#  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 numpy as np
import sys

sys.path.append("..")
import paddle
from op_test import OpTest, skip_check_grad_ci
from op_test_xpu import XPUOpTest
import unittest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper

paddle.enable_static()


class XPUTestElementwiseAddOp(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = 'elementwise_add'
        self.use_dynamic_create_class = False

    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_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 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_ingore_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_ingore_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 = self.in_type

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

        def init_max_relative_error(self):
            self.max_relative_error = 0.006

    @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(1,1) 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

    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 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 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 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 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

    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

    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 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 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

    @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

    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 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 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, 3, 10, 12).astype(self.dtype)
            self.out = self.x + self.y

        def init_axis(self):
            self.axis = 2

    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.XPUPlace(0))
                y1 = fluid.create_lod_tensor(np.array([-1, 3, 5, 5]),
                                             [[1, 1, 1, 1]], fluid.XPUPlace(0))
                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 TestAddOp(unittest.TestCase):

        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 = paddle.add(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 = paddle.add(x, y)

                place = fluid.XPUPlace(0)
                exe = fluid.Executor(place)
                z_value = exe.run(feed=gen_data(), fetch_list=[z.name])
                z_expected = np.array([3., 8., 6.])
                self.assertEqual((z_value == z_expected).all(), True)

        def test_dygraph(self):
            with fluid.dygraph.guard():
                np_x = np.array([2, 3, 4]).astype('float32')
                np_y = np.array([1, 5, 2]).astype('float32')
                x = fluid.dygraph.to_variable(np_x)
                y = fluid.dygraph.to_variable(np_y)
                z = paddle.add(x, y)
                np_z = z.numpy()
                z_expected = np.array([3., 8., 6.])
                self.assertEqual((np_z == z_expected).all(), True)


support_types = get_xpu_op_support_types('elementwise_add')
for stype in support_types:
    create_test_class(globals(), XPUTestElementwiseAddOp, stype)

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