_					:	str = "linspace"	,     lowercase_					:	int = 0	,     )		->      Tuple:
      if trained_betas is not None:
       UpperCAmelCase			:      List[str]		   =					torch.tensor(lowercase_	,     dtype=torch.floataa			)
      elif beta_schedule == "linear":
       UpperCAmelCase			:      Tuple		   =					torch.linspace(lowercase_	,     lowercase_	,     lowercase_	,     dtype=torch.floataa			)
      elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       UpperCAmelCase			:      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
       UpperCAmelCase			:      Tuple		   =					betas_for_alpha_bar(lowercase_			)
      else:
       raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""			)

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

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

      # setable values
      UpperCAmelCase			:      str		   =					None
      UpperCAmelCase			:      List[Any]		   =					np.linspace(0	,     num_train_timesteps - 1	,     lowercase_	,     dtype=lowercase_			)[::-1].copy()
      UpperCAmelCase			:      Optional[Any]		   =					torch.from_numpy(lowercase_			)
      UpperCAmelCase			:      List[str]		   =					False
      UpperCAmelCase			:      Dict		   =					use_karras_sigmas


     @property
     def   UpperCAmelCase_			(						self					:	Tuple			)		->      Any:
      # standard deviation of the initial noise distribution
      if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

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


     def   UpperCAmelCase_			(						self					:	Tuple	,     lowercase_					:	torch.FloatTensor	,     lowercase_					:	Union[float, torch.FloatTensor]			)		->      torch.FloatTensor:
      if isinstance(lowercase_	,     torch.Tensor			):
       UpperCAmelCase			:      int		   =					timestep.to(self.timesteps.device			)
      UpperCAmelCase			:      List[str]		   =					(self.timesteps == timestep).nonzero().item()
      UpperCAmelCase			:      Dict		   =					self.sigmas[step_index]

      UpperCAmelCase			:      Tuple		   =					sample / ((sigma**2 + 1) ** 0.5)

      UpperCAmelCase			:      List[Any]		   =					True
      return sample


     def   UpperCAmelCase_			(						self					:	Optional[int]	,     lowercase_					:	int	,     lowercase_					:	Union[str, torch.device] = None			)		->      Union[str, Any]:
      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			:      str		   =					np.linspace(0	,     self.config.num_train_timesteps - 1	,     lowercase_	,     dtype=lowercase_			)[
           ::-1
       ].copy()
      elif self.config.timestep_spacing == "leading":
       UpperCAmelCase			:      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			:      Optional[int]		   =					(np.arange(0	,     lowercase_			) * step_ratio).round()[::-1].copy().astype(lowercase_			)
       timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
       UpperCAmelCase			:      int		   =					self.config.num_train_timesteps / self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       UpperCAmelCase			:      Any		   =					(np.arange(self.config.num_train_timesteps	,     0	,     -step_ratio			)).round().copy().astype(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'."""			)

      UpperCAmelCase			:      Optional[int]		   =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
      UpperCAmelCase			:      int		   =					np.log(lowercase_			)

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

      UpperCAmelCase			:      Tuple		   =					np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
      UpperCAmelCase			:      Optional[Any]		   =					torch.from_numpy(lowercase_			).to(device=lowercase_			)
      if str(lowercase_			).startswith('mps'			):
       # mps does not support float64
       UpperCAmelCase			:      Union[str, Any]		   =					torch.from_numpy(lowercase_			).to(lowercase_	,     dtype=torch.floataa			)
      else:
       UpperCAmelCase			:      Dict		   =					torch.from_numpy(lowercase_			).to(device=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Optional[int]	,     lowercase_					:	Tuple	,     lowercase_					:	int			)		->      Optional[int]:
      # get log sigma
      UpperCAmelCase			:      Any		   =					np.log(lowercase_			)

      # get distribution
      UpperCAmelCase			:      Dict		   =					log_sigma - log_sigmas[:, np.newaxis]

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

      UpperCAmelCase			:      Any		   =					log_sigmas[low_idx]
      UpperCAmelCase			:      int		   =					log_sigmas[high_idx]

      # interpolate sigmas
      UpperCAmelCase			:      Optional[Any]		   =					(low - log_sigma) / (low - high)
      UpperCAmelCase			:      str		   =					np.clip(lowercase_	,     0	,     1			)

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


     def   UpperCAmelCase_			(						self					:	Tuple	,     lowercase_					:	torch.FloatTensor	,     lowercase_					:	Union[str, Any]			)		->      torch.FloatTensor:
      UpperCAmelCase			:      float		   =					in_sigmas[-1].item()
      UpperCAmelCase			:      float		   =					in_sigmas[0].item()

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


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

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

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

      UpperCAmelCase			:      Dict		   =					min(s_churn / (len(self.sigmas			) - 1)	,     2**0.5 - 1			) if s_tmin <= sigma <= s_tmax else 0.0

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

      UpperCAmelCase			:      Any		   =					noise * s_noise
      UpperCAmelCase			:      List[Any]		   =					sigma * (gamma + 1)

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

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

      UpperCAmelCase			:      str		   =					sample + derivative * dt

      if not return_dict:
       return (prev_sample,)

      return EulerDiscreteSchedulerOutput(prev_sample=lowercase_	,     pred_original_sample=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Dict	,     lowercase_					:	torch.FloatTensor	,     lowercase_					:	torch.FloatTensor	,     lowercase_					:	torch.FloatTensor	,     )		->      torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      UpperCAmelCase			:      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
       UpperCAmelCase			:      Optional[int]		   =					self.timesteps.to(original_samples.device	,     dtype=torch.floataa			)
       UpperCAmelCase			:      Dict		   =					timesteps.to(original_samples.device	,     dtype=torch.floataa			)
      else:
       UpperCAmelCase			:      Union[str, Any]		   =					self.timesteps.to(original_samples.device			)
       UpperCAmelCase			:      Tuple		   =					timesteps.to(original_samples.device			)

      UpperCAmelCase			:      Union[str, Any]		   =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

      UpperCAmelCase			:      Union[str, Any]		   =					original_samples + noise * sigma
      return noisy_samples




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







'''simple docstring'''

from sklearn.metrics import roc_auc_score

import datasets


lowercase__            =     "\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"

lowercase__            =     "\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"

lowercase__            =     "\\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 			A_					(							datasets.Metric				):






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      Dict:
      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   UpperCAmelCase_			(						self					:	Dict	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Dict	,     lowercase_					:	Dict="macro"	,     lowercase_					:	Optional[Any]=None	,     lowercase_					:	List[str]=None	,     lowercase_					:	int="raise"	,     lowercase_					:	List[str]=None	,     )		->      str:
      return {
          "roc_auc": roc_auc_score(
              lowercase_	,     lowercase_	,     average=lowercase_	,     sample_weight=lowercase_	,     max_fpr=lowercase_	,     multi_class=lowercase_	,     labels=lowercase_	,     )
      }




