# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
""" test lazy adam """
import numpy as np
from mindspore.nn.optim import LazyAdam, FTRL, Adam, ProximalAdagrad
import mindspore.nn as nn
from mindspore import Tensor, Parameter, context
from mindspore.ops import operations as P

context.set_context(enable_sparse=True)


class NetWithSparseGatherV2(nn.Cell):
    """ NetWithSparseGatherV2 definition """
    def __init__(self):
        super(NetWithSparseGatherV2, self).__init__()
        self.weight1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="weight1")
        self.weight2 = Parameter(Tensor(np.ones([2, 1, 2]).astype((np.float32))), name="weight2")
        self.axis = 0
        self.gather = P.SparseGatherV2()

    def construct(self, indices, label):
        return self.gather(self.weight1, indices, self.axis) + self.weight2


def test_ftrl_target():
    """ test_ftrl_target """
    net = NetWithSparseGatherV2()
    net.set_train()

    optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
    if optimizer.target not in ('CPU', 'Ascend'):
        raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))


def test_lazyadam_target():
    """ test_lazyadam_target """
    net = NetWithSparseGatherV2()
    net.set_train()

    optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
    if optimizer.target not in ('CPU', 'Ascend'):
        raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))


def test_adam_target():
    """ test_adam_target """
    net = NetWithSparseGatherV2()
    net.set_train()

    optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
    if optimizer.target not in ('CPU', 'Ascend'):
        raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))


def test_proximal_target():
    """ test_proximal_target """
    net = NetWithSparseGatherV2()
    net.set_train()

    optimizer = ProximalAdagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
    if optimizer.target not in ('CPU', 'Ascend'):
        raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))