code: 
  import inspect
  import unittest
  
  from transformers import is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  				import torch
  
  				from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  				from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
  				from PIL import Image
  
  				from transformers import GLPNImageProcessor
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	List[str]			     =  self.config_class(**self.inputs_dict	)
  				self.parent.assertTrue(hasattr(_a		,     "hidden_sizes"	)	)
  				self.parent.assertTrue(hasattr(_a		,     "num_attention_heads"	)	)
  				self.parent.assertTrue(hasattr(_a		,     "num_encoder_blocks"	)	)
  
  
  
  
  
  
  
  class    _snake_case      :
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a=13		,     _a=64		,     _a=3		,     _a=4		,     _a=[2, 2, 2, 2]		,     _a=[8, 4, 2, 1]		,     _a=[16, 32, 64, 128]		,     _a=[1, 4, 8, 16]		,     _a=[1, 2, 4, 8]		,     _a=True		,     _a=True		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=0.02		,     _a=16		,     _a=3		,     _a=None		,     ):
  				__magic_name__	:	int			     =  parent
  				__magic_name__	:	Union[str, Any]			     =  batch_size
  				__magic_name__	:	Any			     =  image_size
  				__magic_name__	:	Tuple			     =  num_channels
  				__magic_name__	:	Union[str, Any]			     =  num_encoder_blocks
  				__magic_name__	:	Tuple			     =  sr_ratios
  				__magic_name__	:	Tuple			     =  depths
  				__magic_name__	:	int			     =  hidden_sizes
  				__magic_name__	:	Optional[int]			     =  downsampling_rates
  				__magic_name__	:	Any			     =  num_attention_heads
  				__magic_name__	:	Tuple			     =  is_training
  				__magic_name__	:	Optional[Any]			     =  use_labels
  				__magic_name__	:	int			     =  hidden_act
  				__magic_name__	:	Any			     =  hidden_dropout_prob
  				__magic_name__	:	List[Any]			     =  attention_probs_dropout_prob
  				__magic_name__	:	Dict			     =  initializer_range
  				__magic_name__	:	str			     =  decoder_hidden_size
  				__magic_name__	:	List[str]			     =  num_labels
  				__magic_name__	:	List[Any]			     =  scope
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Tuple			     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
  				__magic_name__	:	int			     =  None
  				if self.use_labels:
  					__magic_name__	:	Union[str, Any]			     =  ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels	)
  
  				__magic_name__	:	int			     =  self.get_config()
  				return config, pixel_values, labels
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return GLPNConfig(
  				    image_size=self.image_size		,     num_channels=self.num_channels		,     num_encoder_blocks=self.num_encoder_blocks		,     depths=self.depths		,     hidden_sizes=self.hidden_sizes		,     num_attention_heads=self.num_attention_heads		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     initializer_range=self.initializer_range		,     decoder_hidden_size=self.decoder_hidden_size		,     )
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a	):
  				__magic_name__	:	Dict			     =  GLPNModel(config=_a	)
  				model.to(_a	)
  				model.eval()
  				__magic_name__	:	Union[str, Any]			     =  model(_a	)
  				__magic_name__	:	List[Any]			     =  self.image_size // (self.downsampling_rates[-1] * 2)
  				self.parent.assertEqual(
  				    result.last_hidden_state.shape		,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a	):
  				__magic_name__	:	List[Any]			     =  self.num_labels
  				__magic_name__	:	Tuple			     =  GLPNForDepthEstimation(_a	)
  				model.to(_a	)
  				model.eval()
  				__magic_name__	:	Any			     =  model(_a	)
  				self.parent.assertEqual(result.predicted_depth.shape		,     (self.batch_size, self.image_size, self.image_size)	)
  				__magic_name__	:	int			     =  model(_a		,     labels=_a	)
  				self.parent.assertEqual(result.predicted_depth.shape		,     (self.batch_size, self.image_size, self.image_size)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	List[str]			     =  self.prepare_config_and_inputs()
  				__magic_name__	:	str			     =  config_and_inputs
  				__magic_name__	:	Tuple			     =  {"pixel_values": pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class    _snake_case      (  snake_case       ,		snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
  			UpperCamelCase__					   =					(
  			    {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
  			)
  
  			UpperCamelCase__					   =					False
  			UpperCamelCase__					   =					False
  			UpperCamelCase__					   =					False
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	int			     =  GLPNModelTester(self	)
  				__magic_name__	:	Union[str, Any]			     =  GLPNConfigTester(self		,     config_class=_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Any			     =  self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_depth_estimation(*_a	)
  
  
  
  
  
  
  
  			@unittest.skip("GLPN does not use inputs_embeds"	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				pass
  
  
  
  
  
  
  
  			@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				pass
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  					__magic_name__	:	str			     =  model_class(_a	)
  					__magic_name__	:	Optional[Any]			     =  inspect.signature(model.forward	)
  					# signature.parameters is an OrderedDict => so arg_names order is deterministic
  					__magic_name__	:	Optional[int]			     =  [*signature.parameters.keys()]
  
  					__magic_name__	:	int			     =  ["pixel_values"]
  					self.assertListEqual(arg_names[:1]		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[int]			     =  self.model_tester.prepare_config_and_inputs_for_common()
  				__magic_name__	:	Optional[int]			     =  True
  
  				for model_class in self.all_model_classes:
  					__magic_name__	:	List[Any]			     =  True
  					__magic_name__	:	Any			     =  False
  					__magic_name__	:	List[Any]			     =  True
  					__magic_name__	:	Optional[Any]			     =  model_class(_a	)
  					model.to(_a	)
  					model.eval()
  					with torch.no_grad():
  						__magic_name__	:	Optional[Any]			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  					__magic_name__	:	List[str]			     =  outputs.attentions
  
  					__magic_name__	:	Tuple			     =  sum(self.model_tester.depths	)
  					self.assertEqual(len(_a	)		,     _a	)
  
  					# check that output_attentions also work using config
  					del inputs_dict["output_attentions"]
  					__magic_name__	:	int			     =  True
  					__magic_name__	:	Optional[Any]			     =  model_class(_a	)
  					model.to(_a	)
  					model.eval()
  					with torch.no_grad():
  						__magic_name__	:	Tuple			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  					__magic_name__	:	Optional[Any]			     =  outputs.attentions
  
  					self.assertEqual(len(_a	)		,     _a	)
  
  					# verify the first attentions (first block, first layer)
  					__magic_name__	:	int			     =  (self.model_tester.image_size // 4) ** 2
  					__magic_name__	:	Tuple			     =  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
  					self.assertListEqual(
  					    list(attentions[0].shape[-3:]	)		,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,     )
  
  					# verify the last attentions (last block, last layer)
  					__magic_name__	:	Optional[int]			     =  (self.model_tester.image_size // 32) ** 2
  					__magic_name__	:	List[str]			     =  (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
  					self.assertListEqual(
  					    list(attentions[-1].shape[-3:]	)		,     [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]		,     )
  					__magic_name__	:	int			     =  len(_a	)
  
  					# Check attention is always last and order is fine
  					__magic_name__	:	int			     =  True
  					__magic_name__	:	Any			     =  True
  					__magic_name__	:	List[str]			     =  model_class(_a	)
  					model.to(_a	)
  					model.eval()
  					with torch.no_grad():
  						__magic_name__	:	Optional[int]			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  
  					self.assertEqual(out_len + 1		,     len(_a	)	)
  
  					__magic_name__	:	str			     =  outputs.attentions
  
  					self.assertEqual(len(_a	)		,     _a	)
  					# verify the first attentions (first block, first layer)
  					__magic_name__	:	str			     =  (self.model_tester.image_size // 4) ** 2
  					__magic_name__	:	Optional[Any]			     =  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
  					self.assertListEqual(
  					    list(self_attentions[0].shape[-3:]	)		,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,     )
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				def check_hidden_states_output(_a		,     _a		,     _a	):
  					__magic_name__	:	Optional[int]			     =  model_class(_a	)
  					model.to(_a	)
  					model.eval()
  
  					with torch.no_grad():
  						__magic_name__	:	List[str]			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  
  					__magic_name__	:	List[str]			     =  outputs.hidden_states
  
  					__magic_name__	:	Tuple			     =  self.model_tester.num_encoder_blocks
  					self.assertEqual(len(_a	)		,     _a	)
  
  					# verify the first hidden states (first block)
  					self.assertListEqual(
  					    list(hidden_states[0].shape[-3:]	)		,     [
  					        self.model_tester.hidden_sizes[0],
  					        self.model_tester.image_size // 4,
  					        self.model_tester.image_size // 4,
  					    ]		,     )
  
  				__magic_name__	:	Tuple			     =  self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  					__magic_name__	:	Tuple			     =  True
  					check_hidden_states_output(_a		,     _a		,     _a	)
  
  					# check that output_hidden_states also work using config
  					del inputs_dict["output_hidden_states"]
  					__magic_name__	:	Dict			     =  True
  
  					check_hidden_states_output(_a		,     _a		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				if not self.model_tester.is_training:
  					return
  
  				__magic_name__	:	str			     =  self.model_tester.prepare_config_and_inputs_for_common()
  				__magic_name__	:	Union[str, Any]			     =  True
  
  				for model_class in self.all_model_classes:
  					if model_class in get_values(_a	):
  						continue
  					# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
  					# this can then be incorporated into _prepare_for_class in test_modeling_common.py
  					if model_class.__name__ == "GLPNForDepthEstimation":
  						__magic_name__	:	Any			     =  inputs_dict["pixel_values"].shape
  						__magic_name__	:	Union[str, Any]			     =  torch.zeros(
  						    [self.model_tester.batch_size, height, width]		,     device=_a	).long()
  					__magic_name__	:	Union[str, Any]			     =  model_class(_a	)
  					model.to(_a	)
  					model.train()
  					__magic_name__	:	List[Any]			     =  self._prepare_for_class(_a		,     _a		,     return_labels=_a	)
  					__magic_name__	:	str			     =  model(**_a	).loss
  					loss.backward()
  
  
  
  
  
  
  
  			@slow
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  					__magic_name__	:	Union[str, Any]			     =  GLPNModel.from_pretrained(_a	)
  					self.assertIsNotNone(_a	)
  
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	Union[str, Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Tuple			     =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
  	return image
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class    _snake_case      (  unittest.TestCase       ):
  
  
  
  
  
  
  
  			@slow
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[int]			     =  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
  				__magic_name__	:	Optional[int]			     =  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_a	)
  
  				__magic_name__	:	Tuple			     =  prepare_img()
  				__magic_name__	:	Optional[Any]			     =  image_processor(images=_a		,     return_tensors="pt"	).to(_a	)
  
  				# forward pass
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	)
  
  				# verify the predicted depth
  				__magic_name__	:	Optional[Any]			     =  torch.Size([1, 480, 640]	)
  				self.assertEqual(outputs.predicted_depth.shape		,     _a	)
  
  				__magic_name__	:	List[Any]			     =  torch.tensor(
  				    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]	).to(_a	)
  
  				self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]		,     _a		,     atol=1e-4	)	)
  
style_context: 
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  snake_case				:	List[Any]							   =						"\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				:	Any							   =						"\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				:	List[Any]							   =						"\\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    _snake_case      (  datasets.Metric       ):
  
  
  
  
  
  
  
  			def     SCREAMING_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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a="macro"		,     _a=None		,     _a=None		,     _a="raise"		,     _a=None		,     ):
  				return {
  				    "roc_auc": roc_auc_score(
  				        _a		,     _a		,     average=_a		,     sample_weight=_a		,     max_fpr=_a		,     multi_class=_a		,     labels=_a		,     )
  				}
  