# Path: diffprivlib/tools/utils.py
# def count_nonzero(array, epsilon=1.0, accountant=None, axis=None, keepdims=False):
#     r"""Counts the number of non-zero values in the array ``array`` with differential privacy.
# 
#     It is typical to use this function on the result of binary operations, such as ``count_nonzero(array >= 0)``.  If
#     you wish to count the number of elements of an array, use ``count_nonzero(np.ones_like(array))``.
# 
#     The word "non-zero" is in reference to the Python 2.x built-in method ``__nonzero__()`` (renamed ``__bool__()`` in
#     Python 3.x) of Python objects that tests an object's "truthfulness".  For example, any number is considered truthful
#     if it is nonzero, whereas any string is considered truthful if it is not the empty string.  Thus, this function
#     (recursively) counts how many elements in ``array`` (and in sub-arrays thereof) have their ``__nonzero__()`` or
#     ``__bool__()`` method evaluated to ``True``.
# 
#     Parameters
#     ----------
#     array : array_like
#         The array for which to count non-zeros.
# 
#     epsilon : float, default: 1.0
#         Privacy parameter :math:`\epsilon`.
# 
#     accountant : BudgetAccountant, optional
#         Accountant to keep track of privacy budget.
# 
#     axis : int or tuple, optional
#         Axis or tuple of axes along which to count non-zeros.  Default is None, meaning that non-zeros will be counted
#         along a flattened version of ``array``.
# 
#     keepdims : bool, default: False
#         If this is set to True, the axes that are counted are left in the result as dimensions with size one. With this
#         option, the result will broadcast correctly against the input array.
# 
#     Returns
#     -------
#     count : int or array of int
#         Differentially private number of non-zero values in the array along a given axis.  Otherwise, the total number
#         of non-zero values in the array is returned.
# 
#     """
#     array = np.asanyarray(array)
# 
#     if np.issubdtype(array.dtype, np.character):
#         array_bool = array != array.dtype.type()
#     else:
#         array_bool = array.astype(np.bool_, copy=False)
# 
#     return sum(array_bool, axis=axis, dtype=np.intp, bounds=(0, 1), epsilon=epsilon, accountant=accountant,
#                keepdims=keepdims)
# 
# Path: diffprivlib/utils.py
# class BudgetError(ValueError):
#     """Custom exception to capture the privacy budget being exceeded, typically controlled by a
#     :class:`.BudgetAccountant`.
# 
#     For example, this exception may be raised when the user:
# 
#         - Attempts to execute a query which would exceed the privacy budget of the accountant.
#         - Attempts to change the slack of the accountant in such a way that the existing budget spends would exceed the
#           accountant's budget.
# 
#     """


from unittest import TestCase
from diffprivlib.tools.utils import count_nonzero
from diffprivlib.utils import BudgetError
        from diffprivlib.accountant import BudgetAccountant
import numpy as np
        self.assertAlmostEqual(res, res_dp, delta=0.01 * res)

    def test_array_like(self):
        self.assertIsNotNone(count_nonzero([-1, 0, 1]))
        self.assertIsNotNone(count_nonzero((-1, 0, 1)))

    def test_axis(self):
        a = np.random.random((1000, 5)).round()
        res_dp = count_nonzero(a, epsilon=1, axis=0)
        self.assertEqual(res_dp.shape, (5,))

    def test_strings(self):
        a = ["", "", "Diffprivlib", "Diffprivlib", "Python", "Numpy"]
        res = np.count_nonzero(a)
        res_dp = count_nonzero(a, epsilon=float("inf"))

        self.assertEqual(res, res_dp)

    def test_nan(self):
        a = np.random.random((5, 5)).round()
        a[2, 2] = np.nan

        res = count_nonzero(a)
        self.assertFalse(np.isnan(res))

    def test_keepdims(self):
        a = np.random.random((5, 5)).round()

        self.assertEqual(count_nonzero(a, axis=0).ndim, 1)
        self.assertEqual(count_nonzero(a, axis=0, keepdims=True).ndim, 2)

# Path: diffprivlib/tools/utils.py
# def count_nonzero(array, epsilon=1.0, accountant=None, axis=None, keepdims=False):
#     r"""Counts the number of non-zero values in the array ``array`` with differential privacy.
# 
#     It is typical to use this function on the result of binary operations, such as ``count_nonzero(array >= 0)``.  If
#     you wish to count the number of elements of an array, use ``count_nonzero(np.ones_like(array))``.
# 
#     The word "non-zero" is in reference to the Python 2.x built-in method ``__nonzero__()`` (renamed ``__bool__()`` in
#     Python 3.x) of Python objects that tests an object's "truthfulness".  For example, any number is considered truthful
#     if it is nonzero, whereas any string is considered truthful if it is not the empty string.  Thus, this function
#     (recursively) counts how many elements in ``array`` (and in sub-arrays thereof) have their ``__nonzero__()`` or
#     ``__bool__()`` method evaluated to ``True``.
# 
#     Parameters
#     ----------
#     array : array_like
#         The array for which to count non-zeros.
# 
#     epsilon : float, default: 1.0
#         Privacy parameter :math:`\epsilon`.
# 
#     accountant : BudgetAccountant, optional
#         Accountant to keep track of privacy budget.
# 
#     axis : int or tuple, optional
#         Axis or tuple of axes along which to count non-zeros.  Default is None, meaning that non-zeros will be counted
#         along a flattened version of ``array``.
# 
#     keepdims : bool, default: False
#         If this is set to True, the axes that are counted are left in the result as dimensions with size one. With this
#         option, the result will broadcast correctly against the input array.
# 
#     Returns
#     -------
#     count : int or array of int
#         Differentially private number of non-zero values in the array along a given axis.  Otherwise, the total number
#         of non-zero values in the array is returned.
# 
#     """
#     array = np.asanyarray(array)
# 
#     if np.issubdtype(array.dtype, np.character):
#         array_bool = array != array.dtype.type()
#     else:
#         array_bool = array.astype(np.bool_, copy=False)
# 
#     return sum(array_bool, axis=axis, dtype=np.intp, bounds=(0, 1), epsilon=epsilon, accountant=accountant,
#                keepdims=keepdims)
# 
# Path: diffprivlib/utils.py
# class BudgetError(ValueError):
#     """Custom exception to capture the privacy budget being exceeded, typically controlled by a
#     :class:`.BudgetAccountant`.
# 
#     For example, this exception may be raised when the user:
# 
#         - Attempts to execute a query which would exceed the privacy budget of the accountant.
#         - Attempts to change the slack of the accountant in such a way that the existing budget spends would exceed the
#           accountant's budget.
# 
#     """


# Path: tests/tools/test_count_nonzero.py
from unittest import TestCase
from diffprivlib.tools.utils import count_nonzero
from diffprivlib.utils import BudgetError
        from diffprivlib.accountant import BudgetAccountant
import numpy as np

        self.assertAlmostEqual(res, res_dp, delta=0.01 * res)

    def test_array_like(self):
        self.assertIsNotNone(count_nonzero([-1, 0, 1]))
        self.assertIsNotNone(count_nonzero((-1, 0, 1)))

    def test_axis(self):
        a = np.random.random((1000, 5)).round()
        res_dp = count_nonzero(a, epsilon=1, axis=0)
        self.assertEqual(res_dp.shape, (5,))

    def test_strings(self):
        a = ["", "", "Diffprivlib", "Diffprivlib", "Python", "Numpy"]
        res = np.count_nonzero(a)
        res_dp = count_nonzero(a, epsilon=float("inf"))

        self.assertEqual(res, res_dp)

    def test_nan(self):
        a = np.random.random((5, 5)).round()
        a[2, 2] = np.nan

        res = count_nonzero(a)
        self.assertFalse(np.isnan(res))

    def test_keepdims(self):
        a = np.random.random((5, 5)).round()

        self.assertEqual(count_nonzero(a, axis=0).ndim, 1)
        self.assertEqual(count_nonzero(a, axis=0, keepdims=True).ndim, 2)

        self.assertEqual(count_nonzero(a, axis=0, keepdims=True).shape, (1, 5))