#   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 get_test_cover_info import (
    XPUOpTestWrapper,
    create_test_class,
    get_xpu_op_support_types,
)
from op_test_xpu import XPUOpTest

import paddle
from paddle import fluid

paddle.enable_static()


class XPUTestAdadelta(XPUOpTestWrapper):
    def __init__(self):
        self.op_name = 'adadelta'

    class TestAdadeltaOp1(XPUOpTest):
        def setUp(self):
            self.op_type = "adadelta"
            self.dtype = self.in_type
            self.place = paddle.XPUPlace(0)

            param = np.random.uniform(-1, 1, (102, 105)).astype(self.dtype)
            grad = np.random.uniform(-1, 1, (102, 105)).astype(self.dtype)
            # The squared gradient is positive
            avg_squared_grad = np.random.random((102, 105)).astype(self.dtype)
            # The squared update is positive
            avg_squared_update = np.random.random((102, 105)).astype(self.dtype)

            rho = 0.95
            epsilon = 1e-6

            learning_rate = 1.0
            self.inputs = {
                'Param': param,
                'Grad': grad,
                'AvgSquaredGrad': avg_squared_grad,
                'AvgSquaredUpdate': avg_squared_update,
                'LearningRate': np.array([learning_rate]).astype("float32"),
            }

            self.attrs = {'rho': rho, 'epsilon': epsilon}

            avg_squared_grad_out = rho * avg_squared_grad + (
                1 - rho
            ) * np.square(grad)
            update = -np.multiply(
                np.sqrt(
                    np.divide(
                        avg_squared_update + epsilon,
                        avg_squared_grad_out + epsilon,
                    )
                ),
                grad,
            )

            avg_squared_update_out = rho * avg_squared_update + (
                1 - rho
            ) * np.square(update)

            param_out = param + update

            self.outputs = {
                'ParamOut': param_out,
                'AvgSquaredGradOut': avg_squared_grad_out,
                'AvgSquaredUpdateOut': avg_squared_update_out,
            }

        def test_check_output(self):
            self.check_output()

    class TestAdadeltaOp2(XPUOpTest):
        '''Test Adadelta op with default attribute values'''

        def setUp(self):
            self.op_type = "adadelta"
            self.dtype = self.in_type
            self.place = paddle.XPUPlace(0)

            param = np.random.uniform(-1, 1, (102, 105)).astype(self.dtype)
            grad = np.random.uniform(-1, 1, (102, 105)).astype(self.dtype)
            # The squared gradient is positive
            avg_squared_grad = np.random.random((102, 105)).astype(self.dtype)
            # The squared update is positive
            avg_squared_update = np.random.random((102, 105)).astype(self.dtype)

            rho = 0.95
            epsilon = 1e-6

            learning_rate = 1.0
            self.inputs = {
                'Param': param,
                'Grad': grad,
                'AvgSquaredGrad': avg_squared_grad,
                'AvgSquaredUpdate': avg_squared_update,
                'LearningRate': np.array([learning_rate]).astype("float32"),
            }

            avg_squared_grad_out = rho * avg_squared_grad + (
                1 - rho
            ) * np.square(grad)
            update = -np.multiply(
                np.sqrt(
                    np.divide(
                        avg_squared_update + epsilon,
                        avg_squared_grad_out + epsilon,
                    )
                ),
                grad,
            )

            avg_squared_update_out = rho * avg_squared_update + (
                1 - rho
            ) * np.square(update)

            param_out = param + update

            self.outputs = {
                'ParamOut': param_out,
                'AvgSquaredGradOut': avg_squared_grad_out,
                'AvgSquaredUpdateOut': avg_squared_update_out,
            }

        def test_check_output(self):
            self.check_output()

    class TestAdadeltaV2(unittest.TestCase):
        def test_adadelta_dygraph(self):
            self.dtype = self.in_type
            self.place = paddle.XPUPlace(0)

            paddle.disable_static(self.place)
            value = np.arange(26).reshape(2, 13).astype(self.dtype)
            a = paddle.to_tensor(value)
            linear = paddle.nn.Linear(13, 5)
            # This can be any optimizer supported by dygraph.
            adam = paddle.optimizer.Adadelta(
                learning_rate=0.01,
                parameters=linear.parameters(),
                weight_decay=0.01,
            )
            out = linear(a)
            out.backward()
            adam.step()
            adam.clear_gradients()

        def test_adadelta(self):
            self.dtype = self.in_type
            paddle.enable_static()
            place = fluid.XPUPlace(0)
            main = fluid.Program()
            with fluid.program_guard(main):
                x = paddle.static.data(
                    name='x', shape=[-1, 13], dtype=self.dtype
                )
                y = paddle.static.data(
                    name='y', shape=[-1, 1], dtype=self.dtype
                )
                y_predict = paddle.static.nn.fc(x, size=1, activation=None)
                cost = paddle.nn.functional.square_error_cost(
                    input=y_predict, label=y
                )
                avg_cost = paddle.mean(cost)

                rms_optimizer = paddle.optimizer.Adadelta(learning_rate=0.1)
                rms_optimizer.minimize(avg_cost)

                fetch_list = [avg_cost]
                train_reader = paddle.batch(
                    paddle.dataset.uci_housing.train(), batch_size=1
                )
                feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
                exe = fluid.Executor(place)
                exe.run(fluid.default_startup_program())
                for data in train_reader():
                    exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)

        def test_raise_error(self):
            self.assertRaises(ValueError, paddle.optimizer.Adadelta, None)
            self.assertRaises(
                ValueError,
                paddle.optimizer.Adadelta,
                learning_rate=0.1,
                rho=None,
            )
            self.assertRaises(
                ValueError,
                paddle.optimizer.Adadelta,
                learning_rate=0.1,
                epsilon=None,
            )

    class TestAdadeltaV2Group(TestAdadeltaV2):
        def test_adadelta_dygraph(self):
            self.dtype = self.in_type
            self.place = paddle.XPUPlace(0)

            paddle.disable_static(self.place)
            value = np.arange(26).reshape(2, 13).astype(self.dtype)
            a = paddle.to_tensor(value)
            linear_1 = paddle.nn.Linear(13, 5)
            linear_2 = paddle.nn.Linear(5, 5)
            # This can be any optimizer supported by dygraph.
            adam = paddle.optimizer.Adadelta(
                learning_rate=0.01,
                parameters=[
                    {'params': linear_1.parameters()},
                    {
                        'params': linear_2.parameters(),
                        'weight_decay': 0.001,
                    },
                ],
                weight_decay=0.1,
            )
            out = linear_1(a)
            out = linear_2(out)
            out.backward()
            adam.step()
            adam.clear_gradients()


support_types = get_xpu_op_support_types('adadelta')
for stype in support_types:
    create_test_class(globals(), XPUTestAdadelta, stype)


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