# 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_training """
import numpy as np

import mindspore.nn as nn
from mindspore import context
from mindspore.common.tensor import Tensor
from mindspore.nn import WithGradCell, WithLossCell
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P
from mindspore.train.model import Model
from ..ut_filter import non_graph_engine


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


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


@non_graph_engine
def test_loss_cell_wrapper():
    """ test_loss_cell_wrapper """
    data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
    label = Tensor(np.ones([1, 10]).astype(np.float32))
    net = LeNet5()
    loss_fn = nn.SoftmaxCrossEntropyWithLogits()
    loss_net = WithLossCell(net, loss_fn)
    loss_out = loss_net(data, label)
    assert loss_out.asnumpy().dtype == 'float32' or loss_out.asnumpy().dtype == 'float64'


@non_graph_engine
def test_grad_cell_wrapper():
    """ test_grad_cell_wrapper """
    data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
    label = Tensor(np.ones([1, 10]).astype(np.float32))
    dout = Tensor(np.ones([1]).astype(np.float32))
    net = LeNet5()
    loss_fn = nn.SoftmaxCrossEntropyWithLogits()
    grad_net = WithGradCell(net, loss_fn, dout)
    gradients = grad_net(data, label)
    assert isinstance(gradients[0].asnumpy()[0][0][0][0], (np.float32, np.float64))