#  Copyright (c) 2021 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.

from __future__ import print_function

import numpy as np
import unittest
import sys

sys.path.append("..")
from op_test import OpTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from test_momentum_op import calculate_momentum_by_numpy

paddle.enable_static()


class TestMomentumOp1(OpTest):

    def set_npu(self):
        self.__class__.use_npu = True

    def setUp(self):
        self.set_npu()
        self.op_type = "momentum"
        self.init_dtype()
        self.init_case()

        param = np.random.random(self.shape).astype(self.dtype)
        grad = np.random.random(self.shape).astype(self.dtype)
        velocity = np.zeros(self.shape).astype(self.dtype)
        learning_rate = np.array([0.001]).astype(np.float32)
        mu = 0.0001

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Velocity': velocity,
            'LearningRate': learning_rate
        }

        self.attrs = {'mu': mu, 'use_nesterov': self.use_nesterov}

        param_out, velocity_out = calculate_momentum_by_numpy(
            param=param,
            grad=grad,
            mu=mu,
            velocity=velocity,
            use_nesterov=self.use_nesterov,
            learning_rate=learning_rate)

        self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}

    def init_case(self):
        self.shape = (123, 321)
        self.use_nesterov = False

    def init_dtype(self):
        self.dtype = np.float32

    def test_check_output(self):
        self.check_output_with_place(core.NPUPlace(0))


class TestMomentumOpFp16(TestMomentumOp1):

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

    def test_check_output(self):
        self.check_output(atol=1e-3)


class TestMomentumOp2(TestMomentumOp1):

    def init_case(self):
        self.shape = (123, 321)
        self.use_nesterov = True


class TestMomentumV2(unittest.TestCase):

    def test_momentum_dygraph(self):
        paddle.disable_static(place=fluid.NPUPlace(0))
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear = paddle.nn.Linear(13, 5)
        # This can be any optimizer supported by dygraph.
        adam = paddle.optimizer.Momentum(learning_rate=0.01,
                                         momentum=0.9,
                                         parameters=linear.parameters())
        out = linear(a)
        out.backward()
        adam.step()
        adam.clear_gradients()

    def test_momentum(self):
        paddle.enable_static()
        place = fluid.NPUPlace(0)
        main = fluid.Program()
        with fluid.program_guard(main):
            x = fluid.layers.data(name='x', shape=[13], dtype='float32')
            y = fluid.layers.data(name='y', shape=[1], dtype='float32')
            y_predict = fluid.layers.fc(input=x, size=1, act=None)
            cost = fluid.layers.square_error_cost(input=y_predict, label=y)
            avg_cost = paddle.mean(cost)

            rms_optimizer = paddle.optimizer.Momentum(learning_rate=0.1,
                                                      momentum=0.9)
            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.Momentum,
                          learning_rate=None)
        self.assertRaises(ValueError, paddle.optimizer.Momentum, momentum=None)


class TestMomentumOpWithDecay(OpTest):

    def set_npu(self):
        self.__class__.use_npu = True

    def setUp(self):
        self.set_npu()
        self.op_type = "momentum"
        self.dtype = np.float32
        self.use_nesterov = True
        self.regularization_method = 'l2_decay'
        self.regularization_coeff = 0.9
        self.init_config()

        param = np.random.random((123, 321)).astype(self.dtype)
        grad = np.random.random((123, 321)).astype(self.dtype)
        velocity = np.zeros((123, 321)).astype(self.dtype)
        learning_rate = np.array([0.001]).astype(np.float32)
        mu = 0.0001
        use_nesterov = self.use_nesterov
        regularization_method = self.regularization_method
        regularization_coeff = self.regularization_coeff

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Velocity': velocity,
            'LearningRate': learning_rate
        }

        self.attrs = {
            'mu': mu,
            'use_nesterov': use_nesterov,
            'regularization_method': regularization_method,
            'regularization_coeff': regularization_coeff
        }

        grad = grad + regularization_coeff * param

        param_out, velocity_out = calculate_momentum_by_numpy(
            param=param,
            grad=grad,
            mu=mu,
            velocity=velocity,
            use_nesterov=use_nesterov,
            learning_rate=learning_rate)

        self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}

    def init_config(self):
        pass

    def test_check_output(self):
        paddle.enable_static()
        self.check_output_with_place(core.NPUPlace(0), atol=3e-3)


class TestMomentumOpWithDecayFP16(TestMomentumOpWithDecay):

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

    def test_check_output(self):
        paddle.enable_static()
        self.check_output(atol=1e-3)


class TestMomentumOpWithDecay2(TestMomentumOpWithDecay):

    def init_config(self):
        self.use_nesterov = False


class TestMomentumOpWithDecayAPI(unittest.TestCase):

    def _test_momentum_dygraph_common(self, regularization):
        paddle.disable_static(fluid.NPUPlace(0))
        inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
        linear = paddle.nn.Linear(10, 10)
        inp = paddle.to_tensor(inp)
        out = linear(inp)
        loss = paddle.mean(out)
        # This can be any optimizer supported by dygraph.
        momentum = paddle.fluid.contrib.optimizer.Momentum(
            learning_rate=0.01,
            momentum=0.9,
            parameter_list=linear.parameters(),
            regularization=regularization)
        momentum.minimize(loss)

    def test_momentum_dygraph_1(self):
        self._test_momentum_dygraph_common(
            regularization=paddle.fluid.regularizer.L2Decay(
                regularization_coeff=0.1))

    def test_momentum_static(self):
        paddle.enable_static()
        place = fluid.NPUPlace(0)
        main = fluid.Program()
        with fluid.program_guard(main):
            x = fluid.layers.data(name='x', shape=[13], dtype='float32')
            y = fluid.layers.data(name='y', shape=[1], dtype='float32')
            y_predict = fluid.layers.fc(input=x, size=1, act=None)
            cost = fluid.layers.square_error_cost(input=y_predict, label=y)
            avg_cost = paddle.mean(cost)

            momentum_optimizer = paddle.fluid.contrib.optimizer.Momentum(
                learning_rate=0.1, momentum=0.9)
            momentum_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)


class TestMomentumOpVsMomentumOpWithDecayAPI(unittest.TestCase):

    def __update_params(self, momentum, linear):
        for i in range(10):
            inp = paddle.full(shape=[2, 2], fill_value=i,
                              dtype='float32').astype("float32")
            inp = paddle.to_tensor(inp)
            out = linear(inp)
            loss = paddle.mean(out)
            loss.backward()
            momentum.minimize(loss)
            linear.clear_gradients()

    def __test_vs(self, place=fluid.NPUPlace(0)):
        paddle.disable_static(place=place)
        linear_old = paddle.nn.Linear(
            2,
            2,
            weight_attr=paddle.nn.initializer.Constant(value=2.0),
            bias_attr=paddle.nn.initializer.Constant(value=2.0))
        momentum_old = paddle.fluid.optimizer.Momentum(
            learning_rate=0.01,
            momentum=0.9,
            parameter_list=linear_old.parameters(),
            regularization=paddle.fluid.regularizer.L2Decay(
                regularization_coeff=0.1))
        self.__update_params(momentum=momentum_old, linear=linear_old)

        linear_new = paddle.nn.Linear(
            2,
            2,
            weight_attr=paddle.nn.initializer.Constant(value=2.0),
            bias_attr=paddle.nn.initializer.Constant(value=2.0))
        momentum_new = paddle.fluid.contrib.optimizer.Momentum(
            learning_rate=0.01,
            momentum=0.9,
            parameter_list=linear_new.parameters(),
            regularization=paddle.fluid.regularizer.L2Decay(
                regularization_coeff=0.1))
        self.__update_params(momentum=momentum_new, linear=linear_new)

        self.assertEqual(
            (linear_old.weight.numpy() == linear_new.weight.numpy()).all(),
            True,
            'the param weight updated by two Momentum optimizers should equal')

    def test_vs(self, place=fluid.NPUPlace(0)):
        self.__test_vs(place=place)


class TestMomentumV2Group(TestMomentumV2):

    def test_momentum_dygraph(self):
        paddle.disable_static(place=fluid.NPUPlace(0))
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear_1 = paddle.nn.Linear(13, 5)
        linear_2 = paddle.nn.Linear(5, 3)
        # This can be any optimizer supported by dygraph.
        adam = paddle.optimizer.Momentum(learning_rate=0.01,
                                         parameters=[{
                                             'params':
                                             linear_1.parameters()
                                         }, {
                                             'params':
                                             linear_2.parameters(),
                                             'weight_decay':
                                             0.001,
                                             'learning_rate':
                                             0.1,
                                             'momentum':
                                             0.99
                                         }],
                                         weight_decay=0.1,
                                         momentum=0.9)
        out = linear_1(a)
        out = linear_2(out)
        out.backward()
        adam.step()
        adam.clear_gradients()


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