#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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 unittest
import numpy as np
import paddle
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import (
    OpTest,
    skip_check_grad_ci,
    convert_float_to_uint16,
)
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
from paddle.fluid.framework import _test_eager_guard


class TestElementwiseAddOp(OpTest):
    def init_kernel_type(self):
        self.use_mkldnn = False

    def setUp(self):
        self.op_type = "elementwise_add"
        self.python_api = paddle.add
        self.init_dtype()
        self.init_input_output()
        self.init_kernel_type()
        self.init_axis()

        self.inputs = {
            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
            'Y': OpTest.np_dtype_to_fluid_dtype(self.y),
        }
        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
        self.outputs = {'Out': self.out}

    def check_eager(self):
        return self.use_mkldnn == False and self.axis == -1

    def test_check_output(self):
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        self.check_output(
            check_dygraph=(self.use_mkldnn == False),
            check_eager=self.check_eager(),
        )

    def test_check_grad_normal(self):
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        if self.dtype == np.float16:
            return
        self.check_grad(
            ['X', 'Y'],
            'Out',
            check_dygraph=(self.use_mkldnn == False),
            check_eager=self.check_eager(),
        )

    def test_check_grad_ingore_x(self):
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        if self.dtype == np.float16:
            return
        self.check_grad(
            ['Y'],
            'Out',
            no_grad_set=set("X"),
            check_dygraph=(self.use_mkldnn == False),
            check_eager=self.check_eager(),
        )

    def test_check_grad_ingore_y(self):
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        if self.dtype == np.float16:
            return
        self.check_grad(
            ['X'],
            'Out',
            no_grad_set=set('Y'),
            check_dygraph=(self.use_mkldnn == False),
            check_eager=self.check_eager(),
        )

    def init_input_output(self):
        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.out = np.add(self.x, self.y)

    def init_dtype(self):
        self.dtype = np.float64

    def init_axis(self):
        self.axis = -1


@unittest.skipIf(
    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
class TestFP16ElementwiseAddOp(TestElementwiseAddOp):
    def init_dtype(self):
        self.dtype = np.float16

    def test_check_output(self):
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place):
                self.check_output_with_place(
                    place, atol=1e-3, check_dygraph=(self.use_mkldnn == False)
                )


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or core.cudnn_version() < 8100
    or paddle.device.cuda.get_device_capability()[0] < 8,
    "only support compiled with CUDA and cudnn version need larger than 8.1.0 and device's compute capability is at least 8.0",
)
class TestBF16ElementwiseAddOp(OpTest):
    def setUp(self):
        self.op_type = "elementwise_add"
        self.dtype = np.uint16

        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32)
        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32)
        self.out = np.add(self.x, self.y)

        self.axis = -1

        self.inputs = {
            'X': OpTest.np_dtype_to_fluid_dtype(
                convert_float_to_uint16(self.x)
            ),
            'Y': OpTest.np_dtype_to_fluid_dtype(
                convert_float_to_uint16(self.y)
            ),
        }
        self.attrs = {'axis': self.axis, 'use_mkldnn': False}
        self.outputs = {'Out': convert_float_to_uint16(self.out)}

    def test_check_output(self):
        place = core.CUDAPlace(0)
        self.check_output_with_place(place, check_eager=False)

    def test_check_grad_normal(self):
        place = core.CUDAPlace(0)
        self.check_grad_with_place(place, ['X', 'Y'], 'Out', check_eager=False)

    def test_check_grad_ingore_x(self):
        place = core.CUDAPlace(0)
        self.check_grad_with_place(
            place, ['Y'], 'Out', no_grad_set=set("X"), check_eager=False
        )

    def test_check_grad_ingore_y(self):
        place = core.CUDAPlace(0)
        self.check_grad_with_place(
            place, ['X'], 'Out', no_grad_set=set('Y'), check_eager=False
        )


@skip_check_grad_ci(
    reason="[skip shape check] Use y_shape(1) to test broadcast."
)
class TestElementwiseAddOp_scalar(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
        self.y = np.random.rand(1).astype(self.dtype)
        self.out = self.x + self.y


@skip_check_grad_ci(
    reason="[skip shape check] Use y_shape(1) to test broadcast."
)
class TestFP16ElementwiseAddOp_scalar(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
        self.y = np.random.rand(1).astype(self.dtype)
        self.out = self.x + self.y


@skip_check_grad_ci(
    reason="[skip shape check] Use y_shape(1,1) to test broadcast."
)
class TestElementwiseAddOp_scalar2(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
        self.y = np.random.rand(1, 1).astype(self.dtype)
        self.out = self.x + self.y


@skip_check_grad_ci(
    reason="[skip shape check] Use y_shape(1,1) to test broadcast."
)
class TestFP16ElementwiseAddOp_scalar2(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
        self.y = np.random.rand(1, 1).astype(self.dtype)
        self.out = self.x + self.y


class TestElementwiseAddOp_Vector(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.random((100,)).astype(self.dtype)
        self.y = np.random.random((100,)).astype(self.dtype)
        self.out = np.add(self.x, self.y)


class TestFP16ElementwiseAddOp_Vector(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.random((100,)).astype(self.dtype)
        self.y = np.random.random((100,)).astype(self.dtype)
        self.out = np.add(self.x, self.y)


class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
        self.y = np.random.rand(100).astype(self.dtype)
        self.out = self.x + self.y.reshape(100, 1, 1)

    def init_axis(self):
        self.axis = 0


class TestFP16ElementwiseAddOp_broadcast_0(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
        self.y = np.random.rand(100).astype(self.dtype)
        self.out = self.x + self.y.reshape(100, 1, 1)

    def init_axis(self):
        self.axis = 0


class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 100, 3).astype(self.dtype)
        self.y = np.random.rand(100).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 100, 1)

    def init_axis(self):
        self.axis = 1


class TestFP16ElementwiseAddOp_broadcast_1(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 100, 3).astype(self.dtype)
        self.y = np.random.rand(100).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 100, 1)

    def init_axis(self):
        self.axis = 1


class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
        self.y = np.random.rand(100).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 1, 100)


class TestFP16ElementwiseAddOp_broadcast_2(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
        self.y = np.random.rand(100).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 1, 100)


class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 10, 12, 1).astype(self.dtype)
        self.y = np.random.rand(10, 12).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 10, 12, 1)

    def init_axis(self):
        self.axis = 1


class TestFP16ElementwiseAddOp_broadcast_3(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
        self.y = np.random.rand(10, 12).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 10, 12, 1)

    def init_axis(self):
        self.axis = 1


class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 2, 1, 2).astype(self.dtype)
        self.y = np.random.rand(100, 1).astype(self.dtype)
        self.out = self.x + self.y.reshape(100, 1, 1, 1)

    def init_axis(self):
        self.axis = 0


class TestFP16ElementwiseAddOp_broadcast_4(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 2, 1, 2).astype(self.dtype)
        self.y = np.random.rand(100, 1).astype(self.dtype)
        self.out = self.x + self.y.reshape(100, 1, 1, 1)

    def init_axis(self):
        self.axis = 0


class TestElementwiseAddOp_broadcast_5(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(10, 3, 12).astype(self.dtype)
        self.y = np.random.rand(10, 1, 12).astype(self.dtype)
        self.out = self.x + self.y


class TestFP16ElementwiseAddOp_broadcast_5(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(10, 3, 12).astype(self.dtype)
        self.y = np.random.rand(10, 1, 12).astype(self.dtype)
        self.out = self.x + self.y


class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
        self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
        self.out = self.x + self.y


class TestElementwiseAddOp_broadcast_7(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(1, 1, 20, 5).astype(self.dtype)
        self.y = np.random.rand(20, 5, 1, 1).astype(self.dtype)
        self.out = self.x + self.y


class TestFP16ElementwiseAddOp_broadcast_6(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
        self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
        self.out = self.x + self.y


class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 10, 12).astype(self.dtype)
        self.y = np.random.rand(10, 12).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 10, 12)

    def init_axis(self):
        self.axis = 1


class TestFP16ElementwiseAddOp_rowwise_add_0(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 10, 12).astype(self.dtype)
        self.y = np.random.rand(10, 12).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 10, 12)

    def init_axis(self):
        self.axis = 1


@skip_check_grad_ci(
    reason="[skip shape check] Use y_shape(1) to test broadcast."
)
class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 1).astype(self.dtype)
        self.y = np.random.rand(1).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 1)

    def init_axis(self):
        self.axis = 1


@skip_check_grad_ci(
    reason="[skip shape check] Use y_shape(1) to test broadcast."
)
class TestFP16ElementwiseAddOp_rowwise_add_1(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 1).astype(self.dtype)
        self.y = np.random.rand(1).astype(self.dtype)
        self.out = self.x + self.y.reshape(1, 1)

    def init_axis(self):
        self.axis = 1


class TestElementwiseAddOp_channelwise_add(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
        self.y = np.random.rand(100, 1, 1).astype(self.dtype)
        self.out = self.x + self.y

    def init_axis(self):
        self.axis = -1


class TestFP16ElementwiseAddOp_channelwise_add(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
        self.y = np.random.rand(100, 1, 1).astype(self.dtype)
        self.out = self.x + self.y

    def init_axis(self):
        self.axis = -1


class TestElementwiseAddOp_commonuse_add1(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
        self.y = np.random.rand(1, 1, 100).astype(self.dtype)
        self.out = self.x + self.y

    def init_axis(self):
        self.axis = -1


class TestElementwiseFP16AddOp_commonuse_add1(TestFP16ElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
        self.y = np.random.rand(1, 1, 100).astype(self.dtype)
        self.out = self.x + self.y

    def init_axis(self):
        self.axis = -1


class TestElementwiseAddOp_commonuse_add2(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(10, 3, 1, 4).astype(self.dtype)
        self.y = np.random.rand(10, 1, 12, 1).astype(self.dtype)
        self.out = self.x + self.y

    def init_axis(self):
        self.axis = -1


class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(10, 12).astype(self.dtype)
        self.y = np.random.rand(2, 2, 10, 12).astype(self.dtype)
        self.out = self.x + self.y

    def init_axis(self):
        self.axis = 2


class TestElementwiseAddOp_same_shape_ysize_large(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(10, 1, 12).astype(self.dtype)
        self.y = np.random.rand(10, 2, 12).astype(self.dtype)
        self.out = self.x + self.y

    def init_axis(self):
        self.axis = 0


class TestElementwiseAddOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # the input of elementwise_add must be Variable.
            x1 = fluid.create_lod_tensor(
                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
            )
            y1 = fluid.create_lod_tensor(
                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
            )
            self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1)

            # the input dtype of elementwise_add must be float16 or float32 or float64 or int32 or int64
            # float16 only can be set on GPU place
            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
            y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
            self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2)


class TestAddApi(unittest.TestCase):
    def _executed_api(self, x, y, name=None):
        return paddle.add(x, y, name)

    def test_name(self):
        with fluid.program_guard(fluid.Program()):
            x = fluid.data(name="x", shape=[2, 3], dtype="float32")
            y = fluid.data(name='y', shape=[2, 3], dtype='float32')

            y_1 = self._executed_api(x, y, name='add_res')
            self.assertEqual(('add_res' in y_1.name), True)

    def test_declarative(self):
        with fluid.program_guard(fluid.Program()):

            def gen_data():
                return {
                    "x": np.array([2, 3, 4]).astype('float32'),
                    "y": np.array([1, 5, 2]).astype('float32'),
                }

            x = fluid.data(name="x", shape=[3], dtype='float32')
            y = fluid.data(name="y", shape=[3], dtype='float32')
            z = self._executed_api(x, y)

            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            z_value = exe.run(feed=gen_data(), fetch_list=[z.name])
            z_expected = np.array([3.0, 8.0, 6.0])
            self.assertEqual((z_value == z_expected).all(), True)

    def test_dygraph(self):
        with fluid.dygraph.guard():
            np_x = np.array([2, 3, 4]).astype('float64')
            np_y = np.array([1, 5, 2]).astype('float64')
            x = fluid.dygraph.to_variable(np_x)
            y = fluid.dygraph.to_variable(np_y)
            z = self._executed_api(x, y)
            np_z = z.numpy()
            z_expected = np.array([3.0, 8.0, 6.0])
            self.assertEqual((np_z == z_expected).all(), True)


class TestAddInplaceApi(TestAddApi):
    def _executed_api(self, x, y, name=None):
        return x.add_(y, name)


class TestAddInplaceBroadcastSuccess(unittest.TestCase):
    def init_data(self):
        self.x_numpy = np.random.rand(2, 3, 4).astype('float')
        self.y_numpy = np.random.rand(3, 4).astype('float')

    def test_broadcast_success(self):
        paddle.disable_static()
        self.init_data()
        x = paddle.to_tensor(self.x_numpy)
        y = paddle.to_tensor(self.y_numpy)
        inplace_result = x.add_(y)
        numpy_result = self.x_numpy + self.y_numpy
        self.assertEqual((inplace_result.numpy() == numpy_result).all(), True)
        paddle.enable_static()


class TestAddInplaceBroadcastSuccess2(TestAddInplaceBroadcastSuccess):
    def init_data(self):
        self.x_numpy = np.random.rand(1, 2, 3, 1).astype('float')
        self.y_numpy = np.random.rand(3, 1).astype('float')


class TestAddInplaceBroadcastSuccess3(TestAddInplaceBroadcastSuccess):
    def init_data(self):
        self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float')
        self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float')


class TestAddInplaceBroadcastError(unittest.TestCase):
    def init_data(self):
        self.x_numpy = np.random.rand(3, 4).astype('float')
        self.y_numpy = np.random.rand(2, 3, 4).astype('float')

    def test_broadcast_errors(self):
        paddle.disable_static()
        self.init_data()
        x = paddle.to_tensor(self.x_numpy)
        y = paddle.to_tensor(self.y_numpy)

        def broadcast_shape_error():
            x.add_(y)

        self.assertRaises(ValueError, broadcast_shape_error)
        paddle.enable_static()


class TestAddInplaceBroadcastError2(TestAddInplaceBroadcastError):
    def init_data(self):
        self.x_numpy = np.random.rand(2, 1, 4).astype('float')
        self.y_numpy = np.random.rand(2, 3, 4).astype('float')


class TestAddInplaceBroadcastError3(TestAddInplaceBroadcastError):
    def init_data(self):
        self.x_numpy = np.random.rand(5, 2, 1, 4).astype('float')
        self.y_numpy = np.random.rand(2, 3, 4).astype('float')


class TestComplexElementwiseAddOp(OpTest):
    def setUp(self):
        self.op_type = "elementwise_add"
        self.dtype = np.float64
        self.shape = (2, 3, 4, 5)
        self.init_input_output()
        self.init_grad_input_output()

        self.inputs = {
            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
            'Y': OpTest.np_dtype_to_fluid_dtype(self.y),
        }
        self.attrs = {'axis': -1, 'use_mkldnn': False}
        self.outputs = {'Out': self.out}

    def init_base_dtype(self):
        self.dtype = np.float64

    def init_input_output(self):
        self.x = np.random.random(self.shape).astype(
            self.dtype
        ) + 1j * np.random.random(self.shape).astype(self.dtype)
        self.y = np.random.random(self.shape).astype(
            self.dtype
        ) + 1j * np.random.random(self.shape).astype(self.dtype)
        self.out = self.x + self.y

    def init_grad_input_output(self):
        self.grad_out = np.ones(self.shape, self.dtype) + 1j * np.ones(
            self.shape, self.dtype
        )
        self.grad_x = self.grad_out
        self.grad_y = self.grad_out

    def test_check_output(self):
        self.check_output(check_eager=False)

    def test_check_grad_normal(self):
        self.check_grad(
            ['X', 'Y'],
            'Out',
            user_defined_grads=[self.grad_x, self.grad_y],
            user_defined_grad_outputs=[self.grad_out],
        )

    def test_check_grad_ingore_x(self):
        self.check_grad(
            ['Y'],
            'Out',
            no_grad_set=set("X"),
            user_defined_grads=[self.grad_y],
            user_defined_grad_outputs=[self.grad_out],
        )

    def test_check_grad_ingore_y(self):
        self.check_grad(
            ['X'],
            'Out',
            no_grad_set=set('Y'),
            user_defined_grads=[self.grad_x],
            user_defined_grad_outputs=[self.grad_out],
        )


class TestRealComplexElementwiseAddOp(TestComplexElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.random(self.shape).astype(self.dtype)
        self.y = np.random.random(self.shape).astype(
            self.dtype
        ) + 1j * np.random.random(self.shape).astype(self.dtype)
        self.out = self.x + self.y

    def init_grad_input_output(self):
        self.grad_out = np.ones(self.shape, self.dtype) + 1j * np.ones(
            self.shape, self.dtype
        )
        self.grad_x = np.real(self.grad_out)
        self.grad_y = self.grad_out


class TestBoolAddFloatElementwiseAddop(unittest.TestCase):
    def test_static_add(self):
        paddle.enable_static()
        a = 1.5
        b = paddle.full([4, 5, 6], True, dtype='bool')
        c = a + b
        self.assertTrue(c.dtype == core.VarDesc.VarType.FP32)
        paddle.enable_static()

    def func_dygraph_add(self):
        paddle.disable_static()
        a = 1.5
        b = paddle.full([2], True, dtype='bool')
        # special case: scalar + tensor(bool)
        c = a + b
        self.assertTrue(c.dtype == core.VarDesc.VarType.FP32)

        np_a = np.random.random((2, 3, 4)).astype(np.float64)
        np_b = np.random.random((2, 3, 4)).astype(np.float64)

        tensor_a = paddle.to_tensor(np_a, dtype="float32")
        tensor_b = paddle.to_tensor(np_b, dtype="float32")

        # normal case: tensor + tensor
        expect_out = np_a + np_b
        actual_out = tensor_a + tensor_b
        np.testing.assert_allclose(actual_out, expect_out)

        # normal case: tensor + scalar
        expect_out = np_a + 1
        actual_out = tensor_a + 1
        np.testing.assert_allclose(actual_out, expect_out)

        # normal case: scalar + tenor
        expect_out = 1 + np_a
        actual_out = 1 + tensor_a
        np.testing.assert_allclose(actual_out, expect_out)

        paddle.enable_static()

    def test_dygraph_add(self):
        with _test_eager_guard():
            self.func_dygraph_add()
        self.func_dygraph_add()


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()