# Copyright 2023 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 get func graph"""
import numpy as np

import mindspore.nn as nn
from mindspore import context
from mindspore.common.tensor import Tensor
from mindspore.common.initializer import TruncatedNormal
from mindspore.common.parameter import ParameterTuple
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.train.serialization import _get_funcgraph


def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
    weight = TruncatedNormal(0.02)
    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 = TruncatedNormal(0.02)
    bias = TruncatedNormal(0.02)
    return nn.Dense(input_channels, out_channels, weight, bias)


class LeNet5FunGraph(nn.Cell):
    def __init__(self):
        super(LeNet5FunGraph, self).__init__()
        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, 10)
        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 WithLossCellFuncGrpah(nn.Cell):
    def __init__(self, network):
        super(WithLossCellFuncGrpah, self).__init__(auto_prefix=False)
        self.loss = nn.SoftmaxCrossEntropyWithLogits()
        self.network = network

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


class TrainOneStepCellFuncGraph(nn.Cell):
    def __init__(self, network):
        super(TrainOneStepCellFuncGraph, self).__init__(auto_prefix=False)
        self.network = network
        self.network.set_train()
        self.weights = ParameterTuple(network.trainable_params())
        self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
        self.hyper_map = C.HyperMap()
        self.grad = C.GradOperation(get_by_list=True)

    def construct(self, x, label):
        weights = self.weights
        grads = self.grad(self.network, weights)(x, label)
        return self.optimizer(grads)


def test_get_func_graph_lenet():
    """
    Feature: Compile Lenet and get func_graph
    Description: Test get_func_graph API to compile Lenet and get func_graph
    Expectation: get successfully
    """
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    network = LeNet5FunGraph()
    network.set_train()
    predict = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
    label = Tensor(np.zeros([32, 10]).astype(np.float32))
    net = TrainOneStepCellFuncGraph(WithLossCellFuncGrpah(network))
    func_graph = _get_funcgraph(net, predict, label)
    assert func_graph
