from itertools import product, combinations_with_replacement

import numpy as np

from numba import jit, typeof
from numba.core.compiler import compile_isolated
from numba.np.numpy_support import numpy_version
from numba.tests.support import TestCase, MemoryLeakMixin, tag
import unittest


def array_all(arr):
    return arr.all()

def array_all_global(arr):
    return np.all(arr)

def array_any(arr):
    return arr.any()

def array_any_global(arr):
    return np.any(arr)

def array_cumprod(arr):
    return arr.cumprod()

def array_cumprod_global(arr):
    return np.cumprod(arr)

def array_nancumprod(arr):
    return np.nancumprod(arr)

def array_cumsum(arr):
    return arr.cumsum()

def array_cumsum_global(arr):
    return np.cumsum(arr)

def array_nancumsum(arr):
    return np.nancumsum(arr)

def array_sum(arr):
    return arr.sum()

def array_sum_global(arr):
    return np.sum(arr)

def array_prod(arr):
    return arr.prod()

def array_prod_global(arr):
    return np.prod(arr)

def array_mean(arr):
    return arr.mean()

def array_mean_global(arr):
    return np.mean(arr)

def array_var(arr):
    return arr.var()

def array_var_global(arr):
    return np.var(arr)

def array_std(arr):
    return arr.std()

def array_std_global(arr):
    return np.std(arr)

def array_min(arr):
    return arr.min()

def array_min_global(arr):
    return np.min(arr)

def array_amin(arr):
    return np.amin(arr)

def array_max(arr):
    return arr.max()

def array_max_global(arr):
    return np.max(arr)

def array_amax(arr):
    return np.amax(arr)

def array_argmin(arr):
    return arr.argmin()

def array_argmin_global(arr):
    return np.argmin(arr)

def array_argmax(arr):
    return arr.argmax()

def array_argmax_global(arr):
    return np.argmax(arr)

def array_median_global(arr):
    return np.median(arr)

def array_nanmin(arr):
    return np.nanmin(arr)

def array_nanmax(arr):
    return np.nanmax(arr)

def array_nanmean(arr):
    return np.nanmean(arr)

def array_nansum(arr):
    return np.nansum(arr)

def array_nanprod(arr):
    return np.nanprod(arr)

def array_nanstd(arr):
    return np.nanstd(arr)

def array_nanvar(arr):
    return np.nanvar(arr)

def array_nanmedian_global(arr):
    return np.nanmedian(arr)

def array_percentile_global(arr, q):
    return np.percentile(arr, q)

def array_nanpercentile_global(arr, q):
    return np.nanpercentile(arr, q)

def array_ptp_global(a):
    return np.ptp(a)

def array_ptp(a):
    return a.ptp()

def array_quantile_global(arr, q):
    return np.quantile(arr, q)

def array_nanquantile_global(arr, q):
    return np.nanquantile(arr, q)

def base_test_arrays(dtype):
    if dtype == np.bool_:
        def factory(n):
            assert n % 2 == 0
            return np.bool_([0, 1] * (n // 2))
    else:
        def factory(n):
            return np.arange(n, dtype=dtype) + 1

    a1 = factory(10)
    a2 = factory(10).reshape(2, 5)
    # The prod() of this array fits in a 32-bit int
    a3 = (factory(12))[::-1].reshape((2, 3, 2), order='A')
    assert not (a3.flags.c_contiguous or a3.flags.f_contiguous)

    return [a1, a2, a3]

def full_test_arrays(dtype):
    array_list = base_test_arrays(dtype)

    # Add floats with some mantissa
    if dtype == np.float32:
        array_list += [a / 10 for a in array_list]

    # add imaginary part
    if dtype == np.complex64:
        acc = []
        for a in array_list:
            tmp = a / 10 + 1j * a / 11
            tmp[::2] = np.conj(tmp[::2])
            acc.append(tmp)
        array_list.extend(acc)

    for a in array_list:
        assert a.dtype == np.dtype(dtype)
    return array_list

def run_comparative(compare_func, test_array):
    arrty = typeof(test_array)
    cres = compile_isolated(compare_func, [arrty])
    numpy_result = compare_func(test_array)
    numba_result = cres.entry_point(test_array)

    return numpy_result, numba_result


class TestArrayReductions(MemoryLeakMixin, TestCase):
    """
    Test array reduction methods and functions such as .sum(), .max(), etc.
    """

    def setUp(self):
        super(TestArrayReductions, self).setUp()
        np.random.seed(42)

    def check_reduction_basic(self, pyfunc, **kwargs):
        # Basic reduction checks on 1-d float64 arrays
        cfunc = jit(nopython=True)(pyfunc)
        def check(arr):
            self.assertPreciseEqual(pyfunc(arr), cfunc(arr), **kwargs)

        arr = np.float64([1.0, 2.0, 0.0, -0.0, 1.0, -1.5])
        check(arr)
        arr = np.float64([-0.0, -1.5])
        check(arr)
        arr = np.float64([-1.5, 2.5, 'inf'])
        check(arr)
        arr = np.float64([-1.5, 2.5, '-inf'])
        check(arr)
        arr = np.float64([-1.5, 2.5, 'inf', '-inf'])
        check(arr)
        arr = np.float64(['nan', -1.5, 2.5, 'nan', 3.0])
        check(arr)
        arr = np.float64(['nan', -1.5, 2.5, 'nan', 'inf', '-inf', 3.0])
        check(arr)
        arr = np.float64([5.0, 'nan', -1.5, 'nan'])
        check(arr)
        # Only NaNs
        arr = np.float64(['nan', 'nan'])
        check(arr)

    def test_all_basic(self, pyfunc=array_all):
        cfunc = jit(nopython=True)(pyfunc)
        def check(arr):
            self.assertPreciseEqual(pyfunc(arr), cfunc(arr))

        arr = np.float64([1.0, 0.0, float('inf'), float('nan')])
        check(arr)
        arr[1] = -0.0
        check(arr)
        arr[1] = 1.5
        check(arr)
        arr = arr.reshape((2, 2))
        check(arr)
        check(arr[::-1])

    def test_any_basic(self, pyfunc=array_any):
        cfunc = jit(nopython=True)(pyfunc)
        def check(arr):
            self.assertPreciseEqual(pyfunc(arr), cfunc(arr))

        arr = np.float64([0.0, -0.0, 0.0, 0.0])
        check(arr)
        arr[2] = float('nan')
        check(arr)
        arr[2] = float('inf')
        check(arr)
        arr[2] = 1.5
        check(arr)
        arr = arr.reshape((2, 2))
        check(arr)
        check(arr[::-1])

    def test_sum_basic(self):
        self.check_reduction_basic(array_sum)

    def test_mean_basic(self):
        self.check_reduction_basic(array_mean)

    def test_var_basic(self):
        self.check_reduction_basic(array_var, prec='double')

    def test_std_basic(self):
        self.check_reduction_basic(array_std)

    def test_min_basic(self):
        self.check_reduction_basic(array_min)

    def test_max_basic(self):
        self.check_reduction_basic(array_max)

    def test_argmin_basic(self):
        self.check_reduction_basic(array_argmin)

    def test_argmax_basic(self):
        self.check_reduction_basic(array_argmax)

    def test_nanmin_basic(self):
        self.check_reduction_basic(array_nanmin)

    def test_nanmax_basic(self):
        self.check_reduction_basic(array_nanmax)

    def test_nanmean_basic(self):
        self.check_reduction_basic(array_nanmean)

    def test_nansum_basic(self):
        self.check_reduction_basic(array_nansum)

    def test_nanprod_basic(self):
        self.check_reduction_basic(array_nanprod)

    def test_nanstd_basic(self):
        self.check_reduction_basic(array_nanstd)

    def test_nanvar_basic(self):
        self.check_reduction_basic(array_nanvar, prec='double')

    def check_median_basic(self, pyfunc, array_variations):
        cfunc = jit(nopython=True)(pyfunc)
        def check(arr):
            expected = pyfunc(arr)
            got = cfunc(arr)
            self.assertPreciseEqual(got, expected)

        # Odd sizes
        def check_odd(a):
            check(a)
            a = a.reshape((9, 7))
            check(a)
            check(a.T)
        for a in array_variations(np.arange(63) + 10.5):
            check_odd(a)

        # Even sizes
        def check_even(a):
            check(a)
            a = a.reshape((4, 16))
            check(a)
            check(a.T)
        for a in array_variations(np.arange(64) + 10.5):
            check_even(a)

    @staticmethod
    def _array_variations(a):
        # Sorted, reversed, random, many duplicates, many NaNs, all NaNs
        yield a
        a = a[::-1].copy()
        yield a
        np.random.shuffle(a)
        yield a
        a[a % 4 >= 1] = 3.5
        yield a
        a[a % 4 >= 2] = np.nan
        yield a
        a[:] = np.nan
        yield a

    def test_median_basic(self):
        pyfunc = array_median_global

        def variations(a):
            # Sorted, reversed, random, many duplicates
            yield a
            a = a[::-1].copy()
            yield a
            np.random.shuffle(a)
            yield a
            a[a % 4 >= 1] = 3.5
            yield a

        self.check_median_basic(pyfunc, variations)

    def check_percentile_and_quantile(self, pyfunc, q_upper_bound):
        cfunc = jit(nopython=True)(pyfunc)

        def check(a, q, abs_tol=1e-12):
            expected = pyfunc(a, q)
            got = cfunc(a, q)
            # NOTE: inf/nan is not checked, seems to be susceptible to upstream
            # changes
            finite = np.isfinite(expected)
            if np.all(finite):
                self.assertPreciseEqual(got, expected, abs_tol=abs_tol)
            else:
                self.assertPreciseEqual(got[finite], expected[finite],
                                        abs_tol=abs_tol)

        a = self.random.randn(27).reshape(3, 3, 3)
        q = np.linspace(0, q_upper_bound, 14)[::-1]
        check(a, q)
        check(a, 0)
        check(a, q_upper_bound / 2)
        check(a, q_upper_bound)

        not_finite = [np.nan, -np.inf, np.inf]
        a.flat[:10] = self.random.choice(not_finite, 10)
        self.random.shuffle(a)
        self.random.shuffle(q)
        check(a, q)

        a = a.flatten().tolist()
        q = q.flatten().tolist()
        check(a, q)
        check(tuple(a), tuple(q))

        a = self.random.choice([1, 2, 3, 4], 10)
        q = np.linspace(0, q_upper_bound, 5)
        check(a, q)

        # tests inspired by
        # https://github.com/numpy/numpy/blob/345b2f6e/numpy/lib/tests/test_function_base.py
        x = np.arange(8) * 0.5
        np.testing.assert_equal(cfunc(x, 0), 0.)
        np.testing.assert_equal(cfunc(x, q_upper_bound), 3.5)
        np.testing.assert_equal(cfunc(x, q_upper_bound / 2), 1.75)

        x = np.arange(12).reshape(3, 4)
        q = np.array((0.25, 0.5, 1.0)) * q_upper_bound
        np.testing.assert_equal(cfunc(x, q), [2.75, 5.5, 11.0])

        x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
        q = np.array((0.25, 0.50)) * q_upper_bound
        np.testing.assert_equal(cfunc(x, q).shape, (2,))

        q = np.array((0.25, 0.50, 0.75)) * q_upper_bound
        np.testing.assert_equal(cfunc(x, q).shape, (3,))

        x = np.arange(12).reshape(3, 4)
        np.testing.assert_equal(cfunc(x, q_upper_bound / 2), 5.5)
        self.assertTrue(np.isscalar(cfunc(x, q_upper_bound / 2)))

        np.testing.assert_equal(cfunc([1, 2, 3], 0), 1)

        a = np.array([2, 3, 4, 1])
        cfunc(a, [q_upper_bound / 2])
        np.testing.assert_equal(a, np.array([2, 3, 4, 1]))

    def check_percentile_edge_cases(self, pyfunc, q_upper_bound=100):
        cfunc = jit(nopython=True)(pyfunc)

        def check(a, q, abs_tol=1e-14):
            expected = pyfunc(a, q)
            got = cfunc(a, q)
            # NOTE: inf/nan is not checked, seems to be susceptible to upstream
            # changes
            finite = np.isfinite(expected)
            if np.all(finite):
                self.assertPreciseEqual(got, expected, abs_tol=abs_tol)
            else:
                self.assertPreciseEqual(got[finite], expected[finite],
                                        abs_tol=abs_tol)

        def convert_to_float_and_check(a, q, abs_tol=1e-14):
            expected = pyfunc(a, q).astype(np.float64)
            got = cfunc(a, q)
            self.assertPreciseEqual(got, expected, abs_tol=abs_tol)

        def _array_combinations(elements):
            for i in range(1, 10):
                for comb in combinations_with_replacement(elements, i):
                    yield np.array(comb)

        # high number of combinations, many including non-finite values
        q = (0, 0.1 * q_upper_bound, 0.2 * q_upper_bound, q_upper_bound)
        element_pool = (1, -1, np.nan, np.inf, -np.inf)
        for a in _array_combinations(element_pool):
            check(a, q)

        # edge cases - numpy exhibits behavioural differences across
        # platforms, see: https://github.com/numpy/numpy/issues/13272
        if q_upper_bound == 1:
            _check = convert_to_float_and_check
        else:
            _check = check

        a = np.array(5)
        q = np.array(1)
        _check(a, q)

        a = 5
        q = q_upper_bound / 2
        _check(a, q)

    def check_percentile_exceptions(self, pyfunc):
        cfunc = jit(nopython=True)(pyfunc)

        def check_err(a, q):
            with self.assertRaises(ValueError) as raises:
                cfunc(a, q)
            self.assertEqual(
                "Percentiles must be in the range [0, 100]",
                str(raises.exception)
            )

        # Exceptions leak references
        self.disable_leak_check()

        a = np.arange(5)
        check_err(a, -5)  # q less than 0
        check_err(a, (1, 10, 105))  # q contains value greater than 100
        check_err(a, (1, 10, np.nan))  # q contains nan

        with self.assertTypingError() as e:
            a = np.arange(5) * 1j
            q = 0.1
            cfunc(a, q)

        self.assertIn('Not supported for complex dtype', str(e.exception))

    def check_quantile_exceptions(self, pyfunc):
        cfunc = jit(nopython=True)(pyfunc)

        def check_err(a, q):
            with self.assertRaises(ValueError) as raises:
                cfunc(a, q)
            self.assertEqual(
                "Quantiles must be in the range [0, 1]",
                str(raises.exception)
            )

        # Exceptions leak references
        self.disable_leak_check()

        a = np.arange(5)
        check_err(a, -0.5)  # q less than 0
        check_err(a, (0.1, 0.10, 1.05))  # q contains value greater than 1
        check_err(a, (0.1, 0.10, np.nan))  # q contains nan

        with self.assertTypingError() as e:
            a = np.arange(5) * 1j
            q = 0.1
            cfunc(a, q)

        self.assertIn('Not supported for complex dtype', str(e.exception))

    def test_percentile_basic(self):
        pyfunc = array_percentile_global
        self.check_percentile_and_quantile(pyfunc, q_upper_bound=100)
        self.check_percentile_edge_cases(pyfunc, q_upper_bound=100)
        self.check_percentile_exceptions(pyfunc)

    def test_nanpercentile_basic(self):
        pyfunc = array_nanpercentile_global
        self.check_percentile_and_quantile(pyfunc, q_upper_bound=100)
        self.check_percentile_edge_cases(pyfunc, q_upper_bound=100)
        self.check_percentile_exceptions(pyfunc)

    def test_quantile_basic(self):
        pyfunc = array_quantile_global
        self.check_percentile_and_quantile(pyfunc, q_upper_bound=1)
        self.check_percentile_edge_cases(pyfunc, q_upper_bound=1)
        self.check_quantile_exceptions(pyfunc)

    def test_nanquantile_basic(self):
        pyfunc = array_nanquantile_global
        self.check_percentile_and_quantile(pyfunc, q_upper_bound=1)
        self.check_percentile_edge_cases(pyfunc, q_upper_bound=1)
        self.check_quantile_exceptions(pyfunc)

    def test_nanmedian_basic(self):
        pyfunc = array_nanmedian_global
        self.check_median_basic(pyfunc, self._array_variations)

    def test_array_sum_global(self):
        arr = np.arange(10, dtype=np.int32)
        arrty = typeof(arr)
        self.assertEqual(arrty.ndim, 1)
        self.assertEqual(arrty.layout, 'C')

        cres = compile_isolated(array_sum_global, [arrty])
        cfunc = cres.entry_point

        self.assertEqual(np.sum(arr), cfunc(arr))

    def test_array_prod_int_1d(self):
        arr = np.arange(10, dtype=np.int32) + 1
        arrty = typeof(arr)
        self.assertEqual(arrty.ndim, 1)
        self.assertEqual(arrty.layout, 'C')

        cres = compile_isolated(array_prod, [arrty])
        cfunc = cres.entry_point

        self.assertEqual(arr.prod(), cfunc(arr))

    def test_array_prod_float_1d(self):
        arr = np.arange(10, dtype=np.float32) + 1 / 10
        arrty = typeof(arr)
        self.assertEqual(arrty.ndim, 1)
        self.assertEqual(arrty.layout, 'C')

        cres = compile_isolated(array_prod, [arrty])
        cfunc = cres.entry_point

        np.testing.assert_allclose(arr.prod(), cfunc(arr))

    def test_array_prod_global(self):
        arr = np.arange(10, dtype=np.int32)
        arrty = typeof(arr)
        self.assertEqual(arrty.ndim, 1)
        self.assertEqual(arrty.layout, 'C')

        cres = compile_isolated(array_prod_global, [arrty])
        cfunc = cres.entry_point

        np.testing.assert_allclose(np.prod(arr), cfunc(arr))

    def check_cumulative(self, pyfunc):
        arr = np.arange(2, 10, dtype=np.int16)
        expected, got = run_comparative(pyfunc, arr)
        self.assertPreciseEqual(got, expected)
        arr = np.linspace(2, 8, 6)
        expected, got = run_comparative(pyfunc, arr)
        self.assertPreciseEqual(got, expected)
        arr = arr.reshape((3, 2))
        expected, got = run_comparative(pyfunc, arr)
        self.assertPreciseEqual(got, expected)

    def test_array_cumsum(self):
        self.check_cumulative(array_cumsum)

    def test_array_cumsum_global(self):
        self.check_cumulative(array_cumsum_global)

    def test_array_cumprod(self):
        self.check_cumulative(array_cumprod)

    def test_array_cumprod_global(self):
        self.check_cumulative(array_cumprod_global)

    def check_aggregation_magnitude(self, pyfunc, is_prod=False):
        """
        Check that integer overflows are avoided (issue #931).
        """
        # Overflows are avoided here (ints are cast either to intp
        # or float64).
        n_items = 2 if is_prod else 10  # avoid overflow on prod()
        arr = (np.arange(n_items) + 40000).astype('int16')
        npr, nbr = run_comparative(pyfunc, arr)
        self.assertPreciseEqual(npr, nbr)
        # Overflows are avoided for functions returning floats here.
        # Other functions may wrap around.
        arr = (np.arange(10) + 2**60).astype('int64')
        npr, nbr = run_comparative(pyfunc, arr)
        self.assertPreciseEqual(npr, nbr)
        arr = arr.astype('uint64')
        npr, nbr = run_comparative(pyfunc, arr)
        self.assertPreciseEqual(npr, nbr)

    def test_sum_magnitude(self):
        self.check_aggregation_magnitude(array_sum)
        self.check_aggregation_magnitude(array_sum_global)

    def test_cumsum_magnitude(self):
        self.check_aggregation_magnitude(array_cumsum)
        self.check_aggregation_magnitude(array_cumsum_global)

    def test_nancumsum_magnitude(self):
        self.check_aggregation_magnitude(array_nancumsum, is_prod=True)

    def test_prod_magnitude(self):
        self.check_aggregation_magnitude(array_prod, is_prod=True)
        self.check_aggregation_magnitude(array_prod_global, is_prod=True)

    def test_cumprod_magnitude(self):
        self.check_aggregation_magnitude(array_cumprod, is_prod=True)
        self.check_aggregation_magnitude(array_cumprod_global, is_prod=True)

    def test_nancumprod_magnitude(self):
        self.check_aggregation_magnitude(array_nancumprod, is_prod=True)

    def test_mean_magnitude(self):
        self.check_aggregation_magnitude(array_mean)
        self.check_aggregation_magnitude(array_mean_global)

    def test_var_magnitude(self):
        self.check_aggregation_magnitude(array_var)
        self.check_aggregation_magnitude(array_var_global)

    def test_std_magnitude(self):
        self.check_aggregation_magnitude(array_std)
        self.check_aggregation_magnitude(array_std_global)

    def _do_check_nptimedelta(self, pyfunc, arr):
        arrty = typeof(arr)
        cfunc = jit(nopython=True)(pyfunc)

        self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
        # Even vs. odd size, for np.median
        self.assertPreciseEqual(cfunc(arr[:-1]), pyfunc(arr[:-1]))
        # Test with different orders, for np.median
        arr = arr[::-1].copy()  # Keep 'C' layout
        self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
        np.random.shuffle(arr)
        self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
        # Test with a NaT
        if 'median' not in pyfunc.__name__:
            # Test with (val, NaT)^N (and with the random NaT from above)
            # use a loop, there's some weird thing/bug with arr[1::2] = 'NaT'

            # Further Numba has bug(s) relating to NaN/NaT handling in anything
            # using a partition such as np.median
            for x in range(1, len(arr), 2):
                arr[x] = 'NaT'
            self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
        # Test with all NaTs
        arr.fill(arrty.dtype('NaT'))
        self.assertPreciseEqual(cfunc(arr), pyfunc(arr))

    def check_npdatetime(self, pyfunc):
        arr = np.arange(10).astype(dtype='M8[Y]')
        self._do_check_nptimedelta(pyfunc, arr)

    def check_nptimedelta(self, pyfunc):
        arr = np.arange(10).astype(dtype='m8[s]')
        self._do_check_nptimedelta(pyfunc, arr)

    def test_min_npdatetime(self):
        self.check_npdatetime(array_min)
        self.check_nptimedelta(array_min)

    def test_max_npdatetime(self):
        self.check_npdatetime(array_max)
        self.check_nptimedelta(array_max)

    def test_argmin_npdatetime(self):
        self.check_npdatetime(array_argmin)
        self.check_nptimedelta(array_argmin)

    def test_argmax_npdatetime(self):
        self.check_npdatetime(array_argmax)
        self.check_nptimedelta(array_argmax)

    def test_median_npdatetime(self):
        self.check_nptimedelta(array_median_global)

    def test_sum_npdatetime(self):
        self.check_nptimedelta(array_sum)

    def test_cumsum_npdatetime(self):
        self.check_nptimedelta(array_cumsum)

    def test_mean_npdatetime(self):
        self.check_nptimedelta(array_mean)

    def check_nan_cumulative(self, pyfunc):
        cfunc = jit(nopython=True)(pyfunc)

        def check(a):
            expected = pyfunc(a)
            got = cfunc(a)
            self.assertPreciseEqual(expected, got)

        def _set_some_values_to_nan(a):
            p = a.size // 2  # set approx half elements to NaN
            np.put(a, np.random.choice(range(a.size), p, replace=False), np.nan)
            return a

        def a_variations():
            yield np.linspace(-1, 3, 60).reshape(3, 4, 5)
            yield np.array([np.inf, 3, 4])
            yield np.array([True, True, True, False])
            yield np.arange(1, 10)
            yield np.asfortranarray(np.arange(1, 64) - 33.3)
            yield np.arange(1, 10, dtype=np.float32)[::-1]

        for a in a_variations():
            check(a)  # no nans
            check(_set_some_values_to_nan(a.astype(np.float64)))  # about 50% nans

        # edge cases
        check(np.array([]))
        check(np.full(10, np.nan))

        parts = np.array([np.nan, 2, np.nan, 4, 5, 6, 7, 8, 9])

        a = parts + 1j * parts[::-1]
        a = a.reshape(3, 3)
        check(a)

    def test_nancumprod_basic(self):
        self.check_cumulative(array_nancumprod)
        self.check_nan_cumulative(array_nancumprod)

    def test_nancumsum_basic(self):
        self.check_cumulative(array_nancumsum)
        self.check_nan_cumulative(array_nancumsum)

    def test_ptp_basic(self):
        pyfunc = array_ptp_global
        cfunc = jit(nopython=True)(pyfunc)

        def check(a):
            expected = pyfunc(a)
            got = cfunc(a)
            self.assertPreciseEqual(expected, got)

        def a_variations():
            yield np.arange(10)
            yield np.array([-1.1, np.nan, 2.2])
            yield np.array([-np.inf, 5])
            yield (4, 2, 5)
            yield (1,)
            yield np.full(5, 5)
            yield [2.2, -2.3, 0.1]
            a = np.linspace(-10, 10, 16).reshape(4, 2, 2)
            yield a
            yield np.asfortranarray(a)
            yield a[::-1]
            np.random.RandomState(0).shuffle(a)
            yield a
            yield 6
            yield 6.5
            yield -np.inf
            yield 1 + 4j
            yield [2.2, np.nan]
            yield [2.2, np.inf]
            yield ((4.1, 2.0, -7.6), (4.3, 2.7, 5.2))
            yield np.full(5, np.nan)
            yield 1 + np.nan * 1j
            yield np.nan + np.nan * 1j
            yield np.nan

        for a in a_variations():
            check(a)

    def test_ptp_method(self):
        # checks wiring of np.ndarray.ptp() only, `np.ptp` test above checks
        # the actual alg
        pyfunc = array_ptp
        cfunc = jit(nopython=True)(pyfunc)

        a = np.arange(10)
        expected = pyfunc(a)
        got = cfunc(a)
        self.assertPreciseEqual(expected, got)

    def test_ptp_complex(self):
        pyfunc = array_ptp_global
        cfunc = jit(nopython=True)(pyfunc)

        def check(a):
            expected = pyfunc(a)
            got = cfunc(a)
            self.assertPreciseEqual(expected, got)

        def make_array(real_nan=False, imag_nan=False):
            real = np.linspace(-4, 4, 25)
            if real_nan:
                real[4:9] = np.nan
            imag = np.linspace(-5, 5, 25)
            if imag_nan:
                imag[7:12] = np.nan
            return (real + 1j * imag).reshape(5, 5)

        for real_nan, imag_nan in product([True, False], repeat=2):
            comp = make_array(real_nan, imag_nan)
            check(comp)

        real = np.ones(8)
        imag = np.arange(-4, 4)
        comp = real + 1j * imag
        check(comp)
        comp = real - 1j * imag
        check(comp)

        comp = np.full((4, 4), fill_value=(1 - 1j))
        check(comp)

    def test_ptp_exceptions(self):
        pyfunc = array_ptp_global
        cfunc = jit(nopython=True)(pyfunc)

        # Exceptions leak references
        self.disable_leak_check()

        with self.assertTypingError() as e:
            cfunc(np.array((True, True, False)))

        msg = "Boolean dtype is unsupported (as per NumPy)"
        self.assertIn(msg, str(e.exception))

        with self.assertRaises(ValueError) as e:
            cfunc(np.array([]))

        msg = "zero-size array reduction not possible"
        self.assertIn(msg, str(e.exception))

    def test_min_max_complex_basic(self):
        pyfuncs = array_min_global, array_max_global

        for pyfunc in pyfuncs:
            cfunc = jit(nopython=True)(pyfunc)

            def check(a):
                expected = pyfunc(a)
                got = cfunc(a)
                self.assertPreciseEqual(expected, got)

            real = np.linspace(-10, 10, 40)
            real[:4] = real[-1]
            imag = real * 2
            a = real - imag * 1j
            check(a)

            for _ in range(10):
                self.random.shuffle(real)
                self.random.shuffle(imag)
                dtype = self.random.choice([np.complex64, np.complex128])
                a = real - imag * 1j
                a[:4] = a[-1]
                check(a.astype(dtype))

    def test_nanmin_nanmax_complex_basic(self):
        pyfuncs = array_nanmin, array_nanmax

        for pyfunc in pyfuncs:
            cfunc = jit(nopython=True)(pyfunc)

            def check(a):
                expected = pyfunc(a)
                got = cfunc(a)
                self.assertPreciseEqual(expected, got)

            real = np.linspace(-10, 10, 40)
            real[:4] = real[-1]
            real[5:9] = np.nan
            imag = real * 2
            imag[7:12] = np.nan
            a = real - imag * 1j
            check(a)

            for _ in range(10):
                self.random.shuffle(real)
                self.random.shuffle(imag)
                a = real - imag * 1j
                a[:4] = a[-1]
                check(a)

    def test_nanmin_nanmax_non_array_inputs(self):
        pyfuncs = array_nanmin, array_nanmax

        def check(a):
            expected = pyfunc(a)
            got = cfunc(a)
            self.assertPreciseEqual(expected, got)

        def a_variations():
            yield [1, 6, 4, 2]
            yield ((-10, 4, -12), (5, 200, -30))
            yield np.array(3)
            yield (2,)
            yield 3.142
            yield False
            yield (np.nan, 3.142, -5.2, 3.0)
            yield [np.inf, np.nan, -np.inf]
            yield [(np.nan, 1.1), (-4.4, 8.7)]

        for pyfunc in pyfuncs:
            cfunc = jit(nopython=True)(pyfunc)

            for a in a_variations():
                check(a)

    def test_argmax_axis_1d_2d_4d(self):
        arr1d = np.array([0, 20, 3, 4])
        arr2d = np.arange(6).reshape(2, 3)
        arr2d[0,1] += 100

        arr4d = np.arange(120).reshape(2, 3, 4, 5) + 10
        arr4d[0, 1, 1, 2] += 100
        arr4d[1, 0, 0, 0] -= 51

        for arr in [arr1d, arr2d, arr4d]:
            axes = list(range(arr.ndim)) + [
                -(i+1) for i in range(arr.ndim)
            ]
            py_functions = [
                lambda a, _axis=axis: np.argmax(a, axis=_axis)
                for axis in axes
            ]
            c_functions = [
                jit(nopython=True)(pyfunc) for pyfunc in py_functions
            ]
            for cfunc in c_functions:
                self.assertPreciseEqual(cfunc.py_func(arr), cfunc(arr))

    def test_argmax_axis_out_of_range(self):
        arr1d = np.arange(6)
        arr2d = np.arange(6).reshape(2, 3)

        @jit(nopython=True)
        def jitargmax(arr, axis):
            return np.argmax(arr, axis)

        def assert_raises(arr, axis):
            with self.assertRaisesRegex(ValueError, "axis.*out of bounds"):
                jitargmax.py_func(arr, axis)
            with self.assertRaisesRegex(ValueError, "axis.*out of bounds"):
                jitargmax(arr, axis)

        assert_raises(arr1d, 1)
        assert_raises(arr1d, -2)
        assert_raises(arr2d, -3)
        assert_raises(arr2d, 2)
        # Exceptions leak references
        self.disable_leak_check()

    def test_argmax_axis_must_be_integer(self):
        arr = np.arange(6)

        @jit(nopython=True)
        def jitargmax(arr, axis):
            return np.argmax(arr, axis)

        with self.assertTypingError() as e:
            jitargmax(arr, "foo")
        self.assertIn("axis must be an integer", str(e.exception))

    def test_argmax_method_axis(self):
        arr2d = np.arange(6).reshape(2, 3)

        def argmax(arr):
            return arr2d.argmax(axis=0)

        self.assertPreciseEqual(argmax(arr2d),
                                jit(nopython=True)(argmax)(arr2d))

    def test_argmax_return_type(self):
        # See issue #7853, return type should be intp not based on input type
        arr2d = np.arange(6, dtype=np.uint8).reshape(2, 3)

        def argmax(arr):
            return arr2d.argmax(axis=0)

        self.assertPreciseEqual(argmax(arr2d),
                                jit(nopython=True)(argmax)(arr2d))

    def test_argmin_axis_1d_2d_4d(self):
        arr1d = np.array([0, 20, 3, 4])
        arr2d = np.arange(6).reshape(2, 3)
        arr2d[0,1] += 100

        arr4d = np.arange(120).reshape(2, 3, 4, 5) + 10
        arr4d[0, 1, 1, 2] += 100
        arr4d[1, 0, 0, 0] -= 51

        for arr in [arr1d, arr2d, arr4d]:
            axes = list(range(arr.ndim)) + [
                -(i+1) for i in range(arr.ndim)
            ]
            py_functions = [
                lambda a, _axis=axis: np.argmin(a, axis=_axis)
                for axis in axes
            ]
            c_functions = [
                jit(nopython=True)(pyfunc) for pyfunc in py_functions
            ]
            for cfunc in c_functions:
                self.assertPreciseEqual(cfunc.py_func(arr), cfunc(arr))

    def test_argmin_axis_out_of_range(self):
        arr1d = np.arange(6)
        arr2d = np.arange(6).reshape(2, 3)

        @jit(nopython=True)
        def jitargmin(arr, axis):
            return np.argmin(arr, axis)

        def assert_raises(arr, axis):
            with self.assertRaisesRegex(ValueError, "axis.*out of bounds"):
                jitargmin.py_func(arr, axis)
            with self.assertRaisesRegex(ValueError, "axis.*out of bounds"):
                jitargmin(arr, axis)

        assert_raises(arr1d, 1)
        assert_raises(arr1d, -2)
        assert_raises(arr2d, -3)
        assert_raises(arr2d, 2)

        # Exceptions leak references
        self.disable_leak_check()

    def test_argmin_axis_must_be_integer(self):
        arr = np.arange(6)

        @jit(nopython=True)
        def jitargmin(arr, axis):
            return np.argmin(arr, axis)

        with self.assertTypingError() as e:
            jitargmin(arr, "foo")
        self.assertIn("axis must be an integer", str(e.exception))

    def test_argmin_method_axis(self):
        arr2d = np.arange(6).reshape(2, 3)

        def argmin(arr):
            return arr2d.argmin(axis=0)

        self.assertPreciseEqual(argmin(arr2d),
                                jit(nopython=True)(argmin)(arr2d))

    def test_argmin_return_type(self):
        # See issue #7853, return type should be intp not based on input type
        arr2d = np.arange(6, dtype=np.uint8).reshape(2, 3)

        def argmin(arr):
            return arr2d.argmin(axis=0)

        self.assertPreciseEqual(argmin(arr2d),
                                jit(nopython=True)(argmin)(arr2d))

    @classmethod
    def install_generated_tests(cls):
        # These form a testing product where each of the combinations are tested

        # these function are tested in real and complex space
        reduction_funcs = [array_sum, array_sum_global,
                           array_prod, array_prod_global,
                           array_mean, array_mean_global,
                           array_var, array_var_global,
                           array_std, array_std_global,
                           array_all, array_all_global,
                           array_any, array_any_global,
                           array_min, array_min_global,
                           array_amax, array_amin,
                           array_max, array_max_global,
                           array_nanmax, array_nanmin,
                           array_nansum,
                           ]

        # these functions only work in real space as no complex comparison
        # operator is implemented
        reduction_funcs_rspace = [array_argmin, array_argmin_global,
                                  array_argmax, array_argmax_global]

        reduction_funcs += [array_nanmean, array_nanstd, array_nanvar]
        reduction_funcs += [array_nanprod]

        dtypes_to_test = [np.int32, np.float32, np.bool_, np.complex64]

        def install_tests(dtypes, funcs):
            # Install tests on class
            for dt in dtypes:
                test_arrays = full_test_arrays(dt)
                for red_func, test_array in product(funcs, test_arrays):
                    # Create the name for the test function
                    test_name = "test_{0}_{1}_{2}d"
                    test_name = test_name.format(red_func.__name__,
                                                 test_array.dtype.name,
                                                 test_array.ndim)

                    def new_test_function(self, redFunc=red_func,
                                          testArray=test_array,
                                          testName=test_name):
                        ulps = 1
                        if 'prod' in red_func.__name__ and \
                            np.iscomplexobj(testArray):
                            # prod family accumulate slightly more error on
                            # some architectures (power, 32bit) for complex input
                            ulps = 3
                        npr, nbr = run_comparative(redFunc, testArray)
                        self.assertPreciseEqual(npr, nbr, msg=testName,
                                                prec="single", ulps=ulps)

                    # Install it into the class
                    setattr(cls, test_name, new_test_function)

        # install tests for reduction functions that only work in real space
        install_tests(dtypes_to_test[:-1], reduction_funcs_rspace)

        # install tests for reduction functions
        install_tests(dtypes_to_test, reduction_funcs)


TestArrayReductions.install_generated_tests()


class TestArrayReductionsExceptions(MemoryLeakMixin, TestCase):

    # int64, size 0
    zero_size = np.arange(0)

    def check_exception(self, pyfunc, msg):
        cfunc = jit(nopython=True)(pyfunc)
        # make sure NumPy raises consistently/no behaviour change
        with self.assertRaises(BaseException):
            pyfunc(self.zero_size)
        # check numba impl raises expected
        with self.assertRaises(ValueError) as e:
            cfunc(self.zero_size)
        self.assertIn(msg, str(e.exception))

    @classmethod
    def install(cls):

        fn_to_msg = dict()
        empty_seq = "attempt to get {0} of an empty sequence"
        op_no_ident = ("zero-size array to reduction operation "
                       "{0}")
        for x in [array_argmax, array_argmax_global, array_argmin,
                  array_argmin_global]:
            fn_to_msg[x] = empty_seq
        for x in [array_max, array_max, array_min, array_min]:
            fn_to_msg[x] = op_no_ident

        name_template = "test_zero_size_array_{0}"
        for fn, msg in fn_to_msg.items():
            test_name = name_template.format(fn.__name__)

            lmsg = msg.format(fn.__name__)
            lmsg = lmsg.replace('array_','').replace('_global','')
            def test_fn(self, func=fn, message=lmsg):
                self.check_exception(func, message)

            setattr(cls, test_name, test_fn)

TestArrayReductionsExceptions.install()


if __name__ == '__main__':
    unittest.main()
