

'''simple docstring'''






import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
      from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DPTImageProcessor
class      SCREAMING_SNAKE_CASE__      :




    def __init__( self    ,					lowercase__    ,					lowercase__=2    ,					lowercase__=32    ,					lowercase__=16    ,					lowercase__=3    ,					lowercase__=True    ,					lowercase__=True    ,					lowercase__=32    ,					lowercase__=4    ,					lowercase__=[0, 1, 2, 3]    ,					lowercase__=4    ,					lowercase__=37    ,					lowercase__="gelu"    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.02    ,					lowercase__=3    ,					lowercase__=False    ,					lowercase__=None    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	parent
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	batch_size
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	image_size
          SCREAMING_SNAKE_CASE_				:      List[str]					=	patch_size
          SCREAMING_SNAKE_CASE_				:      Any					=	num_channels
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	is_training
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	use_labels
          SCREAMING_SNAKE_CASE_				:      Dict					=	hidden_size
          SCREAMING_SNAKE_CASE_				:      Dict					=	num_hidden_layers
          SCREAMING_SNAKE_CASE_				:      Tuple					=	backbone_out_indices
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	num_attention_heads
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	intermediate_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	hidden_act
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_				:      List[str]					=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_				:      Dict					=	initializer_range
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	num_labels
          SCREAMING_SNAKE_CASE_				:      Any					=	scope
          SCREAMING_SNAKE_CASE_				:      Tuple					=	is_hybrid
          # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
          SCREAMING_SNAKE_CASE_				:      Any					=	(image_size // patch_size) ** 2
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	num_patches + 1




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	None
          if self.use_labels:
                SCREAMING_SNAKE_CASE_				:      Dict					=	ids_tensor([self.batch_size, self.image_size, self.image_size]    ,					self.num_labels      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.get_config()

          return config, pixel_values, labels




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return DPTConfig(
              image_size=self.image_size    ,					patch_size=self.patch_size    ,					num_channels=self.num_channels    ,					hidden_size=self.hidden_size    ,					num_hidden_layers=self.num_hidden_layers    ,					backbone_out_indices=self.backbone_out_indices    ,					num_attention_heads=self.num_attention_heads    ,					intermediate_size=self.intermediate_size    ,					hidden_act=self.hidden_act    ,					hidden_dropout_prob=self.hidden_dropout_prob    ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,					is_decoder=lowercase__    ,					initializer_range=self.initializer_range    ,					is_hybrid=self.is_hybrid    ,					)




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






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	DPTModel(config=lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	model(lowercase__      )
          self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)      )




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






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.num_labels
          SCREAMING_SNAKE_CASE_				:      Dict					=	DPTForDepthEstimation(lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	model(lowercase__      )
          self.parent.assertEqual(result.predicted_depth.shape    ,					(self.batch_size, self.image_size, self.image_size)      )




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






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.num_labels
          SCREAMING_SNAKE_CASE_				:      Tuple					=	DPTForSemanticSegmentation(lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      str					=	model(lowercase__    ,					labels=lowercase__      )
          self.parent.assertEqual(
              result.logits.shape    ,					(self.batch_size, self.num_labels, self.image_size, self.image_size)      )







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	config_and_inputs
          SCREAMING_SNAKE_CASE_				:      str					=	{"pixel_values": pixel_values}
          return config, inputs_dict






@require_torch
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase,_UpperCAmelCase,unittest.TestCase				):
    _A         =							(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    _A         =							(
        {
            "depth-estimation": DPTForDepthEstimation,
            "feature-extraction": DPTModel,
            "image-segmentation": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    _A         =							False
    _A         =							False
    _A         =							False




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	DPTModelTester(self      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ConfigTester(self    ,					config_class=lowercase__    ,					has_text_modality=lowercase__    ,					hidden_size=37      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          self.config_tester.run_common_tests()




    @unittest.skip(reason="DPT does not use inputs_embeds"      )
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          pass




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model_class(lowercase__      )
                self.assertIsInstance(model.get_input_embeddings()    ,					(nn.Module)      )
                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowercase__    ,					nn.Linear      )      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      List[str]					=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE_				:      int					=	model_class(lowercase__      )
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	inspect.signature(model.forward      )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                SCREAMING_SNAKE_CASE_				:      Any					=	[*signature.parameters.keys()]

                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	["pixel_values"]
                self.assertListEqual(arg_names[:1]    ,					lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                      continue

                SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.model_tester.prepare_config_and_inputs_for_common()
                SCREAMING_SNAKE_CASE_				:      Dict					=	True

                if model_class in get_values(lowercase__      ):
                      continue

                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model_class(lowercase__      )
                model.to(lowercase__      )
                model.train()
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	self._prepare_for_class(lowercase__    ,					lowercase__    ,					return_labels=lowercase__      )
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model(**lowercase__      ).loss
                loss.backward()




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                      continue

                SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.model_tester.prepare_config_and_inputs_for_common()
                SCREAMING_SNAKE_CASE_				:      int					=	False
                SCREAMING_SNAKE_CASE_				:      List[str]					=	True

                if model_class in get_values(lowercase__      ) or not model_class.supports_gradient_checkpointing:
                      continue
                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	model_class(lowercase__      )
                model.to(lowercase__      )
                model.gradient_checkpointing_enable()
                model.train()
                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self._prepare_for_class(lowercase__    ,					lowercase__    ,					return_labels=lowercase__      )
                SCREAMING_SNAKE_CASE_				:      List[str]					=	model(**lowercase__      ).loss
                loss.backward()




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.model_tester.prepare_config_and_inputs_for_common()

          SCREAMING_SNAKE_CASE_				:      Dict					=	_config_zero_init(lowercase__      )
          for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE_				:      int					=	model_class(config=lowercase__      )
                # Skip the check for the backbone
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[]
                for name, module in model.named_modules():
                      if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                            SCREAMING_SNAKE_CASE_				:      Any					=	[F"{name}.{key}" for key in module.state_dict().keys()]
                            break

                for name, param in model.named_parameters():
                      if param.requires_grad:
                            if name in backbone_params:
                                  continue
                            self.assertIn(
                                ((param.data.mean() * 1e9).round() / 1e9).item()    ,					[0.0, 1.0]    ,					msg=F"Parameter {name} of model {model_class} seems not properly initialized"    ,					)




    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          pass







    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE_				:      int					=	DPTModel.from_pretrained(lowercase__      )
                self.assertIsNotNone(lowercase__      )






def  __lowerCamelCase      (				)							->       Optional[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
      return image





@require_torch
@require_vision
@slow
class      SCREAMING_SNAKE_CASE__      (    unittest.TestCase				):




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	DPTImageProcessor.from_pretrained("Intel/dpt-large"      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	DPTForDepthEstimation.from_pretrained("Intel/dpt-large"      ).to(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	prepare_img()
          SCREAMING_SNAKE_CASE_				:      int					=	image_processor(images=lowercase__    ,					return_tensors="pt"      ).to(lowercase__      )

          # forward pass
          with torch.no_grad():
                SCREAMING_SNAKE_CASE_				:      Dict					=	model(**lowercase__      )
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	outputs.predicted_depth

          # verify the predicted depth
          SCREAMING_SNAKE_CASE_				:      Any					=	torch.Size((1, 384, 384)      )
          self.assertEqual(predicted_depth.shape    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Any					=	torch.tensor(
              [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]      ).to(lowercase__      )

          self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]    ,					lowercase__    ,					atol=1e-4      )      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
          SCREAMING_SNAKE_CASE_				:      str					=	DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	prepare_img()
          SCREAMING_SNAKE_CASE_				:      str					=	image_processor(images=lowercase__    ,					return_tensors="pt"      ).to(lowercase__      )

          # forward pass
          with torch.no_grad():
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model(**lowercase__      )

          # verify the logits
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	torch.Size((1, 150, 480, 480)      )
          self.assertEqual(outputs.logits.shape    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	torch.tensor(
              [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]      ).to(lowercase__      )

          self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]    ,					lowercase__    ,					atol=1e-4      )      )







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	prepare_img()
          SCREAMING_SNAKE_CASE_				:      int					=	image_processor(images=lowercase__    ,					return_tensors="pt"      ).to(lowercase__      )

          # forward pass
          with torch.no_grad():
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	model(**lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	outputs.logits.detach().cpu()

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	image_processor.post_process_semantic_segmentation(outputs=lowercase__    ,					target_sizes=[(500, 300)]      )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	torch.Size((500, 300)      )
          self.assertEqual(segmentation[0].shape    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Dict					=	image_processor.post_process_semantic_segmentation(outputs=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	torch.Size((480, 480)      )
          self.assertEqual(segmentation[0].shape    ,					lowercase__      )




'''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__    ,					)
          }

