

from sklearn.metrics import roc_auc_score

import datasets


_UpperCamelCase     =    '''
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.
'''

_UpperCamelCase     =    '''
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]
'''

_UpperCamelCase     =    '''\
@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 					lowercase		( datasets.Metric      ):



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      Tuple:






  """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 , __a , __a , __a="macro" , __a=None , __a=None , __a="raise" , __a=None , )				->      str:






  """simple docstring"""





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






import random
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, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 import PIL

 from transformers import FlavaImageProcessor
 from transformers.image_utils import PILImageResampling
 from transformers.models.flava.image_processing_flava import (
     FLAVA_CODEBOOK_MEAN,
     FLAVA_CODEBOOK_STD,
     FLAVA_IMAGE_MEAN,
     FLAVA_IMAGE_STD,
 )
else:
 _UpperCamelCase     =    _UpperCamelCase     =    _UpperCamelCase     =    _UpperCamelCase     =    None



class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 def __init__(self , __a , __a=7 , __a=3 , __a=30 , __a=400 , __a=True , __a=None , __a=True , __a=None , __a=None , __a=True , __a=1 / 255 , __a=True , __a=FLAVA_IMAGE_MEAN , __a=FLAVA_IMAGE_STD , __a=14 , __a=75 , __a=None , __a=16 , __a=0.3 , __a=None , __a=True , __a=None , __a=None , __a=True , __a=None , __a=True , __a=True , __a=FLAVA_CODEBOOK_MEAN , __a=FLAVA_CODEBOOK_STD , )				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       size if size is not None else {'height': 224, 'width': 224}
  UpperCAmelCase__           =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
  UpperCAmelCase__           =       codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
  UpperCAmelCase__           =       codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

  UpperCAmelCase__           =       parent
  UpperCAmelCase__           =       batch_size
  UpperCAmelCase__           =       num_channels
  UpperCAmelCase__           =       do_resize
  UpperCAmelCase__           =       do_rescale
  UpperCAmelCase__           =       rescale_factor
  UpperCAmelCase__           =       min_resolution
  UpperCAmelCase__           =       max_resolution
  UpperCAmelCase__           =       size
  UpperCAmelCase__           =       resample if resample is not None else PILImageResampling.BICUBIC
  UpperCAmelCase__           =       do_normalize
  UpperCAmelCase__           =       image_mean
  UpperCAmelCase__           =       image_std
  UpperCAmelCase__           =       do_center_crop
  UpperCAmelCase__           =       crop_size

  UpperCAmelCase__           =       input_size_patches
  UpperCAmelCase__           =       total_mask_patches
  UpperCAmelCase__           =       mask_group_max_patches
  UpperCAmelCase__           =       mask_group_min_patches
  UpperCAmelCase__           =       mask_group_min_aspect_ratio
  UpperCAmelCase__           =       mask_group_max_aspect_ratio

  UpperCAmelCase__           =       codebook_do_resize
  UpperCAmelCase__           =       codebook_size
  UpperCAmelCase__           =       codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
  UpperCAmelCase__           =       codebook_do_center_crop
  UpperCAmelCase__           =       codebook_crop_size
  UpperCAmelCase__           =       codebook_do_map_pixels
  UpperCAmelCase__           =       codebook_do_normalize
  UpperCAmelCase__           =       codebook_image_mean
  UpperCAmelCase__           =       codebook_image_std




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  return {
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "do_normalize": self.do_normalize,
      "do_resize": self.do_resize,
      "size": self.size,
      "resample": self.resample,
      "do_rescale": self.do_rescale,
      "rescale_factor": self.rescale_factor,
      "do_center_crop": self.do_center_crop,
      "crop_size": self.crop_size,
      "input_size_patches": self.input_size_patches,
      "total_mask_patches": self.total_mask_patches,
      "mask_group_max_patches": self.mask_group_max_patches,
      "mask_group_min_patches": self.mask_group_min_patches,
      "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
      "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
      "codebook_do_resize": self.codebook_do_resize,
      "codebook_size": self.codebook_size,
      "codebook_resample": self.codebook_resample,
      "codebook_do_center_crop": self.codebook_do_center_crop,
      "codebook_crop_size": self.codebook_crop_size,
      "codebook_do_map_pixels": self.codebook_do_map_pixels,
      "codebook_do_normalize": self.codebook_do_normalize,
      "codebook_image_mean": self.codebook_image_mean,
      "codebook_image_std": self.codebook_image_std,
  }




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return (self.size["height"], self.size["width"])




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (
      (self.input_size_patches, self.input_size_patches)
      if not isinstance(self.input_size_patches , __a				)
      else self.input_size_patches
  )




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 					lowercase		( _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       FlavaImageProcessor if is_vision_available() else None
 __SCREAMING_SNAKE_CASE      							=       None




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       FlavaImageProcessingTester(self				)




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  self.assertTrue(hasattr(__a , 'image_mean'				)				)
  self.assertTrue(hasattr(__a , 'image_std'				)				)
  self.assertTrue(hasattr(__a , 'do_normalize'				)				)
  self.assertTrue(hasattr(__a , 'do_resize'				)				)
  self.assertTrue(hasattr(__a , 'resample'				)				)
  self.assertTrue(hasattr(__a , 'crop_size'				)				)
  self.assertTrue(hasattr(__a , 'do_center_crop'				)				)
  self.assertTrue(hasattr(__a , 'do_rescale'				)				)
  self.assertTrue(hasattr(__a , 'rescale_factor'				)				)
  self.assertTrue(hasattr(__a , 'masking_generator'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_resize'				)				)
  self.assertTrue(hasattr(__a , 'codebook_size'				)				)
  self.assertTrue(hasattr(__a , 'codebook_resample'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_center_crop'				)				)
  self.assertTrue(hasattr(__a , 'codebook_crop_size'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_map_pixels'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_normalize'				)				)
  self.assertTrue(hasattr(__a , 'codebook_image_mean'				)				)
  self.assertTrue(hasattr(__a , 'codebook_image_std'				)				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class.from_dict(self.image_processor_dict				)
  self.assertEqual(image_processor.size , {'height': 224, 'width': 224}				)
  self.assertEqual(image_processor.crop_size , {'height': 224, 'width': 224}				)
  self.assertEqual(image_processor.codebook_size , {'height': 112, 'width': 112}				)
  self.assertEqual(image_processor.codebook_crop_size , {'height': 112, 'width': 112}				)

  UpperCAmelCase__           =       self.image_processing_class.from_dict(
      self.image_processor_dict , size=42 , crop_size=84 , codebook_size=33 , codebook_crop_size=66				)
  self.assertEqual(image_processor.size , {'height': 42, 'width': 42}				)
  self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84}				)
  self.assertEqual(image_processor.codebook_size , {'height': 33, 'width': 33}				)
  self.assertEqual(image_processor.codebook_crop_size , {'height': 66, 'width': 66}				)




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random PIL images
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , PIL.Image.Image				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_tensors='pt'				)

  # Test no bool masked pos
  self.assertFalse('bool_masked_pos' in encoded_images				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()

  self.assertEqual(
      encoded_images.pixel_values.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()

  # Test no bool masked pos
  self.assertFalse('bool_masked_pos' in encoded_images				)

  self.assertEqual(
      encoded_images.pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )




 def 						UpperCamelCase__							(self , __a , __a				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random tensors
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , **__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , __a				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_tensors='pt'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  UpperCAmelCase__           =       image_processing(__a , return_image_mask=__a , return_tensors='pt'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_mask_size()
  self.assertEqual(
      encoded_images.bool_masked_pos.shape , (
          self.image_processor_tester.batch_size,
          expected_height,
          expected_width,
      ) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				).pixel_values

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )

  # Test masking
  UpperCAmelCase__           =       image_processing(__a , return_image_mask=__a , return_tensors='pt'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_mask_size()
  self.assertEqual(
      encoded_images.bool_masked_pos.shape , (
          self.image_processor_tester.batch_size,
          expected_height,
          expected_width,
      ) , )




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  self._test_call_framework(np.ndarray , prepare_kwargs={'numpify': True}				)




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  self._test_call_framework(torch.Tensor , prepare_kwargs={'torchify': True}				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  random.seed(1234				)
  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_image_mask=__a , return_tensors='pt'				)
  self.assertEqual(encoded_images.bool_masked_pos.sum().item() , 75				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random PIL images
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , PIL.Image.Image				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_codebook_pixels=__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_codebook_image_size()
  self.assertEqual(
      encoded_images.codebook_pixel_values.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_codebook_pixels=__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_codebook_image_size()
  self.assertEqual(
      encoded_images.codebook_pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )



