#  Copyright (c) 2022 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
from op_test import OpTest, skip_check_grad_ci
from op_test_xpu import XPUOpTest

import paddle
from paddle import base

paddle.enable_static()


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
class TestElementwiseAddOp(XPUOpTest):
    def setUp(self):
        self.op_type = "elementwise_add"
        self.init_dtype()
        self.init_input_output()
        self.init_axis()
        self.init_max_relative_error()
        self.inputs = {
            'X': OpTest.np_dtype_to_base_dtype(self.x),
            'Y': OpTest.np_dtype_to_base_dtype(self.y),
        }
        self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
        self.outputs = {'Out': self.out}

    def test_check_output(self):
        if paddle.is_compiled_with_xpu():
            place = paddle.XPUPlace(0)
            self.check_output_with_place(place)

    def test_check_grad_normal(self):
        if paddle.is_compiled_with_xpu():
            place = paddle.XPUPlace(0)
            self.check_grad_with_place(
                place,
                ['X', 'Y'],
                'Out',
                max_relative_error=self.max_relative_error,
            )

    def test_check_grad_ignore_x(self):
        if paddle.is_compiled_with_xpu():
            place = paddle.XPUPlace(0)
            self.check_grad_with_place(
                place,
                ['Y'],
                'Out',
                no_grad_set=set("X"),
                max_relative_error=self.max_relative_error,
            )

    def test_check_grad_ignore_y(self):
        if paddle.is_compiled_with_xpu():
            place = paddle.XPUPlace(0)
            self.check_grad_with_place(
                place,
                ['X'],
                'Out',
                no_grad_set=set("Y"),
                max_relative_error=self.max_relative_error,
            )

    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.float32

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

    def init_max_relative_error(self):
        self.max_relative_error = 0.006


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
@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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
@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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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)


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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)


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
    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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
    def init_input_output(self):
        self.x = np.random.rand(100, 2, 3, 4).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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
@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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
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, 3, 10, 12).astype(self.dtype)
        self.out = self.x + self.y

    def init_axis(self):
        self.axis = 2


@unittest.skipIf(
    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
)
class TestAddOp(unittest.TestCase):
    def test_name(self):
        with base.program_guard(base.Program()):
            x = paddle.static.data(name="x", shape=[2, 3], dtype="float32")
            y = paddle.static.data(name='y', shape=[2, 3], dtype='float32')

            y_1 = paddle.add(x, y, name='add_res')
            if not paddle.framework.use_pir_api():
                self.assertEqual(('add_res' in y_1.name), True)

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

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

            x = paddle.static.data(name="x", shape=[3], dtype='float32')
            y = paddle.static.data(name="y", shape=[3], dtype='float32')
            z = paddle.add(x, y)

            place = base.XPUPlace(0)
            exe = base.Executor(place)
            z_value = exe.run(feed=gen_data(), fetch_list=[z])
            z_expected = np.array([3.0, 8.0, 6.0])
            self.assertEqual((z_value == z_expected).all(), True)

    def test_dygraph(self):
        with base.dygraph.guard():
            np_x = np.array([2, 3, 4]).astype('float32')
            np_y = np.array([1, 5, 2]).astype('float32')
            x = paddle.to_tensor(np_x)
            y = paddle.to_tensor(np_y)
            z = paddle.add(x, y)
            np_z = z.numpy()
            z_expected = np.array([3.0, 8.0, 6.0])
            self.assertEqual((np_z == z_expected).all(), True)


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