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# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

# pylint: skip-file
from __future__ import absolute_import
from __future__ import division
import os
import numpy as _np
import mxnet as mx
from mxnet import np, npx, autograd
from mxnet.gluon import HybridBlock
from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, assert_exception, use_np
from common import with_seed, TemporaryDirectory
from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
from mxnet.ndarray.ndarray import py_slice
from mxnet.base import integer_types
import scipy.stats as ss


@with_seed()
@use_np
def test_np_empty():
    dtypes = [np.int8, np.int32, np.float16, np.float32, np.float64, None]
    expected_dtypes = [np.int8, np.int32, np.float16, np.float32, np.float64, np.float32]
    orders = ['C', 'F', 'A']
    shapes = [
        (),
        0,
        (0,),
        (0, 0),
        2,
        (2,),
        (3, 0),
        (4, 5),
        (1, 1, 1, 1),
    ]
    ctxes = [npx.current_context(), None]
    for dtype, expected_dtype in zip(dtypes, expected_dtypes):
        for shape in shapes:
            for order in orders:
                for ctx in ctxes:
                    if order == 'C':
                        ret = np.empty(shape, dtype, order, ctx)
                        assert ret.dtype == expected_dtype
                        assert ret.shape == shape if isinstance(shape, tuple) else (shape,)
                        assert ret.ctx == npx.current_context()
                    else:
                        assert_exception(np.empty, NotImplementedError, shape, dtype, order, ctx)


@with_seed()
@use_np
def test_np_array_creation():
    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
    objects = [
        [],
        (),
        [[1, 2], [3, 4]],
        _np.random.uniform(size=rand_shape_nd(3)),
        _np.random.uniform(size=(3, 0, 4))
    ]
    for dtype in dtypes:
        for src in objects:
            mx_arr = np.array(src, dtype=dtype)
            assert mx_arr.context == mx.current_context()
            if isinstance(src, mx.nd.NDArray):
                np_arr = _np.array(src.asnumpy(), dtype=dtype if dtype is not None else _np.float32)
            else:
                np_arr = _np.array(src, dtype=dtype if dtype is not None else _np.float32)
            assert mx_arr.dtype == np_arr.dtype
            assert same(mx_arr.asnumpy(), np_arr)


@with_seed()
@use_np
def test_np_zeros():
    # test np.zeros in Gluon
    class TestZeros(HybridBlock):
        def __init__(self, shape, dtype=None):
            super(TestZeros, self).__init__()
            self._shape = shape
            self._dtype = dtype

        def hybrid_forward(self, F, x, *args, **kwargs):
            return x + F.np.zeros(shape, dtype)

    class TestZerosOutputType(HybridBlock):
        def hybrid_forward(self, F, x, *args, **kwargs):
            return x, F.np.zeros(shape=())

    # test np.zeros in imperative
    def check_zero_array_creation(shape, dtype):
        np_out = _np.zeros(shape=shape, dtype=dtype)
        mx_out = np.zeros(shape=shape, dtype=dtype)
        assert same(mx_out.asnumpy(), np_out)
        if dtype is None:
            assert mx_out.dtype == _np.float32
            assert np_out.dtype == _np.float64

    shapes = [(0,), (2, 0, 2), (0, 0, 0, 0), ()]
    shapes += [rand_shape_nd(ndim, allow_zero_size=True) for ndim in range(5)]
    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
    for shape in shapes:
        for dtype in dtypes:
            check_zero_array_creation(shape, dtype)
            x = np.array(_np.random.uniform(size=shape), dtype=dtype)
            if dtype is None:
                x = x.astype('float32')
            for hybridize in [True, False]:
                test_zeros = TestZeros(shape, dtype)
                test_zeros_output_type = TestZerosOutputType()
                if hybridize:
                    test_zeros.hybridize()
                    test_zeros_output_type.hybridize()
                y = test_zeros(x)
                assert type(y) == np.ndarray
                assert same(x.asnumpy(), y.asnumpy())
                y = test_zeros_output_type(x)
                assert type(y[1]) == np.ndarray


@with_seed()
@use_np
def test_np_ones():
    # test np.ones in Gluon
    class TestOnes(HybridBlock):
        def __init__(self, shape, dtype=None):
            super(TestOnes, self).__init__()
            self._shape = shape
            self._dtype = dtype

        def hybrid_forward(self, F, x, *args, **kwargs):
            return x * F.np.ones(shape, dtype)

    class TestOnesOutputType(HybridBlock):
        def hybrid_forward(self, F, x, *args, **kwargs):
            return x, F.np.ones(shape=())

    # test np.ones in imperative
    def check_ones_array_creation(shape, dtype):
        np_out = _np.ones(shape=shape, dtype=dtype)
        mx_out = np.ones(shape=shape, dtype=dtype)
        assert same(mx_out.asnumpy(), np_out)
        if dtype is None:
            assert mx_out.dtype == _np.float32
            assert np_out.dtype == _np.float64

    shapes = [(0,), (2, 0, 2), (0, 0, 0, 0), ()]
    shapes += [rand_shape_nd(ndim, allow_zero_size=True) for ndim in range(5)]
    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
    for shape in shapes:
        for dtype in dtypes:
            check_ones_array_creation(shape, dtype)
            x = mx.nd.array(_np.random.uniform(size=shape), dtype=dtype).as_np_ndarray()
            if dtype is None:
                x = x.astype('float32')
            for hybridize in [True, False]:
                test_ones = TestOnes(shape, dtype)
                test_ones_output_type = TestOnesOutputType()
                if hybridize:
                    test_ones.hybridize()
                    test_ones_output_type.hybridize()
                y = test_ones(x)
                assert type(y) == np.ndarray
                assert same(x.asnumpy(), y.asnumpy())
                y = test_ones_output_type(x)
                assert type(y[1]) == np.ndarray


@with_seed()
@use_np
def test_identity():
    class TestIdentity(HybridBlock):
        def __init__(self, shape, dtype=None):
            super(TestIdentity, self).__init__()
            self._n = n
            self._dtype = dtype

        def hybrid_forward(self, F, x):
            return x * F.np.identity(self._n, self._dtype)

    class TestIdentityOutputType(HybridBlock):
        def hybrid_forward(self, F, x):
            return x, F.np.identity(0)

    def check_identity_array_creation(shape, dtype):
        np_out = _np.identity(n=n, dtype=dtype)
        mx_out = np.identity(n=n, dtype=dtype)
        assert same(mx_out.asnumpy(), np_out)
        if dtype is None:
            assert mx_out.dtype == _np.float32
            assert np_out.dtype == _np.float64

    ns = [0, 1, 2, 3, 5, 15, 30, 200]
    dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None]
    for n in ns:
        for dtype in dtypes:
            check_identity_array_creation(n, dtype)
            x = mx.nd.array(_np.random.uniform(size=(n, n)), dtype=dtype).as_np_ndarray()
            if dtype is None:
                x = x.astype('float32')
            for hybridize in [True, False]:
                test_identity = TestIdentity(n, dtype)
                test_identity_output_type = TestIdentityOutputType()
                if hybridize:
                    test_identity.hybridize()
                    test_identity_output_type.hybridize()
                y = test_identity(x)
                assert type(y) == np.ndarray
                assert same(x.asnumpy() * _np.identity(n, dtype), y.asnumpy())
                y = test_identity_output_type(x)
                assert type(y[1]) == np.ndarray


@with_seed()
def test_np_ndarray_binary_element_wise_ops():
    np_op_map = {
        '+': _np.add,
        '*': _np.multiply,
        '-': _np.subtract,
        '/': _np.divide,
        'mod': _np.mod,
        'pow': _np.power,
        '==': _np.equal,
        '>': _np.greater,
        '>=': _np.greater_equal,
        '<': _np.less,
        '<=': _np.less_equal
    }

    def get_np_ret(x1, x2, op):
        return np_op_map[op](x1, x2)

    @use_np
    class TestBinaryElementWiseOp(HybridBlock):
        def __init__(self, op, scalar=None, reverse=False):
            super(TestBinaryElementWiseOp, self).__init__()
            self._op = op
            self._scalar = scalar
            self._reverse = reverse  # if false, scalar is the right operand.

        def hybrid_forward(self, F, x, *args):
            if self._op == '+':
                if self._scalar is not None:
                    return x + self._scalar if not self._reverse else self._scalar + x
                else:
                    return x + args[0] if not self._reverse else args[0] + x
            elif self._op == '*':
                if self._scalar is not None:
                    return x * self._scalar if not self._reverse else self._scalar * x
                else:
                    return x * args[0] if not self._reverse else args[0] * x
            elif self._op == '-':
                if self._scalar is not None:
                    return x - self._scalar if not self._reverse else self._scalar - x
                else:
                    return x - args[0] if not self._reverse else args[0] - x
            elif self._op == '/':
                if self._scalar is not None:
                    return x / self._scalar if not self._reverse else self._scalar / x
                else:
                    return x / args[0] if not self._reverse else args[0] / x
            elif self._op == 'mod':
                if self._scalar is not None:
                    return x % self._scalar if not self._reverse else self._scalar % x
                else:
                    return x % args[0] if not self._reverse else args[0] % x
            elif self._op == 'pow':
                if self._scalar is not None:
                    return x ** self._scalar if not self._reverse else self._scalar ** x
                else:
                    return x ** args[0] if not self._reverse else args[0] ** x
            elif self._op == '>':
                if self._scalar is not None:
                    return x > self._scalar if not self._reverse else self._scalar > x
                else:
                    return x > args[0]
            elif self._op == '>=':
                if self._scalar is not None:
                    return x >= self._scalar if not self._reverse else self._scalar >= x
                else:
                    return x >= args[0]
            elif self._op == '<':
                if self._scalar is not None:
                    return x < self._scalar if not self._reverse else self._scalar < x
                else:
                    return x < args[0]
            elif self._op == '<=':
                if self._scalar is not None:
                    return x <= self._scalar if not self._reverse else self._scalar <= x
                else:
                    return x <= args[0]
            elif self._op == '==':
                if self._scalar is not None:
                    return x == self._scalar if not self._reverse else self._scalar == x
                else:
                    return x == args[0]
            else:
                print(self._op)
                assert False

    @use_np
    def check_binary_op_result(shape1, shape2, op, dtype=None):
        if shape1 is None:
            mx_input1 = abs(_np.random.uniform()) + 1
            np_input1 = mx_input1
        else:
            mx_input1 = rand_ndarray(shape1, dtype=dtype).abs() + 1
            np_input1 = mx_input1.asnumpy()
        if shape2 is None:
            mx_input2 = abs(_np.random.uniform()) + 1
            np_input2 = mx_input2
        else:
            mx_input2 = rand_ndarray(shape2, dtype=dtype).abs() + 1
            np_input2 = mx_input2.asnumpy()

        scalar = None
        reverse = False
        if isinstance(mx_input1, mx.nd.NDArray) and not isinstance(mx_input2, mx.nd.NDArray):
            scalar = mx_input2
            reverse = False
        elif isinstance(mx_input2, mx.nd.NDArray) and not isinstance(mx_input1, mx.nd.NDArray):
            scalar = mx_input1
            reverse = True

        np_out = get_np_ret(np_input1, np_input2, op)
        for hybridize in [True, False]:
            if scalar is None:
                get_mx_ret_np = TestBinaryElementWiseOp(op)
                get_mx_ret_classic = TestBinaryElementWiseOp(op)
                if hybridize:
                    get_mx_ret_np.hybridize()
                    get_mx_ret_classic.hybridize()
                mx_out = get_mx_ret_np(mx_input1.as_np_ndarray(), mx_input2.as_np_ndarray())
                assert type(mx_out) == np.ndarray
                assert np_out.shape == mx_out.shape
                assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
            else:
                get_mx_ret = TestBinaryElementWiseOp(op, scalar=scalar, reverse=reverse)
                if hybridize:
                    get_mx_ret.hybridize()
                if reverse:
                    mx_out = get_mx_ret(mx_input2.as_np_ndarray())
                    assert type(mx_out) == np.ndarray
                else:
                    mx_out = get_mx_ret(mx_input1.as_np_ndarray())
                    assert type(mx_out) == np.ndarray
                assert np_out.shape == mx_out.shape
                assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)

    dtypes = [_np.float32, _np.float64, None]
    ops = np_op_map.keys()
    for dtype in dtypes:
        for op in ops:
            check_binary_op_result((3, 4), (3, 4), op, dtype)
            check_binary_op_result(None, (3, 4), op, dtype)
            check_binary_op_result((3, 4), None, op, dtype)
            check_binary_op_result((1, 4), (3, 1), op, dtype)
            check_binary_op_result(None, (3, 1), op, dtype)
            check_binary_op_result((1, 4), None, op, dtype)
            check_binary_op_result((1, 4), (3, 5, 4), op, dtype)
            check_binary_op_result((), (3, 5, 4), op, dtype)
            check_binary_op_result((), None, op, dtype)
            check_binary_op_result(None, (), op, dtype)
            check_binary_op_result((0, 2), (1, 1), op, dtype)
            check_binary_op_result((0, 2), None, op, dtype)
            check_binary_op_result(None, (0, 2), op, dtype)


@with_seed()
def test_np_hybrid_block_multiple_outputs():
    @use_np
    class TestAllNumpyOutputs(HybridBlock):
        def hybrid_forward(self, F, x, *args, **kwargs):
            return F.np.add(x, x), F.np.multiply(x, x)

    class TestAllClassicOutputs(HybridBlock):
        def hybrid_forward(self, F, x, *args, **kwargs):
            return x.as_nd_ndarray() + x.as_nd_ndarray(), x.as_nd_ndarray() * x.as_nd_ndarray()

    data_np = np.ones((2, 3))
    for block, expected_out_type in [(TestAllClassicOutputs, mx.nd.NDArray),
                                     (TestAllNumpyOutputs, np.ndarray)]:
        net = block()
        for hybridize in [True, False]:
            if hybridize:
                net.hybridize()
            out1, out2 = net(data_np)
            assert type(out1) is expected_out_type
            assert type(out2) is expected_out_type

    @use_np
    class TestMixedTypeOutputsFailure(HybridBlock):
        def hybrid_forward(self, F, x, *args, **kwargs):
            return x.as_nd_ndarray() + x.as_nd_ndarray(), F.np.multiply(x, x)

    net = TestMixedTypeOutputsFailure()
    assert_exception(net, TypeError, data_np)
    net.hybridize()
    assert_exception(net, TypeError, data_np)


@with_seed()
@use_np
def test_np_grad_ndarray_type():
    data = np.array(2, dtype=_np.float32)
    data.attach_grad()
    assert type(data.grad) == np.ndarray
    assert type(data.detach()) == np.ndarray


@with_seed()
def test_np_ndarray_astype():
    mx_data = np.array([2, 3, 4, 5], dtype=_np.int32)
    np_data = mx_data.asnumpy()

    def check_astype_equal(dtype, copy, expect_zero_copy=False):
        mx_ret = mx_data.astype(dtype=dtype, copy=copy)
        assert type(mx_ret) is np.ndarray
        np_ret = np_data.astype(dtype=dtype, copy=copy)
        assert mx_ret.dtype == np_ret.dtype
        assert same(mx_ret.asnumpy(), np_ret)
        if expect_zero_copy:
            assert id(mx_ret) == id(mx_data)
            assert id(np_ret) == id(np_data)

    for dtype in [_np.int8, _np.uint8, _np.int32, _np.float16, _np.float32, _np.float64]:
        for copy in [True, False]:
            check_astype_equal(dtype, copy, copy is False and mx_data.dtype == dtype)


@with_seed()
def test_np_ndarray_copy():
    mx_data = np.array([2, 3, 4, 5], dtype=_np.int32)
    assert_exception(mx_data.copy, NotImplementedError, order='F')
    mx_ret = mx_data.copy()
    np_ret = mx_data.asnumpy().copy()
    assert same(mx_ret.asnumpy(), np_ret)


@with_seed()
@use_np
def test_np_ndarray_indexing():
    def np_int(index, int_type=np.int32):
        """
        Helper function for testing indexing that converts slices to slices of ints or None, and tuples to
        tuples of ints or None.
        """
        def convert(num):
            if num is None:
                return num
            else:
                return int_type(num)

        if isinstance(index, slice):
            return slice(convert(index.start), convert(index.stop), convert(index.step))
        elif isinstance(index, tuple):  # tuple of slices and integers
            ret = []
            for elem in index:
                if isinstance(elem, slice):
                    ret.append(slice(convert(elem.start), convert(elem.stop), convert(elem.step)))
                else:
                    ret.append(convert(elem))
            return tuple(ret)
        else:
            assert False

    # Copied from test_ndarray.py. Under construction.
    def test_getitem(np_array, index):
        np_index = index
        if type(index) == mx.nd.NDArray:  # use of NDArray is prohibited
            assert False
        if isinstance(index, np.ndarray):
            np_index = index.asnumpy()
        if isinstance(index, tuple):
            np_index = tuple([
                idx.asnumpy() if isinstance(idx, mx.nd.NDArray) else idx
                for idx in index]
            )
        np_indexed_array = np_array[np_index]
        mx_np_array = np.array(np_array, dtype=np_array.dtype)
        try:
            mx_indexed_array = mx_np_array[index]
        except Exception as e:
            print('Failed with index = {}'.format(index))
            raise e
        mx_indexed_array = mx_indexed_array.asnumpy()
        assert same(np_indexed_array, mx_indexed_array), 'Failed with index = {}'.format(index)

    def test_setitem(np_array, index):
        def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None):
            if np_value is not None:
                np_array[np_index] = np_value
            elif isinstance(mx_value, np.ndarray):
                np_array[np_index] = mx_value.asnumpy()
            else:
                np_array[np_index] = mx_value
            try:
                mx_array[mx_index] = mx_value
            except Exception as e:
                print('Failed with index = {}, value.shape = {}'.format(mx_index, mx_value.shape))
                raise e

            assert same(np_array, mx_array.asnumpy())

        def _is_basic_index(index):
            if isinstance(index, (integer_types, py_slice)):
                return True
            if isinstance(index, tuple) and all(isinstance(i, (integer_types, py_slice)) for i in index):
                return True
            return False

        np_index = index  # keep this native numpy type
        if isinstance(index, np.ndarray):
            np_index = index.asnumpy()
        if isinstance(index, tuple):
            np_index = []
            for idx in index:
                if isinstance(idx, np.ndarray):
                    np_index.append(idx.asnumpy())
                else:
                    np_index.append(idx)
            np_index = tuple(np_index)

        mx_array = np.array(np_array, dtype=np_array.dtype)  # mxnet.np.ndarray
        np_array = mx_array.asnumpy()  # native numpy array
        indexed_array_shape = np_array[np_index].shape
        np_indexed_array = _np.random.randint(low=-10000, high=0, size=indexed_array_shape)
        # test value is a native numpy array without broadcast
        assert_same(np_array, np_index, mx_array, index, np_indexed_array)
        # test value is a mxnet numpy array without broadcast
        assert_same(np_array, np_index, mx_array, index, np.array(np_indexed_array))
        # test value is an numeric_type
        assert_same(np_array, np_index, mx_array, index, _np.random.randint(low=-10000, high=0))
        if len(indexed_array_shape) > 1:
            np_value = _np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],))
            # test mxnet ndarray with broadcast
            assert_same(np_array, np_index, mx_array, index, np.array(np_value))
            # test native numpy array with broadcast
            assert_same(np_array, np_index, mx_array, index, np_value)

            # test value shape are expanded to be longer than index array's shape
            # this is currently only supported in basic indexing
            if _is_basic_index(index):
                expanded_value_shape = (1, 1, 1) + np_value.shape
                assert_same(np_array, np_index, mx_array, index, np.array(np_value.reshape(expanded_value_shape)))
                assert_same(np_array, np_index, mx_array, index, np_value.reshape(expanded_value_shape))
            # test list with broadcast
            assert_same(np_array, np_index, mx_array, index,
                        [_np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1])

    def test_getitem_autograd(np_array, index):
        """
        np_array: native numpy array.
        """
        x = np.array(np_array, dtype=np_array.dtype)
        x.attach_grad()
        with mx.autograd.record():
            y = x[index]
        y.backward()
        value = np.ones_like(y)
        x_grad = np.zeros_like(x)
        x_grad[index] = value
        assert same(x_grad.asnumpy(), x.grad.asnumpy())

    def test_setitem_autograd(np_array, index):
        """
        np_array: native numpy array.
        """
        x = np.array(np_array, dtype=np_array.dtype)
        out_shape = x[index].shape
        y = np.array(_np.random.uniform(size=out_shape))
        y.attach_grad()
        try:
            with mx.autograd.record():
                x[index] = y
                x.backward()
                y_grad = np.ones_like(y)
                assert same(y_grad.asnumpy(), y.grad.asnumpy())
        except mx.base.MXNetError as err:
            assert str(err).find('Inplace operations (+=, -=, x[:]=, etc) are not supported when recording with') != -1

    shape = (8, 16, 9, 9)
    np_array = _np.arange(_np.prod(_np.array(shape)), dtype='int32').reshape(shape)  # native np array

    # Test sliced output being ndarray:
    index_list = [
        (),
        # Basic indexing
        # Single int as index
        0,
        np.int32(0),
        np.int64(0),
        5,
        np.int32(5),
        np.int64(5),
        -1,
        np.int32(-1),
        np.int64(-1),
        # Slicing as index
        slice(5),
        np_int(slice(5), np.int32),
        np_int(slice(5), np.int64),
        slice(1, 5),
        np_int(slice(1, 5), np.int32),
        np_int(slice(1, 5), np.int64),
        slice(1, 5, 2),
        np_int(slice(1, 5, 2), np.int32),
        np_int(slice(1, 5, 2), np.int64),
        slice(7, 0, -1),
        np_int(slice(7, 0, -1)),
        np_int(slice(7, 0, -1), np.int64),
        slice(None, 6),
        np_int(slice(None, 6)),
        np_int(slice(None, 6), np.int64),
        slice(None, 6, 3),
        np_int(slice(None, 6, 3)),
        np_int(slice(None, 6, 3), np.int64),
        slice(1, None),
        np_int(slice(1, None)),
        np_int(slice(1, None), np.int64),
        slice(1, None, 3),
        np_int(slice(1, None, 3)),
        np_int(slice(1, None, 3), np.int64),
        slice(None, None, 2),
        np_int(slice(None, None, 2)),
        np_int(slice(None, None, 2), np.int64),
        slice(None, None, -1),
        np_int(slice(None, None, -1)),
        np_int(slice(None, None, -1), np.int64),
        slice(None, None, -2),
        np_int(slice(None, None, -2), np.int32),
        np_int(slice(None, None, -2), np.int64),
        # Multiple ints as indices
        (1, 2, 3),
        np_int((1, 2, 3)),
        np_int((1, 2, 3), np.int64),
        (-1, -2, -3),
        np_int((-1, -2, -3)),
        np_int((-1, -2, -3), np.int64),
        (1, 2, 3, 4),
        np_int((1, 2, 3, 4)),
        np_int((1, 2, 3, 4), np.int64),
        (-4, -3, -2, -1),
        np_int((-4, -3, -2, -1)),
        np_int((-4, -3, -2, -1), np.int64),
        # slice(None) as indices
        (slice(None), slice(None), 1, 8),
        (slice(None), slice(None), -1, 8),
        (slice(None), slice(None), 1, -8),
        (slice(None), slice(None), -1, -8),
        np_int((slice(None), slice(None), 1, 8)),
        np_int((slice(None), slice(None), 1, 8), np.int64),
        (slice(None), slice(None), 1, 8),
        np_int((slice(None), slice(None), -1, -8)),
        np_int((slice(None), slice(None), -1, -8), np.int64),
        (slice(None), 2, slice(1, 5), 1),
        np_int((slice(None), 2, slice(1, 5), 1)),
        np_int((slice(None), 2, slice(1, 5), 1), np.int64),
        # Mixture of ints and slices as indices
        (slice(None, None, -1), 2, slice(1, 5), 1),
        np_int((slice(None, None, -1), 2, slice(1, 5), 1)),
        np_int((slice(None, None, -1), 2, slice(1, 5), 1), np.int64),
        (slice(None, None, -1), 2, slice(1, 7, 2), 1),
        np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)),
        np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), np.int64),
        (slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)),
        np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3))),
        np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), np.int64),
        (slice(1, 8, 2), 1, slice(3, 8), 2),
        np_int((slice(1, 8, 2), 1, slice(3, 8), 2)),
        np_int((slice(1, 8, 2), 1, slice(3, 8), 2), np.int64),
        # Test Ellipsis ('...')
        (1, Ellipsis, -1),
        (slice(2), Ellipsis, None, 0),
        # Test newaxis
        None,
        (1, None, -2, 3, -4),
        (1, slice(2, 5), None),
        (slice(None), slice(1, 4), None, slice(2, 3)),
        (slice(1, 3), slice(1, 3), slice(1, 3), slice(1, 3), None),
        (slice(1, 3), slice(1, 3), None, slice(1, 3), slice(1, 3)),
        (None, slice(1, 2), 3, None),
        (1, None, 2, 3, None, None, 4),
        # Advanced indexing
        ([1, 2], slice(3, 5), None, None, [3, 4]),
        (slice(None), slice(3, 5), None, None, [2, 3], [3, 4]),
        (slice(None), slice(3, 5), None, [2, 3], None, [3, 4]),
        (None, slice(None), slice(3, 5), [2, 3], None, [3, 4]),
        [1],
        [1, 2],
        [2, 1, 3],
        [7, 5, 0, 3, 6, 2, 1],
        np.array([6, 3], dtype=np.int32),
        np.array([[3, 4], [0, 6]], dtype=np.int32),
        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),
        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),
        np.array([[2], [0], [1]], dtype=np.int32),
        np.array([[2], [0], [1]], dtype=np.int64),
        np.array([4, 7], dtype=np.int32),
        np.array([4, 7], dtype=np.int64),
        np.array([[3, 6], [2, 1]], dtype=np.int32),
        np.array([[3, 6], [2, 1]], dtype=np.int64),
        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),
        np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),
        (1, [2, 3]),
        (1, [2, 3], np.array([[3], [0]], dtype=np.int32)),
        (1, [2, 3]),
        (1, [2, 3], np.array([[3], [0]], dtype=np.int64)),
        (1, [2], np.array([[5], [3]], dtype=np.int32), slice(None)),
        (1, [2], np.array([[5], [3]], dtype=np.int64), slice(None)),
        (1, [2, 3], np.array([[6], [0]], dtype=np.int32), slice(2, 5)),
        (1, [2, 3], np.array([[6], [0]], dtype=np.int64), slice(2, 5)),
        (1, [2, 3], np.array([[4], [7]], dtype=np.int32), slice(2, 5, 2)),
        (1, [2, 3], np.array([[4], [7]], dtype=np.int64), slice(2, 5, 2)),
        (1, [2], np.array([[3]], dtype=np.int32), slice(None, None, -1)),
        (1, [2], np.array([[3]], dtype=np.int64), slice(None, None, -1)),
        (1, [2], np.array([[3]], dtype=np.int32), np.array([[5, 7], [2, 4]], dtype=np.int64)),
        (1, [2], np.array([[4]], dtype=np.int32), np.array([[1, 3], [5, 7]], dtype='int64')),
        [0],
        [0, 1],
        [1, 2, 3],
        [2, 0, 5, 6],
        ([1, 1], [2, 3]),
        ([1], [4], [5]),
        ([1], [4], [5], [6]),
        ([[1]], [[2]]),
        ([[1]], [[2]], [[3]], [[4]]),
        (slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)),
        ([[[[1]]]], [[1]], slice(0, 3), [1, 5]),
        ([[[[1]]]], 3, slice(0, 3), [1, 3]),
        ([[[[1]]]], 3, slice(0, 3), 0),
        ([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)),
        ([1, 2], slice(3, 5), [2, 3], [3, 4]),
        ([1, 2], slice(3, 5), (2, 3), [3, 4]),
        range(4),
        range(3, 0, -1),
        (range(4,), [1]),
    ]
    for index in index_list:
        test_getitem(np_array, index)
        test_setitem(np_array, index)
        test_getitem_autograd(np_array, index)
        test_setitem_autograd(np_array, index)

    # Test indexing to zero-size tensors
    index_list = [
        (slice(0, 0), slice(0, 0), 1, 2),
        (slice(0, 0), slice(0, 0), slice(0, 0), slice(0, 0)),
    ]
    for index in index_list:
        test_getitem(np_array, index)
        test_setitem(np_array, index)
        test_getitem_autograd(np_array, index)
        test_setitem_autograd(np_array, index)

    # test zero-size tensors get and setitem
    shapes_indices = [
                        ((0), [slice(None, None, None)]),
                        ((3, 0), [2, (slice(None, None, None)), (slice(None, None, None), None)]),
    ]
    for shape, indices in shapes_indices:
        np_array = _np.zeros(shape)
        for index in indices:
            test_getitem(np_array, index)
            test_setitem(np_array, index)
            test_getitem_autograd(np_array, index)
            test_setitem_autograd(np_array, index)


@with_seed()
@use_np
def test_np_save_load_ndarrays():
    shapes = [(2, 0, 1), (0,), (), (), (0, 4), (), (3, 0, 0, 0), (2, 1), (0, 5, 0), (4, 5, 6), (0, 0, 0)]
    array_list = [_np.random.randint(0, 10, size=shape) for shape in shapes]
    array_list = [np.array(arr, dtype=arr.dtype) for arr in array_list]
    # test save/load single ndarray
    for i, arr in enumerate(array_list):
        with TemporaryDirectory() as work_dir:
            fname = os.path.join(work_dir, 'dataset.npy')
            npx.save(fname, arr)
            arr_loaded = npx.load(fname)
            assert isinstance(arr_loaded, list)
            assert len(arr_loaded) == 1
            assert _np.array_equal(arr_loaded[0].asnumpy(), array_list[i].asnumpy())

    # test save/load a list of ndarrays
    with TemporaryDirectory() as work_dir:
        fname = os.path.join(work_dir, 'dataset.npy')
        npx.save(fname, array_list)
        array_list_loaded = mx.nd.load(fname)
        assert isinstance(arr_loaded, list)
        assert len(array_list) == len(array_list_loaded)
        assert all(isinstance(arr, np.ndarray) for arr in arr_loaded)
        for a1, a2 in zip(array_list, array_list_loaded):
            assert _np.array_equal(a1.asnumpy(), a2.asnumpy())

    # test save/load a dict of str->ndarray
    arr_dict = {}
    keys = [str(i) for i in range(len(array_list))]
    for k, v in zip(keys, array_list):
        arr_dict[k] = v
    with TemporaryDirectory() as work_dir:
        fname = os.path.join(work_dir, 'dataset.npy')
        npx.save(fname, arr_dict)
        arr_dict_loaded = npx.load(fname)
        assert isinstance(arr_dict_loaded, dict)
        assert len(arr_dict_loaded) == len(arr_dict)
        for k, v in arr_dict_loaded.items():
            assert k in arr_dict
            assert _np.array_equal(v.asnumpy(), arr_dict[k].asnumpy())


@retry(5)
@with_seed()
@use_np
def test_np_uniform():
    types = [None, "float32", "float64"]
    ctx = mx.context.current_context()
    samples = 1000000
    # Generation test
    trials = 8
    num_buckets = 5
    for dtype in types:
        for low, high in [(-100.0, -98.0), (99.0, 101.0)]:
            scale = high - low
            buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.uniform.ppf(x, loc=low, scale=scale), num_buckets)
            buckets = np.array(buckets, dtype=dtype).tolist()
            probs = [(buckets[i][1] - buckets[i][0])/scale for i in range(num_buckets)]
            generator_mx_np = lambda x: mx.np.random.uniform(low, high, size=x, ctx=ctx, dtype=dtype).asnumpy()
            verify_generator(generator=generator_mx_np, buckets=buckets, probs=probs, nsamples=samples, nrepeat=trials)

    # Broadcasting test
    params = [
        (1.0, mx.np.ones((4,4)) + 2.0),
        (mx.np.zeros((4,4)) + 1, 2.0),
        (mx.np.zeros((1,4)), mx.np.ones((4,4)) + mx.np.array([1, 2, 3, 4])),
        (mx.np.array([1, 2, 3, 4]), mx.np.ones((2,4,4)) * 5)
    ]
    for dtype in types:
        for low, high in params:
            expect_mean = (low + high) / 2
            expanded_size = (samples,) + expect_mean.shape
            uniform_samples = mx.np.random.uniform(low, high, size=expanded_size, dtype=dtype)
            mx.test_utils.assert_almost_equal(uniform_samples.asnumpy().mean(0), expect_mean.asnumpy(), rtol=0.20, atol=1e-1)


@retry(5)
@with_seed()
@use_np
def test_np_multinomial():
    pvals_list = [[0.0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0.0]]
    sizes = [None, (), (3,), (2, 5, 7), (4, 9)]
    experiements = 10000
    for pvals_mx_np_array in [False, True]:
        for have_size in [False, True]:
            for pvals in pvals_list:
                if pvals_mx_np_array:
                    pvals = mx.np.array(pvals)
                if have_size:
                    for size in sizes:
                        freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy() / _np.float32(experiements)
                        # for those cases that didn't need reshape
                        if size in [None, ()]:
                            if type(pvals) == np.ndarray:
                                mx.test_utils.assert_almost_equal(freq, pvals.asnumpy(), rtol=0.20, atol=1e-1)
                            else:
                                mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
                        else:
                            # check the shape
                            assert freq.shape == size + (len(pvals),), 'freq.shape={}, size + (len(pvals))={}'.format(freq.shape, size + (len(pvals)))
                            freq = freq.reshape((-1, len(pvals)))
                            # check the value for each row
                            for i in range(freq.shape[0]):
                                if type(pvals) == np.ndarray:
                                    mx.test_utils.assert_almost_equal(freq[i, :], pvals.asnumpy(), rtol=0.20, atol=1e-1)
                                else:
                                    mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)
                else:
                    freq = mx.np.random.multinomial(experiements, pvals).asnumpy() / _np.float32(experiements)
                    if type(pvals) == np.ndarray:
                        mx.test_utils.assert_almost_equal(freq, pvals.asnumpy(), rtol=0.20, atol=1e-1)
                    else:
                        mx.test_utils.assert_almost_equal(freq, pvals, rtol=0.20, atol=1e-1)
    # check the zero dimension
    sizes = [(0), (0, 2), (4, 0, 2), (3, 0, 1, 2, 0)]
    for pvals_mx_np_array in [False, True]:
        for pvals in pvals_list:
            for size in sizes:
                if pvals_mx_np_array:
                    pvals = mx.np.array(pvals)
                freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy()
                assert freq.size == 0
    # check [] as pvals
    for pvals_mx_np_array in [False, True]:
        for pvals in [[], ()]:
            if pvals_mx_np_array:
                pvals = mx.np.array(pvals)
            freq = mx.np.random.multinomial(experiements, pvals).asnumpy()
            assert freq.size == 0
            for size in sizes:
                freq = mx.np.random.multinomial(experiements, pvals, size=size).asnumpy()
                assert freq.size == 0
    # test small experiment for github issue
    # https://github.com/apache/incubator-mxnet/issues/15383
    small_exp, total_exp = 20, 10000
    for pvals_mx_np_array in [False, True]:
        for pvals in pvals_list:
            if pvals_mx_np_array:
                pvals = mx.np.array(pvals)
            x = np.random.multinomial(small_exp, pvals)
            for i in range(total_exp // small_exp):
                x = x + np.random.multinomial(20, pvals)
        freq = (x.asnumpy() / _np.float32(total_exp)).reshape((-1, len(pvals)))
        for i in range(freq.shape[0]):
            if type(pvals) == np.ndarray:
                mx.test_utils.assert_almost_equal(freq[i, :], pvals.asnumpy(), rtol=0.20, atol=1e-1)
            else:
                mx.test_utils.assert_almost_equal(freq[i, :], pvals, rtol=0.20, atol=1e-1)


if __name__ == '__main__':
    import nose
    nose.runmodule()