'''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from nbdt import data, analysis, loss, models

import torchvision
import torchvision.transforms as transforms

import os
import argparse
import onnxruntime as ort
import numpy as np

from nbdt.utils import (
    progress_bar, generate_fname, generate_kwargs, Colors, maybe_install_wordnet
)

maybe_install_wordnet()

datasets = ('CIFAR10', 'CIFAR100') + data.imagenet.names + data.custom.names


parser = argparse.ArgumentParser(description='PyTorch CIFAR Training')
parser.add_argument('--batch-size', default=512, type=int,
                    help='Batch size used for training')
parser.add_argument('--epochs', '-e', default=200, type=int,
                    help='By default, lr schedule is scaled accordingly')
parser.add_argument('--dataset', default='CIFAR10', choices=datasets)
parser.add_argument('--arch', default='ResNet18', choices=list(models.get_model_choices()))
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')

# extra general options for main script
parser.add_argument('--path-resume', default='',
                    help='Overrides checkpoint path generation')
parser.add_argument('--name', default='',
                    help='Name of experiment. Used for checkpoint filename')
parser.add_argument('--pretrained', action='store_true',
                    help='Download pretrained model. Not all models support this.')
parser.add_argument('--eval', help='eval only', action='store_true')
parser.add_argument('--onnx', help='export only', action='store_true')

# options specific to this project and its dataloaders
parser.add_argument('--loss', choices=loss.names, default='CrossEntropyLoss')
parser.add_argument('--analysis', choices=analysis.names, help='Run analysis after each epoch')
parser.add_argument('--input-size', type=int,
                    help='Set transform train and val. Samples are resized to '
                    'input-size + 32.')
parser.add_argument('--lr-decay-every', type=int, default=0)

data.custom.add_arguments(parser)
loss.add_arguments(parser)
analysis.add_arguments(parser)

args = parser.parse_args()

loss.set_default_values(args)

device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0  # best test accuracy
start_epoch = 0  # start from epoch 0 or last checkpoint epoch

# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

dataset = getattr(data, args.dataset)

if args.dataset in ('TinyImagenet200', 'Imagenet1000'):
    default_input_size = 64 if args.dataset == 'TinyImagenet200' else 224
    input_size = args.input_size or default_input_size
    transform_train = dataset.transform_train(input_size)
    transform_test = dataset.transform_val(input_size)
elif args.input_size is not None and args.input_size > 32:
    transform_train = transforms.Compose([
        transforms.Resize(args.input_size + 32),
        transforms.RandomCrop(args.input_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    transform_test = transforms.Compose([
        transforms.Resize(args.input_size + 32),
        transforms.CenterCrop(args.input_size),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

dataset_kwargs = generate_kwargs(args, dataset,
    name=f'Dataset {args.dataset}',
    keys=data.custom.keys,
    globals=globals())

trainset = dataset(**dataset_kwargs, root='./data', train=True, download=True, transform=transform_train)
testset = dataset(**dataset_kwargs, root='./data', train=False, download=True, transform=transform_test)

assert trainset.classes == testset.classes, (trainset.classes, testset.classes)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

Colors.cyan(f'Training with dataset {args.dataset} and {len(trainset.classes)} classes')

# Model
print('==> Building model..')
model = getattr(models, args.arch)
model_kwargs = {'num_classes': len(trainset.classes) }

if args.pretrained:
    print('==> Loading pretrained model..')
    try:
        net = model(pretrained=True, dataset=args.dataset, **model_kwargs)
    except TypeError as e:  # likely because `dataset` not allowed arg
        print(e)

    try:
        net = model(pretrained=True, **model_kwargs)
    except Exception as e:
        Colors.red(f'Fatal error: {e}')
        exit()
else:
    net = model(**model_kwargs)

net = net.to(device)
if device == 'cuda':
    net = torch.nn.DataParallel(net)
    cudnn.benchmark = True

checkpoint_fname = generate_fname(**vars(args))
checkpoint_path = './checkpoint/{}.pth'.format(checkpoint_fname)
print(f'==> Checkpoints will be saved to: {checkpoint_path}')


# TODO(alvin): fix checkpoint structure so that this isn't neededd
def load_state_dict(state_dict):
    try:
        net.load_state_dict(state_dict)
    except RuntimeError as e:
        if 'Missing key(s) in state_dict:' in str(e):
            net.load_state_dict({
                key.replace('module.', '', 1): value
                for key, value in state_dict.items()
            })


resume_path = args.path_resume or checkpoint_path
if args.resume:
    # Load checkpoint.
    print('==> Resuming from checkpoint..')
    assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
    if not os.path.exists(resume_path):
        print('==> No checkpoint found. Skipping...')
    else:
        checkpoint = torch.load(resume_path, map_location=torch.device(device))

        if 'net' in checkpoint:
            load_state_dict(checkpoint['net'])
            best_acc = checkpoint['acc']
            start_epoch = checkpoint['epoch']
            Colors.cyan(f'==> Checkpoint found for epoch {start_epoch} with accuracy '
                  f'{best_acc} at {resume_path}')
        else:
            load_state_dict(checkpoint)
            Colors.cyan(f'==> Checkpoint found at {resume_path}')


criterion = nn.CrossEntropyLoss()
class_criterion = getattr(loss, args.loss)
loss_kwargs = generate_kwargs(args, class_criterion,
    name=f'Loss {args.loss}',
    keys=loss.keys,
    globals=globals())
criterion = class_criterion(**loss_kwargs)

optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)

def adjust_learning_rate(epoch, lr):
    if args.lr_decay_every:
        steps = epoch // args.lr_decay_every
        return lr / (10 ** steps)
    if epoch <= 150 / 350. * args.epochs:  # 32k iterations
        return lr
    elif epoch <= 250 / 350. * args.epochs:  # 48k iterations
        return lr/10
    else:
        return lr/100

# Training
def train(epoch, analyzer):
    analyzer.start_train(epoch)
    lr = adjust_learning_rate(epoch, args.lr)
    optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)

    print('\nEpoch: %d' % epoch)
    net.train()
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

        stat = analyzer.update_batch(outputs, targets)
        extra = f'| {stat}' if stat else ''

        progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d) %s'
            % (train_loss/(batch_idx+1), 100.*correct/total, correct, total, extra))

    analyzer.end_train(epoch)

def test(epoch, analyzer, checkpoint=True):
    analyzer.start_test(epoch)

    global best_acc
    net.eval()
    test_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(testloader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = net(inputs)
            loss = criterion(outputs, targets)

            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

            if device == 'cuda':
                predicted = predicted.cpu()
                targets = targets.cpu()

            stat = analyzer.update_batch(outputs, targets)
            extra = f'| {stat}' if stat else ''

            progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d) %s'
                % (test_loss/(batch_idx+1), 100.*correct/total, correct, total, extra))

    # Save checkpoint.
    acc = 100.*correct/total
    print("Accuracy: {}, {}/{}".format(acc, correct, total))
    if acc > best_acc and checkpoint:
        state = {
            'net': net.state_dict(),
            'acc': acc,
            'epoch': epoch,
        }
        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')

        print(f'Saving to {checkpoint_fname} ({acc})..')
        torch.save(state, f'./checkpoint/{checkpoint_fname}.pth')
        best_acc = acc

    analyzer.end_test(epoch)


class_analysis = getattr(analysis, args.analysis or 'Noop')
analyzer_kwargs = generate_kwargs(args, class_analysis,
    name=f'Analyzer {args.analysis}',
    keys=analysis.keys,
    globals=globals())
analyzer = class_analysis(**analyzer_kwargs)


if args.onnx:
    if not args.resume and not args.pretrained:
        Colors.red(' * Warning: Model is not loaded from checkpoint. '
        'Use --resume or --pretrained (if supported)')

    fname = f"out/{checkpoint_fname}.onnx"
    dummy_input = torch.randn(1, 3, 32, 32)
    torch.onnx.export(
        net, dummy_input, fname,
        input_names=["x"], output_names=["outputs"])
    print(f"=> Wrote ONNX export to {fname}")

    outputs_torch = net(dummy_input)
    outputs_torch = outputs_torch.detach().numpy()

    ort_session = ort.InferenceSession(fname)
    outputs_onnx = ort_session.run(None, {
        'x': dummy_input.numpy()
    })

    if (outputs_torch == outputs_onnx).all():
        Colors.green("=> ONNX export check succeeded: Outputs match.")
    else:
        Colors.red("=> ONNX export check failed: Outputs do not match.")

    exit()


if args.eval:
    if not args.resume and not args.pretrained:
        Colors.red(' * Warning: Model is not loaded from checkpoint. '
        'Use --resume or --pretrained (if supported)')

    analyzer.start_epoch(0)
    test(0, analyzer, checkpoint=False)
    exit()

for epoch in range(start_epoch, args.epochs):
    analyzer.start_epoch(epoch)
    train(epoch, analyzer)
    test(epoch, analyzer)
    analyzer.end_epoch(epoch)

if args.epochs == 0:
    analyzer.start_epoch(0)
    test(0, analyzer)
    analyzer.end_epoch(0)
print(f'Best accuracy: {best_acc} // Checkpoint name: {checkpoint_fname}')