mindspore/tests/st/numpy_native/test_array_ops.py

# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed 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.
# ============================================================================
"""unit tests for numpy array operations"""

import functools

import pytest
import numpy as onp

import mindspore.numpy as mnp
from mindspore.nn import Cell

from .utils import rand_int, run_non_kw_test, check_all_results, match_array, \
    rand_bool, match_res, run_multi_test, to_tensor, match_all_arrays


class Cases():
    def __init__(self):
        self.all_shapes = [
            1, 2, (1,), (2,), (1, 2, 3), [1], [2], [1, 2, 3]
        ]
        self.onp_dtypes = [onp.int32, 'int32', int,
                           onp.float32, 'float32', float,
                           onp.uint32, 'uint32',
                           onp.bool_, 'bool', bool]

        self.mnp_dtypes = [mnp.int32, 'int32', int,
                           mnp.float32, 'float32', float,
                           mnp.uint32, 'uint32',
                           mnp.bool_, 'bool', bool]

        self.array_sets = [1, 1.1, True, [1, 0, True], [1, 1.0, 2], (1,),
                           [(1, 2, 3), (4, 5, 6)], onp.random.random(  # pylint: disable=no-member
                               (100, 100)).astype(onp.float32),
                           onp.random.random((100, 100)).astype(onp.bool)]

        self.arrs = [
            rand_int(2),
            rand_int(2, 3),
            rand_int(2, 3, 4),
            rand_int(2, 3, 4, 5),
        ]

        # scalars expanded across the 0th dimension
        self.scalars = [
            rand_int(),
            rand_int(1),
            rand_int(1, 1),
            rand_int(1, 1, 1),
        ]

        # arrays of the same size expanded across the 0th dimension
        self.expanded_arrs = [
            rand_int(2, 3),
            rand_int(1, 2, 3),
            rand_int(1, 1, 2, 3),
            rand_int(1, 1, 1, 2, 3),
        ]

        # arrays with dimensions of size 1
        self.nested_arrs = [
            rand_int(1),
            rand_int(1, 2),
            rand_int(3, 1, 8),
            rand_int(1, 3, 9, 1),
        ]

        # arrays which can be broadcast
        self.broadcastables = [
            rand_int(5),
            rand_int(6, 1),
            rand_int(7, 1, 5),
            rand_int(8, 1, 6, 1)
        ]

        # boolean arrays which can be broadcast
        self.bool_broadcastables = [
            rand_bool(),
            rand_bool(1),
            rand_bool(5),
            rand_bool(6, 1),
            rand_bool(7, 1, 5),
            rand_bool(8, 1, 6, 1),
        ]

        self.mnp_prototypes = [
            mnp.ones((2, 3, 4)),
            [mnp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]],
            ([(1, 2), mnp.ones(2)], (onp.ones(2), [3, 4])),
        ]

        self.onp_prototypes = [
            onp.ones((2, 3, 4)),
            [onp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]],
            ([(1, 2), onp.ones(2)], (onp.ones(2), [3, 4])),
        ]


# Test np.transpose and np.ndarray.transpose
def mnp_transpose(input_tensor):
    a = mnp.transpose(input_tensor, (0, 2, 1))
    b = mnp.transpose(input_tensor, [2, 1, 0])
    c = mnp.transpose(input_tensor, (1, 0, 2))
    d = mnp.transpose(input_tensor)
    return a, b, c, d


def onp_transpose(input_array):
    a = onp.transpose(input_array, (0, 2, 1))
    b = onp.transpose(input_array, [2, 1, 0])
    c = onp.transpose(input_array, (1, 0, 2))
    d = onp.transpose(input_array)
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_transpose():
    onp_array = onp.random.random((3, 4, 5)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_transposed = onp_transpose(onp_array)
    m_transposed = mnp_transpose(mnp_array)
    check_all_results(o_transposed, m_transposed)


# Test np.expand_dims
def mnp_expand_dims(input_tensor):
    a = mnp.expand_dims(input_tensor, 0)
    b = mnp.expand_dims(input_tensor, -1)
    c = mnp.expand_dims(input_tensor, axis=2)
    d = mnp.expand_dims(input_tensor, axis=-2)
    return a, b, c, d


def onp_expand_dims(input_array):
    a = onp.expand_dims(input_array, 0)
    b = onp.expand_dims(input_array, -1)
    c = onp.expand_dims(input_array, axis=2)
    d = onp.expand_dims(input_array, axis=-2)
    return a, b, c, d


@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
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def test_expand_dims():
    onp_array = onp.random.random((3, 4, 5)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_expanded = onp_expand_dims(onp_array)
    m_expanded = mnp_expand_dims(mnp_array)
    check_all_results(o_expanded, m_expanded)


# Test np.squeeze
def mnp_squeeze(input_tensor):
    a = mnp.squeeze(input_tensor)
    b = mnp.squeeze(input_tensor, 0)
    c = mnp.squeeze(input_tensor, axis=None)
    d = mnp.squeeze(input_tensor, axis=-3)
    e = mnp.squeeze(input_tensor, (2,))
    f = mnp.squeeze(input_tensor, (0, 2))
    return a, b, c, d, e, f


def onp_squeeze(input_array):
    a = onp.squeeze(input_array)
    b = onp.squeeze(input_array, 0)
    c = onp.squeeze(input_array, axis=None)
    d = onp.squeeze(input_array, axis=-3)
    e = onp.squeeze(input_array, (2,))
    f = onp.squeeze(input_array, (0, 2))
    return a, b, c, d, e, f


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_squeeze():
    onp_array = onp.random.random((1, 3, 1, 4, 2)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_squeezed = onp_squeeze(onp_array)
    m_squeezed = mnp_squeeze(mnp_array)
    check_all_results(o_squeezed, m_squeezed)

    onp_array = onp.random.random((1, 1, 1, 1, 1)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_squeezed = onp_squeeze(onp_array)
    m_squeezed = mnp_squeeze(mnp_array)
    check_all_results(o_squeezed, m_squeezed)


# Test np.rollaxis
def mnp_rollaxis(input_tensor):
    a = mnp.rollaxis(input_tensor, 0, 1)
    b = mnp.rollaxis(input_tensor, 0, 2)
    c = mnp.rollaxis(input_tensor, 2, 1)
    d = mnp.rollaxis(input_tensor, 2, 2)
    e = mnp.rollaxis(input_tensor, 0)
    f = mnp.rollaxis(input_tensor, 1)
    return a, b, c, d, e, f


def onp_rollaxis(input_array):
    a = onp.rollaxis(input_array, 0, 1)
    b = onp.rollaxis(input_array, 0, 2)
    c = onp.rollaxis(input_array, 2, 1)
    d = onp.rollaxis(input_array, 2, 2)
    e = onp.rollaxis(input_array, 0)
    f = onp.rollaxis(input_array, 1)
    return a, b, c, d, e, f


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_rollaxis():
    onp_array = onp.random.random((3, 4, 5)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_rolled = onp_rollaxis(onp_array)
    m_rolled = mnp_rollaxis(mnp_array)
    check_all_results(o_rolled, m_rolled)


# Test np.swapaxes
def mnp_swapaxes(input_tensor):
    a = mnp.swapaxes(input_tensor, 0, 1)
    b = mnp.swapaxes(input_tensor, 1, 0)
    c = mnp.swapaxes(input_tensor, 1, 1)
    d = mnp.swapaxes(input_tensor, 2, 1)
    e = mnp.swapaxes(input_tensor, 1, 2)
    f = mnp.swapaxes(input_tensor, 2, 2)
    return a, b, c, d, e, f


def onp_swapaxes(input_array):
    a = onp.swapaxes(input_array, 0, 1)
    b = onp.swapaxes(input_array, 1, 0)
    c = onp.swapaxes(input_array, 1, 1)
    d = onp.swapaxes(input_array, 2, 1)
    e = onp.swapaxes(input_array, 1, 2)
    f = onp.swapaxes(input_array, 2, 2)
    return a, b, c, d, e, f


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_swapaxes():
    onp_array = onp.random.random((3, 4, 5)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_swaped = onp_swapaxes(onp_array)
    m_swaped = mnp_swapaxes(mnp_array)
    check_all_results(o_swaped, m_swaped)


# Test np.reshape
def mnp_reshape(input_tensor):
    a = mnp.reshape(input_tensor, (3, 8))
    b = mnp.reshape(input_tensor, [3, -1])
    c = mnp.reshape(input_tensor, (-1, 12))
    d = mnp.reshape(input_tensor, (-1,))
    e = mnp.reshape(input_tensor, 24)
    f = mnp.reshape(input_tensor, [2, 4, -1])
    g = input_tensor.reshape(3, 8)
    h = input_tensor.reshape(3, -1)
    i = input_tensor.reshape([-1, 3])
    j = input_tensor.reshape(-1)
    return a, b, c, d, e, f, g, h, i, j


def onp_reshape(input_array):
    a = onp.reshape(input_array, (3, 8))
    b = onp.reshape(input_array, [3, -1])
    c = onp.reshape(input_array, (-1, 12))
    d = onp.reshape(input_array, (-1,))
    e = onp.reshape(input_array, 24)
    f = onp.reshape(input_array, [2, 4, -1])
    g = input_array.reshape(3, 8)
    h = input_array.reshape(3, -1)
    i = input_array.reshape([-1, 3])
    j = input_array.reshape(-1)
    return a, b, c, d, e, f, g, h, i, j


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_reshape():
    onp_array = onp.random.random((2, 3, 4)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_reshaped = onp_reshape(onp_array)
    m_reshaped = mnp_reshape(mnp_array)
    check_all_results(o_reshaped, m_reshaped)


# Test np.ravel
def mnp_ravel(input_tensor):
    a = mnp.ravel(input_tensor)
    return a


def onp_ravel(input_array):
    a = onp.ravel(input_array)
    return a


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_ravel():
    onp_array = onp.random.random((2, 3, 4)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_ravel = onp_ravel(onp_array)
    m_ravel = mnp_ravel(mnp_array).asnumpy()
    match_array(o_ravel, m_ravel)


# Test np.concatenate
def mnp_concatenate(input_tensor):
    a = mnp.concatenate(input_tensor, None)
    b = mnp.concatenate(input_tensor, 0)
    c = mnp.concatenate(input_tensor, 1)
    d = mnp.concatenate(input_tensor, 2)
    return a, b, c, d


def onp_concatenate(input_array):
    a = onp.concatenate(input_array, None)
    b = onp.concatenate(input_array, 0)
    c = onp.concatenate(input_array, 1)
    d = onp.concatenate(input_array, 2)
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_concatenate():
    onp_array = onp.random.random((5, 4, 3, 2)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_concatenate = onp_concatenate(onp_array)
    m_concatenate = mnp_concatenate(mnp_array)
    check_all_results(o_concatenate, m_concatenate)


def mnp_append(arr1, arr2):
    a = mnp.append(arr1, arr2)
    b = mnp.append(arr1, arr2, axis=0)
    c = mnp.append(arr1, arr2, axis=-1)
    return a, b, c

def onp_append(arr1, arr2):
    a = onp.append(arr1, arr2)
    b = onp.append(arr1, arr2, axis=0)
    c = onp.append(arr1, arr2, axis=-1)
    return a, b, c

@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_append():
    onp_array = onp.random.random((4, 3, 2)).astype('float32')
    onp_value = onp.random.random((4, 3, 2)).astype('float32')
    mnp_array = to_tensor(onp_array)
    mnp_value = to_tensor(onp_value)
    onp_res = onp_append(onp_array, onp_value)
    mnp_res = mnp_append(mnp_array, mnp_value)
    check_all_results(onp_res, mnp_res)


def construct_arrays(n=1, ndim=1, axis=None, low=1, high=5):
    onp_array_lst = []
    mnp_array_lst = []
    shape = onp.random.randint(low=low, high=high, size=ndim)
    new_shape = [sh for sh in shape]
    while n > 0:
        n -= 1
        onp_array1 = onp.random.randint(
            low=low, high=high, size=shape).astype(onp.float32)
        onp_array_lst.append(onp_array1)
        mnp_array_lst.append(to_tensor(onp_array1))
        if axis is not None and axis < ndim:
            new_shape[axis] += onp.random.randint(2)
            onp_array2 = onp.random.randint(
                low=low, high=high, size=new_shape).astype(onp.float32)
            onp_array_lst.append(onp_array2)
            mnp_array_lst.append(to_tensor(onp_array2))
    return onp_array_lst, mnp_array_lst

# Test np.xstack


def prepare_array_sequences(n_lst, ndim_lst, axis=None, low=1, high=5):
    onp_seq_lst = []
    mnp_seq_lst = []
    for n in n_lst:
        for ndim in ndim_lst:
            onp_array_lst, mnp_array_lst = construct_arrays(
                n=n, ndim=ndim, axis=axis, low=low, high=high)
            onp_seq_lst.append(onp_array_lst)
            mnp_seq_lst.append(mnp_array_lst)
    return onp_seq_lst, mnp_seq_lst


def mnp_column_stack(input_tensor):
    return mnp.column_stack(input_tensor)


def onp_column_stack(input_array):
    return onp.column_stack(input_array)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_column_stack():
    onp_seq_lst, mnp_seq_lst = prepare_array_sequences(
        n_lst=[1, 5], ndim_lst=[1, 2, 3, 4], axis=1)
    for i, onp_seq in enumerate(onp_seq_lst):
        onp_seq = onp_seq_lst[i]
        mnp_seq = mnp_seq_lst[i]
        o_column_stack = onp_column_stack(onp_seq)
        m_column_stack = mnp_column_stack(mnp_seq)
        check_all_results(o_column_stack, m_column_stack)


def mnp_hstack(input_tensor):
    return mnp.hstack(input_tensor)


def onp_hstack(input_array):
    return onp.hstack(input_array)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_hstack():
    onp_seq_lst0, mnp_seq_lst0 = prepare_array_sequences(
        n_lst=[1, 5], ndim_lst=[2, 3, 4], axis=1)
    onp_seq_lst1, mnp_seq_lst1 = prepare_array_sequences(
        n_lst=[1, 5], ndim_lst=[1], axis=0)
    onp_seq_lst = onp_seq_lst0 + onp_seq_lst1
    mnp_seq_lst = mnp_seq_lst0 + mnp_seq_lst1
    for i, onp_seq in enumerate(onp_seq_lst):
        mnp_seq = mnp_seq_lst[i]
        o_hstack = onp_hstack(onp_seq)
        m_hstack = mnp_hstack(mnp_seq)
        check_all_results(o_hstack, m_hstack)


def mnp_dstack(input_tensor):
    return mnp.dstack(input_tensor)


def onp_dstack(input_array):
    return onp.dstack(input_array)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_dstack():
    onp_seq_lst, mnp_seq_lst = prepare_array_sequences(
        n_lst=[1, 5], ndim_lst=[1, 2, 3, 4], axis=2)
    for i, onp_seq in enumerate(onp_seq_lst):
        mnp_seq = mnp_seq_lst[i]
        o_dstack = onp_dstack(onp_seq)
        m_dstack = mnp_dstack(mnp_seq)
        check_all_results(o_dstack, m_dstack)


def mnp_vstack(input_tensor):
    return mnp.vstack(input_tensor)


def onp_vstack(input_array):
    return onp.vstack(input_array)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_vstack():
    onp_seq_lst0, mnp_seq_lst0 = prepare_array_sequences(
        n_lst=[1, 5], ndim_lst=[2, 3, 4], axis=0)
    onp_seq_lst1, mnp_seq_lst1 = prepare_array_sequences(
        n_lst=[1, 5], ndim_lst=[1])
    onp_seq_lst = onp_seq_lst0 + onp_seq_lst1
    mnp_seq_lst = mnp_seq_lst0 + mnp_seq_lst1
    for i, onp_seq in enumerate(onp_seq_lst):
        mnp_seq = mnp_seq_lst[i]
        o_vstack = onp_vstack(onp_seq)
        m_vstack = mnp_vstack(mnp_seq)
        check_all_results(o_vstack, m_vstack)
# Test np.atleastxd


def mnp_atleast1d(*arys):
    return mnp.atleast_1d(*arys)


def onp_atleast1d(*arys):
    return onp.atleast_1d(*arys)


def mnp_atleast2d(*arys):
    return mnp.atleast_2d(*arys)


def onp_atleast2d(*arys):
    return onp.atleast_2d(*arys)


def mnp_atleast3d(*arys):
    return mnp.atleast_3d(*arys)


def onp_atleast3d(*arys):
    return onp.atleast_3d(*arys)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_atleast1d():
    run_non_kw_test(mnp_atleast1d, onp_atleast1d, Cases())


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_atleast2d():
    run_non_kw_test(mnp_atleast2d, onp_atleast2d, Cases())


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_atleast3d():
    run_non_kw_test(mnp_atleast3d, onp_atleast3d, Cases())


# Test np.where
def mnp_where(condition, x, y):
    return mnp.where(condition, x, y)


def onp_where(condition, x, y):
    return onp.where(condition, x, y)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_where():
    test_case = Cases()
    for condition1 in test_case.bool_broadcastables[:2]:
        for x in test_case.broadcastables[:2]:
            for y in test_case.broadcastables[:2]:
                for condition2 in test_case.broadcastables[:2]:
                    match_res(mnp_where, onp_where, condition1, x, y)
                    match_res(mnp_where, onp_where, condition2, x, y)


# Test ndarray.flatten
def mnp_ndarray_flatten(input_tensor):
    a = input_tensor.flatten()
    b = input_tensor.flatten(order='F')
    c = input_tensor.flatten(order='C')
    return a, b, c


def onp_ndarray_flatten(input_array):
    a = input_array.flatten()
    b = input_array.flatten(order='F')
    c = input_array.flatten(order='C')
    return a, b, c


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_ndarray_flatten():
    onp_array = onp.random.random((3, 4, 5)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_flatten = onp_ndarray_flatten(onp_array)
    m_flatten = mnp_ndarray_flatten(mnp_array)
    check_all_results(o_flatten, m_flatten)


# Test ndarray.transpose
def mnp_ndarray_transpose(input_tensor):
    a = input_tensor.T
    b = input_tensor.transpose()
    c = input_tensor.transpose((0, 2, 1))
    d = input_tensor.transpose([0, 2, 1])
    return a, b, c, d


def onp_ndarray_transpose(input_array):
    a = input_array.T
    b = input_array.transpose()
    c = input_array.transpose((0, 2, 1))
    d = input_array.transpose([0, 2, 1])
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_ndarray_transpose():
    onp_array = onp.random.random((3, 4, 5)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_transposed = onp_ndarray_transpose(onp_array)
    m_transposed = mnp_ndarray_transpose(mnp_array)
    check_all_results(o_transposed, m_transposed)


# Test ndarray.astype
def mnp_ndarray_astype(input_tensor):
    a = input_tensor.astype("float16")
    b = input_tensor.astype(onp.float64)
    c = input_tensor.astype(mnp.bool_)
    return a, b, c


def onp_ndarray_astype(input_array):
    a = input_array.astype("float16")
    b = input_array.astype(onp.float64)
    c = input_array.astype(onp.bool_)
    return a, b, c


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_ndarray_astype():
    onp_array = onp.random.random((3, 4, 5)).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_astype = onp_ndarray_astype(onp_array)
    m_astype = mnp_ndarray_astype(mnp_array)
    for arr1, arr2 in zip(o_astype, m_astype):
        assert arr1.dtype == arr2.asnumpy().dtype


def onp_concatenate_type_promotion(onp_array1, onp_array2, onp_array3, onp_array4):
    o_concatenate = onp.concatenate((onp_array1,
                                     onp_array2,
                                     onp_array3,
                                     onp_array4), -1)
    return o_concatenate


def mnp_concatenate_type_promotion(mnp_array1, mnp_array2, mnp_array3, mnp_array4):
    m_concatenate = mnp.concatenate([mnp_array1,
                                     mnp_array2,
                                     mnp_array3,
                                     mnp_array4], -1)
    return m_concatenate


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_concatenate_type_promotion():
    onp_array = onp.random.random((5, 1)).astype('float32')
    mnp_array = to_tensor(onp_array)
    onp_array1 = onp_array.astype(onp.float16)
    onp_array2 = onp_array.astype(onp.bool_)
    onp_array3 = onp_array.astype(onp.float32)
    onp_array4 = onp_array.astype(onp.int32)

    mnp_array1 = mnp_array.astype(onp.float16)
    mnp_array2 = mnp_array.astype(onp.bool_)
    mnp_array3 = mnp_array.astype(onp.float32)
    mnp_array4 = mnp_array.astype(onp.int32)
    o_concatenate = onp_concatenate_type_promotion(
        onp_array1, onp_array2, onp_array3, onp_array4).astype('float32')
    m_concatenate = mnp_concatenate_type_promotion(
        mnp_array1, mnp_array2, mnp_array3, mnp_array4)
    check_all_results(o_concatenate, m_concatenate, error=1e-7)


def mnp_stack(*arrs):
    a = mnp.stack(arrs, axis=-4)
    b = mnp.stack(arrs, axis=-3)
    c = mnp.stack(arrs, axis=0)
    d = mnp.stack(arrs, axis=3)
    e = mnp.stack(arrs, axis=2)
    return a, b, c, d, e


def onp_stack(*arrs):
    a = onp.stack(arrs, axis=-4)
    b = onp.stack(arrs, axis=-3)
    c = onp.stack(arrs, axis=0)
    d = onp.stack(arrs, axis=3)
    e = onp.stack(arrs, axis=2)
    return a, b, c, d, e


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_stack():
    arr = rand_int(3, 4, 5, 6)
    match_res(mnp.stack, onp.stack, arr)
    for i in range(-4, 4):
        match_res(mnp.stack, onp.stack, arr, axis=i)

    arrs = [rand_int(3, 4, 5) for i in range(10)]
    match_res(mnp.stack, onp.stack, arrs)
    match_res(mnp.stack, onp.stack, tuple(arrs))
    match_res(mnp_stack, onp_stack, *arrs)
    for i in range(-4, 4):
        match_res(mnp.stack, onp.stack, arrs, axis=i)


def mnp_roll(input_tensor):
    a = mnp.roll(input_tensor, -3)
    b = mnp.roll(input_tensor, [-2, -3], 1)
    c = mnp.roll(input_tensor, (3, 0, -5), (-1, -2, 0))
    d = mnp.roll(input_tensor, (4,), [0, 0, 1])
    return a, b, c, d


def onp_roll(input_array):
    a = onp.roll(input_array, -3)
    b = onp.roll(input_array, [-2, -3], 1)
    c = onp.roll(input_array, (3, 0, -5), (-1, -2, 0))
    d = onp.roll(input_array, (4,), [0, 0, 1])
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_roll():
    arr = rand_int(3, 4, 5)
    match_res(mnp_roll, onp_roll, arr)
    arr = rand_int(1, 4, 6).astype("int64")
    match_res(mnp_roll, onp_roll, arr)


def mnp_moveaxis(a):
    a = mnp.moveaxis(a, 3, 3)
    b = mnp.moveaxis(a, -1, 4)
    c = mnp.moveaxis(a, (2, 1, 4), (0, 3, 2))
    d = mnp.moveaxis(a, [-2, -5], [2, -4])
    return a, b, c, d


def onp_moveaxis(a):
    a = onp.moveaxis(a, 3, 3)
    b = onp.moveaxis(a, -1, 4)
    c = onp.moveaxis(a, (2, 1, 4), (0, 3, 2))
    d = onp.moveaxis(a, [-2, -5], [2, -4])
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_moveaxis():
    a = rand_int(2, 4, 5, 9, 6)
    match_res(mnp_moveaxis, onp_moveaxis, a)


def mnp_tile(x):
    a = mnp.tile(x, 1)
    b = mnp.tile(x, 3)
    c = mnp.tile(x, [5, 1])
    d = mnp.tile(x, [5, 1, 2, 3, 7])
    return a, b, c, d


def onp_tile(x):
    a = onp.tile(x, 1)
    b = onp.tile(x, 3)
    c = onp.tile(x, [5, 1])
    d = onp.tile(x, [5, 1, 2, 3, 7])
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_tile():
    a = rand_int(2, 3, 4)
    match_res(mnp_tile, onp_tile, a)


def mnp_broadcast_to(x):
    a = mnp.broadcast_to(x, (2, 3))
    b = mnp.broadcast_to(x, (8, 1, 3))
    return a, b


def onp_broadcast_to(x):
    a = onp.broadcast_to(x, (2, 3))
    b = onp.broadcast_to(x, (8, 1, 3))
    return a, b


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_broadcast_to():
    x = rand_int()
    match_res(mnp_broadcast_to, onp_broadcast_to, x)
    x = rand_int(3)
    match_res(mnp_broadcast_to, onp_broadcast_to, x)
    x = rand_int(1, 3)
    match_res(mnp_broadcast_to, onp_broadcast_to, x)


def mnp_broadcast_arrays(*args):
    return mnp.broadcast_arrays(*args)


def onp_broadcast_arrays(*args):
    return onp.broadcast_arrays(*args)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_broadcast_arrays():
    test_case = Cases()
    broadcastables = test_case.broadcastables
    for i in range(len(broadcastables)):
        arrs = broadcastables[i:]
        match_res(mnp_broadcast_arrays, onp_broadcast_arrays, *arrs)


def mnp_flip(x):
    a = mnp.flip(x)
    b = mnp.flip(x, 0)
    c = mnp.flip(x, 1)
    d = mnp.flip(x, (-3, -1))
    return a, b, c, d


def onp_flip(x):
    a = onp.flip(x)
    b = onp.flip(x, 0)
    c = onp.flip(x, 1)
    d = onp.flip(x, (-3, -1))
    return a, b, c, d


@pytest.mark.level2
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_flip():
    x = rand_int(2, 3, 4)
    run_multi_test(mnp_flip, onp_flip, (x,))


def mnp_flipud(x):
    return mnp.flipud(x)


def onp_flipud(x):
    return  onp.flipud(x)


@pytest.mark.level2
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_flipud():
    x = rand_int(2, 3, 4)
    run_multi_test(mnp_flipud, onp_flipud, (x,))


def mnp_fliplr(x):
    return mnp.fliplr(x)


def onp_fliplr(x):
    return onp.fliplr(x)


@pytest.mark.level2
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_fliplr():
    x = rand_int(2, 3, 4)
    run_multi_test(mnp_fliplr, onp_fliplr, (x,))


def mnp_split(input_tensor):
    a = mnp.split(input_tensor, indices_or_sections=1)
    b = mnp.split(input_tensor, indices_or_sections=3)
    return a, b


def onp_split(input_array):
    a = onp.split(input_array, indices_or_sections=1)
    b = onp.split(input_array, indices_or_sections=3)
    return a, b


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_split():
    onp_arrs = [
        onp.random.randint(1, 5, size=(9, 4, 5)).astype('float32')
    ]
    mnp_arrs = [to_tensor(arr) for arr in onp_arrs]
    for onp_arr, mnp_arr in zip(onp_arrs, mnp_arrs):
        o_split = onp_split(onp_arr)
        m_split = mnp_split(mnp_arr)
        for expect_lst, actual_lst in zip(o_split, m_split):
            for expect, actual in zip(expect_lst, actual_lst):
                match_array(expect, actual.asnumpy())


def mnp_array_split(input_tensor):
    a = mnp.array_split(input_tensor, indices_or_sections=4, axis=2)
    b = mnp.array_split(input_tensor, indices_or_sections=3, axis=1)
    c = mnp.array_split(input_tensor, indices_or_sections=6)
    return a, b, c


def onp_array_split(input_array):
    a = onp.array_split(input_array, indices_or_sections=4, axis=2)
    b = onp.array_split(input_array, indices_or_sections=3, axis=1)
    c = onp.array_split(input_array, indices_or_sections=6)
    return a, b, c


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_array_split():
    onp_arr = onp.random.randint(1, 5, size=(9, 7, 13)).astype('float32')
    mnp_arr = to_tensor(onp_arr)
    o_split = onp_split(onp_arr)
    m_split = mnp_split(mnp_arr)
    for expect_lst, actual_lst in zip(o_split, m_split):
        for expect, actual in zip(expect_lst, actual_lst):
            match_array(expect, actual.asnumpy())


def mnp_vsplit(input_tensor):
    a = mnp.vsplit(input_tensor, indices_or_sections=3)
    return a


def onp_vsplit(input_array):
    a = onp.vsplit(input_array, indices_or_sections=3)
    return a


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_vsplit():
    onp_arrs = [
        onp.random.randint(1, 5, size=(9, 4, 5)).astype('float32')
    ]
    mnp_arrs = [to_tensor(arr) for arr in onp_arrs]
    for onp_arr, mnp_arr in zip(onp_arrs, mnp_arrs):
        o_vsplit = onp_vsplit(onp_arr)
        m_vsplit = mnp_vsplit(mnp_arr)
        for expect_lst, actual_lst in zip(o_vsplit, m_vsplit):
            for expect, actual in zip(expect_lst, actual_lst):
                match_array(expect, actual.asnumpy())


def mnp_hsplit(input_tensor):
    a = mnp.hsplit(input_tensor, indices_or_sections=3)
    return a


def onp_hsplit(input_array):
    a = onp.hsplit(input_array, indices_or_sections=3)
    return a


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_hsplit():
    onp_arrs = [
        onp.random.randint(1, 5, size=(4, 9, 5)).astype('float32')
    ]
    mnp_arrs = [to_tensor(arr) for arr in onp_arrs]
    for onp_arr, mnp_arr in zip(onp_arrs, mnp_arrs):
        o_hsplit = onp_hsplit(onp_arr)
        m_hsplit = mnp_hsplit(mnp_arr)
        for expect_lst, actual_lst in zip(o_hsplit, m_hsplit):
            for expect, actual in zip(expect_lst, actual_lst):
                match_array(expect, actual.asnumpy())


def mnp_dsplit(input_tensor):
    a = mnp.dsplit(input_tensor, indices_or_sections=3)
    return a

def onp_dsplit(input_array):
    a = onp.dsplit(input_array, indices_or_sections=3)
    return a

@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_dsplit():
    onp_arrs = [
        onp.random.randint(1, 5, size=(5, 4, 9)).astype('float32')
    ]
    mnp_arrs = [to_tensor(arr) for arr in onp_arrs]
    for onp_arr, mnp_arr in zip(onp_arrs, mnp_arrs):
        o_dsplit = onp_dsplit(onp_arr)
        m_dsplit = mnp_dsplit(mnp_arr)
        for expect_lst, actual_lst in zip(o_dsplit, m_dsplit):
            for expect, actual in zip(expect_lst, actual_lst):
                match_array(expect, actual.asnumpy())


def mnp_take_along_axis(*arrs):
    x = arrs[0]
    a = mnp.take_along_axis(x, arrs[1], axis=None)
    b = mnp.take_along_axis(x, arrs[2], axis=1)
    c = mnp.take_along_axis(x, arrs[3], axis=-1)
    d = mnp.take_along_axis(x, arrs[4], axis=0)
    return a, b, c, d


def onp_take_along_axis(*arrs):
    x = arrs[0]
    a = onp.take_along_axis(x, arrs[1], axis=None)
    b = onp.take_along_axis(x, arrs[2], axis=1)
    c = onp.take_along_axis(x, arrs[3], axis=-1)
    d = onp.take_along_axis(x, arrs[4], axis=0)
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_take_along_axis():
    x = rand_int(6, 7, 8, 9)
    indices1 = rand_int(2).astype(onp.int32)
    indices2 = rand_int(6, 3, 8, 1).astype(onp.int32)
    indices3 = rand_int(6, 1, 8, 5).astype(onp.int32)
    indices4 = rand_int(4, 1, 1, 1).astype(onp.int32)
    run_multi_test(mnp_take_along_axis, onp_take_along_axis,
                   (x, indices1, indices2, indices3, indices4))


def mnp_take(x, indices):
    a = mnp.take(x, indices)
    b = mnp.take(x, indices, axis=-1)
    c = mnp.take(x, indices, axis=0, mode='wrap')
    d = mnp.take(x, indices, axis=1, mode='clip')
    return a, b, c, d


def onp_take(x, indices):
    a = onp.take(x, indices)
    b = onp.take(x, indices, axis=-1)
    c = onp.take(x, indices, axis=0, mode='wrap')
    d = onp.take(x, indices, axis=1, mode='clip')
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_take():
    x = rand_int(2, 3, 4, 5)
    indices = rand_int(2, 3).astype(onp.int32)
    run_multi_test(mnp_take, onp_take, (x, indices))


def mnp_repeat(x):
    a = mnp.repeat(x, 2)
    b = mnp.repeat(x, 3, axis=0)
    c = mnp.repeat(x, (4, 1, 5), axis=1)
    d = mnp.repeat(x, (3, 2, 1, 0, 4), axis=-1)
    e = mnp.repeat(x, 0)
    return a, b, c, d, e


def onp_repeat(x):
    a = onp.repeat(x, 2)
    b = onp.repeat(x, 3, axis=0)
    c = onp.repeat(x, (4, 1, 5), axis=1)
    d = onp.repeat(x, (3, 2, 1, 0, 4), axis=-1)
    e = onp.repeat(x, 0)
    return a, b, c, d, e


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_repeat():
    x = rand_int(2, 3, 4, 5)
    run_multi_test(mnp_repeat, onp_repeat, (x,))


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_select():
    choicelist = rand_int(2, 3, 4, 5)
    condlist = choicelist > 2
    match_res(mnp.select, onp.select, condlist, choicelist)
    match_res(mnp.select, onp.select, condlist, choicelist, default=10)

    condlist = rand_bool(5, 4, 1, 3)
    choicelist = rand_int(5, 3)
    match_res(mnp.select, onp.select, condlist, choicelist)
    match_res(mnp.select, onp.select, condlist, choicelist, default=10)

    condlist = rand_bool(3, 1, 7)
    choicelist = rand_int(3, 5, 2, 1)
    match_res(mnp.select, onp.select, condlist, choicelist)
    match_res(mnp.select, onp.select, condlist, choicelist, default=10)


def test_choose():
    x = rand_int(2, 1, 4).astype(onp.int32)
    y = rand_int(3, 2, 5, 4).astype(onp.int32)
    match_res(mnp.choose, onp.choose, x, y, mode='wrap')
    match_res(mnp.choose, onp.choose, x, y, mode='clip')

    x = rand_int(5, 3, 1, 7).astype(onp.int32)
    y1 = rand_int(7).astype(onp.int32)
    y2 = rand_int(1, 3, 1).astype(onp.int32)
    y3 = rand_int(5, 1, 1, 7).astype(onp.int32)
    onp_arrays = (x, (y1, y2, y3))
    mnp_arrays = (to_tensor(x), tuple(map(to_tensor, (y1, y2, y3))))
    match_all_arrays(mnp.choose(*mnp_arrays, mode='wrap'), onp.choose(*onp_arrays, mode='wrap'))
    match_all_arrays(mnp.choose(*mnp_arrays, mode='clip'), onp.choose(*onp_arrays, mode='clip'))


class ReshapeExpandSqueeze(Cell):
    def __init__(self):
        super(ReshapeExpandSqueeze, self).__init__()

    def construct(self, x):
        x = mnp.expand_dims(x, 2)
        x = mnp.reshape(x, (1, 2, 3, 4, 1, 1))
        x = mnp.squeeze(x)
        return x


class TransposeConcatRavel(Cell):
    def __init__(self):
        super(TransposeConcatRavel, self).__init__()

    def construct(self, x1, x2, x3):
        x1 = mnp.transpose(x1, [0, 2, 1])
        x2 = x2.transpose(0, 2, 1)
        x = mnp.concatenate((x1, x2, x3), -1)
        x = mnp.ravel(x)
        return x


class RollSwap(Cell):
    def __init__(self):
        super(RollSwap, self).__init__()

    def construct(self, x):
        x = mnp.rollaxis(x, 2)
        x = mnp.swapaxes(x, 0, 1)
        return x


test_case_array_ops = [
    ('ReshapeExpandSqueeze', {
        'block': ReshapeExpandSqueeze(),
        'desc_inputs': [mnp.ones((2, 3, 4))]}),

    ('TransposeConcatRavel', {
        'block': TransposeConcatRavel(),
        'desc_inputs': [mnp.ones((2, 3, 4)),
                        mnp.ones((2, 3, 4)),
                        mnp.ones((2, 4, 1))]}),

    ('RollSwap', {
        'block': RollSwap(),
        'desc_inputs': [mnp.ones((2, 3, 4))]})
]

test_case_lists = [test_case_array_ops]
test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
# use -k to select certain testcast
# pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_expand_dims_exception():
    with pytest.raises(TypeError):
        mnp.expand_dims(mnp.ones((3, 3)), 1.2)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_swapaxes_exception():
    with pytest.raises(ValueError):
        mnp.swapaxes(mnp.ones((3, 3)), 1, 10)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_tensor_flatten():
    lst = [[1.0, 2.0], [3.0, 4.0]]
    tensor_list = to_tensor(lst)
    assert tensor_list.flatten().asnumpy().tolist() == [1.0, 2.0, 3.0, 4.0]
    assert tensor_list.flatten(order='F').asnumpy().tolist() == [
        1.0, 3.0, 2.0, 4.0]


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_tensor_reshape():
    lst = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
    tensor_list = to_tensor(lst)
    with pytest.raises(TypeError):
        tensor_list = tensor_list.reshape({0, 1, 2})
    with pytest.raises(ValueError):
        tensor_list = tensor_list.reshape(1, 2, 3)
    assert tensor_list.reshape([-1, 4]).shape == (2, 4)
    assert tensor_list.reshape(1, -1, 4).shape == (1, 2, 4)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_tensor_squeeze():
    lst = [[[1.0], [2.0], [3.0]]]
    tensor_list = to_tensor(lst)
    with pytest.raises(TypeError):
        tensor_list = tensor_list.squeeze(1.2)
    with pytest.raises(ValueError):
        tensor_list = tensor_list.squeeze(4)
    assert tensor_list.squeeze().shape == (3,)
    assert tensor_list.squeeze(axis=2).shape == (1, 3)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_tensor_ravel():
    lst = [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]
    tensor_list = to_tensor(lst)
    assert tensor_list.ravel().shape == (8,)
    assert tensor_list.ravel().asnumpy().tolist() == [
        1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_tensor_swapaxes():
    lst = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
    tensor_list = to_tensor(lst)
    with pytest.raises(TypeError):
        tensor_list = tensor_list.swapaxes(0, (1,))
    with pytest.raises(ValueError):
        tensor_list = tensor_list.swapaxes(0, 3)
    assert tensor_list.swapaxes(0, 1).shape == (3, 2)


def mnp_rot90(input_tensor):
    a = mnp.rot90(input_tensor)
    b = mnp.rot90(input_tensor, 2)
    c = mnp.rot90(input_tensor, 3)
    d = mnp.rot90(input_tensor, 4)
    e = mnp.rot90(input_tensor, 5, (0, -1))
    f = mnp.rot90(input_tensor, 1, (2, 0))
    g = mnp.rot90(input_tensor, -3, (-1, -2))
    h = mnp.rot90(input_tensor, 3, (2, 1))
    return a, b, c, d, e, f, g, h


def onp_rot90(input_array):
    a = onp.rot90(input_array)
    b = onp.rot90(input_array, 2)
    c = onp.rot90(input_array, 3)
    d = onp.rot90(input_array, 4)
    e = onp.rot90(input_array, 5, (0, -1))
    f = onp.rot90(input_array, 1, (2, 0))
    g = onp.rot90(input_array, -3, (-1, -2))
    h = onp.rot90(input_array, 3, (2, 1))
    return a, b, c, d, e, f, g, h


@pytest.mark.level2
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_rot90():
    onp_array = rand_int(3, 4, 5).astype('float32')
    mnp_array = to_tensor(onp_array)
    o_rot = onp_rot90(onp_array)
    m_rot = mnp_rot90(mnp_array)
    check_all_results(o_rot, m_rot)


def mnp_size(x):
    a = mnp.size(x)
    b = mnp.size(x, axis=0)
    return a, b


def onp_size(x):
    a = onp.size(x)
    b = onp.size(x, axis=0)
    return a, b


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_size():
    onp_arr = onp.random.rand(2, 3, 4).astype('float32')
    mnp_arr = to_tensor(onp_arr)
    for actual, expected in zip(mnp_size(mnp_arr), onp_size(onp_arr)):
        match_array(actual, expected)


def mnp_array_str(x):
    return mnp.array_str(x)


def onp_array_str(x):
    return onp.array_str(x)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_array_str():
    onp_arr = onp.random.rand(2, 3, 4).astype('float32')
    mnp_arr = to_tensor(onp_arr)
    for actual, expected in zip(mnp_size(mnp_arr), onp_size(onp_arr)):
        match_array(actual, expected)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_apply_along_axis():
    onp_arr = rand_int(5, 3, 7)
    mnp_arr = to_tensor(onp_arr)
    for i in range(-3, 3):
        mnp_res = mnp.apply_along_axis(mnp.diag, i, mnp_arr)
        onp_res = onp.apply_along_axis(onp.diag, i, onp_arr)
        match_all_arrays(mnp_res, onp_res)
    mnp_res = mnp.apply_along_axis(lambda x: x[0], 2, mnp_arr)
    onp_res = onp.apply_along_axis(lambda x: x[0], 2, onp_arr)
    match_all_arrays(mnp_res, onp_res)
    mnp_res = mnp.apply_along_axis(lambda x, y, offset=0: (x[4] - y)*offset, 2, mnp_arr, 1, offset=3)
    onp_res = onp.apply_along_axis(lambda x, y, offset=0: (x[4] - y)*offset, 2, onp_arr, 1, offset=3)
    match_all_arrays(mnp_res, onp_res)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_piecewise():
    x = rand_int(2, 4)
    mnp_x = to_tensor(x)
    condlist = [x < 2, x == 2, x > 2]
    mnp_condlist = [mnp_x < 2, mnp_x == 2, mnp_x > 2]
    funclist = [lambda x, offset=0: x - offset, lambda x, offset=0: x, lambda x, offset=0: x*offset]
    mnp_res = mnp.piecewise(mnp_x, mnp_condlist, funclist, offset=2)
    onp_res = onp.piecewise(x, condlist, funclist, offset=2)
    match_all_arrays(mnp_res, onp_res)

    funclist = [-1, 0, 1]
    mnp_res = mnp.piecewise(mnp_x, mnp_condlist, funclist)
    onp_res = onp.piecewise(x, condlist, funclist)
    match_all_arrays(mnp_res, onp_res)

    condlist = [x > 10, x < 0]
    mnp_x = to_tensor(x)
    mnp_condlist = [mnp_x > 10, mnp_x < 0]
    funclist = [lambda x: x - 2, lambda x: x - 1, lambda x: x*2]
    mnp_res = mnp.piecewise(mnp_x, mnp_condlist, funclist)
    onp_res = onp.piecewise(x, condlist, funclist)
    match_all_arrays(mnp_res, onp_res)

    x = 2
    condlist = True
    funclist = [lambda x: x - 1]
    mnp_res = mnp.piecewise(x, condlist, funclist)
    onp_res = onp.piecewise(x, condlist, funclist)
    match_all_arrays(mnp_res, onp_res)


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_unravel_index():
    shapes = [(), 1, 3, (5, 1), (2, 6, 3)]
    dims = [(5, 4, 7), (5*4, 7), 5*4*7]
    for shape in shapes:
        x = onp.random.randint(0, 5*4*7, shape)
        for dim in dims:
            for order in ('C', 'F'):
                mnp_res = mnp.unravel_index(to_tensor(x), dim, order=order)
                onp_res = onp.unravel_index(x, dim, order=order)
                match_all_arrays(mnp_res, onp_res)


def mnp_apply_over_axes(x):
    a = mnp.apply_over_axes(mnp.sum, x, axes=0)
    b = mnp.apply_over_axes(mnp.sum, x, axes=(0, 1))
    c = mnp.apply_over_axes(mnp.std, x, axes=1)
    d = mnp.apply_over_axes(mnp.mean, x, axes=(-1,))
    return a, b, c, d


def onp_apply_over_axes(x):
    a = onp.apply_over_axes(onp.sum, x, axes=0)
    b = onp.apply_over_axes(onp.sum, x, axes=(0, 1))
    c = onp.apply_over_axes(onp.std, x, axes=1)
    d = onp.apply_over_axes(onp.mean, x, axes=(-1,))
    return a, b, c, d


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_apply_over_axes():
    arrs = [
        onp.random.rand(2, 2).astype('float32'),
        onp.random.rand(3, 2, 2).astype('float32'),
        onp.random.rand(5, 4, 3, 3).astype('float32'),
    ]
    for x in arrs:
        for expected, actual in zip(onp_apply_over_axes(x),
                                    mnp_apply_over_axes(to_tensor(x))):
            match_array(actual.asnumpy(), expected, error=5)