#  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
import paddle
import paddle.fluid.core as core
import sys

sys.path.append('..')
from op_test import OpTest, skip_check_grad_ci
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard

paddle.enable_static()


class TestElementwiseAddOp(OpTest):

    def set_mlu(self):
        self.place = paddle.device.MLUPlace(0)
        self.__class__.use_mlu = True

    def setUp(self):
        self.op_type = "elementwise_add"
        self.set_mlu()
        self.init_dtype()
        self.init_input_output()
        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}
        self.outputs = {'Out': self.out}

    def test_check_output(self):
        self.check_output_with_place(self.place)

    def test_check_grad_normal(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(self.place, ['X', 'Y'],
                                   'Out',
                                   max_relative_error=0.01)

    def test_check_grad_ingore_x(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(self.place, ['Y'],
                                   'Out',
                                   no_grad_set=set("X"),
                                   max_relative_error=0.01)

    def test_check_grad_ingore_y(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(self.place, ['X'],
                                   'Out',
                                   no_grad_set=set('Y'),
                                   max_relative_error=0.01)

    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


class TestFP16ElementwiseAddOp(TestElementwiseAddOp):

    def init_dtype(self):
        self.dtype = np.float16

    def test_check_output(self):
        self.check_output_with_place(self.place, atol=1e-3)


@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.MLUPlace(0))
            y1 = fluid.create_lod_tensor(np.array([-1, 3, 5, 5]),
                                         [[1, 1, 1, 1]], fluid.MLUPlace(0))
            self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1)

            # the input dtype of elementwise_add must be float16 or float32
            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.MLUPlace(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 = self._executed_api(x, y)
            np_z = z.numpy()
            z_expected = np.array([3., 8., 6.])
            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('float32')
        self.y_numpy = np.random.rand(3, 4).astype('float32')

    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('float32')
        self.y_numpy = np.random.rand(3, 1).astype('float32')


class TestAddInplaceBroadcastSuccess3(TestAddInplaceBroadcastSuccess):

    def init_data(self):
        self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float32')
        self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float32')


class TestAddInplaceBroadcastError(unittest.TestCase):

    def init_data(self):
        self.x_numpy = np.random.rand(3, 4).astype('float32')
        self.y_numpy = np.random.rand(2, 3, 4).astype('float32')

    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('float32')
        self.y_numpy = np.random.rand(2, 3, 4).astype('float32')


class TestAddInplaceBroadcastError3(TestAddInplaceBroadcastError):

    def init_data(self):
        self.x_numpy = np.random.rand(5, 2, 1, 4).astype('float32')
        self.y_numpy = np.random.rand(2, 3, 4).astype('float32')


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 test_dygraph_add(self):
        paddle.disable_static()
        a = 1.5
        b = paddle.full([4, 5, 6], True, dtype='bool')
        c = a + b
        self.assertTrue(c.dtype == core.VarDesc.VarType.FP32)


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