# Copyright 2022 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.
# ============================================================================
"""test python built-in functions in graph mode"""
import pytest
import numpy as np
from mindspore import Tensor, context, nn
from mindspore import dtype as mstype

context.set_context(mode=context.GRAPH_MODE)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_fallback_round_tensor():
    """
    Feature: JIT Fallback
    Description: Test round(Tensor) with a variable tensor in graph mode
    Expectation: No exception
    """

    class Net(nn.Cell):
        def construct(self, x):
            return round(x)

    net = Net()
    x = Tensor(np.array([0.1, 4.51, 9.9]), mstype.float32)
    out = net(x)
    expect = Tensor(np.array([0.0, 5.0, 10.0]))
    np.testing.assert_almost_equal(out.asnumpy(), expect.asnumpy())


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_fallback_round_tensor_constant():
    """
    Feature: JIT Fallback
    Description: Test any(Tensor) with a constant tensor in graph mode
    Expectation: No exception.
    """
    class Net(nn.Cell):
        def construct(self):
            x = Tensor(np.array([0.1, 4.51, 9.9]), mstype.float32)
            return round(x)

    net = Net()
    out = net()
    expect = Tensor(np.array([0.0, 5.0, 10.0]))
    np.testing.assert_almost_equal(out.asnumpy(), expect.asnumpy())
