





import numpy as np





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    _A    =       np.floor(_SCREAMING_SNAKE_CASE							)
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    _A    =       int(n_draws - floored.sum()							)
    if need_to_add > 0:
        _A    =       continuous - floored
        _A    =       np.sort(np.unique(_SCREAMING_SNAKE_CASE							)							)[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((_A), )    =       np.where(remainder == value							)
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            _A    =       min(len(_SCREAMING_SNAKE_CASE							)       ,    _SCREAMING_SNAKE_CASE							)
            _A    =       rng.choice(_SCREAMING_SNAKE_CASE       ,    size=_SCREAMING_SNAKE_CASE       ,    replace=_SCREAMING_SNAKE_CASE							)
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa							)





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=10							)       ->   Optional[int]:




    """simple docstring"""




    _A, _A    =       np.unique(_SCREAMING_SNAKE_CASE       ,    return_inverse=_SCREAMING_SNAKE_CASE							)
    _A    =       classes.shape[0]
    _A    =       np.bincount(_SCREAMING_SNAKE_CASE							)
    if np.min(_SCREAMING_SNAKE_CASE							) < 2:
        raise ValueError('Minimum class count error'							)
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)							)
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)							)
    _A    =       np.split(np.argsort(_SCREAMING_SNAKE_CASE       ,    kind='mergesort'							)       ,    np.cumsum(_SCREAMING_SNAKE_CASE							)[:-1]							)
    for _ in range(_SCREAMING_SNAKE_CASE							):
        _A    =       approximate_mode(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
        _A    =       class_counts - n_i
        _A    =       approximate_mode(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)

        _A    =       []
        _A    =       []

        for i in range(_SCREAMING_SNAKE_CASE							):
            _A    =       rng.permutation(class_counts[i]							)
            _A    =       class_indices[i].take(_SCREAMING_SNAKE_CASE       ,    mode='clip'							)
            train.extend(perm_indices_class_i[: n_i[i]]							)
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]							)
        _A    =       rng.permutation(_SCREAMING_SNAKE_CASE							)
        _A    =       rng.permutation(_SCREAMING_SNAKE_CASE							)

        yield train, test












from sklearn.metrics import accuracy_score

import datasets


__A       : Union[str, Any]						    = "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


__A       : List[Any]						    = "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


__A       : List[str]						    = "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION  )
class     lowerCamelCase(	datasets.Metric  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         return datasets.MetricInfo(
             description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'       )       ),
                     'references': datasets.Sequence(datasets.Value('int32'       )       ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'       ),
                     'references': datasets.Value('int32'       ),
                 }       )							,    reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_=True							,    snake_case_=None       ):
         return {
             "accuracy": float(
                 accuracy_score(snake_case_							,    snake_case_							,    normalize=snake_case_							,    sample_weight=snake_case_       )       )
         }





