






import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput





class __lowerCamelCase					( nn.Module):





	"""simple docstring"""






	def __init__(				self			,       UpperCAmelCase			,       UpperCAmelCase=1.0       ):
							"""simple docstring"""







							super().__init__()

							_UpperCAmelCase							   =     cut_size
							_UpperCAmelCase							   =     cut_power

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     pixel_values.shape[2:4]
							_UpperCAmelCase							   =     min(UpperCAmelCase			,       UpperCAmelCase       )
							_UpperCAmelCase							   =     min(UpperCAmelCase			,       UpperCAmelCase			,       self.cut_size       )
							_UpperCAmelCase							   =     []
							for _ in range(UpperCAmelCase       ):
													_UpperCAmelCase							   =     int(torch.rand([]       ) ** self.cut_power * (max_size - min_size) + min_size       )
													_UpperCAmelCase							   =     torch.randint(0			,       sideX - size + 1			,       ()       )
													_UpperCAmelCase							   =     torch.randint(0			,       sideY - size + 1			,       ()       )
													_UpperCAmelCase							   =     pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
													cutouts.append(F.adaptive_avg_poolad(UpperCAmelCase			,       self.cut_size       )       )
							return torch.cat(UpperCAmelCase       )



def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    List[str]:





						"""simple docstring"""



						_UpperCAmelCase							   =     F.normalize(__lowerCAmelCase					,						dim=-1      )
						_UpperCAmelCase							   =     F.normalize(__lowerCAmelCase					,						dim=-1      )
						return (x - y).norm(dim=-1      ).div(2      ).arcsin().pow(2      ).mul(2      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Dict:





						"""simple docstring"""



						for param in model.parameters():
												_UpperCAmelCase							   =     value





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	def __init__(				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       ):
							"""simple docstring"""







							super().__init__()
							self.register_modules(
							    vae=UpperCAmelCase			,       text_encoder=UpperCAmelCase			,       clip_model=UpperCAmelCase			,       tokenizer=UpperCAmelCase			,       unet=UpperCAmelCase			,       scheduler=UpperCAmelCase			,       feature_extractor=UpperCAmelCase			,       )

							_UpperCAmelCase							   =     transforms.Normalize(mean=feature_extractor.image_mean			,       std=feature_extractor.image_std       )
							_UpperCAmelCase							   =     (
							    feature_extractor.size
							    if isinstance(feature_extractor.size			,       UpperCAmelCase       )
							    else feature_extractor.size['shortest_edge']
							)
							_UpperCAmelCase							   =     MakeCutouts(self.cut_out_size       )

							set_requires_grad(self.text_encoder			,       UpperCAmelCase       )
							set_requires_grad(self.clip_model			,       UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase = "auto"       ):
							"""simple docstring"""







							if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													_UpperCAmelCase							   =     self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							self.enable_attention_slicing(UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							set_requires_grad(self.vae			,       UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							set_requires_grad(self.vae			,       UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							set_requires_grad(self.unet			,       UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							set_requires_grad(self.unet			,       UpperCAmelCase       )

	@torch.enable_grad()
	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase=True			,       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     latents.detach().requires_grad_()

							_UpperCAmelCase							   =     self.scheduler.scale_model_input(UpperCAmelCase			,       UpperCAmelCase       )

							# predict the noise residual
							_UpperCAmelCase							   =     self.unet(UpperCAmelCase			,       UpperCAmelCase			,       encoder_hidden_states=UpperCAmelCase       ).sample

							if isinstance(self.scheduler			,       (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)       ):
													_UpperCAmelCase							   =     self.scheduler.alphas_cumprod[timestep]
													_UpperCAmelCase							   =     1 - alpha_prod_t
													# compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													_UpperCAmelCase							   =     (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

													_UpperCAmelCase							   =     torch.sqrt(UpperCAmelCase       )
													_UpperCAmelCase							   =     pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     self.scheduler.sigmas[index]
													_UpperCAmelCase							   =     latents - sigma * noise_pred
							else:
													raise ValueError(F"""scheduler type {type(self.scheduler       )} not supported"""       )

							_UpperCAmelCase							   =     1 / self.vae.config.scaling_factor * sample
							_UpperCAmelCase							   =     self.vae.decode(UpperCAmelCase       ).sample
							_UpperCAmelCase							   =     (image / 2 + 0.5).clamp(0			,       1       )

							if use_cutouts:
													_UpperCAmelCase							   =     self.make_cutouts(UpperCAmelCase			,       UpperCAmelCase       )
							else:
													_UpperCAmelCase							   =     transforms.Resize(self.cut_out_size       )(UpperCAmelCase       )
							_UpperCAmelCase							   =     self.normalize(UpperCAmelCase       ).to(latents.dtype       )

							_UpperCAmelCase							   =     self.clip_model.get_image_features(UpperCAmelCase       )
							_UpperCAmelCase							   =     image_embeddings_clip / image_embeddings_clip.norm(p=2			,       dim=-1			,       keepdim=UpperCAmelCase       )

							if use_cutouts:
													_UpperCAmelCase							   =     spherical_dist_loss(UpperCAmelCase			,       UpperCAmelCase       )
													_UpperCAmelCase							   =     dists.view([num_cutouts, sample.shape[0], -1]       )
													_UpperCAmelCase							   =     dists.sum(2       ).mean(0       ).sum() * clip_guidance_scale
							else:
													_UpperCAmelCase							   =     spherical_dist_loss(UpperCAmelCase			,       UpperCAmelCase       ).mean() * clip_guidance_scale

							_UpperCAmelCase							   =     -torch.autograd.grad(UpperCAmelCase			,       UpperCAmelCase       )[0]

							if isinstance(self.scheduler			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     latents.detach() + grads * (sigma**2)
													_UpperCAmelCase							   =     noise_pred_original
							else:
													_UpperCAmelCase							   =     noise_pred_original - torch.sqrt(UpperCAmelCase       ) * grads
							return noise_pred, latents

	@torch.no_grad()
	def __call__(				self			,       UpperCAmelCase			,       UpperCAmelCase = 512			,       UpperCAmelCase = 512			,       UpperCAmelCase = 50			,       UpperCAmelCase = 7.5			,       UpperCAmelCase = 1			,       UpperCAmelCase = 0.0			,       UpperCAmelCase = 100			,       UpperCAmelCase = None			,       UpperCAmelCase = 4			,       UpperCAmelCase = True			,       UpperCAmelCase = None			,       UpperCAmelCase = None			,       UpperCAmelCase = "pil"			,       UpperCAmelCase = True			,       ):
							"""simple docstring"""







							if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     1
							elif isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     len(UpperCAmelCase       )
							else:
													raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase       )}"""       )

							if height % 8 != 0 or width % 8 != 0:
													raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""       )

							# get prompt text embeddings
							_UpperCAmelCase							   =     self.tokenizer(
							    UpperCAmelCase			,       padding='max_length'			,       max_length=self.tokenizer.model_max_length			,       truncation=UpperCAmelCase			,       return_tensors='pt'			,       )
							_UpperCAmelCase							   =     self.text_encoder(text_input.input_ids.to(self.device       )       )[0]
							# duplicate text embeddings for each generation per prompt
							_UpperCAmelCase							   =     text_embeddings.repeat_interleave(UpperCAmelCase			,       dim=0       )

							if clip_guidance_scale > 0:
													if clip_prompt is not None:
																			_UpperCAmelCase							   =     self.tokenizer(
																			    UpperCAmelCase			,       padding='max_length'			,       max_length=self.tokenizer.model_max_length			,       truncation=UpperCAmelCase			,       return_tensors='pt'			,       ).input_ids.to(self.device       )
													else:
																			_UpperCAmelCase							   =     text_input.input_ids.to(self.device       )
													_UpperCAmelCase							   =     self.clip_model.get_text_features(UpperCAmelCase       )
													_UpperCAmelCase							   =     text_embeddings_clip / text_embeddings_clip.norm(p=2			,       dim=-1			,       keepdim=UpperCAmelCase       )
													# duplicate text embeddings clip for each generation per prompt
													_UpperCAmelCase							   =     text_embeddings_clip.repeat_interleave(UpperCAmelCase			,       dim=0       )

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							_UpperCAmelCase							   =     guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
													_UpperCAmelCase							   =     text_input.input_ids.shape[-1]
													_UpperCAmelCase							   =     self.tokenizer(['']			,       padding='max_length'			,       max_length=UpperCAmelCase			,       return_tensors='pt'       )
													_UpperCAmelCase							   =     self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
													# duplicate unconditional embeddings for each generation per prompt
													_UpperCAmelCase							   =     uncond_embeddings.repeat_interleave(UpperCAmelCase			,       dim=0       )

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													_UpperCAmelCase							   =     torch.cat([uncond_embeddings, text_embeddings]       )

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							_UpperCAmelCase							   =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							_UpperCAmelCase							   =     text_embeddings.dtype
							if latents is None:
													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			_UpperCAmelCase							   =     torch.randn(UpperCAmelCase			,       generator=UpperCAmelCase			,       device='cpu'			,       dtype=UpperCAmelCase       ).to(
																			    self.device       )
													else:
																			_UpperCAmelCase							   =     torch.randn(UpperCAmelCase			,       generator=UpperCAmelCase			,       device=self.device			,       dtype=UpperCAmelCase       )
							else:
													if latents.shape != latents_shape:
																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
													_UpperCAmelCase							   =     latents.to(self.device       )

							# set timesteps
							_UpperCAmelCase							   =     'offset' in set(inspect.signature(self.scheduler.set_timesteps       ).parameters.keys()       )
							_UpperCAmelCase							   =     {}
							if accepts_offset:
													_UpperCAmelCase							   =     1

							self.scheduler.set_timesteps(UpperCAmelCase			,       **UpperCAmelCase       )

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							_UpperCAmelCase							   =     self.scheduler.timesteps.to(self.device       )

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCAmelCase							   =     latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							_UpperCAmelCase							   =     'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
							_UpperCAmelCase							   =     {}
							if accepts_eta:
													_UpperCAmelCase							   =     eta

							# check if the scheduler accepts generator
							_UpperCAmelCase							   =     'generator' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
							if accepts_generator:
													_UpperCAmelCase							   =     generator

							for i, t in enumerate(self.progress_bar(UpperCAmelCase       )       ):
													# expand the latents if we are doing classifier free guidance
													_UpperCAmelCase							   =     torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
													_UpperCAmelCase							   =     self.scheduler.scale_model_input(UpperCAmelCase			,       UpperCAmelCase       )

													# predict the noise residual
													_UpperCAmelCase							   =     self.unet(UpperCAmelCase			,       UpperCAmelCase			,       encoder_hidden_states=UpperCAmelCase       ).sample

													# perform classifier free guidance
													if do_classifier_free_guidance:
																			_UpperCAmelCase   ,					_UpperCAmelCase							   =     noise_pred.chunk(2       )
																			_UpperCAmelCase							   =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# perform clip guidance
													if clip_guidance_scale > 0:
																			_UpperCAmelCase							   =     (
																			    text_embeddings.chunk(2       )[1] if do_classifier_free_guidance else text_embeddings
																			)
																			_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.cond_fn(
																			    UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       )

													# compute the previous noisy sample x_t -> x_t-1
													_UpperCAmelCase							   =     self.scheduler.step(UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       **UpperCAmelCase       ).prev_sample

							# scale and decode the image latents with vae
							_UpperCAmelCase							   =     1 / self.vae.config.scaling_factor * latents
							_UpperCAmelCase							   =     self.vae.decode(UpperCAmelCase       ).sample

							_UpperCAmelCase							   =     (image / 2 + 0.5).clamp(0			,       1       )
							_UpperCAmelCase							   =     image.cpu().permute(0			,       2			,       3			,       1       ).numpy()

							if output_type == "pil":
													_UpperCAmelCase							   =     self.numpy_to_pil(UpperCAmelCase       )

							if not return_dict:
													return (image, None)

							return StableDiffusionPipelineOutput(images=UpperCAmelCase			,       nsfw_content_detected=UpperCAmelCase       )








from sklearn.metrics import roc_auc_score

import datasets


_a       =    '''
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.
'''

_a       =    '''
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]
'''

_a       =    '''\
@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):





	"""simple docstring"""






	def 			UpperCamelCase       (				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 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase="macro"			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase="raise"			,       UpperCAmelCase=None			,       ):
							"""simple docstring"""







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