# 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.
# ============================================================================
import pytest
import numpy as np
import time, math
import mindspore.nn as nn
from mindspore import context, Tensor, ParameterTuple
from mindspore.ops import operations as P
from mindspore.common.initializer import TruncatedNormal
from mindspore.ops import functional as F
from mindspore.ops import composite as C
from mindspore.common import dtype as mstype

from mindspore.nn.wrap.cell_wrapper import WithLossCell
from mindspore.nn.optim import Momentum

np.random.seed(1)

def weight_variable():
    """weight initial"""
    return TruncatedNormal(0.02)


def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
    """weight initial for conv layer"""
    weight = weight_variable()
    return nn.Conv2d(in_channels, out_channels,
                     kernel_size=kernel_size, stride=stride, padding=padding,
                     weight_init=weight, has_bias=False, pad_mode="valid")


def fc_with_initialize(input_channels, out_channels):
    """weight initial for fc layer"""
    weight = weight_variable()
    bias = weight_variable()
    return nn.Dense(input_channels, out_channels, weight, bias)


class LeNet(nn.Cell):
    """
    Lenet network
    Args:
        num_class (int): Num classes, Default: 10.
    Returns:
        Tensor, output tensor
    Examples:
        >>> LeNet(num_class=10)
    """
    def __init__(self, num_class=10):
        super(LeNet, self).__init__()
        self.num_class = num_class
        self.batch_size = 32
        self.conv1 = conv(1, 6, 5)
        self.conv2 = conv(6, 16, 5)
        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
        self.fc2 = fc_with_initialize(120, 84)
        self.fc3 = fc_with_initialize(84, self.num_class)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        self.reshape = P.Reshape()

    def construct(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.reshape(x, (self.batch_size, -1))
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.fc3(x)
        return x


class CrossEntropyLoss(nn.Cell):
    """
    Define loss for network
    """
    def __init__(self):
        super(CrossEntropyLoss, self).__init__()
        self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
        self.mean = P.ReduceMean()
        self.one_hot = P.OneHot()
        self.on_value = Tensor(1.0, mstype.float32)
        self.off_value = Tensor(0.0, mstype.float32)
        self.num = Tensor(32.0, mstype.float32)

    def construct(self, logits, label):
        label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
        loss = self.cross_entropy(logits, label)[0]
        loss = P.RealDiv()(P.ReduceSum()(loss, -1), self.num)
        return loss


class GradWrap(nn.Cell):
    """
    GradWrap definition
    """
    def __init__(self, network):
        super(GradWrap, self).__init__()
        self.network = network
        self.weights = ParameterTuple(filter(lambda x: x.requires_grad, network.get_parameters()))

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


@pytest.mark.level0
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_single
def test_ascend_pynative_lenet():
    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")

    epoch_size = 20
    batch_size = 32
    inputs = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32))
    labels = Tensor(np.ones([batch_size]).astype(np.int32))

    net = LeNet()
    criterion = CrossEntropyLoss()
    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)

    net_with_criterion = WithLossCell(net, criterion)
    train_network = GradWrap(net_with_criterion)
    train_network.set_train()
    total_time = 0

    for epoch in range(0, epoch_size):
        start_time = time.time()
        fw_output = net(inputs)
        loss_output = criterion(fw_output, labels)
        grads = train_network(inputs, labels)
        success = optimizer(grads)
        end_time = time.time()
        cost_time = end_time - start_time
        total_time = total_time + cost_time

        print("======epoch: ", epoch, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
    assert(total_time < 20.0)
    assert(loss_output.asnumpy() < 0.01)