

from sklearn.metrics import roc_auc_score

import datasets


A_		:			Tuple										=   '\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'

A_		:			Optional[int]										=   '\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'

A_		:			int										=   '\\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      lowerCAmelCase_						(self  )     ->      Tuple:
        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     ,      )     ->      List[Any]:
        return {
            "roc_auc": roc_auc_score(
                lowercase__     ,      lowercase__     ,      average=lowercase__     ,      sample_weight=lowercase__     ,      max_fpr=lowercase__     ,      multi_class=lowercase__     ,      labels=lowercase__     ,      )
        }







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import SwinaSRImageProcessor
       from transformers.image_transforms import get_image_size
class 					A_   (      unittest.TestCase						):


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__=7     ,      lowercase__=3     ,      lowercase__=18     ,      lowercase__=30     ,      lowercase__=400     ,      lowercase__=True     ,      lowercase__=1 / 255     ,      lowercase__=True     ,      lowercase__=8     ,      )     ->      List[str]:
        __UpperCAmelCase									=			parent
        __UpperCAmelCase									=			batch_size
        __UpperCAmelCase									=			num_channels
        __UpperCAmelCase									=			image_size
        __UpperCAmelCase									=			min_resolution
        __UpperCAmelCase									=			max_resolution
        __UpperCAmelCase									=			do_rescale
        __UpperCAmelCase									=			rescale_factor
        __UpperCAmelCase									=			do_pad
        __UpperCAmelCase									=			pad_size


    def      lowerCAmelCase_						(self  )     ->      int:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }






    def      lowerCAmelCase_						(self     ,      lowercase__=False     ,      lowercase__=False     ,      lowercase__=False  )     ->      Tuple:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
            __UpperCAmelCase									=			[]
            for i in range(self.batch_size  ):
                image_inputs.append(
                    np.random.randint(
                        255     ,      size=(self.num_channels, self.max_resolution, self.max_resolution)     ,      dtype=np.uinta  )  )
        else:
            __UpperCAmelCase									=			[]
            for i in range(self.batch_size  ):
                __UpperCAmelCase					,     __UpperCAmelCase									=			np.random.choice(np.arange(self.min_resolution     ,      self.max_resolution  )     ,      2  )
                image_inputs.append(np.random.randint(255     ,      size=(self.num_channels, width, height)     ,      dtype=np.uinta  )  )

        if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            __UpperCAmelCase									=			[Image.fromarray(np.moveaxis(lowercase__     ,      0     ,      -1  )  ) for x in image_inputs]

        if torchify:
            __UpperCAmelCase									=			[torch.from_numpy(lowercase__  ) for x in image_inputs]

        return image_inputs






@require_torch
@require_vision
class 					A_   (      _a ,     unittest.TestCase						):


    '''simple docstring'''






    a__     							=       SwinaSRImageProcessor if is_vision_available() else None


    def      lowerCAmelCase_						(self  )     ->      str:
        __UpperCAmelCase									=			SwinaSRImageProcessingTester(self  )


    @property
    def      lowerCAmelCase_						(self  )     ->      str:
        return self.image_processor_tester.prepare_image_processor_dict()


    def      lowerCAmelCase_						(self  )     ->      List[Any]:
        __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(lowercase__     ,      '''do_rescale'''  )  )
        self.assertTrue(hasattr(lowercase__     ,      '''rescale_factor'''  )  )
        self.assertTrue(hasattr(lowercase__     ,      '''do_pad'''  )  )
        self.assertTrue(hasattr(lowercase__     ,      '''pad_size'''  )  )


    def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
        pass


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
        __UpperCAmelCase					,     __UpperCAmelCase									=			get_image_size(lowercase__  )
        __UpperCAmelCase									=			self.image_processor_tester.pad_size

        __UpperCAmelCase									=			(old_height // size + 1) * size - old_height
        __UpperCAmelCase									=			(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width


    def      lowerCAmelCase_						(self  )     ->      Optional[int]:
        # Initialize image_processor
        __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__  )
        for image in image_inputs:
            self.assertIsInstance(lowercase__     ,      Image.Image  )

        # Test not batched input
        __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape     ,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,      )


    def      lowerCAmelCase_						(self  )     ->      str:
        # Initialize image_processor
        __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      numpify=lowercase__  )
        for image in image_inputs:
            self.assertIsInstance(lowercase__     ,      np.ndarray  )

        # Test not batched input
        __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape     ,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,      )






    def      lowerCAmelCase_						(self  )     ->      int:
        # Initialize image_processor
        __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      torchify=lowercase__  )
        for image in image_inputs:
            self.assertIsInstance(lowercase__     ,      torch.Tensor  )

        # Test not batched input
        __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape     ,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,      )




