# 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 model train """
import numpy as np
import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore.common.initializer import initializer
from mindspore import Tensor, Parameter, Model
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.optim import Momentum

# fn is a funcation use i as input
def lr_gen(fn, epoch_size):
    for i in range(epoch_size):
        yield fn(i)

def me_train_tensor(net, input_np, label_np, epoch_size=2):
    """me_train_tensor"""
    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr_gen(lambda i: 0.1, epoch_size), 0.9, 0.01, 1024)
    Model(net, loss, opt)
    _network = nn.WithLossCell(net, loss)
    _train_net = nn.TrainOneStepCell(_network, opt)
    _train_net.set_train()
    label_np = np.argmax(label_np, axis=-1).astype(np.int32)
    for epoch in range(0, epoch_size):
        print(f"epoch %d"%(epoch))
        _train_net(Tensor(input_np), Tensor(label_np))


def test_bias_add(test_with_simu):
    """test_bias_add"""
    import mindspore.context as context
    is_pynative_mode = (context.get_context("mode") == context.PYNATIVE_MODE)
    # training api is implemented under Graph mode
    if is_pynative_mode:
        context.set_context(mode=context.GRAPH_MODE)
    if test_with_simu:
        return

    class Net(nn.Cell):
        """Net definition"""
        def __init__(self,
                     output_channels,
                     bias_init='zeros',
                     ):
            super(Net, self).__init__()
            self.biasAdd = P.BiasAdd()

            if isinstance(bias_init, Tensor):
                if bias_init.dim() != 1 or bias_init.shape()[0] != output_channels:
                    raise ValueError("bias_init shape error")

            self.bias = Parameter(initializer(
                bias_init, [output_channels]), name="bias")

        def construct(self, input_x):
            return self.biasAdd(input_x, self.bias)

    bias_init = Tensor(np.ones([3]).astype(np.float32))
    input_np = np.ones([1, 3, 3, 3], np.float32)
    label_np = np.ones([1, 3, 3, 3], np.int32) * 2
    me_train_tensor(Net(3, bias_init=bias_init), input_np, label_np)


def test_conv(test_with_simu):
    """test_conv"""
    import mindspore.context as context
    is_pynative_mode = (context.get_context("mode") == context.PYNATIVE_MODE)
    # training api is implemented under Graph mode
    if is_pynative_mode:
        context.set_context(mode=context.GRAPH_MODE)
    if test_with_simu:
        return

    class Net(nn.Cell):
        "Net definition"""
        def __init__(self,
                     cin,
                     cout,
                     kernel_size):
            super(Net, self).__init__()
            Tensor(np.ones([6, 3, 3, 3]).astype(np.float32) * 0.01)
            self.conv = nn.Conv2d(cin,
                                  cout,
                                  kernel_size)

        def construct(self, input_x):
            return self.conv(input_x)

    net = Net(3, 6, (3, 3))
    input_np = np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01
    label_np = np.ones([1, 6, 32, 32]).astype(np.int32)
    me_train_tensor(net, input_np, label_np)


def test_net():
    """test_net"""
    import mindspore.context as context
    is_pynative_mode = (context.get_context("mode") == context.PYNATIVE_MODE)
    # training api is implemented under Graph mode
    if is_pynative_mode:
        context.set_context(mode=context.GRAPH_MODE)

    class Net(nn.Cell):
        """Net definition"""
        def __init__(self):
            super(Net, self).__init__()
            Tensor(np.ones([64, 3, 7, 7]).astype(np.float32) * 0.01)
            self.conv = nn.Conv2d(3, 64, (7, 7), pad_mode="same", stride=2)
            self.relu = nn.ReLU()
            self.bn = nn.BatchNorm2d(64)
            self.mean = P.ReduceMean(keep_dims=True)
            self.flatten = nn.Flatten()
            self.dense = nn.Dense(64, 12)

        def construct(self, input_x):
            output = input_x
            output = self.conv(output)
            output = self.bn(output)
            output = self.relu(output)
            output = self.mean(output, (-2, -1))
            output = self.flatten(output)
            output = self.dense(output)
            return output

    net = Net()
    input_np = np.ones([32, 3, 224, 224]).astype(np.float32) * 0.01
    label_np = np.ones([32, 12]).astype(np.int32)
    me_train_tensor(net, input_np, label_np)

def test_bn():
    """test_bn"""
    import mindspore.context as context
    is_pynative_mode = (context.get_context("mode") == context.PYNATIVE_MODE)
    # training api is implemented under Graph mode
    if is_pynative_mode:
        context.set_context(mode=context.GRAPH_MODE)

    class Net(nn.Cell):
        """Net definition"""
        def __init__(self, cin, cout):
            super(Net, self).__init__()
            self.bn = nn.BatchNorm2d(cin)
            self.flatten = nn.Flatten()
            self.dense = nn.Dense(cin, cout)

        def construct(self, input_x):
            output = input_x
            output = self.bn(output)
            output = self.flatten(output)
            output = self.dense(output)
            return output

    net = Net(2048, 16)
    input_np = np.ones([32, 2048, 1, 1]).astype(np.float32) * 0.01
    label_np = np.ones([32, 16]).astype(np.int32)
    me_train_tensor(net, input_np, label_np)