# Path: diffprivlib/tools/utils.py
# def nansum(array, epsilon=1.0, bounds=None, accountant=None, axis=None, dtype=None, keepdims=False, **unused_args):
#     r"""Sum of array elements over a given axis with differential privacy, ignoring NaNs.
# 
#     Parameters
#     ----------
#     array : array_like
#         Elements to sum.
# 
#     epsilon : float, default: 1.0
#         Privacy parameter :math:`\epsilon`.
# 
#     bounds : tuple, optional
#         Bounds of the values of the array, of the form (min, max).
# 
#     accountant : BudgetAccountant, optional
#         Accountant to keep track of privacy budget.
# 
#     axis : None or int or tuple of ints, optional
#         Axis or axes along which a sum is performed.  The default, axis=None, will sum all of the elements of the input
#         array.  If axis is negative it counts from the last to the first axis.
# 
#         If axis is a tuple of ints, a sum is performed on all of the axes specified in the tuple instead of a single
#         axis or all the axes as before.
# 
#     dtype : dtype, optional
#         The type of the returned array and of the accumulator in which the elements are summed.  The dtype of `array` is
#         used by default unless `array` has an integer dtype of less precision than the default platform integer.  In
#         that case, if `array` is signed then the platform integer is used while if `array` is unsigned then an unsigned
#         integer of the same precision as the platform integer is used.
# 
#     keepdims : bool, default: False
#         If this is set to True, the axes which are reduced are left in the result as dimensions with size one.  With
#         this option, the result will broadcast correctly against the input array.
# 
#     Returns
#     -------
#     sum_along_axis : ndarray
#         An array with the same shape as `array`, with the specified axis removed.   If `array` is a 0-d array, or if
#         `axis` is None, a scalar is returned.  If an output array is specified, a reference to `out` is returned.
# 
#     See Also
#     --------
#     ndarray.sum : Equivalent non-private method.
# 
#     mean, sum
# 
#     """
#     warn_unused_args(unused_args)
# 
#     return _sum(array, epsilon=epsilon, bounds=bounds, accountant=accountant, axis=axis, dtype=dtype,
#                 keepdims=keepdims, nan=True)
# 
# Path: diffprivlib/utils.py
# class PrivacyLeakWarning(RuntimeWarning):
#     """Custom warning to capture privacy leaks resulting from incorrect parameter setting.
# 
#     For example, this warning may occur when the user:
# 
#         - fails to specify the bounds or range of data to a model where required (e.g., `bounds=None` to
#           :class:`.GaussianNB`).
#         - inputs data to a model that falls outside the bounds or range originally specified.
# 
#     """
# 
# 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 nansum
from diffprivlib.utils import PrivacyLeakWarning, BudgetError
        from diffprivlib.accountant import BudgetAccountant
import numpy as np
        self.assertIsNotNone(nansum((1, 2, 3), bounds=(1, 3)))

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

    def test_clipped_output(self):
        a = np.random.random((10,))

        for i in range(100):
            self.assertTrue(0 <= nansum(a, epsilon=1e-5, bounds=(0, 1)) <= 10)

    def test_int_output(self):
        a = np.random.random(1000) * 10
        res_int = nansum(a, dtype=int, bounds=(0, 10))
        self.assertIsInstance(res_int, int)

        res = np.nansum(a, dtype=int)
        res_inf = nansum(a, epsilon=float("inf"), dtype=int, bounds=(0, 10))
        self.assertEqual(res, res_inf)

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

        res = nansum(a, bounds=(0, 1))
        self.assertFalse(np.isnan(res))

        a = np.array([np.nan] * 10)

# Path: diffprivlib/tools/utils.py
# def nansum(array, epsilon=1.0, bounds=None, accountant=None, axis=None, dtype=None, keepdims=False, **unused_args):
#     r"""Sum of array elements over a given axis with differential privacy, ignoring NaNs.
# 
#     Parameters
#     ----------
#     array : array_like
#         Elements to sum.
# 
#     epsilon : float, default: 1.0
#         Privacy parameter :math:`\epsilon`.
# 
#     bounds : tuple, optional
#         Bounds of the values of the array, of the form (min, max).
# 
#     accountant : BudgetAccountant, optional
#         Accountant to keep track of privacy budget.
# 
#     axis : None or int or tuple of ints, optional
#         Axis or axes along which a sum is performed.  The default, axis=None, will sum all of the elements of the input
#         array.  If axis is negative it counts from the last to the first axis.
# 
#         If axis is a tuple of ints, a sum is performed on all of the axes specified in the tuple instead of a single
#         axis or all the axes as before.
# 
#     dtype : dtype, optional
#         The type of the returned array and of the accumulator in which the elements are summed.  The dtype of `array` is
#         used by default unless `array` has an integer dtype of less precision than the default platform integer.  In
#         that case, if `array` is signed then the platform integer is used while if `array` is unsigned then an unsigned
#         integer of the same precision as the platform integer is used.
# 
#     keepdims : bool, default: False
#         If this is set to True, the axes which are reduced are left in the result as dimensions with size one.  With
#         this option, the result will broadcast correctly against the input array.
# 
#     Returns
#     -------
#     sum_along_axis : ndarray
#         An array with the same shape as `array`, with the specified axis removed.   If `array` is a 0-d array, or if
#         `axis` is None, a scalar is returned.  If an output array is specified, a reference to `out` is returned.
# 
#     See Also
#     --------
#     ndarray.sum : Equivalent non-private method.
# 
#     mean, sum
# 
#     """
#     warn_unused_args(unused_args)
# 
#     return _sum(array, epsilon=epsilon, bounds=bounds, accountant=accountant, axis=axis, dtype=dtype,
#                 keepdims=keepdims, nan=True)
# 
# Path: diffprivlib/utils.py
# class PrivacyLeakWarning(RuntimeWarning):
#     """Custom warning to capture privacy leaks resulting from incorrect parameter setting.
# 
#     For example, this warning may occur when the user:
# 
#         - fails to specify the bounds or range of data to a model where required (e.g., `bounds=None` to
#           :class:`.GaussianNB`).
#         - inputs data to a model that falls outside the bounds or range originally specified.
# 
#     """
# 
# 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_nansum.py
from unittest import TestCase
from diffprivlib.tools.utils import nansum
from diffprivlib.utils import PrivacyLeakWarning, BudgetError
        from diffprivlib.accountant import BudgetAccountant
import numpy as np

        self.assertIsNotNone(nansum((1, 2, 3), bounds=(1, 3)))

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

    def test_clipped_output(self):
        a = np.random.random((10,))

        for i in range(100):
            self.assertTrue(0 <= nansum(a, epsilon=1e-5, bounds=(0, 1)) <= 10)

    def test_int_output(self):
        a = np.random.random(1000) * 10
        res_int = nansum(a, dtype=int, bounds=(0, 10))
        self.assertIsInstance(res_int, int)

        res = np.nansum(a, dtype=int)
        res_inf = nansum(a, epsilon=float("inf"), dtype=int, bounds=(0, 10))
        self.assertEqual(res, res_inf)

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

        res = nansum(a, bounds=(0, 1))
        self.assertFalse(np.isnan(res))

        a = np.array([np.nan] * 10)

        res = nansum(a, epsilon=float("inf"), bounds=(0, 1))