





"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


A_     :      Any								=logging.get_logger(__name__)







class 	__a   (  lowerCAmelCase__		):
     SCREAMING_SNAKE_CASE__ :      Dict       		=			["pixel_values"]



     def __init__(       self					,     a__ = True					,     a__ = None					,     a__ = PIL.Image.NEAREST					,     a__ = False					,     a__ = None					,     a__ = 1 / 2_55					,     a__ = False					,     a__ = True					,     a__ = True					,     a__ = None					,     a__ = None					,     a__ = True					,     **a__					,     ):
            super().__init__(**a__   )
            _lowerCamelCase    	=					size if size is not None else {'height': 3_46, 'width': 3_46}
            _lowerCamelCase    	=					get_size_dict(a__   )
            _lowerCamelCase    	=					crop_size if crop_size is not None else {'height': 2_89, 'width': 2_89}
            _lowerCamelCase    	=					get_size_dict(a__					,     param_name='crop_size'   )

            _lowerCamelCase    	=					do_resize
            _lowerCamelCase    	=					size
            _lowerCamelCase    	=					resample
            _lowerCamelCase    	=					do_center_crop
            _lowerCamelCase    	=					crop_size
            _lowerCamelCase    	=					do_rescale
            _lowerCamelCase    	=					rescale_factor
            _lowerCamelCase    	=					rescale_offset
            _lowerCamelCase    	=					do_normalize
            _lowerCamelCase    	=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            _lowerCamelCase    	=					image_std if image_std is not None else IMAGENET_STANDARD_STD
            _lowerCamelCase    	=					include_top



     def 						snake_case_			(       self					,     a__					,     a__					,     a__ = PIL.Image.NEAREST					,     a__ = None					,     **a__					,     ):
            _lowerCamelCase    	=					get_size_dict(a__   )
            if "height" not in size or "width" not in size:
                   raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'   )
            return resize(
                a__					,     size=(size['height'], size['width'])					,     resample=a__					,     data_format=a__					,     **a__   )



     def 						snake_case_			(       self					,     a__					,     a__					,     a__ = None					,     **a__					,     ):
            _lowerCamelCase    	=					get_size_dict(a__   )
            if "height" not in size or "width" not in size:
                   raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'   )
            return center_crop(a__					,     size=(size['height'], size['width'])					,     data_format=a__					,     **a__   )



     def 						snake_case_			(       self					,     a__					,     a__					,     a__ = True					,     a__ = None					,     **a__					,     ):
            if offset:
                   _lowerCamelCase    	=					(image - 127.5) * scale
                   if data_format is not None:
                          _lowerCamelCase    	=					to_channel_dimension_format(a__					,     a__   )
                   _lowerCamelCase    	=					rescaled_image.astype(np.floataa   )
            else:
                   _lowerCamelCase    	=					rescale(a__					,     scale=a__					,     data_format=a__					,     **a__   )
            return rescaled_image



     def 						snake_case_			(       self					,     a__					,     a__					,     a__					,     a__ = None					,     **a__					,     ):
            return normalize(a__					,     mean=a__					,     std=a__					,     data_format=a__					,     **a__   )



     def 						snake_case_			(       self					,     a__					,     a__ = None					,     a__ = None					,     a__=None					,     a__ = None					,     a__ = None					,     a__ = None					,     a__ = None					,     a__ = None					,     a__ = None					,     a__ = None					,     a__ = None					,     a__ = None					,     a__ = None					,     a__ = ChannelDimension.FIRST					,     **a__					,     ):
            _lowerCamelCase    	=					do_resize if do_resize is not None else self.do_resize
            _lowerCamelCase    	=					resample if resample is not None else self.resample
            _lowerCamelCase    	=					do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCamelCase    	=					do_rescale if do_rescale is not None else self.do_rescale
            _lowerCamelCase    	=					rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCamelCase    	=					rescale_offset if rescale_offset is not None else self.rescale_offset
            _lowerCamelCase    	=					do_normalize if do_normalize is not None else self.do_normalize
            _lowerCamelCase    	=					image_mean if image_mean is not None else self.image_mean
            _lowerCamelCase    	=					image_std if image_std is not None else self.image_std
            _lowerCamelCase    	=					include_top if include_top is not None else self.include_top

            _lowerCamelCase    	=					size if size is not None else self.size
            _lowerCamelCase    	=					get_size_dict(a__   )
            _lowerCamelCase    	=					crop_size if crop_size is not None else self.crop_size
            _lowerCamelCase    	=					get_size_dict(a__					,     param_name='crop_size'   )

            _lowerCamelCase    	=					make_list_of_images(a__   )

            if not valid_images(a__   ):
                   raise ValueError(
                       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                       'torch.Tensor, tf.Tensor or jax.ndarray.'   )

            if do_resize and size is None or resample is None:
                   raise ValueError('Size and resample must be specified if do_resize is True.'   )

            if do_center_crop and crop_size is None:
                   raise ValueError('Crop size must be specified if do_center_crop is True.'   )

            if do_rescale and rescale_factor is None:
                   raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

            if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

            # All transformations expect numpy arrays.
            _lowerCamelCase    	=					[to_numpy_array(a__   ) for image in images]

            if do_resize:
                   _lowerCamelCase    	=					[self.resize(image=a__					,     size=a__					,     resample=a__   ) for image in images]

            if do_center_crop:
                   _lowerCamelCase    	=					[self.center_crop(image=a__					,     size=a__   ) for image in images]

            if do_rescale:
                   _lowerCamelCase    	=					[self.rescale(image=a__					,     scale=a__					,     offset=a__   ) for image in images]

            if do_normalize:
                   _lowerCamelCase    	=					[self.normalize(image=a__					,     mean=a__					,     std=a__   ) for image in images]

            if include_top:
                   _lowerCamelCase    	=					[self.normalize(image=a__					,     mean=[0, 0, 0]					,     std=a__   ) for image in images]

            _lowerCamelCase    	=					[to_channel_dimension_format(a__					,     a__   ) for image in images]

            _lowerCamelCase    	=					{'pixel_values': images}
            return BatchFeature(data=a__					,     tensor_type=a__   )













"""simple docstring"""


from sklearn.metrics import roc_auc_score

import datasets


A_     :      Optional[int]								="""
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
"""

A_     :      Dict								="""
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results['roc_auc'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
        >>> print(round(results['roc_auc'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results['roc_auc']])
        [0.83, 0.38, 0.94]
"""

A_     :      List[str]								="""\
@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 	__a   (  datasets.Metric		):



     def 						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 						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__					,     )
            }






