# Copyright 2020 Adap GmbH. All Rights Reserved.
#
# 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.
# ==============================================================================
"""PyTorch CIFAR-10 image classification.

The code is generally adapted from 'PyTorch: A 60 Minute Blitz'. Further
explanations are given in the official PyTorch tutorial:

https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
"""


# mypy: ignore-errors
# pylint: disable=W0223


from typing import Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch import Tensor

DATA_ROOT = "~/.flower/data/cifar-10"

# pylint: disable=unsubscriptable-object
class Net(nn.Module):
    """Simple CNN adapted from 'PyTorch: A 60 Minute Blitz'."""

    def __init__(self) -> None:
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    # pylint: disable=arguments-differ,invalid-name
    def forward(self, x: Tensor) -> Tensor:
        """Compute forward pass."""
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


# pylint: disable=unused-argument
def load_data() -> Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader]:
    """Load CIFAR-10 (training and test set)."""
    transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    )

    # Training set
    trainset = torchvision.datasets.CIFAR10(
        root=DATA_ROOT, train=True, download=True, transform=transform
    )
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)

    # Test set
    testset = torchvision.datasets.CIFAR10(
        root=DATA_ROOT, train=False, download=True, transform=transform
    )
    testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)

    return trainloader, testloader


def train(
    net: Net,
    trainloader: torch.utils.data.DataLoader,
    epochs: int,
    device: torch.device,  # pylint: disable=no-member
) -> None:
    """Train the network."""
    # Define loss and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

    print(f"Training {epochs} epoch(s) w/ {len(trainloader)} batches each")

    # Train the network
    for epoch in range(epochs):  # loop over the dataset multiple times
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            images, labels = data[0].to(device), data[1].to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 100 == 99:  # print every 100 mini-batches
                print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0


def test(
    net: Net,
    testloader: torch.utils.data.DataLoader,
    device: torch.device,  # pylint: disable=no-member
) -> Tuple[float, float]:
    """Validate the network on the entire test set."""
    criterion = nn.CrossEntropyLoss()
    correct = 0
    total = 0
    loss = 0.0
    with torch.no_grad():
        for data in testloader:
            images, labels = data[0].to(device), data[1].to(device)
            outputs = net(images)
            loss += criterion(outputs, labels).item()
            _, predicted = torch.max(outputs.data, 1)  # pylint: disable=no-member
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = correct / total
    return loss, accuracy


def main():
    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("Central PyTorch Training")
    print("Load data")
    trainloader, testloader = load_data()
    print("Start training")
    train(net=Net(), trainloader=trainloader, epochs=2, device=DEVICE)
    print("Start Testing")
    loss, accuracy = test(net=Net(), testloader=testloader, device=DEVICE)
    print("Loss: ", loss)
    print("Accuracy: ", accuracy)


if __name__ == "__main__":
    main()
