# 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 optimizer """
import numpy as np
import pytest
from mindspore import Tensor
from mindspore.nn.optim import Optimizer, SGD, Adam, AdamWeightDecay, AdamWeightDecayDynamicLR
from mindspore.common.parameter import Parameter


class IterableObjc:
    def __iter__(self):
        cont = 0
        while cont < 3:
            cont += 1
            yield Parameter(Tensor(cont), name="cont")


params = IterableObjc()

class TestOptimizer():
    def test_init(self):
        Optimizer(0.5, params)
        with pytest.raises(ValueError):
            Optimizer(-0.5, params)

    def test_construct(self):
        opt_2 = Optimizer(0.5, params)
        with pytest.raises(NotImplementedError):
            opt_2.construct()


class TestAdam():
    """ TestAdam definition """
    def test_init(self):
        Adam(params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
             use_nesterov=False, weight_decay=0.0, loss_scale=1.0)

    def test_construct(self):
        with pytest.raises(TypeError):
            gradient = Tensor(np.zeros([1, 2, 3]))
            adam = Adam(params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
                        use_nesterov=False, weight_decay=0.0, loss_scale=1.0)
            adam.construct(gradient)


class TestSGD():
    """ TestSGD definition """
    def test_init(self):
        with pytest.raises(ValueError):
            SGD(params, learning_rate=0.1, momentum=-0.1, dampening=0, weight_decay=0, nesterov=False)
        with pytest.raises(ValueError):
            SGD(params, learning_rate=0.12, momentum=-0.1, dampening=0, weight_decay=0, nesterov=False)
        SGD(params)


class TestNullParam():
    """ TestNullParam definition """
    def test_optim_init(self):
        with pytest.raises(TypeError):
            Optimizer(0.1, None)

    def test_AdamWightDecay_init(self):
        with pytest.raises(TypeError):
            AdamWeightDecay(None)

    def test_AdamWeightDecayDynamicLR_init(self):
        with pytest.raises(TypeError):
            AdamWeightDecayDynamicLR(None, 10)

    def test_Sgd_init(self):
        with pytest.raises(TypeError):
            SGD(None)

class TestUnsupportParam():
    """ TestUnsupportParam definition """
    def test_optim_init(self):
        with pytest.raises(ValueError):
            Optimizer(0.1, (1, 2, 3))

    def test_AdamWightDecay_init(self):
        with pytest.raises(TypeError):
            AdamWeightDecay(9)

    def test_AdamWeightDecayDynamicLR_init(self):
        with pytest.raises(TypeError):
            AdamWeightDecayDynamicLR(0.5, 10)

    def test_Sgd_init(self):
        with pytest.raises(TypeError):
            paramsTensor = Tensor(np.zeros([1, 2, 3]))
            SGD(paramsTensor)