# Copyright 2019-2021 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.
# ============================================================================

import numpy as np
import pytest

import mindspore.context as context
from mindspore.common.api import ms_function
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
from mindspore.nn import Cell
from mindspore.ops.operations import Tile


class TileNet(Cell):
    def __init__(self, numpy_input):
        super(TileNet, self).__init__()
        self.Tile = Tile()

        self.input_parameter = Parameter(initializer(Tensor(numpy_input), numpy_input.shape), name='x')

    @ms_function
    def construct(self, mul):
        return self.Tile(self.input_parameter, mul)


def ms_tile(nptype):
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")

    input_0 = np.arange(2).reshape((2, 1, 1)).astype(nptype)
    mul_0 = (8, 1, 1)
    input_1 = np.arange(32).reshape((2, 4, 4)).astype(nptype)
    mul_1 = (2, 2, 2)
    input_2 = np.arange(1).reshape((1, 1, 1)).astype(nptype)
    mul_2 = (1, 1, 1)

    tile_net = TileNet(input_0)
    np_expected = np.tile(input_0, mul_0)
    ms_output = tile_net(mul_0).asnumpy()
    np.testing.assert_array_equal(ms_output, np_expected)

    tile_net = TileNet(input_1)
    np_expected = np.tile(input_1, mul_1)
    ms_output = tile_net(mul_1).asnumpy()
    np.testing.assert_array_equal(ms_output, np_expected)

    tile_net = TileNet(input_2)
    np_expected = np.tile(input_2, mul_2)
    ms_output = tile_net(mul_2).asnumpy()
    np.testing.assert_array_equal(ms_output, np_expected)

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_float16():
    ms_tile(np.float16)

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_float32():
    ms_tile(np.float32)

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_float64():
    ms_tile(np.float64)

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_int16():
    ms_tile(np.int16)

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_int32():
    ms_tile(np.int32)

@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_int64():
    ms_tile(np.int64)