import subprocess
import sys
import textwrap

import numpy._core._multiarray_tests as _multiarray_tests
import pytest

import numpy as np
import numpy._core.umath as ncu
from numpy import all, arange, array, nditer
from numpy.testing import (
    HAS_REFCOUNT,
    IS_WASM,
    assert_,
    assert_array_equal,
    assert_equal,
    assert_raises,
    suppress_warnings,
)
from numpy.testing._private.utils import requires_memory


def iter_multi_index(i):
    ret = []
    while not i.finished:
        ret.append(i.multi_index)
        i.iternext()
    return ret

def iter_indices(i):
    ret = []
    while not i.finished:
        ret.append(i.index)
        i.iternext()
    return ret

def iter_iterindices(i):
    ret = []
    while not i.finished:
        ret.append(i.iterindex)
        i.iternext()
    return ret

@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
def test_iter_refcount():
    # Make sure the iterator doesn't leak

    # Basic
    a = arange(6)
    dt = np.dtype('f4').newbyteorder()
    rc_a = sys.getrefcount(a)
    rc_dt = sys.getrefcount(dt)
    with nditer(a, [],
                [['readwrite', 'updateifcopy']],
                casting='unsafe',
                op_dtypes=[dt]) as it:
        assert_(not it.iterationneedsapi)
        assert_(sys.getrefcount(a) > rc_a)
        assert_(sys.getrefcount(dt) > rc_dt)
    # del 'it'
    it = None
    assert_equal(sys.getrefcount(a), rc_a)
    assert_equal(sys.getrefcount(dt), rc_dt)

    # With a copy
    a = arange(6, dtype='f4')
    dt = np.dtype('f4')
    rc_a = sys.getrefcount(a)
    rc_dt = sys.getrefcount(dt)
    it = nditer(a, [],
                [['readwrite']],
                op_dtypes=[dt])
    rc2_a = sys.getrefcount(a)
    rc2_dt = sys.getrefcount(dt)
    it2 = it.copy()
    assert_(sys.getrefcount(a) > rc2_a)
    if sys.version_info < (3, 13):
        # np.dtype('f4') is immortal after Python 3.13
        assert_(sys.getrefcount(dt) > rc2_dt)
    it = None
    assert_equal(sys.getrefcount(a), rc2_a)
    assert_equal(sys.getrefcount(dt), rc2_dt)
    it2 = None
    assert_equal(sys.getrefcount(a), rc_a)
    assert_equal(sys.getrefcount(dt), rc_dt)

def test_iter_best_order():
    # The iterator should always find the iteration order
    # with increasing memory addresses

    # Test the ordering for 1-D to 5-D shapes
    for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
        a = arange(np.prod(shape))
        # Test each combination of positive and negative strides
        for dirs in range(2**len(shape)):
            dirs_index = [slice(None)] * len(shape)
            for bit in range(len(shape)):
                if ((2**bit) & dirs):
                    dirs_index[bit] = slice(None, None, -1)
            dirs_index = tuple(dirs_index)

            aview = a.reshape(shape)[dirs_index]
            # C-order
            i = nditer(aview, [], [['readonly']])
            assert_equal(list(i), a)
            # Fortran-order
            i = nditer(aview.T, [], [['readonly']])
            assert_equal(list(i), a)
            # Other order
            if len(shape) > 2:
                i = nditer(aview.swapaxes(0, 1), [], [['readonly']])
                assert_equal(list(i), a)

def test_iter_c_order():
    # Test forcing C order

    # Test the ordering for 1-D to 5-D shapes
    for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
        a = arange(np.prod(shape))
        # Test each combination of positive and negative strides
        for dirs in range(2**len(shape)):
            dirs_index = [slice(None)] * len(shape)
            for bit in range(len(shape)):
                if ((2**bit) & dirs):
                    dirs_index[bit] = slice(None, None, -1)
            dirs_index = tuple(dirs_index)

            aview = a.reshape(shape)[dirs_index]
            # C-order
            i = nditer(aview, order='C')
            assert_equal(list(i), aview.ravel(order='C'))
            # Fortran-order
            i = nditer(aview.T, order='C')
            assert_equal(list(i), aview.T.ravel(order='C'))
            # Other order
            if len(shape) > 2:
                i = nditer(aview.swapaxes(0, 1), order='C')
                assert_equal(list(i),
                                    aview.swapaxes(0, 1).ravel(order='C'))

def test_iter_f_order():
    # Test forcing F order

    # Test the ordering for 1-D to 5-D shapes
    for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
        a = arange(np.prod(shape))
        # Test each combination of positive and negative strides
        for dirs in range(2**len(shape)):
            dirs_index = [slice(None)] * len(shape)
            for bit in range(len(shape)):
                if ((2**bit) & dirs):
                    dirs_index[bit] = slice(None, None, -1)
            dirs_index = tuple(dirs_index)

            aview = a.reshape(shape)[dirs_index]
            # C-order
            i = nditer(aview, order='F')
            assert_equal(list(i), aview.ravel(order='F'))
            # Fortran-order
            i = nditer(aview.T, order='F')
            assert_equal(list(i), aview.T.ravel(order='F'))
            # Other order
            if len(shape) > 2:
                i = nditer(aview.swapaxes(0, 1), order='F')
                assert_equal(list(i),
                                    aview.swapaxes(0, 1).ravel(order='F'))

def test_iter_c_or_f_order():
    # Test forcing any contiguous (C or F) order

    # Test the ordering for 1-D to 5-D shapes
    for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
        a = arange(np.prod(shape))
        # Test each combination of positive and negative strides
        for dirs in range(2**len(shape)):
            dirs_index = [slice(None)] * len(shape)
            for bit in range(len(shape)):
                if ((2**bit) & dirs):
                    dirs_index[bit] = slice(None, None, -1)
            dirs_index = tuple(dirs_index)

            aview = a.reshape(shape)[dirs_index]
            # C-order
            i = nditer(aview, order='A')
            assert_equal(list(i), aview.ravel(order='A'))
            # Fortran-order
            i = nditer(aview.T, order='A')
            assert_equal(list(i), aview.T.ravel(order='A'))
            # Other order
            if len(shape) > 2:
                i = nditer(aview.swapaxes(0, 1), order='A')
                assert_equal(list(i),
                                    aview.swapaxes(0, 1).ravel(order='A'))

def test_nditer_multi_index_set():
    # Test the multi_index set
    a = np.arange(6).reshape(2, 3)
    it = np.nditer(a, flags=['multi_index'])

    # Removes the iteration on two first elements of a[0]
    it.multi_index = (0, 2,)

    assert_equal(list(it), [2, 3, 4, 5])

@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
def test_nditer_multi_index_set_refcount():
    # Test if the reference count on index variable is decreased

    index = 0
    i = np.nditer(np.array([111, 222, 333, 444]), flags=['multi_index'])

    start_count = sys.getrefcount(index)
    i.multi_index = (index,)
    end_count = sys.getrefcount(index)

    assert_equal(start_count, end_count)

def test_iter_best_order_multi_index_1d():
    # The multi-indices should be correct with any reordering

    a = arange(4)
    # 1D order
    i = nditer(a, ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(0,), (1,), (2,), (3,)])
    # 1D reversed order
    i = nditer(a[::-1], ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(3,), (2,), (1,), (0,)])

def test_iter_best_order_multi_index_2d():
    # The multi-indices should be correct with any reordering

    a = arange(6)
    # 2D C-order
    i = nditer(a.reshape(2, 3), ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)])
    # 2D Fortran-order
    i = nditer(a.reshape(2, 3).copy(order='F'), ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(0, 0), (1, 0), (0, 1), (1, 1), (0, 2), (1, 2)])
    # 2D reversed C-order
    i = nditer(a.reshape(2, 3)[::-1], ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(1, 0), (1, 1), (1, 2), (0, 0), (0, 1), (0, 2)])
    i = nditer(a.reshape(2, 3)[:, ::-1], ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(0, 2), (0, 1), (0, 0), (1, 2), (1, 1), (1, 0)])
    i = nditer(a.reshape(2, 3)[::-1, ::-1], ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(1, 2), (1, 1), (1, 0), (0, 2), (0, 1), (0, 0)])
    # 2D reversed Fortran-order
    i = nditer(a.reshape(2, 3).copy(order='F')[::-1], ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(1, 0), (0, 0), (1, 1), (0, 1), (1, 2), (0, 2)])
    i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
                                                   ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(0, 2), (1, 2), (0, 1), (1, 1), (0, 0), (1, 0)])
    i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
                                                   ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i), [(1, 2), (0, 2), (1, 1), (0, 1), (1, 0), (0, 0)])

def test_iter_best_order_multi_index_3d():
    # The multi-indices should be correct with any reordering

    a = arange(12)
    # 3D C-order
    i = nditer(a.reshape(2, 3, 2), ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i),
                            [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (0, 2, 0), (0, 2, 1),
                             (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1), (1, 2, 0), (1, 2, 1)])
    # 3D Fortran-order
    i = nditer(a.reshape(2, 3, 2).copy(order='F'), ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i),
                            [(0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 2, 0), (1, 2, 0),
                             (0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1), (0, 2, 1), (1, 2, 1)])
    # 3D reversed C-order
    i = nditer(a.reshape(2, 3, 2)[::-1], ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i),
                            [(1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1), (1, 2, 0), (1, 2, 1),
                             (0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (0, 2, 0), (0, 2, 1)])
    i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i),
                            [(0, 2, 0), (0, 2, 1), (0, 1, 0), (0, 1, 1), (0, 0, 0), (0, 0, 1),
                             (1, 2, 0), (1, 2, 1), (1, 1, 0), (1, 1, 1), (1, 0, 0), (1, 0, 1)])
    i = nditer(a.reshape(2, 3, 2)[:, :, ::-1], ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i),
                            [(0, 0, 1), (0, 0, 0), (0, 1, 1), (0, 1, 0), (0, 2, 1), (0, 2, 0),
                             (1, 0, 1), (1, 0, 0), (1, 1, 1), (1, 1, 0), (1, 2, 1), (1, 2, 0)])
    # 3D reversed Fortran-order
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
                                                    ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i),
                            [(1, 0, 0), (0, 0, 0), (1, 1, 0), (0, 1, 0), (1, 2, 0), (0, 2, 0),
                             (1, 0, 1), (0, 0, 1), (1, 1, 1), (0, 1, 1), (1, 2, 1), (0, 2, 1)])
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
                                                    ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i),
                            [(0, 2, 0), (1, 2, 0), (0, 1, 0), (1, 1, 0), (0, 0, 0), (1, 0, 0),
                             (0, 2, 1), (1, 2, 1), (0, 1, 1), (1, 1, 1), (0, 0, 1), (1, 0, 1)])
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, :, ::-1],
                                                    ['multi_index'], [['readonly']])
    assert_equal(iter_multi_index(i),
                            [(0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1), (0, 2, 1), (1, 2, 1),
                             (0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 2, 0), (1, 2, 0)])

def test_iter_best_order_c_index_1d():
    # The C index should be correct with any reordering

    a = arange(4)
    # 1D order
    i = nditer(a, ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [0, 1, 2, 3])
    # 1D reversed order
    i = nditer(a[::-1], ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [3, 2, 1, 0])

def test_iter_best_order_c_index_2d():
    # The C index should be correct with any reordering

    a = arange(6)
    # 2D C-order
    i = nditer(a.reshape(2, 3), ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [0, 1, 2, 3, 4, 5])
    # 2D Fortran-order
    i = nditer(a.reshape(2, 3).copy(order='F'),
                                    ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [0, 3, 1, 4, 2, 5])
    # 2D reversed C-order
    i = nditer(a.reshape(2, 3)[::-1], ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [3, 4, 5, 0, 1, 2])
    i = nditer(a.reshape(2, 3)[:, ::-1], ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [2, 1, 0, 5, 4, 3])
    i = nditer(a.reshape(2, 3)[::-1, ::-1], ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [5, 4, 3, 2, 1, 0])
    # 2D reversed Fortran-order
    i = nditer(a.reshape(2, 3).copy(order='F')[::-1],
                                    ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [3, 0, 4, 1, 5, 2])
    i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
                                    ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [2, 5, 1, 4, 0, 3])
    i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
                                    ['c_index'], [['readonly']])
    assert_equal(iter_indices(i), [5, 2, 4, 1, 3, 0])

def test_iter_best_order_c_index_3d():
    # The C index should be correct with any reordering

    a = arange(12)
    # 3D C-order
    i = nditer(a.reshape(2, 3, 2), ['c_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
    # 3D Fortran-order
    i = nditer(a.reshape(2, 3, 2).copy(order='F'),
                                    ['c_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [0, 6, 2, 8, 4, 10, 1, 7, 3, 9, 5, 11])
    # 3D reversed C-order
    i = nditer(a.reshape(2, 3, 2)[::-1], ['c_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5])
    i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['c_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [4, 5, 2, 3, 0, 1, 10, 11, 8, 9, 6, 7])
    i = nditer(a.reshape(2, 3, 2)[:, :, ::-1], ['c_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10])
    # 3D reversed Fortran-order
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
                                    ['c_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [6, 0, 8, 2, 10, 4, 7, 1, 9, 3, 11, 5])
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
                                    ['c_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [4, 10, 2, 8, 0, 6, 5, 11, 3, 9, 1, 7])
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, :, ::-1],
                                    ['c_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [1, 7, 3, 9, 5, 11, 0, 6, 2, 8, 4, 10])

def test_iter_best_order_f_index_1d():
    # The Fortran index should be correct with any reordering

    a = arange(4)
    # 1D order
    i = nditer(a, ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [0, 1, 2, 3])
    # 1D reversed order
    i = nditer(a[::-1], ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [3, 2, 1, 0])

def test_iter_best_order_f_index_2d():
    # The Fortran index should be correct with any reordering

    a = arange(6)
    # 2D C-order
    i = nditer(a.reshape(2, 3), ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [0, 2, 4, 1, 3, 5])
    # 2D Fortran-order
    i = nditer(a.reshape(2, 3).copy(order='F'),
                                    ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [0, 1, 2, 3, 4, 5])
    # 2D reversed C-order
    i = nditer(a.reshape(2, 3)[::-1], ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [1, 3, 5, 0, 2, 4])
    i = nditer(a.reshape(2, 3)[:, ::-1], ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [4, 2, 0, 5, 3, 1])
    i = nditer(a.reshape(2, 3)[::-1, ::-1], ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [5, 3, 1, 4, 2, 0])
    # 2D reversed Fortran-order
    i = nditer(a.reshape(2, 3).copy(order='F')[::-1],
                                    ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [1, 0, 3, 2, 5, 4])
    i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
                                    ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [4, 5, 2, 3, 0, 1])
    i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
                                    ['f_index'], [['readonly']])
    assert_equal(iter_indices(i), [5, 4, 3, 2, 1, 0])

def test_iter_best_order_f_index_3d():
    # The Fortran index should be correct with any reordering

    a = arange(12)
    # 3D C-order
    i = nditer(a.reshape(2, 3, 2), ['f_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [0, 6, 2, 8, 4, 10, 1, 7, 3, 9, 5, 11])
    # 3D Fortran-order
    i = nditer(a.reshape(2, 3, 2).copy(order='F'),
                                    ['f_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
    # 3D reversed C-order
    i = nditer(a.reshape(2, 3, 2)[::-1], ['f_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [1, 7, 3, 9, 5, 11, 0, 6, 2, 8, 4, 10])
    i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['f_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [4, 10, 2, 8, 0, 6, 5, 11, 3, 9, 1, 7])
    i = nditer(a.reshape(2, 3, 2)[:, :, ::-1], ['f_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [6, 0, 8, 2, 10, 4, 7, 1, 9, 3, 11, 5])
    # 3D reversed Fortran-order
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
                                    ['f_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10])
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
                                    ['f_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [4, 5, 2, 3, 0, 1, 10, 11, 8, 9, 6, 7])
    i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, :, ::-1],
                                    ['f_index'], [['readonly']])
    assert_equal(iter_indices(i),
                            [6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5])

def test_iter_no_inner_full_coalesce():
    # Check no_inner iterators which coalesce into a single inner loop

    for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
        size = np.prod(shape)
        a = arange(size)
        # Test each combination of forward and backwards indexing
        for dirs in range(2**len(shape)):
            dirs_index = [slice(None)] * len(shape)
            for bit in range(len(shape)):
                if ((2**bit) & dirs):
                    dirs_index[bit] = slice(None, None, -1)
            dirs_index = tuple(dirs_index)

            aview = a.reshape(shape)[dirs_index]
            # C-order
            i = nditer(aview, ['external_loop'], [['readonly']])
            assert_equal(i.ndim, 1)
            assert_equal(i[0].shape, (size,))
            # Fortran-order
            i = nditer(aview.T, ['external_loop'], [['readonly']])
            assert_equal(i.ndim, 1)
            assert_equal(i[0].shape, (size,))
            # Other order
            if len(shape) > 2:
                i = nditer(aview.swapaxes(0, 1),
                                    ['external_loop'], [['readonly']])
                assert_equal(i.ndim, 1)
                assert_equal(i[0].shape, (size,))

def test_iter_no_inner_dim_coalescing():
    # Check no_inner iterators whose dimensions may not coalesce completely

    # Skipping the last element in a dimension prevents coalescing
    # with the next-bigger dimension
    a = arange(24).reshape(2, 3, 4)[:, :, :-1]
    i = nditer(a, ['external_loop'], [['readonly']])
    assert_equal(i.ndim, 2)
    assert_equal(i[0].shape, (3,))
    a = arange(24).reshape(2, 3, 4)[:, :-1, :]
    i = nditer(a, ['external_loop'], [['readonly']])
    assert_equal(i.ndim, 2)
    assert_equal(i[0].shape, (8,))
    a = arange(24).reshape(2, 3, 4)[:-1, :, :]
    i = nditer(a, ['external_loop'], [['readonly']])
    assert_equal(i.ndim, 1)
    assert_equal(i[0].shape, (12,))

    # Even with lots of 1-sized dimensions, should still coalesce
    a = arange(24).reshape(1, 1, 2, 1, 1, 3, 1, 1, 4, 1, 1)
    i = nditer(a, ['external_loop'], [['readonly']])
    assert_equal(i.ndim, 1)
    assert_equal(i[0].shape, (24,))

def test_iter_dim_coalescing():
    # Check that the correct number of dimensions are coalesced

    # Tracking a multi-index disables coalescing
    a = arange(24).reshape(2, 3, 4)
    i = nditer(a, ['multi_index'], [['readonly']])
    assert_equal(i.ndim, 3)

    # A tracked index can allow coalescing if it's compatible with the array
    a3d = arange(24).reshape(2, 3, 4)
    i = nditer(a3d, ['c_index'], [['readonly']])
    assert_equal(i.ndim, 1)
    i = nditer(a3d.swapaxes(0, 1), ['c_index'], [['readonly']])
    assert_equal(i.ndim, 3)
    i = nditer(a3d.T, ['c_index'], [['readonly']])
    assert_equal(i.ndim, 3)
    i = nditer(a3d.T, ['f_index'], [['readonly']])
    assert_equal(i.ndim, 1)
    i = nditer(a3d.T.swapaxes(0, 1), ['f_index'], [['readonly']])
    assert_equal(i.ndim, 3)

    # When C or F order is forced, coalescing may still occur
    a3d = arange(24).reshape(2, 3, 4)
    i = nditer(a3d, order='C')
    assert_equal(i.ndim, 1)
    i = nditer(a3d.T, order='C')
    assert_equal(i.ndim, 3)
    i = nditer(a3d, order='F')
    assert_equal(i.ndim, 3)
    i = nditer(a3d.T, order='F')
    assert_equal(i.ndim, 1)
    i = nditer(a3d, order='A')
    assert_equal(i.ndim, 1)
    i = nditer(a3d.T, order='A')
    assert_equal(i.ndim, 1)

def test_iter_broadcasting():
    # Standard NumPy broadcasting rules

    # 1D with scalar
    i = nditer([arange(6), np.int32(2)], ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 6)
    assert_equal(i.shape, (6,))

    # 2D with scalar
    i = nditer([arange(6).reshape(2, 3), np.int32(2)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 6)
    assert_equal(i.shape, (2, 3))
    # 2D with 1D
    i = nditer([arange(6).reshape(2, 3), arange(3)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 6)
    assert_equal(i.shape, (2, 3))
    i = nditer([arange(2).reshape(2, 1), arange(3)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 6)
    assert_equal(i.shape, (2, 3))
    # 2D with 2D
    i = nditer([arange(2).reshape(2, 1), arange(3).reshape(1, 3)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 6)
    assert_equal(i.shape, (2, 3))

    # 3D with scalar
    i = nditer([np.int32(2), arange(24).reshape(4, 2, 3)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))
    # 3D with 1D
    i = nditer([arange(3), arange(24).reshape(4, 2, 3)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))
    i = nditer([arange(3), arange(8).reshape(4, 2, 1)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))
    # 3D with 2D
    i = nditer([arange(6).reshape(2, 3), arange(24).reshape(4, 2, 3)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))
    i = nditer([arange(2).reshape(2, 1), arange(24).reshape(4, 2, 3)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))
    i = nditer([arange(3).reshape(1, 3), arange(8).reshape(4, 2, 1)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))
    # 3D with 3D
    i = nditer([arange(2).reshape(1, 2, 1), arange(3).reshape(1, 1, 3),
                        arange(4).reshape(4, 1, 1)],
                        ['multi_index'], [['readonly']] * 3)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))
    i = nditer([arange(6).reshape(1, 2, 3), arange(4).reshape(4, 1, 1)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))
    i = nditer([arange(24).reshape(4, 2, 3), arange(12).reshape(4, 1, 3)],
                        ['multi_index'], [['readonly']] * 2)
    assert_equal(i.itersize, 24)
    assert_equal(i.shape, (4, 2, 3))

def test_iter_itershape():
    # Check that allocated outputs work with a specified shape
    a = np.arange(6, dtype='i2').reshape(2, 3)
    i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
                            op_axes=[[0, 1, None], None],
                            itershape=(-1, -1, 4))
    assert_equal(i.operands[1].shape, (2, 3, 4))
    assert_equal(i.operands[1].strides, (24, 8, 2))

    i = nditer([a.T, None], [], [['readonly'], ['writeonly', 'allocate']],
                            op_axes=[[0, 1, None], None],
                            itershape=(-1, -1, 4))
    assert_equal(i.operands[1].shape, (3, 2, 4))
    assert_equal(i.operands[1].strides, (8, 24, 2))

    i = nditer([a.T, None], [], [['readonly'], ['writeonly', 'allocate']],
                            order='F',
                            op_axes=[[0, 1, None], None],
                            itershape=(-1, -1, 4))
    assert_equal(i.operands[1].shape, (3, 2, 4))
    assert_equal(i.operands[1].strides, (2, 6, 12))

    # If we specify 1 in the itershape, it shouldn't allow broadcasting
    # of that dimension to a bigger value
    assert_raises(ValueError, nditer, [a, None], [],
                            [['readonly'], ['writeonly', 'allocate']],
                            op_axes=[[0, 1, None], None],
                            itershape=(-1, 1, 4))
    # Test bug that for no op_axes but itershape, they are NULLed correctly
    i = np.nditer([np.ones(2), None, None], itershape=(2,))

def test_iter_broadcasting_errors():
    # Check that errors are thrown for bad broadcasting shapes

    # 1D with 1D
    assert_raises(ValueError, nditer, [arange(2), arange(3)],
                    [], [['readonly']] * 2)
    # 2D with 1D
    assert_raises(ValueError, nditer,
                    [arange(6).reshape(2, 3), arange(2)],
                    [], [['readonly']] * 2)
    # 2D with 2D
    assert_raises(ValueError, nditer,
                    [arange(6).reshape(2, 3), arange(9).reshape(3, 3)],
                    [], [['readonly']] * 2)
    assert_raises(ValueError, nditer,
                    [arange(6).reshape(2, 3), arange(4).reshape(2, 2)],
                    [], [['readonly']] * 2)
    # 3D with 3D
    assert_raises(ValueError, nditer,
                    [arange(36).reshape(3, 3, 4), arange(24).reshape(2, 3, 4)],
                    [], [['readonly']] * 2)
    assert_raises(ValueError, nditer,
                    [arange(8).reshape(2, 4, 1), arange(24).reshape(2, 3, 4)],
                    [], [['readonly']] * 2)

    # Verify that the error message mentions the right shapes
    try:
        nditer([arange(2).reshape(1, 2, 1),
                arange(3).reshape(1, 3),
                arange(6).reshape(2, 3)],
               [],
               [['readonly'], ['readonly'], ['writeonly', 'no_broadcast']])
        raise AssertionError('Should have raised a broadcast error')
    except ValueError as e:
        msg = str(e)
        # The message should contain the shape of the 3rd operand
        assert_(msg.find('(2,3)') >= 0,
                f'Message "{msg}" doesn\'t contain operand shape (2,3)')
        # The message should contain the broadcast shape
        assert_(msg.find('(1,2,3)') >= 0,
                f'Message "{msg}" doesn\'t contain broadcast shape (1,2,3)')

    try:
        nditer([arange(6).reshape(2, 3), arange(2)],
               [],
               [['readonly'], ['readonly']],
               op_axes=[[0, 1], [0, np.newaxis]],
               itershape=(4, 3))
        raise AssertionError('Should have raised a broadcast error')
    except ValueError as e:
        msg = str(e)
        # The message should contain "shape->remappedshape" for each operand
        assert_(msg.find('(2,3)->(2,3)') >= 0,
            f'Message "{msg}" doesn\'t contain operand shape (2,3)->(2,3)')
        assert_(msg.find('(2,)->(2,newaxis)') >= 0,
                ('Message "%s" doesn\'t contain remapped operand shape'
                '(2,)->(2,newaxis)') % msg)
        # The message should contain the itershape parameter
        assert_(msg.find('(4,3)') >= 0,
                f'Message "{msg}" doesn\'t contain itershape parameter (4,3)')

    try:
        nditer([np.zeros((2, 1, 1)), np.zeros((2,))],
               [],
               [['writeonly', 'no_broadcast'], ['readonly']])
        raise AssertionError('Should have raised a broadcast error')
    except ValueError as e:
        msg = str(e)
        # The message should contain the shape of the bad operand
        assert_(msg.find('(2,1,1)') >= 0,
            f'Message "{msg}" doesn\'t contain operand shape (2,1,1)')
        # The message should contain the broadcast shape
        assert_(msg.find('(2,1,2)') >= 0,
                f'Message "{msg}" doesn\'t contain the broadcast shape (2,1,2)')

def test_iter_flags_errors():
    # Check that bad combinations of flags produce errors

    a = arange(6)

    # Not enough operands
    assert_raises(ValueError, nditer, [], [], [])
    # Bad global flag
    assert_raises(ValueError, nditer, [a], ['bad flag'], [['readonly']])
    # Bad op flag
    assert_raises(ValueError, nditer, [a], [], [['readonly', 'bad flag']])
    # Bad order parameter
    assert_raises(ValueError, nditer, [a], [], [['readonly']], order='G')
    # Bad casting parameter
    assert_raises(ValueError, nditer, [a], [], [['readonly']], casting='noon')
    # op_flags must match ops
    assert_raises(ValueError, nditer, [a] * 3, [], [['readonly']] * 2)
    # Cannot track both a C and an F index
    assert_raises(ValueError, nditer, a,
                ['c_index', 'f_index'], [['readonly']])
    # Inner iteration and multi-indices/indices are incompatible
    assert_raises(ValueError, nditer, a,
                ['external_loop', 'multi_index'], [['readonly']])
    assert_raises(ValueError, nditer, a,
                ['external_loop', 'c_index'], [['readonly']])
    assert_raises(ValueError, nditer, a,
                ['external_loop', 'f_index'], [['readonly']])
    # Must specify exactly one of readwrite/readonly/writeonly per operand
    assert_raises(ValueError, nditer, a, [], [[]])
    assert_raises(ValueError, nditer, a, [], [['readonly', 'writeonly']])
    assert_raises(ValueError, nditer, a, [], [['readonly', 'readwrite']])
    assert_raises(ValueError, nditer, a, [], [['writeonly', 'readwrite']])
    assert_raises(ValueError, nditer, a,
                [], [['readonly', 'writeonly', 'readwrite']])
    # Python scalars are always readonly
    assert_raises(TypeError, nditer, 1.5, [], [['writeonly']])
    assert_raises(TypeError, nditer, 1.5, [], [['readwrite']])
    # Array scalars are always readonly
    assert_raises(TypeError, nditer, np.int32(1), [], [['writeonly']])
    assert_raises(TypeError, nditer, np.int32(1), [], [['readwrite']])
    # Check readonly array
    a.flags.writeable = False
    assert_raises(ValueError, nditer, a, [], [['writeonly']])
    assert_raises(ValueError, nditer, a, [], [['readwrite']])
    a.flags.writeable = True
    # Multi-indices available only with the multi_index flag
    i = nditer(arange(6), [], [['readonly']])
    assert_raises(ValueError, lambda i: i.multi_index, i)
    # Index available only with an index flag
    assert_raises(ValueError, lambda i: i.index, i)
    # GotoCoords and GotoIndex incompatible with buffering or no_inner

    def assign_multi_index(i):
        i.multi_index = (0,)

    def assign_index(i):
        i.index = 0

    def assign_iterindex(i):
        i.iterindex = 0

    def assign_iterrange(i):
        i.iterrange = (0, 1)
    i = nditer(arange(6), ['external_loop'])
    assert_raises(ValueError, assign_multi_index, i)
    assert_raises(ValueError, assign_index, i)
    assert_raises(ValueError, assign_iterindex, i)
    assert_raises(ValueError, assign_iterrange, i)
    i = nditer(arange(6), ['buffered'])
    assert_raises(ValueError, assign_multi_index, i)
    assert_raises(ValueError, assign_index, i)
    assert_raises(ValueError, assign_iterrange, i)
    # Can't iterate if size is zero
    assert_raises(ValueError, nditer, np.array([]))

def test_iter_slice():
    a, b, c = np.arange(3), np.arange(3), np.arange(3.)
    i = nditer([a, b, c], [], ['readwrite'])
    with i:
        i[0:2] = (3, 3)
        assert_equal(a, [3, 1, 2])
        assert_equal(b, [3, 1, 2])
        assert_equal(c, [0, 1, 2])
        i[1] = 12
        assert_equal(i[0:2], [3, 12])

def test_iter_assign_mapping():
    a = np.arange(24, dtype='f8').reshape(2, 3, 4).T
    it = np.nditer(a, [], [['readwrite', 'updateifcopy']],
                       casting='same_kind', op_dtypes=[np.dtype('f4')])
    with it:
        it.operands[0][...] = 3
        it.operands[0][...] = 14
    assert_equal(a, 14)
    it = np.nditer(a, [], [['readwrite', 'updateifcopy']],
                       casting='same_kind', op_dtypes=[np.dtype('f4')])
    with it:
        x = it.operands[0][-1:1]
        x[...] = 14
        it.operands[0][...] = -1234
    assert_equal(a, -1234)
    # check for no warnings on dealloc
    x = None
    it = None

def test_iter_nbo_align_contig():
    # Check that byte order, alignment, and contig changes work

    # Byte order change by requesting a specific dtype
    a = np.arange(6, dtype='f4')
    au = a.byteswap()
    au = au.view(au.dtype.newbyteorder())
    assert_(a.dtype.byteorder != au.dtype.byteorder)
    i = nditer(au, [], [['readwrite', 'updateifcopy']],
                        casting='equiv',
                        op_dtypes=[np.dtype('f4')])
    with i:
        # context manager triggers WRITEBACKIFCOPY on i at exit
        assert_equal(i.dtypes[0].byteorder, a.dtype.byteorder)
        assert_equal(i.operands[0].dtype.byteorder, a.dtype.byteorder)
        assert_equal(i.operands[0], a)
        i.operands[0][:] = 2
    assert_equal(au, [2] * 6)
    del i  # should not raise a warning
    # Byte order change by requesting NBO
    a = np.arange(6, dtype='f4')
    au = a.byteswap()
    au = au.view(au.dtype.newbyteorder())
    assert_(a.dtype.byteorder != au.dtype.byteorder)
    with nditer(au, [], [['readwrite', 'updateifcopy', 'nbo']],
                        casting='equiv') as i:
        # context manager triggers UPDATEIFCOPY on i at exit
        assert_equal(i.dtypes[0].byteorder, a.dtype.byteorder)
        assert_equal(i.operands[0].dtype.byteorder, a.dtype.byteorder)
        assert_equal(i.operands[0], a)
        i.operands[0][:] = 12345
        i.operands[0][:] = 2
    assert_equal(au, [2] * 6)

    # Unaligned input
    a = np.zeros((6 * 4 + 1,), dtype='i1')[1:]
    a.dtype = 'f4'
    a[:] = np.arange(6, dtype='f4')
    assert_(not a.flags.aligned)
    # Without 'aligned', shouldn't copy
    i = nditer(a, [], [['readonly']])
    assert_(not i.operands[0].flags.aligned)
    assert_equal(i.operands[0], a)
    # With 'aligned', should make a copy
    with nditer(a, [], [['readwrite', 'updateifcopy', 'aligned']]) as i:
        assert_(i.operands[0].flags.aligned)
        # context manager triggers UPDATEIFCOPY on i at exit
        assert_equal(i.operands[0], a)
        i.operands[0][:] = 3
    assert_equal(a, [3] * 6)

    # Discontiguous input
    a = arange(12)
    # If it is contiguous, shouldn't copy
    i = nditer(a[:6], [], [['readonly']])
    assert_(i.operands[0].flags.contiguous)
    assert_equal(i.operands[0], a[:6])
    # If it isn't contiguous, should buffer
    i = nditer(a[::2], ['buffered', 'external_loop'],
                        [['readonly', 'contig']],
                        buffersize=10)
    assert_(i[0].flags.contiguous)
    assert_equal(i[0], a[::2])

def test_iter_array_cast():
    # Check that arrays are cast as requested

    # No cast 'f4' -> 'f4'
    a = np.arange(6, dtype='f4').reshape(2, 3)
    i = nditer(a, [], [['readwrite']], op_dtypes=[np.dtype('f4')])
    with i:
        assert_equal(i.operands[0], a)
        assert_equal(i.operands[0].dtype, np.dtype('f4'))

    # Byte-order cast '<f4' -> '>f4'
    a = np.arange(6, dtype='<f4').reshape(2, 3)
    with nditer(a, [], [['readwrite', 'updateifcopy']],
            casting='equiv',
            op_dtypes=[np.dtype('>f4')]) as i:
        assert_equal(i.operands[0], a)
        assert_equal(i.operands[0].dtype, np.dtype('>f4'))

    # Safe case 'f4' -> 'f8'
    a = np.arange(24, dtype='f4').reshape(2, 3, 4).swapaxes(1, 2)
    i = nditer(a, [], [['readonly', 'copy']],
            casting='safe',
            op_dtypes=[np.dtype('f8')])
    assert_equal(i.operands[0], a)
    assert_equal(i.operands[0].dtype, np.dtype('f8'))
    # The memory layout of the temporary should match a (a is (48,4,16))
    # except negative strides get flipped to positive strides.
    assert_equal(i.operands[0].strides, (96, 8, 32))
    a = a[::-1, :, ::-1]
    i = nditer(a, [], [['readonly', 'copy']],
            casting='safe',
            op_dtypes=[np.dtype('f8')])
    assert_equal(i.operands[0], a)
    assert_equal(i.operands[0].dtype, np.dtype('f8'))
    assert_equal(i.operands[0].strides, (96, 8, 32))

    # Same-kind cast 'f8' -> 'f4' -> 'f8'
    a = np.arange(24, dtype='f8').reshape(2, 3, 4).T
    with nditer(a, [],
            [['readwrite', 'updateifcopy']],
            casting='same_kind',
            op_dtypes=[np.dtype('f4')]) as i:
        assert_equal(i.operands[0], a)
        assert_equal(i.operands[0].dtype, np.dtype('f4'))
        assert_equal(i.operands[0].strides, (4, 16, 48))
        # Check that WRITEBACKIFCOPY is activated at exit
        i.operands[0][2, 1, 1] = -12.5
        assert_(a[2, 1, 1] != -12.5)
    assert_equal(a[2, 1, 1], -12.5)

    a = np.arange(6, dtype='i4')[::-2]
    with nditer(a, [],
            [['writeonly', 'updateifcopy']],
            casting='unsafe',
            op_dtypes=[np.dtype('f4')]) as i:
        assert_equal(i.operands[0].dtype, np.dtype('f4'))
        # Even though the stride was negative in 'a', it
        # becomes positive in the temporary
        assert_equal(i.operands[0].strides, (4,))
        i.operands[0][:] = [1, 2, 3]
    assert_equal(a, [1, 2, 3])

def test_iter_array_cast_errors():
    # Check that invalid casts are caught

    # Need to enable copying for casts to occur
    assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
                [['readonly']], op_dtypes=[np.dtype('f8')])
    # Also need to allow casting for casts to occur
    assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
                [['readonly', 'copy']], casting='no',
                op_dtypes=[np.dtype('f8')])
    assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
                [['readonly', 'copy']], casting='equiv',
                op_dtypes=[np.dtype('f8')])
    assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
                [['writeonly', 'updateifcopy']],
                casting='no',
                op_dtypes=[np.dtype('f4')])
    assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
                [['writeonly', 'updateifcopy']],
                casting='equiv',
                op_dtypes=[np.dtype('f4')])
    # '<f4' -> '>f4' should not work with casting='no'
    assert_raises(TypeError, nditer, arange(2, dtype='<f4'), [],
                [['readonly', 'copy']], casting='no',
                op_dtypes=[np.dtype('>f4')])
    # 'f4' -> 'f8' is a safe cast, but 'f8' -> 'f4' isn't
    assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
                [['readwrite', 'updateifcopy']],
                casting='safe',
                op_dtypes=[np.dtype('f8')])
    assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
                [['readwrite', 'updateifcopy']],
                casting='safe',
                op_dtypes=[np.dtype('f4')])
    # 'f4' -> 'i4' is neither a safe nor a same-kind cast
    assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
                [['readonly', 'copy']],
                casting='same_kind',
                op_dtypes=[np.dtype('i4')])
    assert_raises(TypeError, nditer, arange(2, dtype='i4'), [],
                [['writeonly', 'updateifcopy']],
                casting='same_kind',
                op_dtypes=[np.dtype('f4')])

def test_iter_scalar_cast():
    # Check that scalars are cast as requested

    # No cast 'f4' -> 'f4'
    i = nditer(np.float32(2.5), [], [['readonly']],
                    op_dtypes=[np.dtype('f4')])
    assert_equal(i.dtypes[0], np.dtype('f4'))
    assert_equal(i.value.dtype, np.dtype('f4'))
    assert_equal(i.value, 2.5)
    # Safe cast 'f4' -> 'f8'
    i = nditer(np.float32(2.5), [],
                    [['readonly', 'copy']],
                    casting='safe',
                    op_dtypes=[np.dtype('f8')])
    assert_equal(i.dtypes[0], np.dtype('f8'))
    assert_equal(i.value.dtype, np.dtype('f8'))
    assert_equal(i.value, 2.5)
    # Same-kind cast 'f8' -> 'f4'
    i = nditer(np.float64(2.5), [],
                    [['readonly', 'copy']],
                    casting='same_kind',
                    op_dtypes=[np.dtype('f4')])
    assert_equal(i.dtypes[0], np.dtype('f4'))
    assert_equal(i.value.dtype, np.dtype('f4'))
    assert_equal(i.value, 2.5)
    # Unsafe cast 'f8' -> 'i4'
    i = nditer(np.float64(3.0), [],
                    [['readonly', 'copy']],
                    casting='unsafe',
                    op_dtypes=[np.dtype('i4')])
    assert_equal(i.dtypes[0], np.dtype('i4'))
    assert_equal(i.value.dtype, np.dtype('i4'))
    assert_equal(i.value, 3)
    # Readonly scalars may be cast even without setting COPY or BUFFERED
    i = nditer(3, [], [['readonly']], op_dtypes=[np.dtype('f8')])
    assert_equal(i[0].dtype, np.dtype('f8'))
    assert_equal(i[0], 3.)

def test_iter_scalar_cast_errors():
    # Check that invalid casts are caught

    # Need to allow copying/buffering for write casts of scalars to occur
    assert_raises(TypeError, nditer, np.float32(2), [],
                [['readwrite']], op_dtypes=[np.dtype('f8')])
    assert_raises(TypeError, nditer, 2.5, [],
                [['readwrite']], op_dtypes=[np.dtype('f4')])
    # 'f8' -> 'f4' isn't a safe cast if the value would overflow
    assert_raises(TypeError, nditer, np.float64(1e60), [],
                [['readonly']],
                casting='safe',
                op_dtypes=[np.dtype('f4')])
    # 'f4' -> 'i4' is neither a safe nor a same-kind cast
    assert_raises(TypeError, nditer, np.float32(2), [],
                [['readonly']],
                casting='same_kind',
                op_dtypes=[np.dtype('i4')])

def test_iter_object_arrays_basic():
    # Check that object arrays work

    obj = {'a': 3, 'b': 'd'}
    a = np.array([[1, 2, 3], None, obj, None], dtype='O')
    if HAS_REFCOUNT:
        rc = sys.getrefcount(obj)

    # Need to allow references for object arrays
    assert_raises(TypeError, nditer, a)
    if HAS_REFCOUNT:
        assert_equal(sys.getrefcount(obj), rc)

    i = nditer(a, ['refs_ok'], ['readonly'])
    vals = [x_[()] for x_ in i]
    assert_equal(np.array(vals, dtype='O'), a)
    vals, i, x = [None] * 3
    if HAS_REFCOUNT:
        assert_equal(sys.getrefcount(obj), rc)

    i = nditer(a.reshape(2, 2).T, ['refs_ok', 'buffered'],
                        ['readonly'], order='C')
    assert_(i.iterationneedsapi)
    vals = [x_[()] for x_ in i]
    assert_equal(np.array(vals, dtype='O'), a.reshape(2, 2).ravel(order='F'))
    vals, i, x = [None] * 3
    if HAS_REFCOUNT:
        assert_equal(sys.getrefcount(obj), rc)

    i = nditer(a.reshape(2, 2).T, ['refs_ok', 'buffered'],
                        ['readwrite'], order='C')
    with i:
        for x in i:
            x[...] = None
        vals, i, x = [None] * 3
    if HAS_REFCOUNT:
        assert_(sys.getrefcount(obj) == rc - 1)
    assert_equal(a, np.array([None] * 4, dtype='O'))

def test_iter_object_arrays_conversions():
    # Conversions to/from objects
    a = np.arange(6, dtype='O')
    i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
                    casting='unsafe', op_dtypes='i4')
    with i:
        for x in i:
            x[...] += 1
    assert_equal(a, np.arange(6) + 1)

    a = np.arange(6, dtype='i4')
    i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
                    casting='unsafe', op_dtypes='O')
    with i:
        for x in i:
            x[...] += 1
    assert_equal(a, np.arange(6) + 1)

    # Non-contiguous object array
    a = np.zeros((6,), dtype=[('p', 'i1'), ('a', 'O')])
    a = a['a']
    a[:] = np.arange(6)
    i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
                    casting='unsafe', op_dtypes='i4')
    with i:
        for x in i:
            x[...] += 1
    assert_equal(a, np.arange(6) + 1)

    # Non-contiguous value array
    a = np.zeros((6,), dtype=[('p', 'i1'), ('a', 'i4')])
    a = a['a']
    a[:] = np.arange(6) + 98172488
    i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
                    casting='unsafe', op_dtypes='O')
    with i:
        ob = i[0][()]
        if HAS_REFCOUNT:
            rc = sys.getrefcount(ob)
        for x in i:
            x[...] += 1
        if HAS_REFCOUNT:
            newrc = sys.getrefcount(ob)
            assert_(newrc == rc - 1)
    assert_equal(a, np.arange(6) + 98172489)

def test_iter_common_dtype():
    # Check that the iterator finds a common data type correctly
    # (some checks are somewhat duplicate after adopting NEP 50)

    i = nditer([array([3], dtype='f4'), array([0], dtype='f8')],
                    ['common_dtype'],
                    [['readonly', 'copy']] * 2,
                    casting='safe')
    assert_equal(i.dtypes[0], np.dtype('f8'))
    assert_equal(i.dtypes[1], np.dtype('f8'))
    i = nditer([array([3], dtype='i4'), array([0], dtype='f4')],
                    ['common_dtype'],
                    [['readonly', 'copy']] * 2,
                    casting='safe')
    assert_equal(i.dtypes[0], np.dtype('f8'))
    assert_equal(i.dtypes[1], np.dtype('f8'))
    i = nditer([array([3], dtype='f4'), array(0, dtype='f8')],
                    ['common_dtype'],
                    [['readonly', 'copy']] * 2,
                    casting='same_kind')
    assert_equal(i.dtypes[0], np.dtype('f8'))
    assert_equal(i.dtypes[1], np.dtype('f8'))
    i = nditer([array([3], dtype='u4'), array(0, dtype='i4')],
                    ['common_dtype'],
                    [['readonly', 'copy']] * 2,
                    casting='safe')
    assert_equal(i.dtypes[0], np.dtype('i8'))
    assert_equal(i.dtypes[1], np.dtype('i8'))
    i = nditer([array([3], dtype='u4'), array(-12, dtype='i4')],
                    ['common_dtype'],
                    [['readonly', 'copy']] * 2,
                    casting='safe')
    assert_equal(i.dtypes[0], np.dtype('i8'))
    assert_equal(i.dtypes[1], np.dtype('i8'))
    i = nditer([array([3], dtype='u4'), array(-12, dtype='i4'),
                 array([2j], dtype='c8'), array([9], dtype='f8')],
                    ['common_dtype'],
                    [['readonly', 'copy']] * 4,
                    casting='safe')
    assert_equal(i.dtypes[0], np.dtype('c16'))
    assert_equal(i.dtypes[1], np.dtype('c16'))
    assert_equal(i.dtypes[2], np.dtype('c16'))
    assert_equal(i.dtypes[3], np.dtype('c16'))
    assert_equal(i.value, (3, -12, 2j, 9))

    # When allocating outputs, other outputs aren't factored in
    i = nditer([array([3], dtype='i4'), None, array([2j], dtype='c16')], [],
                    [['readonly', 'copy'],
                     ['writeonly', 'allocate'],
                     ['writeonly']],
                    casting='safe')
    assert_equal(i.dtypes[0], np.dtype('i4'))
    assert_equal(i.dtypes[1], np.dtype('i4'))
    assert_equal(i.dtypes[2], np.dtype('c16'))
    # But, if common data types are requested, they are
    i = nditer([array([3], dtype='i4'), None, array([2j], dtype='c16')],
                    ['common_dtype'],
                    [['readonly', 'copy'],
                     ['writeonly', 'allocate'],
                     ['writeonly']],
                    casting='safe')
    assert_equal(i.dtypes[0], np.dtype('c16'))
    assert_equal(i.dtypes[1], np.dtype('c16'))
    assert_equal(i.dtypes[2], np.dtype('c16'))

def test_iter_copy_if_overlap():
    # Ensure the iterator makes copies on read/write overlap, if requested

    # Copy not needed, 1 op
    for flag in ['readonly', 'writeonly', 'readwrite']:
        a = arange(10)
        i = nditer([a], ['copy_if_overlap'], [[flag]])
        with i:
            assert_(i.operands[0] is a)

    # Copy needed, 2 ops, read-write overlap
    x = arange(10)
    a = x[1:]
    b = x[:-1]
    with nditer([a, b], ['copy_if_overlap'], [['readonly'], ['readwrite']]) as i:
        assert_(not np.shares_memory(*i.operands))

    # Copy not needed with elementwise, 2 ops, exactly same arrays
    x = arange(10)
    a = x
    b = x
    i = nditer([a, b], ['copy_if_overlap'], [['readonly', 'overlap_assume_elementwise'],
                                             ['readwrite', 'overlap_assume_elementwise']])
    with i:
        assert_(i.operands[0] is a and i.operands[1] is b)
    with nditer([a, b], ['copy_if_overlap'], [['readonly'], ['readwrite']]) as i:
        assert_(i.operands[0] is a and not np.shares_memory(i.operands[1], b))

    # Copy not needed, 2 ops, no overlap
    x = arange(10)
    a = x[::2]
    b = x[1::2]
    i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['writeonly']])
    assert_(i.operands[0] is a and i.operands[1] is b)

    # Copy needed, 2 ops, read-write overlap
    x = arange(4, dtype=np.int8)
    a = x[3:]
    b = x.view(np.int32)[:1]
    with nditer([a, b], ['copy_if_overlap'], [['readonly'], ['writeonly']]) as i:
        assert_(not np.shares_memory(*i.operands))

    # Copy needed, 3 ops, read-write overlap
    for flag in ['writeonly', 'readwrite']:
        x = np.ones([10, 10])
        a = x
        b = x.T
        c = x
        with nditer([a, b, c], ['copy_if_overlap'],
                   [['readonly'], ['readonly'], [flag]]) as i:
            a2, b2, c2 = i.operands
            assert_(not np.shares_memory(a2, c2))
            assert_(not np.shares_memory(b2, c2))

    # Copy not needed, 3 ops, read-only overlap
    x = np.ones([10, 10])
    a = x
    b = x.T
    c = x
    i = nditer([a, b, c], ['copy_if_overlap'],
               [['readonly'], ['readonly'], ['readonly']])
    a2, b2, c2 = i.operands
    assert_(a is a2)
    assert_(b is b2)
    assert_(c is c2)

    # Copy not needed, 3 ops, read-only overlap
    x = np.ones([10, 10])
    a = x
    b = np.ones([10, 10])
    c = x.T
    i = nditer([a, b, c], ['copy_if_overlap'],
               [['readonly'], ['writeonly'], ['readonly']])
    a2, b2, c2 = i.operands
    assert_(a is a2)
    assert_(b is b2)
    assert_(c is c2)

    # Copy not needed, 3 ops, write-only overlap
    x = np.arange(7)
    a = x[:3]
    b = x[3:6]
    c = x[4:7]
    i = nditer([a, b, c], ['copy_if_overlap'],
               [['readonly'], ['writeonly'], ['writeonly']])
    a2, b2, c2 = i.operands
    assert_(a is a2)
    assert_(b is b2)
    assert_(c is c2)

def test_iter_op_axes():
    # Check that custom axes work

    # Reverse the axes
    a = arange(6).reshape(2, 3)
    i = nditer([a, a.T], [], [['readonly']] * 2, op_axes=[[0, 1], [1, 0]])
    assert_(all([x == y for (x, y) in i]))
    a = arange(24).reshape(2, 3, 4)
    i = nditer([a.T, a], [], [['readonly']] * 2, op_axes=[[2, 1, 0], None])
    assert_(all([x == y for (x, y) in i]))

    # Broadcast 1D to any dimension
    a = arange(1, 31).reshape(2, 3, 5)
    b = arange(1, 3)
    i = nditer([a, b], [], [['readonly']] * 2, op_axes=[None, [0, -1, -1]])
    assert_equal([x * y for (x, y) in i], (a * b.reshape(2, 1, 1)).ravel())
    b = arange(1, 4)
    i = nditer([a, b], [], [['readonly']] * 2, op_axes=[None, [-1, 0, -1]])
    assert_equal([x * y for (x, y) in i], (a * b.reshape(1, 3, 1)).ravel())
    b = arange(1, 6)
    i = nditer([a, b], [], [['readonly']] * 2,
                            op_axes=[None, [np.newaxis, np.newaxis, 0]])
    assert_equal([x * y for (x, y) in i], (a * b.reshape(1, 1, 5)).ravel())

    # Inner product-style broadcasting
    a = arange(24).reshape(2, 3, 4)
    b = arange(40).reshape(5, 2, 4)
    i = nditer([a, b], ['multi_index'], [['readonly']] * 2,
                            op_axes=[[0, 1, -1, -1], [-1, -1, 0, 1]])
    assert_equal(i.shape, (2, 3, 5, 2))

    # Matrix product-style broadcasting
    a = arange(12).reshape(3, 4)
    b = arange(20).reshape(4, 5)
    i = nditer([a, b], ['multi_index'], [['readonly']] * 2,
                            op_axes=[[0, -1], [-1, 1]])
    assert_equal(i.shape, (3, 5))

def test_iter_op_axes_errors():
    # Check that custom axes throws errors for bad inputs

    # Wrong number of items in op_axes
    a = arange(6).reshape(2, 3)
    assert_raises(ValueError, nditer, [a, a], [], [['readonly']] * 2,
                                    op_axes=[[0], [1], [0]])
    # Out of bounds items in op_axes
    assert_raises(ValueError, nditer, [a, a], [], [['readonly']] * 2,
                                    op_axes=[[2, 1], [0, 1]])
    assert_raises(ValueError, nditer, [a, a], [], [['readonly']] * 2,
                                    op_axes=[[0, 1], [2, -1]])
    # Duplicate items in op_axes
    assert_raises(ValueError, nditer, [a, a], [], [['readonly']] * 2,
                                    op_axes=[[0, 0], [0, 1]])
    assert_raises(ValueError, nditer, [a, a], [], [['readonly']] * 2,
                                    op_axes=[[0, 1], [1, 1]])

    # Different sized arrays in op_axes
    assert_raises(ValueError, nditer, [a, a], [], [['readonly']] * 2,
                                    op_axes=[[0, 1], [0, 1, 0]])

    # Non-broadcastable dimensions in the result
    assert_raises(ValueError, nditer, [a, a], [], [['readonly']] * 2,
                                    op_axes=[[0, 1], [1, 0]])

def test_iter_copy():
    # Check that copying the iterator works correctly
    a = arange(24).reshape(2, 3, 4)

    # Simple iterator
    i = nditer(a)
    j = i.copy()
    assert_equal([x[()] for x in i], [x[()] for x in j])

    i.iterindex = 3
    j = i.copy()
    assert_equal([x[()] for x in i], [x[()] for x in j])

    # Buffered iterator
    i = nditer(a, ['buffered', 'ranged'], order='F', buffersize=3)
    j = i.copy()
    assert_equal([x[()] for x in i], [x[()] for x in j])

    i.iterindex = 3
    j = i.copy()
    assert_equal([x[()] for x in i], [x[()] for x in j])

    i.iterrange = (3, 9)
    j = i.copy()
    assert_equal([x[()] for x in i], [x[()] for x in j])

    i.iterrange = (2, 18)
    next(i)
    next(i)
    j = i.copy()
    assert_equal([x[()] for x in i], [x[()] for x in j])

    # Casting iterator
    with nditer(a, ['buffered'], order='F', casting='unsafe',
                op_dtypes='f8', buffersize=5) as i:
        j = i.copy()
    assert_equal([x[()] for x in j], a.ravel(order='F'))

    a = arange(24, dtype='<i4').reshape(2, 3, 4)
    with nditer(a, ['buffered'], order='F', casting='unsafe',
                op_dtypes='>f8', buffersize=5) as i:
        j = i.copy()
    assert_equal([x[()] for x in j], a.ravel(order='F'))


@pytest.mark.parametrize("dtype", np.typecodes["All"])
@pytest.mark.parametrize("loop_dtype", np.typecodes["All"])
@pytest.mark.filterwarnings("ignore::numpy.exceptions.ComplexWarning")
def test_iter_copy_casts(dtype, loop_dtype):
    # Ensure the dtype is never flexible:
    if loop_dtype.lower() == "m":
        loop_dtype = loop_dtype + "[ms]"
    elif np.dtype(loop_dtype).itemsize == 0:
        loop_dtype = loop_dtype + "50"

    # Make things a bit more interesting by requiring a byte-swap as well:
    arr = np.ones(1000, dtype=np.dtype(dtype).newbyteorder())
    try:
        expected = arr.astype(loop_dtype)
    except Exception:
        # Some casts are not possible, do not worry about them
        return

    it = np.nditer((arr,), ["buffered", "external_loop", "refs_ok"],
                   op_dtypes=[loop_dtype], casting="unsafe")

    if np.issubdtype(np.dtype(loop_dtype), np.number):
        # Casting to strings may be strange, but for simple dtypes do not rely
        # on the cast being correct:
        assert_array_equal(expected, np.ones(1000, dtype=loop_dtype))

    it_copy = it.copy()
    res = next(it)
    del it
    res_copy = next(it_copy)
    del it_copy

    assert_array_equal(res, expected)
    assert_array_equal(res_copy, expected)


def test_iter_copy_casts_structured():
    # Test a complicated structured dtype for casting, as it requires
    # both multiple steps and a more complex casting setup.
    # Includes a structured -> unstructured (any to object), and many other
    # casts, which cause this to require all steps in the casting machinery
    # one level down as well as the iterator copy (which uses NpyAuxData clone)
    in_dtype = np.dtype([("a", np.dtype("i,")),
                         ("b", np.dtype(">i,<i,>d,S17,>d,3f,O,i1"))])
    out_dtype = np.dtype([("a", np.dtype("O")),
                          ("b", np.dtype(">i,>i,S17,>d,>U3,3d,i1,O"))])
    arr = np.ones(1000, dtype=in_dtype)

    it = np.nditer((arr,), ["buffered", "external_loop", "refs_ok"],
                   op_dtypes=[out_dtype], casting="unsafe")
    it_copy = it.copy()

    res1 = next(it)
    del it
    res2 = next(it_copy)
    del it_copy

    expected = arr["a"].astype(out_dtype["a"])
    assert_array_equal(res1["a"], expected)
    assert_array_equal(res2["a"], expected)

    for field in in_dtype["b"].names:
        # Note that the .base avoids the subarray field
        expected = arr["b"][field].astype(out_dtype["b"][field].base)
        assert_array_equal(res1["b"][field], expected)
        assert_array_equal(res2["b"][field], expected)


def test_iter_copy_casts_structured2():
    # Similar to the above, this is a fairly arcane test to cover internals
    in_dtype = np.dtype([("a", np.dtype("O,O")),
                         ("b", np.dtype("5O,3O,(1,)O,(1,)i,(1,)O"))])
    out_dtype = np.dtype([("a", np.dtype("O")),
                          ("b", np.dtype("O,3i,4O,4O,4i"))])

    arr = np.ones(1, dtype=in_dtype)
    it = np.nditer((arr,), ["buffered", "external_loop", "refs_ok"],
                   op_dtypes=[out_dtype], casting="unsafe")
    it_copy = it.copy()

    res1 = next(it)
    del it
    res2 = next(it_copy)
    del it_copy

    # Array of two structured scalars:
    for res in res1, res2:
        # Cast to tuple by getitem, which may be weird and changeable?:
        assert isinstance(res["a"][0], tuple)
        assert res["a"][0] == (1, 1)

    for res in res1, res2:
        assert_array_equal(res["b"]["f0"][0], np.ones(5, dtype=object))
        assert_array_equal(res["b"]["f1"], np.ones((1, 3), dtype="i"))
        assert res["b"]["f2"].shape == (1, 4)
        assert_array_equal(res["b"]["f2"][0], np.ones(4, dtype=object))
        assert_array_equal(res["b"]["f3"][0], np.ones(4, dtype=object))
        assert_array_equal(res["b"]["f3"][0], np.ones(4, dtype="i"))


def test_iter_allocate_output_simple():
    # Check that the iterator will properly allocate outputs

    # Simple case
    a = arange(6)
    i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
                        op_dtypes=[None, np.dtype('f4')])
    assert_equal(i.operands[1].shape, a.shape)
    assert_equal(i.operands[1].dtype, np.dtype('f4'))

def test_iter_allocate_output_buffered_readwrite():
    # Allocated output with buffering + delay_bufalloc

    a = arange(6)
    i = nditer([a, None], ['buffered', 'delay_bufalloc'],
                        [['readonly'], ['allocate', 'readwrite']])
    with i:
        i.operands[1][:] = 1
        i.reset()
        for x in i:
            x[1][...] += x[0][...]
        assert_equal(i.operands[1], a + 1)

def test_iter_allocate_output_itorder():
    # The allocated output should match the iteration order

    # C-order input, best iteration order
    a = arange(6, dtype='i4').reshape(2, 3)
    i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
                        op_dtypes=[None, np.dtype('f4')])
    assert_equal(i.operands[1].shape, a.shape)
    assert_equal(i.operands[1].strides, a.strides)
    assert_equal(i.operands[1].dtype, np.dtype('f4'))
    # F-order input, best iteration order
    a = arange(24, dtype='i4').reshape(2, 3, 4).T
    i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
                        op_dtypes=[None, np.dtype('f4')])
    assert_equal(i.operands[1].shape, a.shape)
    assert_equal(i.operands[1].strides, a.strides)
    assert_equal(i.operands[1].dtype, np.dtype('f4'))
    # Non-contiguous input, C iteration order
    a = arange(24, dtype='i4').reshape(2, 3, 4).swapaxes(0, 1)
    i = nditer([a, None], [],
                        [['readonly'], ['writeonly', 'allocate']],
                        order='C',
                        op_dtypes=[None, np.dtype('f4')])
    assert_equal(i.operands[1].shape, a.shape)
    assert_equal(i.operands[1].strides, (32, 16, 4))
    assert_equal(i.operands[1].dtype, np.dtype('f4'))

def test_iter_allocate_output_opaxes():
    # Specifying op_axes should work

    a = arange(24, dtype='i4').reshape(2, 3, 4)
    i = nditer([None, a], [], [['writeonly', 'allocate'], ['readonly']],
                        op_dtypes=[np.dtype('u4'), None],
                        op_axes=[[1, 2, 0], None])
    assert_equal(i.operands[0].shape, (4, 2, 3))
    assert_equal(i.operands[0].strides, (4, 48, 16))
    assert_equal(i.operands[0].dtype, np.dtype('u4'))

def test_iter_allocate_output_types_promotion():
    # Check type promotion of automatic outputs (this was more interesting
    # before NEP 50...)

    i = nditer([array([3], dtype='f4'), array([0], dtype='f8'), None], [],
                    [['readonly']] * 2 + [['writeonly', 'allocate']])
    assert_equal(i.dtypes[2], np.dtype('f8'))
    i = nditer([array([3], dtype='i4'), array([0], dtype='f4'), None], [],
                    [['readonly']] * 2 + [['writeonly', 'allocate']])
    assert_equal(i.dtypes[2], np.dtype('f8'))
    i = nditer([array([3], dtype='f4'), array(0, dtype='f8'), None], [],
                    [['readonly']] * 2 + [['writeonly', 'allocate']])
    assert_equal(i.dtypes[2], np.dtype('f8'))
    i = nditer([array([3], dtype='u4'), array(0, dtype='i4'), None], [],
                    [['readonly']] * 2 + [['writeonly', 'allocate']])
    assert_equal(i.dtypes[2], np.dtype('i8'))
    i = nditer([array([3], dtype='u4'), array(-12, dtype='i4'), None], [],
                    [['readonly']] * 2 + [['writeonly', 'allocate']])
    assert_equal(i.dtypes[2], np.dtype('i8'))

def test_iter_allocate_output_types_byte_order():
    # Verify the rules for byte order changes

    # When there's just one input, the output type exactly matches
    a = array([3], dtype='u4')
    a = a.view(a.dtype.newbyteorder())
    i = nditer([a, None], [],
                    [['readonly'], ['writeonly', 'allocate']])
    assert_equal(i.dtypes[0], i.dtypes[1])
    # With two or more inputs, the output type is in native byte order
    i = nditer([a, a, None], [],
                    [['readonly'], ['readonly'], ['writeonly', 'allocate']])
    assert_(i.dtypes[0] != i.dtypes[2])
    assert_equal(i.dtypes[0].newbyteorder('='), i.dtypes[2])

def test_iter_allocate_output_types_scalar():
    # If the inputs are all scalars, the output should be a scalar

    i = nditer([None, 1, 2.3, np.float32(12), np.complex128(3)], [],
                [['writeonly', 'allocate']] + [['readonly']] * 4)
    assert_equal(i.operands[0].dtype, np.dtype('complex128'))
    assert_equal(i.operands[0].ndim, 0)

def test_iter_allocate_output_subtype():
    # Make sure that the subtype with priority wins
    class MyNDArray(np.ndarray):
        __array_priority__ = 15

    # subclass vs ndarray
    a = np.array([[1, 2], [3, 4]]).view(MyNDArray)
    b = np.arange(4).reshape(2, 2).T
    i = nditer([a, b, None], [],
               [['readonly'], ['readonly'], ['writeonly', 'allocate']])
    assert_equal(type(a), type(i.operands[2]))
    assert_(type(b) is not type(i.operands[2]))
    assert_equal(i.operands[2].shape, (2, 2))

    # If subtypes are disabled, we should get back an ndarray.
    i = nditer([a, b, None], [],
               [['readonly'], ['readonly'],
                ['writeonly', 'allocate', 'no_subtype']])
    assert_equal(type(b), type(i.operands[2]))
    assert_(type(a) is not type(i.operands[2]))
    assert_equal(i.operands[2].shape, (2, 2))

def test_iter_allocate_output_errors():
    # Check that the iterator will throw errors for bad output allocations

    # Need an input if no output data type is specified
    a = arange(6)
    assert_raises(TypeError, nditer, [a, None], [],
                        [['writeonly'], ['writeonly', 'allocate']])
    # Allocated output should be flagged for writing
    assert_raises(ValueError, nditer, [a, None], [],
                        [['readonly'], ['allocate', 'readonly']])
    # Allocated output can't have buffering without delayed bufalloc
    assert_raises(ValueError, nditer, [a, None], ['buffered'],
                                            ['allocate', 'readwrite'])
    # Must specify dtype if there are no inputs (cannot promote existing ones;
    # maybe this should use the 'f4' here, but it does not historically.)
    assert_raises(TypeError, nditer, [None, None], [],
                        [['writeonly', 'allocate'],
                         ['writeonly', 'allocate']],
                        op_dtypes=[None, np.dtype('f4')])
    # If using op_axes, must specify all the axes
    a = arange(24, dtype='i4').reshape(2, 3, 4)
    assert_raises(ValueError, nditer, [a, None], [],
                        [['readonly'], ['writeonly', 'allocate']],
                        op_dtypes=[None, np.dtype('f4')],
                        op_axes=[None, [0, np.newaxis, 1]])
    # If using op_axes, the axes must be within bounds
    assert_raises(ValueError, nditer, [a, None], [],
                        [['readonly'], ['writeonly', 'allocate']],
                        op_dtypes=[None, np.dtype('f4')],
                        op_axes=[None, [0, 3, 1]])
    # If using op_axes, there can't be duplicates
    assert_raises(ValueError, nditer, [a, None], [],
                        [['readonly'], ['writeonly', 'allocate']],
                        op_dtypes=[None, np.dtype('f4')],
                        op_axes=[None, [0, 2, 1, 0]])
    # Not all axes may be specified if a reduction. If there is a hole
    # in op_axes, this is an error.
    a = arange(24, dtype='i4').reshape(2, 3, 4)
    assert_raises(ValueError, nditer, [a, None], ["reduce_ok"],
                        [['readonly'], ['readwrite', 'allocate']],
                        op_dtypes=[None, np.dtype('f4')],
                        op_axes=[None, [0, np.newaxis, 2]])

def test_all_allocated():
    # When no output and no shape is given, `()` is used as shape.
    i = np.nditer([None], op_dtypes=["int64"])
    assert i.operands[0].shape == ()
    assert i.dtypes == (np.dtype("int64"),)

    i = np.nditer([None], op_dtypes=["int64"], itershape=(2, 3, 4))
    assert i.operands[0].shape == (2, 3, 4)

def test_iter_remove_axis():
    a = arange(24).reshape(2, 3, 4)

    i = nditer(a, ['multi_index'])
    i.remove_axis(1)
    assert_equal(list(i), a[:, 0, :].ravel())

    a = a[::-1, :, :]
    i = nditer(a, ['multi_index'])
    i.remove_axis(0)
    assert_equal(list(i), a[0, :, :].ravel())

def test_iter_remove_multi_index_inner_loop():
    # Check that removing multi-index support works

    a = arange(24).reshape(2, 3, 4)

    i = nditer(a, ['multi_index'])
    assert_equal(i.ndim, 3)
    assert_equal(i.shape, (2, 3, 4))
    assert_equal(i.itviews[0].shape, (2, 3, 4))

    # Removing the multi-index tracking causes all dimensions to coalesce
    before = list(i)
    i.remove_multi_index()
    after = list(i)

    assert_equal(before, after)
    assert_equal(i.ndim, 1)
    assert_raises(ValueError, lambda i: i.shape, i)
    assert_equal(i.itviews[0].shape, (24,))

    # Removing the inner loop means there's just one iteration
    i.reset()
    assert_equal(i.itersize, 24)
    assert_equal(i[0].shape, ())
    i.enable_external_loop()
    assert_equal(i.itersize, 24)
    assert_equal(i[0].shape, (24,))
    assert_equal(i.value, arange(24))

def test_iter_iterindex():
    # Make sure iterindex works

    buffersize = 5
    a = arange(24).reshape(4, 3, 2)
    for flags in ([], ['buffered']):
        i = nditer(a, flags, buffersize=buffersize)
        assert_equal(iter_iterindices(i), list(range(24)))
        i.iterindex = 2
        assert_equal(iter_iterindices(i), list(range(2, 24)))

        i = nditer(a, flags, order='F', buffersize=buffersize)
        assert_equal(iter_iterindices(i), list(range(24)))
        i.iterindex = 5
        assert_equal(iter_iterindices(i), list(range(5, 24)))

        i = nditer(a[::-1], flags, order='F', buffersize=buffersize)
        assert_equal(iter_iterindices(i), list(range(24)))
        i.iterindex = 9
        assert_equal(iter_iterindices(i), list(range(9, 24)))

        i = nditer(a[::-1, ::-1], flags, order='C', buffersize=buffersize)
        assert_equal(iter_iterindices(i), list(range(24)))
        i.iterindex = 13
        assert_equal(iter_iterindices(i), list(range(13, 24)))

        i = nditer(a[::1, ::-1], flags, buffersize=buffersize)
        assert_equal(iter_iterindices(i), list(range(24)))
        i.iterindex = 23
        assert_equal(iter_iterindices(i), list(range(23, 24)))
        i.reset()
        i.iterindex = 2
        assert_equal(iter_iterindices(i), list(range(2, 24)))

def test_iter_iterrange():
    # Make sure getting and resetting the iterrange works

    buffersize = 5
    a = arange(24, dtype='i4').reshape(4, 3, 2)
    a_fort = a.ravel(order='F')

    i = nditer(a, ['ranged'], ['readonly'], order='F',
                buffersize=buffersize)
    assert_equal(i.iterrange, (0, 24))
    assert_equal([x[()] for x in i], a_fort)
    for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
        i.iterrange = r
        assert_equal(i.iterrange, r)
        assert_equal([x[()] for x in i], a_fort[r[0]:r[1]])

    i = nditer(a, ['ranged', 'buffered'], ['readonly'], order='F',
                op_dtypes='f8', buffersize=buffersize)
    assert_equal(i.iterrange, (0, 24))
    assert_equal([x[()] for x in i], a_fort)
    for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
        i.iterrange = r
        assert_equal(i.iterrange, r)
        assert_equal([x[()] for x in i], a_fort[r[0]:r[1]])

    def get_array(i):
        val = np.array([], dtype='f8')
        for x in i:
            val = np.concatenate((val, x))
        return val

    i = nditer(a, ['ranged', 'buffered', 'external_loop'],
                ['readonly'], order='F',
                op_dtypes='f8', buffersize=buffersize)
    assert_equal(i.iterrange, (0, 24))
    assert_equal(get_array(i), a_fort)
    for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
        i.iterrange = r
        assert_equal(i.iterrange, r)
        assert_equal(get_array(i), a_fort[r[0]:r[1]])

def test_iter_buffering():
    # Test buffering with several buffer sizes and types
    arrays = []
    # F-order swapped array
    _tmp = np.arange(24, dtype='c16').reshape(2, 3, 4).T
    _tmp = _tmp.view(_tmp.dtype.newbyteorder()).byteswap()
    arrays.append(_tmp)
    # Contiguous 1-dimensional array
    arrays.append(np.arange(10, dtype='f4'))
    # Unaligned array
    a = np.zeros((4 * 16 + 1,), dtype='i1')[1:]
    a.dtype = 'i4'
    a[:] = np.arange(16, dtype='i4')
    arrays.append(a)
    # 4-D F-order array
    arrays.append(np.arange(120, dtype='i4').reshape(5, 3, 2, 4).T)
    for a in arrays:
        for buffersize in (1, 2, 3, 5, 8, 11, 16, 1024):
            vals = []
            i = nditer(a, ['buffered', 'external_loop'],
                           [['readonly', 'nbo', 'aligned']],
                           order='C',
                           casting='equiv',
                           buffersize=buffersize)
            while not i.finished:
                assert_(i[0].size <= buffersize)
                vals.append(i[0].copy())
                i.iternext()
            assert_equal(np.concatenate(vals), a.ravel(order='C'))

def test_iter_write_buffering():
    # Test that buffering of writes is working

    # F-order swapped array
    a = np.arange(24).reshape(2, 3, 4).T
    a = a.view(a.dtype.newbyteorder()).byteswap()
    i = nditer(a, ['buffered'],
                   [['readwrite', 'nbo', 'aligned']],
                   casting='equiv',
                   order='C',
                   buffersize=16)
    x = 0
    with i:
        while not i.finished:
            i[0] = x
            x += 1
            i.iternext()
    assert_equal(a.ravel(order='C'), np.arange(24))

def test_iter_buffering_delayed_alloc():
    # Test that delaying buffer allocation works

    a = np.arange(6)
    b = np.arange(1, dtype='f4')
    i = nditer([a, b], ['buffered', 'delay_bufalloc', 'multi_index', 'reduce_ok'],
                    ['readwrite'],
                    casting='unsafe',
                    op_dtypes='f4')
    assert_(i.has_delayed_bufalloc)
    assert_raises(ValueError, lambda i: i.multi_index, i)
    assert_raises(ValueError, lambda i: i[0], i)
    assert_raises(ValueError, lambda i: i[0:2], i)

    def assign_iter(i):
        i[0] = 0
    assert_raises(ValueError, assign_iter, i)

    i.reset()
    assert_(not i.has_delayed_bufalloc)
    assert_equal(i.multi_index, (0,))
    with i:
        assert_equal(i[0], 0)
        i[1] = 1
        assert_equal(i[0:2], [0, 1])
        assert_equal([[x[0][()], x[1][()]] for x in i], list(zip(range(6), [1] * 6)))

def test_iter_buffered_cast_simple():
    # Test that buffering can handle a simple cast

    a = np.arange(10, dtype='f4')
    i = nditer(a, ['buffered', 'external_loop'],
                   [['readwrite', 'nbo', 'aligned']],
                   casting='same_kind',
                   op_dtypes=[np.dtype('f8')],
                   buffersize=3)
    with i:
        for v in i:
            v[...] *= 2

    assert_equal(a, 2 * np.arange(10, dtype='f4'))

def test_iter_buffered_cast_byteswapped():
    # Test that buffering can handle a cast which requires swap->cast->swap

    a = np.arange(10, dtype='f4')
    a = a.view(a.dtype.newbyteorder()).byteswap()
    i = nditer(a, ['buffered', 'external_loop'],
                   [['readwrite', 'nbo', 'aligned']],
                   casting='same_kind',
                   op_dtypes=[np.dtype('f8').newbyteorder()],
                   buffersize=3)
    with i:
        for v in i:
            v[...] *= 2

    assert_equal(a, 2 * np.arange(10, dtype='f4'))

    with suppress_warnings() as sup:
        sup.filter(np.exceptions.ComplexWarning)

        a = np.arange(10, dtype='f8')
        a = a.view(a.dtype.newbyteorder()).byteswap()
        i = nditer(a, ['buffered', 'external_loop'],
                       [['readwrite', 'nbo', 'aligned']],
                       casting='unsafe',
                       op_dtypes=[np.dtype('c8').newbyteorder()],
                       buffersize=3)
        with i:
            for v in i:
                v[...] *= 2

        assert_equal(a, 2 * np.arange(10, dtype='f8'))

def test_iter_buffered_cast_byteswapped_complex():
    # Test that buffering can handle a cast which requires swap->cast->copy

    a = np.arange(10, dtype='c8')
    a = a.view(a.dtype.newbyteorder()).byteswap()
    a += 2j
    i = nditer(a, ['buffered', 'external_loop'],
                   [['readwrite', 'nbo', 'aligned']],
                   casting='same_kind',
                   op_dtypes=[np.dtype('c16')],
                   buffersize=3)
    with i:
        for v in i:
            v[...] *= 2
    assert_equal(a, 2 * np.arange(10, dtype='c8') + 4j)

    a = np.arange(10, dtype='c8')
    a += 2j
    i = nditer(a, ['buffered', 'external_loop'],
                   [['readwrite', 'nbo', 'aligned']],
                   casting='same_kind',
                   op_dtypes=[np.dtype('c16').newbyteorder()],
                   buffersize=3)
    with i:
        for v in i:
            v[...] *= 2
    assert_equal(a, 2 * np.arange(10, dtype='c8') + 4j)

    a = np.arange(10, dtype=np.clongdouble)
    a = a.view(a.dtype.newbyteorder()).byteswap()
    a += 2j
    i = nditer(a, ['buffered', 'external_loop'],
                   [['readwrite', 'nbo', 'aligned']],
                   casting='same_kind',
                   op_dtypes=[np.dtype('c16')],
                   buffersize=3)
    with i:
        for v in i:
            v[...] *= 2
    assert_equal(a, 2 * np.arange(10, dtype=np.clongdouble) + 4j)

    a = np.arange(10, dtype=np.longdouble)
    a = a.view(a.dtype.newbyteorder()).byteswap()
    i = nditer(a, ['buffered', 'external_loop'],
                   [['readwrite', 'nbo', 'aligned']],
                   casting='same_kind',
                   op_dtypes=[np.dtype('f4')],
                   buffersize=7)
    with i:
        for v in i:
            v[...] *= 2
    assert_equal(a, 2 * np.arange(10, dtype=np.longdouble))

def test_iter_buffered_cast_structured_type():
    # Tests buffering of structured types

    # simple -> struct type (duplicates the value)
    sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
    a = np.arange(3, dtype='f4') + 0.5
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt)
    vals = [np.array(x) for x in i]
    assert_equal(vals[0]['a'], 0.5)
    assert_equal(vals[0]['b'], 0)
    assert_equal(vals[0]['c'], [[(0.5)] * 3] * 2)
    assert_equal(vals[0]['d'], 0.5)
    assert_equal(vals[1]['a'], 1.5)
    assert_equal(vals[1]['b'], 1)
    assert_equal(vals[1]['c'], [[(1.5)] * 3] * 2)
    assert_equal(vals[1]['d'], 1.5)
    assert_equal(vals[0].dtype, np.dtype(sdt))

    # object -> struct type
    sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
    a = np.zeros((3,), dtype='O')
    a[0] = (0.5, 0.5, [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]], 0.5)
    a[1] = (1.5, 1.5, [[1.5, 1.5, 1.5], [1.5, 1.5, 1.5]], 1.5)
    a[2] = (2.5, 2.5, [[2.5, 2.5, 2.5], [2.5, 2.5, 2.5]], 2.5)
    if HAS_REFCOUNT:
        rc = sys.getrefcount(a[0])
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt)
    vals = [x.copy() for x in i]
    assert_equal(vals[0]['a'], 0.5)
    assert_equal(vals[0]['b'], 0)
    assert_equal(vals[0]['c'], [[(0.5)] * 3] * 2)
    assert_equal(vals[0]['d'], 0.5)
    assert_equal(vals[1]['a'], 1.5)
    assert_equal(vals[1]['b'], 1)
    assert_equal(vals[1]['c'], [[(1.5)] * 3] * 2)
    assert_equal(vals[1]['d'], 1.5)
    assert_equal(vals[0].dtype, np.dtype(sdt))
    vals, i, x = [None] * 3
    if HAS_REFCOUNT:
        assert_equal(sys.getrefcount(a[0]), rc)

    # single-field struct type -> simple
    sdt = [('a', 'f4')]
    a = np.array([(5.5,), (8,)], dtype=sdt)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes='i4')
    assert_equal([x_[()] for x_ in i], [5, 8])

    # make sure multi-field struct type -> simple doesn't work
    sdt = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
    a = np.array([(5.5, 7, 'test'), (8, 10, 11)], dtype=sdt)
    assert_raises(TypeError, lambda: (
        nditer(a, ['buffered', 'refs_ok'], ['readonly'],
               casting='unsafe',
               op_dtypes='i4')))

    # struct type -> struct type (field-wise copy)
    sdt1 = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
    sdt2 = [('d', 'u2'), ('a', 'O'), ('b', 'f8')]
    a = np.array([(1, 2, 3), (4, 5, 6)], dtype=sdt1)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    assert_equal([np.array(x_) for x_ in i],
                 [np.array((1, 2, 3), dtype=sdt2),
                  np.array((4, 5, 6), dtype=sdt2)])


def test_iter_buffered_cast_structured_type_failure_with_cleanup():
    # make sure struct type -> struct type with different
    # number of fields fails
    sdt1 = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
    sdt2 = [('b', 'O'), ('a', 'f8')]
    a = np.array([(1, 2, 3), (4, 5, 6)], dtype=sdt1)

    for intent in ["readwrite", "readonly", "writeonly"]:
        # This test was initially designed to test an error at a different
        # place, but will now raise earlier to to the cast not being possible:
        # `assert np.can_cast(a.dtype, sdt2, casting="unsafe")` fails.
        # Without a faulty DType, there is probably no reliable
        # way to get the initial tested behaviour.
        simple_arr = np.array([1, 2], dtype="i,i")  # requires clean up
        with pytest.raises(TypeError):
            nditer((simple_arr, a), ['buffered', 'refs_ok'], [intent, intent],
                   casting='unsafe', op_dtypes=["f,f", sdt2])


def test_buffered_cast_error_paths():
    with pytest.raises(ValueError):
        # The input is cast into an `S3` buffer
        np.nditer((np.array("a", dtype="S1"),), op_dtypes=["i"],
                  casting="unsafe", flags=["buffered"])

    # The `M8[ns]` is cast into the `S3` output
    it = np.nditer((np.array(1, dtype="i"),), op_dtypes=["S1"],
                   op_flags=["writeonly"], casting="unsafe", flags=["buffered"])
    with pytest.raises(ValueError):
        with it:
            buf = next(it)
            buf[...] = "a"  # cannot be converted to int.

@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
@pytest.mark.skipif(not HAS_REFCOUNT, reason="PyPy seems to not hit this.")
def test_buffered_cast_error_paths_unraisable():
    # The following gives an unraisable error. Pytest sometimes captures that
    # (depending python and/or pytest version). So with Python>=3.8 this can
    # probably be cleaned out in the future to check for
    # pytest.PytestUnraisableExceptionWarning:
    code = textwrap.dedent("""
        import numpy as np

        it = np.nditer((np.array(1, dtype="i"),), op_dtypes=["S1"],
                       op_flags=["writeonly"], casting="unsafe", flags=["buffered"])
        buf = next(it)
        buf[...] = "a"
        del buf, it  # Flushing only happens during deallocate right now.
        """)
    res = subprocess.check_output([sys.executable, "-c", code],
                                  stderr=subprocess.STDOUT, text=True)
    assert "ValueError" in res


def test_iter_buffered_cast_subarray():
    # Tests buffering of subarrays

    # one element -> many (copies it to all)
    sdt1 = [('a', 'f4')]
    sdt2 = [('a', 'f8', (3, 2, 2))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'] = np.arange(6)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    for x, count in zip(i, list(range(6))):
        assert_(np.all(x['a'] == count))

    # one element -> many -> back (copies it to all)
    sdt1 = [('a', 'O', (1, 1))]
    sdt2 = [('a', 'O', (3, 2, 2))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'][:, 0, 0] = np.arange(6)
    i = nditer(a, ['buffered', 'refs_ok'], ['readwrite'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    with i:
        assert_equal(i[0].dtype, np.dtype(sdt2))
        count = 0
        for x in i:
            assert_(np.all(x['a'] == count))
            x['a'][0] += 2
            count += 1
    assert_equal(a['a'], np.arange(6).reshape(6, 1, 1) + 2)

    # many -> one element -> back (copies just element 0)
    sdt1 = [('a', 'O', (3, 2, 2))]
    sdt2 = [('a', 'O', (1,))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'][:, 0, 0, 0] = np.arange(6)
    i = nditer(a, ['buffered', 'refs_ok'], ['readwrite'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    with i:
        assert_equal(i[0].dtype, np.dtype(sdt2))
        count = 0
        for x in i:
            assert_equal(x['a'], count)
            x['a'] += 2
            count += 1
    assert_equal(a['a'], np.arange(6).reshape(6, 1, 1, 1) * np.ones((1, 3, 2, 2)) + 2)

    # many -> one element -> back (copies just element 0)
    sdt1 = [('a', 'f8', (3, 2, 2))]
    sdt2 = [('a', 'O', (1,))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'][:, 0, 0, 0] = np.arange(6)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    count = 0
    for x in i:
        assert_equal(x['a'], count)
        count += 1

    # many -> one element (copies just element 0)
    sdt1 = [('a', 'O', (3, 2, 2))]
    sdt2 = [('a', 'f4', (1,))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'][:, 0, 0, 0] = np.arange(6)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    count = 0
    for x in i:
        assert_equal(x['a'], count)
        count += 1

    # many -> matching shape (straightforward copy)
    sdt1 = [('a', 'O', (3, 2, 2))]
    sdt2 = [('a', 'f4', (3, 2, 2))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'] = np.arange(6 * 3 * 2 * 2).reshape(6, 3, 2, 2)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    count = 0
    for x in i:
        assert_equal(x['a'], a[count]['a'])
        count += 1

    # vector -> smaller vector (truncates)
    sdt1 = [('a', 'f8', (6,))]
    sdt2 = [('a', 'f4', (2,))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'] = np.arange(6 * 6).reshape(6, 6)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    count = 0
    for x in i:
        assert_equal(x['a'], a[count]['a'][:2])
        count += 1

    # vector -> bigger vector (pads with zeros)
    sdt1 = [('a', 'f8', (2,))]
    sdt2 = [('a', 'f4', (6,))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'] = np.arange(6 * 2).reshape(6, 2)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    count = 0
    for x in i:
        assert_equal(x['a'][:2], a[count]['a'])
        assert_equal(x['a'][2:], [0, 0, 0, 0])
        count += 1

    # vector -> matrix (broadcasts)
    sdt1 = [('a', 'f8', (2,))]
    sdt2 = [('a', 'f4', (2, 2))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'] = np.arange(6 * 2).reshape(6, 2)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    count = 0
    for x in i:
        assert_equal(x['a'][0], a[count]['a'])
        assert_equal(x['a'][1], a[count]['a'])
        count += 1

    # vector -> matrix (broadcasts and zero-pads)
    sdt1 = [('a', 'f8', (2, 1))]
    sdt2 = [('a', 'f4', (3, 2))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'] = np.arange(6 * 2).reshape(6, 2, 1)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    count = 0
    for x in i:
        assert_equal(x['a'][:2, 0], a[count]['a'][:, 0])
        assert_equal(x['a'][:2, 1], a[count]['a'][:, 0])
        assert_equal(x['a'][2, :], [0, 0])
        count += 1

    # matrix -> matrix (truncates and zero-pads)
    sdt1 = [('a', 'f8', (2, 3))]
    sdt2 = [('a', 'f4', (3, 2))]
    a = np.zeros((6,), dtype=sdt1)
    a['a'] = np.arange(6 * 2 * 3).reshape(6, 2, 3)
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe',
                    op_dtypes=sdt2)
    assert_equal(i[0].dtype, np.dtype(sdt2))
    count = 0
    for x in i:
        assert_equal(x['a'][:2, 0], a[count]['a'][:, 0])
        assert_equal(x['a'][:2, 1], a[count]['a'][:, 1])
        assert_equal(x['a'][2, :], [0, 0])
        count += 1

def test_iter_buffering_badwriteback():
    # Writing back from a buffer cannot combine elements

    # a needs write buffering, but had a broadcast dimension
    a = np.arange(6).reshape(2, 3, 1)
    b = np.arange(12).reshape(2, 3, 2)
    assert_raises(ValueError, nditer, [a, b],
                  ['buffered', 'external_loop'],
                  [['readwrite'], ['writeonly']],
                  order='C')

    # But if a is readonly, it's fine
    nditer([a, b], ['buffered', 'external_loop'],
           [['readonly'], ['writeonly']],
           order='C')

    # If a has just one element, it's fine too (constant 0 stride, a reduction)
    a = np.arange(1).reshape(1, 1, 1)
    nditer([a, b], ['buffered', 'external_loop', 'reduce_ok'],
           [['readwrite'], ['writeonly']],
           order='C')

    # check that it fails on other dimensions too
    a = np.arange(6).reshape(1, 3, 2)
    assert_raises(ValueError, nditer, [a, b],
                  ['buffered', 'external_loop'],
                  [['readwrite'], ['writeonly']],
                  order='C')
    a = np.arange(4).reshape(2, 1, 2)
    assert_raises(ValueError, nditer, [a, b],
                  ['buffered', 'external_loop'],
                  [['readwrite'], ['writeonly']],
                  order='C')

def test_iter_buffering_string():
    # Safe casting disallows shrinking strings
    a = np.array(['abc', 'a', 'abcd'], dtype=np.bytes_)
    assert_equal(a.dtype, np.dtype('S4'))
    assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
                  op_dtypes='S2')
    i = nditer(a, ['buffered'], ['readonly'], op_dtypes='S6')
    assert_equal(i[0], b'abc')
    assert_equal(i[0].dtype, np.dtype('S6'))

    a = np.array(['abc', 'a', 'abcd'], dtype=np.str_)
    assert_equal(a.dtype, np.dtype('U4'))
    assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
                    op_dtypes='U2')
    i = nditer(a, ['buffered'], ['readonly'], op_dtypes='U6')
    assert_equal(i[0], 'abc')
    assert_equal(i[0].dtype, np.dtype('U6'))

def test_iter_buffering_growinner():
    # Test that the inner loop grows when no buffering is needed
    a = np.arange(30)
    i = nditer(a, ['buffered', 'growinner', 'external_loop'],
                           buffersize=5)
    # Should end up with just one inner loop here
    assert_equal(i[0].size, a.size)


@pytest.mark.parametrize("read_or_readwrite", ["readonly", "readwrite"])
def test_iter_contig_flag_reduce_error(read_or_readwrite):
    # Test that a non-contiguous operand is rejected without buffering.
    # NOTE: This is true even for a reduction, where we return a 0-stride
    #       below!
    with pytest.raises(TypeError, match="Iterator operand required buffering"):
        it = np.nditer(
            (np.zeros(()),), flags=["external_loop", "reduce_ok"],
            op_flags=[(read_or_readwrite, "contig"),], itershape=(10,))


@pytest.mark.parametrize("arr", [
        lambda: np.zeros(()),
        lambda: np.zeros((20, 1))[::20],
        lambda: np.zeros((1, 20))[:, ::20]
    ])
def test_iter_contig_flag_single_operand_strides(arr):
    """
    Tests the strides with the contig flag for both broadcast and non-broadcast
    operands in 3 cases where the logic is needed:
    1. When everything has a zero stride, the broadcast op needs to repeated
    2. When the reduce axis is the last axis (first to iterate).
    3. When the reduce axis is the first axis (last to iterate).

    NOTE: The semantics of the cast flag are not clearly defined when
          it comes to reduction.  It is unclear that there are any users.
    """
    first_op = np.ones((10, 10))
    broadcast_op = arr()
    red_op = arr()
    # Add a first operand to ensure no axis-reordering and the result shape.
    iterator = np.nditer(
        (first_op, broadcast_op, red_op),
        flags=["external_loop", "reduce_ok", "buffered", "delay_bufalloc"],
        op_flags=[("readonly", "contig")] * 2 + [("readwrite", "contig")])

    with iterator:
        iterator.reset()
        for f, b, r in iterator:
            # The first operand is contigouos, we should have a view
            assert np.shares_memory(f, first_op)
            # Although broadcast, the second op always has a contiguous stride
            assert b.strides[0] == 8
            assert not np.shares_memory(b, broadcast_op)
            # The reduction has a contiguous stride or a 0 stride
            if red_op.ndim == 0 or red_op.shape[-1] == 1:
                assert r.strides[0] == 0
            else:
                # The stride is 8, although it was not originally:
                assert r.strides[0] == 8
            # If the reduce stride is 0, buffering makes no difference, but we
            # do it anyway right now:
            assert not np.shares_memory(r, red_op)


@pytest.mark.xfail(reason="The contig flag was always buggy.")
def test_iter_contig_flag_incorrect():
    # This case does the wrong thing...
    iterator = np.nditer(
        (np.ones((10, 10)).T, np.ones((1, 10))),
        flags=["external_loop", "reduce_ok", "buffered", "delay_bufalloc"],
        op_flags=[("readonly", "contig")] * 2)

    with iterator:
        iterator.reset()
        for a, b in iterator:
            # Remove a and b from locals (pytest may want to format them)
            a, b = a.strides, b.strides
            assert a == 8
            assert b == 8  # should be 8 but is 0 due to axis reorder


@pytest.mark.slow
def test_iter_buffered_reduce_reuse():
    # large enough array for all views, including negative strides.
    a = np.arange(2 * 3**5)[3**5:3**5 + 1]
    flags = ['buffered', 'delay_bufalloc', 'multi_index', 'reduce_ok', 'refs_ok']
    op_flags = [('readonly',), ('readwrite', 'allocate')]
    op_axes_list = [[(0, 1, 2), (0, 1, -1)], [(0, 1, 2), (0, -1, -1)]]
    # wrong dtype to force buffering
    op_dtypes = [float, a.dtype]

    def get_params():
        for xs in range(-3**2, 3**2 + 1):
            for ys in range(xs, 3**2 + 1):
                for op_axes in op_axes_list:
                    # last stride is reduced and because of that not
                    # important for this test, as it is the inner stride.
                    strides = (xs * a.itemsize, ys * a.itemsize, a.itemsize)
                    arr = np.lib.stride_tricks.as_strided(a, (3, 3, 3), strides)

                    for skip in [0, 1]:
                        yield arr, op_axes, skip

    for arr, op_axes, skip in get_params():
        nditer2 = np.nditer([arr.copy(), None],
                            op_axes=op_axes, flags=flags, op_flags=op_flags,
                            op_dtypes=op_dtypes)
        with nditer2:
            nditer2.operands[-1][...] = 0
            nditer2.reset()
            nditer2.iterindex = skip

            for (a2_in, b2_in) in nditer2:
                b2_in += a2_in.astype(np.int_)

            comp_res = nditer2.operands[-1]

        for bufsize in range(3**3):
            nditer1 = np.nditer([arr, None],
                                op_axes=op_axes, flags=flags, op_flags=op_flags,
                                buffersize=bufsize, op_dtypes=op_dtypes)
            with nditer1:
                nditer1.operands[-1][...] = 0
                nditer1.reset()
                nditer1.iterindex = skip

                for (a1_in, b1_in) in nditer1:
                    b1_in += a1_in.astype(np.int_)

                res = nditer1.operands[-1]
            assert_array_equal(res, comp_res)


def test_iter_buffered_reduce_reuse_core():
    # NumPy re-uses buffers for broadcast operands (as of writing when reading).
    # Test this even if the offset is manually set at some point during
    # the iteration.  (not a particularly tricky path)
    arr = np.empty((1, 6, 4, 1)).reshape(1, 6, 4, 1)[:, ::3, ::2, :]
    arr[...] = np.arange(arr.size).reshape(arr.shape)
    # First and last dimension are broadcast dimensions.
    arr = np.broadcast_to(arr, (100, 2, 2, 2))

    flags = ['buffered', 'reduce_ok', 'refs_ok', 'multi_index']
    op_flags = [('readonly',)]

    buffersize = 100  # small enough to not fit the whole array
    it = np.nditer(arr, flags=flags, op_flags=op_flags, buffersize=100)

    # Iterate a bit (this will cause buffering internally)
    expected = [next(it) for i in range(11)]
    # Now, manually advance to inside the core (the +1)
    it.iterindex = 10 * (2 * 2 * 2) + 1
    result = [next(it) for i in range(10)]

    assert expected[1:] == result


def test_iter_no_broadcast():
    # Test that the no_broadcast flag works
    a = np.arange(24).reshape(2, 3, 4)
    b = np.arange(6).reshape(2, 3, 1)
    c = np.arange(12).reshape(3, 4)

    nditer([a, b, c], [],
           [['readonly', 'no_broadcast'],
            ['readonly'], ['readonly']])
    assert_raises(ValueError, nditer, [a, b, c], [],
                  [['readonly'], ['readonly', 'no_broadcast'], ['readonly']])
    assert_raises(ValueError, nditer, [a, b, c], [],
                  [['readonly'], ['readonly'], ['readonly', 'no_broadcast']])


class TestIterNested:

    def test_basic(self):
        # Test nested iteration basic usage
        a = arange(12).reshape(2, 3, 2)

        i, j = np.nested_iters(a, [[0], [1, 2]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])

        i, j = np.nested_iters(a, [[0, 1], [2]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])

        i, j = np.nested_iters(a, [[0, 2], [1]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])

    def test_reorder(self):
        # Test nested iteration basic usage
        a = arange(12).reshape(2, 3, 2)

        # In 'K' order (default), it gets reordered
        i, j = np.nested_iters(a, [[0], [2, 1]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])

        i, j = np.nested_iters(a, [[1, 0], [2]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])

        i, j = np.nested_iters(a, [[2, 0], [1]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])

        # In 'C' order, it doesn't
        i, j = np.nested_iters(a, [[0], [2, 1]], order='C')
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 2, 4, 1, 3, 5], [6, 8, 10, 7, 9, 11]])

        i, j = np.nested_iters(a, [[1, 0], [2]], order='C')
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1], [6, 7], [2, 3], [8, 9], [4, 5], [10, 11]])

        i, j = np.nested_iters(a, [[2, 0], [1]], order='C')
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 2, 4], [6, 8, 10], [1, 3, 5], [7, 9, 11]])

    def test_flip_axes(self):
        # Test nested iteration with negative axes
        a = arange(12).reshape(2, 3, 2)[::-1, ::-1, ::-1]

        # In 'K' order (default), the axes all get flipped
        i, j = np.nested_iters(a, [[0], [1, 2]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])

        i, j = np.nested_iters(a, [[0, 1], [2]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])

        i, j = np.nested_iters(a, [[0, 2], [1]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])

        # In 'C' order, flipping axes is disabled
        i, j = np.nested_iters(a, [[0], [1, 2]], order='C')
        vals = [list(j) for _ in i]
        assert_equal(vals, [[11, 10, 9, 8, 7, 6], [5, 4, 3, 2, 1, 0]])

        i, j = np.nested_iters(a, [[0, 1], [2]], order='C')
        vals = [list(j) for _ in i]
        assert_equal(vals, [[11, 10], [9, 8], [7, 6], [5, 4], [3, 2], [1, 0]])

        i, j = np.nested_iters(a, [[0, 2], [1]], order='C')
        vals = [list(j) for _ in i]
        assert_equal(vals, [[11, 9, 7], [10, 8, 6], [5, 3, 1], [4, 2, 0]])

    def test_broadcast(self):
        # Test nested iteration with broadcasting
        a = arange(2).reshape(2, 1)
        b = arange(3).reshape(1, 3)

        i, j = np.nested_iters([a, b], [[0], [1]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[[0, 0], [0, 1], [0, 2]], [[1, 0], [1, 1], [1, 2]]])

        i, j = np.nested_iters([a, b], [[1], [0]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[[0, 0], [1, 0]], [[0, 1], [1, 1]], [[0, 2], [1, 2]]])

    def test_dtype_copy(self):
        # Test nested iteration with a copy to change dtype

        # copy
        a = arange(6, dtype='i4').reshape(2, 3)
        i, j = np.nested_iters(a, [[0], [1]],
                            op_flags=['readonly', 'copy'],
                            op_dtypes='f8')
        assert_equal(j[0].dtype, np.dtype('f8'))
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1, 2], [3, 4, 5]])
        vals = None

        # writebackifcopy - using context manager
        a = arange(6, dtype='f4').reshape(2, 3)
        i, j = np.nested_iters(a, [[0], [1]],
                            op_flags=['readwrite', 'updateifcopy'],
                            casting='same_kind',
                            op_dtypes='f8')
        with i, j:
            assert_equal(j[0].dtype, np.dtype('f8'))
            for x in i:
                for y in j:
                    y[...] += 1
            assert_equal(a, [[0, 1, 2], [3, 4, 5]])
        assert_equal(a, [[1, 2, 3], [4, 5, 6]])

        # writebackifcopy - using close()
        a = arange(6, dtype='f4').reshape(2, 3)
        i, j = np.nested_iters(a, [[0], [1]],
                            op_flags=['readwrite', 'updateifcopy'],
                            casting='same_kind',
                            op_dtypes='f8')
        assert_equal(j[0].dtype, np.dtype('f8'))
        for x in i:
            for y in j:
                y[...] += 1
        assert_equal(a, [[0, 1, 2], [3, 4, 5]])
        i.close()
        j.close()
        assert_equal(a, [[1, 2, 3], [4, 5, 6]])

    def test_dtype_buffered(self):
        # Test nested iteration with buffering to change dtype

        a = arange(6, dtype='f4').reshape(2, 3)
        i, j = np.nested_iters(a, [[0], [1]],
                            flags=['buffered'],
                            op_flags=['readwrite'],
                            casting='same_kind',
                            op_dtypes='f8')
        assert_equal(j[0].dtype, np.dtype('f8'))
        for x in i:
            for y in j:
                y[...] += 1
        assert_equal(a, [[1, 2, 3], [4, 5, 6]])

    def test_0d(self):
        a = np.arange(12).reshape(2, 3, 2)
        i, j = np.nested_iters(a, [[], [1, 0, 2]])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])

        i, j = np.nested_iters(a, [[1, 0, 2], []])
        vals = [list(j) for _ in i]
        assert_equal(vals, [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]])

        i, j, k = np.nested_iters(a, [[2, 0], [], [1]])
        vals = []
        for x in i:
            for y in j:
                vals.append(list(k))
        assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])

    def test_iter_nested_iters_dtype_buffered(self):
        # Test nested iteration with buffering to change dtype

        a = arange(6, dtype='f4').reshape(2, 3)
        i, j = np.nested_iters(a, [[0], [1]],
                            flags=['buffered'],
                            op_flags=['readwrite'],
                            casting='same_kind',
                            op_dtypes='f8')
        with i, j:
            assert_equal(j[0].dtype, np.dtype('f8'))
            for x in i:
                for y in j:
                    y[...] += 1
        assert_equal(a, [[1, 2, 3], [4, 5, 6]])

def test_iter_reduction_error():

    a = np.arange(6)
    assert_raises(ValueError, nditer, [a, None], [],
                    [['readonly'], ['readwrite', 'allocate']],
                    op_axes=[[0], [-1]])

    a = np.arange(6).reshape(2, 3)
    assert_raises(ValueError, nditer, [a, None], ['external_loop'],
                    [['readonly'], ['readwrite', 'allocate']],
                    op_axes=[[0, 1], [-1, -1]])

def test_iter_reduction():
    # Test doing reductions with the iterator

    a = np.arange(6)
    i = nditer([a, None], ['reduce_ok'],
                    [['readonly'], ['readwrite', 'allocate']],
                    op_axes=[[0], [-1]])
    # Need to initialize the output operand to the addition unit
    with i:
        i.operands[1][...] = 0
        # Do the reduction
        for x, y in i:
            y[...] += x
        # Since no axes were specified, should have allocated a scalar
        assert_equal(i.operands[1].ndim, 0)
        assert_equal(i.operands[1], np.sum(a))

    a = np.arange(6).reshape(2, 3)
    i = nditer([a, None], ['reduce_ok', 'external_loop'],
                    [['readonly'], ['readwrite', 'allocate']],
                    op_axes=[[0, 1], [-1, -1]])
    # Need to initialize the output operand to the addition unit
    with i:
        i.operands[1][...] = 0
        # Reduction shape/strides for the output
        assert_equal(i[1].shape, (6,))
        assert_equal(i[1].strides, (0,))
        # Do the reduction
        for x, y in i:
            # Use a for loop instead of ``y[...] += x``
            # (equivalent to ``y[...] = y[...].copy() + x``),
            # because y has zero strides we use for the reduction
            for j in range(len(y)):
                y[j] += x[j]
        # Since no axes were specified, should have allocated a scalar
        assert_equal(i.operands[1].ndim, 0)
        assert_equal(i.operands[1], np.sum(a))

    # This is a tricky reduction case for the buffering double loop
    # to handle
    a = np.ones((2, 3, 5))
    it1 = nditer([a, None], ['reduce_ok', 'external_loop'],
                    [['readonly'], ['readwrite', 'allocate']],
                    op_axes=[None, [0, -1, 1]])
    it2 = nditer([a, None], ['reduce_ok', 'external_loop',
                            'buffered', 'delay_bufalloc'],
                    [['readonly'], ['readwrite', 'allocate']],
                    op_axes=[None, [0, -1, 1]], buffersize=10)
    with it1, it2:
        it1.operands[1].fill(0)
        it2.operands[1].fill(0)
        it2.reset()
        for x in it1:
            x[1][...] += x[0]
        for x in it2:
            x[1][...] += x[0]
        assert_equal(it1.operands[1], it2.operands[1])
        assert_equal(it2.operands[1].sum(), a.size)

def test_iter_buffering_reduction():
    # Test doing buffered reductions with the iterator

    a = np.arange(6)
    b = np.array(0., dtype='f8').byteswap()
    b = b.view(b.dtype.newbyteorder())
    i = nditer([a, b], ['reduce_ok', 'buffered'],
                    [['readonly'], ['readwrite', 'nbo']],
                    op_axes=[[0], [-1]])
    with i:
        assert_equal(i[1].dtype, np.dtype('f8'))
        assert_(i[1].dtype != b.dtype)
        # Do the reduction
        for x, y in i:
            y[...] += x
    # Since no axes were specified, should have allocated a scalar
    assert_equal(b, np.sum(a))

    a = np.arange(6).reshape(2, 3)
    b = np.array([0, 0], dtype='f8').byteswap()
    b = b.view(b.dtype.newbyteorder())
    i = nditer([a, b], ['reduce_ok', 'external_loop', 'buffered'],
                    [['readonly'], ['readwrite', 'nbo']],
                    op_axes=[[0, 1], [0, -1]])
    # Reduction shape/strides for the output
    with i:
        assert_equal(i[1].shape, (3,))
        assert_equal(i[1].strides, (0,))
        # Do the reduction
        for x, y in i:
            # Use a for loop instead of ``y[...] += x``
            # (equivalent to ``y[...] = y[...].copy() + x``),
            # because y has zero strides we use for the reduction
            for j in range(len(y)):
                y[j] += x[j]
    assert_equal(b, np.sum(a, axis=1))

    # Iterator inner double loop was wrong on this one
    p = np.arange(2) + 1
    it = np.nditer([p, None],
            ['delay_bufalloc', 'reduce_ok', 'buffered', 'external_loop'],
            [['readonly'], ['readwrite', 'allocate']],
            op_axes=[[-1, 0], [-1, -1]],
            itershape=(2, 2))
    with it:
        it.operands[1].fill(0)
        it.reset()
        assert_equal(it[0], [1, 2, 1, 2])

    # Iterator inner loop should take argument contiguity into account
    x = np.ones((7, 13, 8), np.int8)[4:6, 1:11:6, 1:5].transpose(1, 2, 0)
    x[...] = np.arange(x.size).reshape(x.shape)
    y_base = np.arange(4 * 4, dtype=np.int8).reshape(4, 4)
    y_base_copy = y_base.copy()
    y = y_base[::2, :, None]

    it = np.nditer([y, x],
                   ['buffered', 'external_loop', 'reduce_ok'],
                   [['readwrite'], ['readonly']])
    with it:
        for a, b in it:
            a.fill(2)

    assert_equal(y_base[1::2], y_base_copy[1::2])
    assert_equal(y_base[::2], 2)

def test_iter_buffering_reduction_reuse_reduce_loops():
    # There was a bug triggering reuse of the reduce loop inappropriately,
    # which caused processing to happen in unnecessarily small chunks
    # and overran the buffer.

    a = np.zeros((2, 7))
    b = np.zeros((1, 7))
    it = np.nditer([a, b], flags=['reduce_ok', 'external_loop', 'buffered'],
                    op_flags=[['readonly'], ['readwrite']],
                    buffersize=5)

    with it:
        bufsizes = [x.shape[0] for x, y in it]
    assert_equal(bufsizes, [5, 2, 5, 2])
    assert_equal(sum(bufsizes), a.size)

def test_iter_writemasked_badinput():
    a = np.zeros((2, 3))
    b = np.zeros((3,))
    m = np.array([[True, True, False], [False, True, False]])
    m2 = np.array([True, True, False])
    m3 = np.array([0, 1, 1], dtype='u1')
    mbad1 = np.array([0, 1, 1], dtype='i1')
    mbad2 = np.array([0, 1, 1], dtype='f4')

    # Need an 'arraymask' if any operand is 'writemasked'
    assert_raises(ValueError, nditer, [a, m], [],
                    [['readwrite', 'writemasked'], ['readonly']])

    # A 'writemasked' operand must not be readonly
    assert_raises(ValueError, nditer, [a, m], [],
                    [['readonly', 'writemasked'], ['readonly', 'arraymask']])

    # 'writemasked' and 'arraymask' may not be used together
    assert_raises(ValueError, nditer, [a, m], [],
                    [['readonly'], ['readwrite', 'arraymask', 'writemasked']])

    # 'arraymask' may only be specified once
    assert_raises(ValueError, nditer, [a, m, m2], [],
                    [['readwrite', 'writemasked'],
                     ['readonly', 'arraymask'],
                     ['readonly', 'arraymask']])

    # An 'arraymask' with nothing 'writemasked' also doesn't make sense
    assert_raises(ValueError, nditer, [a, m], [],
                    [['readwrite'], ['readonly', 'arraymask']])

    # A writemasked reduction requires a similarly smaller mask
    assert_raises(ValueError, nditer, [a, b, m], ['reduce_ok'],
                    [['readonly'],
                     ['readwrite', 'writemasked'],
                     ['readonly', 'arraymask']])
    # But this should work with a smaller/equal mask to the reduction operand
    np.nditer([a, b, m2], ['reduce_ok'],
                    [['readonly'],
                     ['readwrite', 'writemasked'],
                     ['readonly', 'arraymask']])
    # The arraymask itself cannot be a reduction
    assert_raises(ValueError, nditer, [a, b, m2], ['reduce_ok'],
                    [['readonly'],
                     ['readwrite', 'writemasked'],
                     ['readwrite', 'arraymask']])

    # A uint8 mask is ok too
    np.nditer([a, m3], ['buffered'],
                    [['readwrite', 'writemasked'],
                     ['readonly', 'arraymask']],
                    op_dtypes=['f4', None],
                    casting='same_kind')
    # An int8 mask isn't ok
    assert_raises(TypeError, np.nditer, [a, mbad1], ['buffered'],
                    [['readwrite', 'writemasked'],
                     ['readonly', 'arraymask']],
                    op_dtypes=['f4', None],
                    casting='same_kind')
    # A float32 mask isn't ok
    assert_raises(TypeError, np.nditer, [a, mbad2], ['buffered'],
                    [['readwrite', 'writemasked'],
                     ['readonly', 'arraymask']],
                    op_dtypes=['f4', None],
                    casting='same_kind')


def _is_buffered(iterator):
    try:
        iterator.itviews
    except ValueError:
        return True
    return False

@pytest.mark.parametrize("a",
        [np.zeros((3,), dtype='f8'),
         np.zeros((9876, 3 * 5), dtype='f8')[::2, :],
         np.zeros((4, 312, 124, 3), dtype='f8')[::2, :, ::2, :],
         # Also test with the last dimension strided (so it does not fit if
         # there is repeated access)
         np.zeros((9,), dtype='f8')[::3],
         np.zeros((9876, 3 * 10), dtype='f8')[::2, ::5],
         np.zeros((4, 312, 124, 3), dtype='f8')[::2, :, ::2, ::-1]])
def test_iter_writemasked(a):
    # Note, the slicing above is to ensure that nditer cannot combine multiple
    # axes into one.  The repetition is just to make things a bit more
    # interesting.
    shape = a.shape
    reps = shape[-1] // 3
    msk = np.empty(shape, dtype=bool)
    msk[...] = [True, True, False] * reps

    # When buffering is unused, 'writemasked' effectively does nothing.
    # It's up to the user of the iterator to obey the requested semantics.
    it = np.nditer([a, msk], [],
                [['readwrite', 'writemasked'],
                 ['readonly', 'arraymask']])
    with it:
        for x, m in it:
            x[...] = 1
    # Because we violated the semantics, all the values became 1
    assert_equal(a, np.broadcast_to([1, 1, 1] * reps, shape))

    # Even if buffering is enabled, we still may be accessing the array
    # directly.
    it = np.nditer([a, msk], ['buffered'],
                [['readwrite', 'writemasked'],
                 ['readonly', 'arraymask']])
    # @seberg: I honestly don't currently understand why a "buffered" iterator
    # would end up not using a buffer for the small array here at least when
    # "writemasked" is used, that seems confusing...  Check by testing for
    # actual memory overlap!
    is_buffered = True
    with it:
        for x, m in it:
            x[...] = 2.5
            if np.may_share_memory(x, a):
                is_buffered = False

    if not is_buffered:
        # Because we violated the semantics, all the values became 2.5
        assert_equal(a, np.broadcast_to([2.5, 2.5, 2.5] * reps, shape))
    else:
        # For large sizes, the iterator may be buffered:
        assert_equal(a, np.broadcast_to([2.5, 2.5, 1] * reps, shape))
        a[...] = 2.5

    # If buffering will definitely happening, for instance because of
    # a cast, only the items selected by the mask will be copied back from
    # the buffer.
    it = np.nditer([a, msk], ['buffered'],
                [['readwrite', 'writemasked'],
                 ['readonly', 'arraymask']],
                op_dtypes=['i8', None],
                casting='unsafe')
    with it:
        for x, m in it:
            x[...] = 3
    # Even though we violated the semantics, only the selected values
    # were copied back
    assert_equal(a, np.broadcast_to([3, 3, 2.5] * reps, shape))


@pytest.mark.parametrize(["mask", "mask_axes"], [
        # Allocated operand (only broadcasts with -1)
        (None, [-1, 0]),
        # Reduction along the first dimension (with and without op_axes)
        (np.zeros((1, 4), dtype="bool"), [0, 1]),
        (np.zeros((1, 4), dtype="bool"), None),
        # Test 0-D and -1 op_axes
        (np.zeros(4, dtype="bool"), [-1, 0]),
        (np.zeros((), dtype="bool"), [-1, -1]),
        (np.zeros((), dtype="bool"), None)])
def test_iter_writemasked_broadcast_error(mask, mask_axes):
    # This assumes that a readwrite mask makes sense. This is likely not the
    # case and should simply be deprecated.
    arr = np.zeros((3, 4))
    itflags = ["reduce_ok"]
    mask_flags = ["arraymask", "readwrite", "allocate"]
    a_flags = ["writeonly", "writemasked"]
    if mask_axes is None:
        op_axes = None
    else:
        op_axes = [mask_axes, [0, 1]]

    with assert_raises(ValueError):
        np.nditer((mask, arr), flags=itflags, op_flags=[mask_flags, a_flags],
                  op_axes=op_axes)


def test_iter_writemasked_decref():
    # force casting (to make it interesting) by using a structured dtype.
    arr = np.arange(10000).astype(">i,O")
    original = arr.copy()
    mask = np.random.randint(0, 2, size=10000).astype(bool)

    it = np.nditer([arr, mask], ['buffered', "refs_ok"],
                   [['readwrite', 'writemasked'],
                    ['readonly', 'arraymask']],
                   op_dtypes=["<i,O", "?"])
    singleton = object()
    if HAS_REFCOUNT:
        count = sys.getrefcount(singleton)
    for buf, mask_buf in it:
        buf[...] = (3, singleton)

    del buf, mask_buf, it   # delete everything to ensure correct cleanup

    if HAS_REFCOUNT:
        # The buffer would have included additional items, they must be
        # cleared correctly:
        assert sys.getrefcount(singleton) - count == np.count_nonzero(mask)

    assert_array_equal(arr[~mask], original[~mask])
    assert (arr[mask] == np.array((3, singleton), arr.dtype)).all()
    del arr

    if HAS_REFCOUNT:
        assert sys.getrefcount(singleton) == count


def test_iter_non_writable_attribute_deletion():
    it = np.nditer(np.ones(2))
    attr = ["value", "shape", "operands", "itviews", "has_delayed_bufalloc",
            "iterationneedsapi", "has_multi_index", "has_index", "dtypes",
            "ndim", "nop", "itersize", "finished"]

    for s in attr:
        assert_raises(AttributeError, delattr, it, s)


def test_iter_writable_attribute_deletion():
    it = np.nditer(np.ones(2))
    attr = ["multi_index", "index", "iterrange", "iterindex"]
    for s in attr:
        assert_raises(AttributeError, delattr, it, s)


def test_iter_element_deletion():
    it = np.nditer(np.ones(3))
    try:
        del it[1]
        del it[1:2]
    except TypeError:
        pass
    except Exception:
        raise AssertionError

def test_iter_allocated_array_dtypes():
    # If the dtype of an allocated output has a shape, the shape gets
    # tacked onto the end of the result.
    it = np.nditer(([1, 3, 20], None), op_dtypes=[None, ('i4', (2,))])
    for a, b in it:
        b[0] = a - 1
        b[1] = a + 1
    assert_equal(it.operands[1], [[0, 2], [2, 4], [19, 21]])

    # Check the same (less sensitive) thing when `op_axes` with -1 is given.
    it = np.nditer(([[1, 3, 20]], None), op_dtypes=[None, ('i4', (2,))],
                   flags=["reduce_ok"], op_axes=[None, (-1, 0)])
    for a, b in it:
        b[0] = a - 1
        b[1] = a + 1
    assert_equal(it.operands[1], [[0, 2], [2, 4], [19, 21]])

    # Make sure this works for scalars too
    it = np.nditer((10, 2, None), op_dtypes=[None, None, ('i4', (2, 2))])
    for a, b, c in it:
        c[0, 0] = a - b
        c[0, 1] = a + b
        c[1, 0] = a * b
        c[1, 1] = a / b
    assert_equal(it.operands[2], [[8, 12], [20, 5]])


def test_0d_iter():
    # Basic test for iteration of 0-d arrays:
    i = nditer([2, 3], ['multi_index'], [['readonly']] * 2)
    assert_equal(i.ndim, 0)
    assert_equal(next(i), (2, 3))
    assert_equal(i.multi_index, ())
    assert_equal(i.iterindex, 0)
    assert_raises(StopIteration, next, i)
    # test reset:
    i.reset()
    assert_equal(next(i), (2, 3))
    assert_raises(StopIteration, next, i)

    # test forcing to 0-d
    i = nditer(np.arange(5), ['multi_index'], [['readonly']], op_axes=[()])
    assert_equal(i.ndim, 0)
    assert_equal(len(i), 1)

    i = nditer(np.arange(5), ['multi_index'], [['readonly']],
               op_axes=[()], itershape=())
    assert_equal(i.ndim, 0)
    assert_equal(len(i), 1)

    # passing an itershape alone is not enough, the op_axes are also needed
    with assert_raises(ValueError):
        nditer(np.arange(5), ['multi_index'], [['readonly']], itershape=())

    # Test a more complex buffered casting case (same as another test above)
    sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
    a = np.array(0.5, dtype='f4')
    i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
                    casting='unsafe', op_dtypes=sdt)
    vals = next(i)
    assert_equal(vals['a'], 0.5)
    assert_equal(vals['b'], 0)
    assert_equal(vals['c'], [[(0.5)] * 3] * 2)
    assert_equal(vals['d'], 0.5)

def test_object_iter_cleanup():
    # see gh-18450
    # object arrays can raise a python exception in ufunc inner loops using
    # nditer, which should cause iteration to stop & cleanup. There were bugs
    # in the nditer cleanup when decref'ing object arrays.
    # This test would trigger valgrind "uninitialized read" before the bugfix.
    assert_raises(TypeError, lambda: np.zeros((17000, 2), dtype='f4') * None)

    # this more explicit code also triggers the invalid access
    arr = np.arange(ncu.BUFSIZE * 10).reshape(10, -1).astype(str)
    oarr = arr.astype(object)
    oarr[:, -1] = None
    assert_raises(TypeError, lambda: np.add(oarr[:, ::-1], arr[:, ::-1]))

    # followup: this tests for a bug introduced in the first pass of gh-18450,
    # caused by an incorrect fallthrough of the TypeError
    class T:
        def __bool__(self):
            raise TypeError("Ambiguous")
    assert_raises(TypeError, np.logical_or.reduce,
                             np.array([T(), T()], dtype='O'))

def test_object_iter_cleanup_reduce():
    # Similar as above, but a complex reduction case that was previously
    # missed (see gh-18810).
    # The following array is special in that it cannot be flattened:
    arr = np.array([[None, 1], [-1, -1], [None, 2], [-1, -1]])[::2]
    with pytest.raises(TypeError):
        np.sum(arr)

@pytest.mark.parametrize("arr", [
        np.ones((8000, 4, 2), dtype=object)[:, ::2, :],
        np.ones((8000, 4, 2), dtype=object, order="F")[:, ::2, :],
        np.ones((8000, 4, 2), dtype=object)[:, ::2, :].copy("F")])
def test_object_iter_cleanup_large_reduce(arr):
    # More complicated calls are possible for large arrays:
    out = np.ones(8000, dtype=np.intp)
    # force casting with `dtype=object`
    res = np.sum(arr, axis=(1, 2), dtype=object, out=out)
    assert_array_equal(res, np.full(8000, 4, dtype=object))

def test_iter_too_large():
    # The total size of the iterator must not exceed the maximum intp due
    # to broadcasting. Dividing by 1024 will keep it small enough to
    # give a legal array.
    size = np.iinfo(np.intp).max // 1024
    arr = np.lib.stride_tricks.as_strided(np.zeros(1), (size,), (0,))
    assert_raises(ValueError, nditer, (arr, arr[:, None]))
    # test the same for multiindex. That may get more interesting when
    # removing 0 dimensional axis is allowed (since an iterator can grow then)
    assert_raises(ValueError, nditer,
                  (arr, arr[:, None]), flags=['multi_index'])


def test_iter_too_large_with_multiindex():
    # When a multi index is being tracked, the error is delayed this
    # checks the delayed error messages and getting below that by
    # removing an axis.
    base_size = 2**10
    num = 1
    while base_size**num < np.iinfo(np.intp).max:
        num += 1

    shape_template = [1, 1] * num
    arrays = []
    for i in range(num):
        shape = shape_template[:]
        shape[i * 2] = 2**10
        arrays.append(np.empty(shape))
    arrays = tuple(arrays)

    # arrays are now too large to be broadcast. The different modes test
    # different nditer functionality with or without GIL.
    for mode in range(6):
        with assert_raises(ValueError):
            _multiarray_tests.test_nditer_too_large(arrays, -1, mode)
    # but if we do nothing with the nditer, it can be constructed:
    _multiarray_tests.test_nditer_too_large(arrays, -1, 7)

    # When an axis is removed, things should work again (half the time):
    for i in range(num):
        for mode in range(6):
            # an axis with size 1024 is removed:
            _multiarray_tests.test_nditer_too_large(arrays, i * 2, mode)
            # an axis with size 1 is removed:
            with assert_raises(ValueError):
                _multiarray_tests.test_nditer_too_large(arrays, i * 2 + 1, mode)

def test_writebacks():
    a = np.arange(6, dtype='f4')
    au = a.byteswap()
    au = au.view(au.dtype.newbyteorder())
    assert_(a.dtype.byteorder != au.dtype.byteorder)
    it = nditer(au, [], [['readwrite', 'updateifcopy']],
                        casting='equiv', op_dtypes=[np.dtype('f4')])
    with it:
        it.operands[0][:] = 100
    assert_equal(au, 100)
    # do it again, this time raise an error,
    it = nditer(au, [], [['readwrite', 'updateifcopy']],
                        casting='equiv', op_dtypes=[np.dtype('f4')])
    try:
        with it:
            assert_equal(au.flags.writeable, False)
            it.operands[0][:] = 0
            raise ValueError('exit context manager on exception')
    except Exception:
        pass
    assert_equal(au, 0)
    assert_equal(au.flags.writeable, True)
    # cannot reuse i outside context manager
    assert_raises(ValueError, getattr, it, 'operands')

    it = nditer(au, [], [['readwrite', 'updateifcopy']],
                        casting='equiv', op_dtypes=[np.dtype('f4')])
    with it:
        x = it.operands[0]
        x[:] = 6
        assert_(x.flags.writebackifcopy)
    assert_equal(au, 6)
    assert_(not x.flags.writebackifcopy)
    x[:] = 123  # x.data still valid
    assert_equal(au, 6)  # but not connected to au

    it = nditer(au, [],
                 [['readwrite', 'updateifcopy']],
                 casting='equiv', op_dtypes=[np.dtype('f4')])
    # reentering works
    with it:
        with it:
            for x in it:
                x[...] = 123

    it = nditer(au, [],
                 [['readwrite', 'updateifcopy']],
                 casting='equiv', op_dtypes=[np.dtype('f4')])
    # make sure exiting the inner context manager closes the iterator
    with it:
        with it:
            for x in it:
                x[...] = 123
        assert_raises(ValueError, getattr, it, 'operands')
    # do not crash if original data array is decrefed
    it = nditer(au, [],
                 [['readwrite', 'updateifcopy']],
                 casting='equiv', op_dtypes=[np.dtype('f4')])
    del au
    with it:
        for x in it:
            x[...] = 123
    # make sure we cannot reenter the closed iterator
    enter = it.__enter__
    assert_raises(RuntimeError, enter)

def test_close_equivalent():
    ''' using a context amanger and using nditer.close are equivalent
    '''
    def add_close(x, y, out=None):
        addop = np.add
        it = np.nditer([x, y, out], [],
                    [['readonly'], ['readonly'], ['writeonly', 'allocate']])
        for (a, b, c) in it:
            addop(a, b, out=c)
        ret = it.operands[2]
        it.close()
        return ret

    def add_context(x, y, out=None):
        addop = np.add
        it = np.nditer([x, y, out], [],
                    [['readonly'], ['readonly'], ['writeonly', 'allocate']])
        with it:
            for (a, b, c) in it:
                addop(a, b, out=c)
            return it.operands[2]
    z = add_close(range(5), range(5))
    assert_equal(z, range(0, 10, 2))
    z = add_context(range(5), range(5))
    assert_equal(z, range(0, 10, 2))

def test_close_raises():
    it = np.nditer(np.arange(3))
    assert_equal(next(it), 0)
    it.close()
    assert_raises(StopIteration, next, it)
    assert_raises(ValueError, getattr, it, 'operands')

def test_close_parameters():
    it = np.nditer(np.arange(3))
    assert_raises(TypeError, it.close, 1)

@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
def test_warn_noclose():
    a = np.arange(6, dtype='f4')
    au = a.byteswap()
    au = au.view(au.dtype.newbyteorder())
    with suppress_warnings() as sup:
        sup.record(RuntimeWarning)
        it = np.nditer(au, [], [['readwrite', 'updateifcopy']],
                        casting='equiv', op_dtypes=[np.dtype('f4')])
        del it
        assert len(sup.log) == 1


@pytest.mark.parametrize(["in_dtype", "buf_dtype"],
        [("i", "O"), ("O", "i"),  # most simple cases
         ("i,O", "O,O"),  # structured partially only copying O
         ("O,i", "i,O"),  # structured casting to and from O
         ])
@pytest.mark.parametrize("steps", [1, 2, 3])
def test_partial_iteration_cleanup(in_dtype, buf_dtype, steps):
    """
    Checks for reference counting leaks during cleanup.  Using explicit
    reference counts lead to occasional false positives (at least in parallel
    test setups).  This test now should still test leaks correctly when
    run e.g. with pytest-valgrind or pytest-leaks
    """
    value = 2**30 + 1  # just a random value that Python won't intern
    arr = np.full(int(ncu.BUFSIZE * 2.5), value).astype(in_dtype)

    it = np.nditer(arr, op_dtypes=[np.dtype(buf_dtype)],
            flags=["buffered", "external_loop", "refs_ok"], casting="unsafe")
    for step in range(steps):
        # The iteration finishes in 3 steps, the first two are partial
        next(it)

    del it  # not necessary, but we test the cleanup

    # Repeat the test with `iternext`
    it = np.nditer(arr, op_dtypes=[np.dtype(buf_dtype)],
                   flags=["buffered", "external_loop", "refs_ok"], casting="unsafe")
    for step in range(steps):
        it.iternext()

    del it  # not necessary, but we test the cleanup

@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
@pytest.mark.parametrize(["in_dtype", "buf_dtype"],
         [("O", "i"),  # most simple cases
          ("O,i", "i,O"),  # structured casting to and from O
          ])
def test_partial_iteration_error(in_dtype, buf_dtype):
    value = 123  # relies on python cache (leak-check will still find it)
    arr = np.full(int(ncu.BUFSIZE * 2.5), value).astype(in_dtype)
    if in_dtype == "O":
        arr[int(ncu.BUFSIZE * 1.5)] = None
    else:
        arr[int(ncu.BUFSIZE * 1.5)]["f0"] = None

    count = sys.getrefcount(value)

    it = np.nditer(arr, op_dtypes=[np.dtype(buf_dtype)],
            flags=["buffered", "external_loop", "refs_ok"], casting="unsafe")
    with pytest.raises(TypeError):
        # pytest.raises seems to have issues with the error originating
        # in the for loop, so manually unravel:
        next(it)
        next(it)  # raises TypeError

    # Repeat the test with `iternext` after resetting, the buffers should
    # already be cleared from any references, so resetting is sufficient.
    it.reset()
    with pytest.raises(TypeError):
        it.iternext()
        it.iternext()

    assert count == sys.getrefcount(value)


def test_arbitrary_number_of_ops():
    # 2*16 + 1 is still just a few kiB, so should be fast and easy to deal with
    # but larger than any small custom integer.
    ops = [np.arange(10) for a in range(2**16 + 1)]

    it = np.nditer(ops)
    for i, vals in enumerate(it):
        assert all(v == i for v in vals)


def test_arbitrary_number_of_ops_nested():
    # 2*16 + 1 is still just a few kiB, so should be fast and easy to deal with
    # but larger than any small custom integer.
    ops = [np.arange(10) for a in range(2**16 + 1)]

    it = np.nested_iters(ops, [[0], []])
    for i, vals in enumerate(it):
        assert all(v == i for v in vals)


@pytest.mark.slow
@requires_memory(9 * np.iinfo(np.intc).max)
def test_arbitrary_number_of_ops_error():
    # A different error may happen for more than integer operands, but that
    # is too large to test nicely.
    a = np.ones(1)
    args = [a] * (np.iinfo(np.intc).max + 1)
    with pytest.raises(ValueError, match="Too many operands to nditer"):
        np.nditer(args)

    with pytest.raises(ValueError, match="Too many operands to nditer"):
        np.nested_iters(args, [[0], []])


def test_debug_print(capfd):
    """
    Matches the expected output of a debug print with the actual output.
    Note that the iterator dump should not be considered stable API,
    this test is mainly to ensure the print does not crash.

    Currently uses a subprocess to avoid dealing with the C level `printf`s.
    """
    # the expected output with all addresses and sizes stripped (they vary
    # and/or are platform dependent).
    expected = """
    ------ BEGIN ITERATOR DUMP ------
    | Iterator Address:
    | ItFlags: BUFFER REDUCE
    | NDim: 2
    | NOp: 2
    | IterSize: 50
    | IterStart: 0
    | IterEnd: 50
    | IterIndex: 0
    | Iterator SizeOf:
    | BufferData SizeOf:
    | AxisData SizeOf:
    |
    | Perm: 0 1
    | DTypes:
    | DTypes: dtype('float64') dtype('int32')
    | InitDataPtrs:
    | BaseOffsets: 0 0
    | Ptrs:
    | User/buffer ptrs:
    | Operands:
    | Operand DTypes: dtype('int64') dtype('float64')
    | OpItFlags:
    |   Flags[0]: READ CAST
    |   Flags[1]: READ WRITE CAST REDUCE
    |
    | BufferData:
    |   BufferSize: 50
    |   Size: 5
    |   BufIterEnd: 5
    |   BUFFER CoreSize: 5
    |   REDUCE Pos: 0
    |   REDUCE OuterSize: 10
    |   REDUCE OuterDim: 1
    |   Strides: 8 4
    |   REDUCE Outer Strides: 40 0
    |   REDUCE Outer Ptrs:
    |   ReadTransferFn:
    |   ReadTransferData:
    |   WriteTransferFn:
    |   WriteTransferData:
    |   Buffers:
    |
    | AxisData[0]:
    |   Shape: 5
    |   Index: 0
    |   Strides: 16 8
    | AxisData[1]:
    |   Shape: 10
    |   Index: 0
    |   Strides: 80 0
    ------- END ITERATOR DUMP -------
    """.strip().splitlines()

    arr1 = np.arange(100, dtype=np.int64).reshape(10, 10)[:, ::2]
    arr2 = np.arange(5.)
    it = np.nditer((arr1, arr2), op_dtypes=["d", "i4"], casting="unsafe",
                   flags=["reduce_ok", "buffered"],
                   op_flags=[["readonly"], ["readwrite"]])
    it.debug_print()
    res = capfd.readouterr().out
    res = res.strip().splitlines()

    assert len(res) == len(expected)
    for res_line, expected_line in zip(res, expected):
        # The actual output may have additional pointers listed that are
        # stripped from the example output:
        assert res_line.startswith(expected_line.strip())
