# 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_pynative_model """
import pytest
import numpy as np

import mindspore.nn as nn
from mindspore import Parameter, ParameterTuple, Tensor
from mindspore import context
from mindspore.nn.optim import Momentum
from mindspore.ops import composite as C
from mindspore.ops import operations as P


grad_by_list = C.GradOperation(get_by_list=True)


def setup_module(module):
    context.set_context(mode=context.PYNATIVE_MODE)


class GradWrap(nn.Cell):
    """ GradWrap definition """

    def __init__(self, network):
        super(GradWrap, self).__init__()
        self.network = network
        self.weights = ParameterTuple(network.get_parameters())

    def construct(self, x, label):
        weights = self.weights
        return grad_by_list(self.network, weights)(x, label)


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def METHOD_NAME():
    """ test_softmaxloss_grad """

    class NetWithLossClass(nn.Cell):
        """ NetWithLossClass definition """

        def __init__(self, network):
            super(NetWithLossClass, self).__init__()
            self.loss = nn.SoftmaxCrossEntropyWithLogits()
            self.network = network

        def construct(self, x, label):
            predict = self.network(x)
            return self.loss(predict, label)

    class Net(nn.Cell):
        """ Net definition """

        def __init__(self):
            super(Net, self).__init__()
            self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
            self.bias = Parameter(Tensor(np.ones([10]).astype(np.float32)), name="bias")
            self.fc = P.MatMul()
            self.bias_add = P.BiasAdd()

        def construct(self, x):
            x = self.bias_add(self.fc(x, self.weight), self.bias)
            return x

    net = GradWrap(NetWithLossClass(Net()))

    predict = Tensor(np.ones([1, 64]).astype(np.float32))
    label = Tensor(np.zeros([1, 10]).astype(np.float32))
    print("pynative run")
    out = net.construct(predict, label)
    print("out:", out)
    print(out[0], (out[0]).asnumpy(), ":result")


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_lenet_grad():
    """ test_lenet_grad """

    class NetWithLossClass(nn.Cell):
        """ NetWithLossClass definition """

        def __init__(self, network):
            super(NetWithLossClass, self).__init__()
            self.loss = nn.SoftmaxCrossEntropyWithLogits()
            self.network = network

        def construct(self, x, label):
            predict = self.network(x)
            return self.loss(predict, label)

    class LeNet5(nn.Cell):
        """ LeNet5 definition """

        def __init__(self):
            super(LeNet5, self).__init__()
            self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
            self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
            self.fc1 = nn.Dense(16 * 5 * 5, 120)
            self.fc2 = nn.Dense(120, 84)
            self.fc3 = nn.Dense(84, 10)
            self.relu = nn.ReLU()
            self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
            self.flatten = P.Flatten()

        def construct(self, x):
            x = self.max_pool2d(self.relu(self.conv1(x)))
            x = self.max_pool2d(self.relu(self.conv2(x)))
            x = self.flatten(x)
            x = self.relu(self.fc1(x))
            x = self.relu(self.fc2(x))
            x = self.fc3(x)
            return x

    input_data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
    label = Tensor(np.ones([1, 10]).astype(np.float32))
    iteration_num = 1
    verification_step = 0

    net = LeNet5()
    loss = nn.SoftmaxCrossEntropyWithLogits()
    momen_opti = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
    train_net = GradWrap(NetWithLossClass(net))
    train_net.set_train()

    for i in range(0, iteration_num):
        # get the gradients
        grads = train_net(input_data, label)
        # update parameters
        success = momen_opti(grads)
        if success is False:
            print("fail to run optimizer")
        # verification
        if i == verification_step:
            fw_output = net(input_data)
            loss_output = loss(fw_output, label)
            print("The loss of %s-th iteration is %s" % (i, loss_output.asnumpy()))