# -*- coding: utf-8 -*-
# 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.

'''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
import torch.distributed as dist
from torch_npu.contrib import transfer_to_npu

import torchvision
import torchvision.transforms as transforms

import os
import argparse

from models import *
from bugfix import progress_bar

import apex
from apex import amp


parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--batch_size', default=128, type=int, help='train batch size')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--n_epochs', type=int, default='200', help='total training epochs')
parser.add_argument('--num_workers', type=int, default='2', help='dataset parallel workers num')
parser.add_argument('--resume', '-r', action='store_true',
                    help='resume from checkpoint')

# add extra parameter for adp
parser.add_argument('--local_rank', type=int, default=0, help='local rank in ddp')
parser.add_argument('--world_size', type=int, default=1, help='world size in ddp')

args = parser.parse_args()

local_rank = args.local_rank
torch.npu.set_device(local_rank)
dist.init_process_group(backend='hccl', init_method='tcp://127.0.0.1:23333', 
                        world_size=args.world_size, rank=args.local_rank)

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
train_epoch = args.n_epochs

# 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)),
])

trainset = torchvision.datasets.CIFAR10(
    root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(
    trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)

testset = torchvision.datasets.CIFAR10(
    root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(
    testset, batch_size=100, shuffle=False, num_workers=args.num_workers)

classes = ('plane', 'car', 'bird', 'cat', 'deer',
           'dog', 'frog', 'horse', 'ship', 'truck')

# Model
print('==> Building model..')

net = ResNet50()
net = net.npu()

if args.resume:
    # Load checkpoint.
    print('==> Resuming from checkpoint..')
    assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
    checkpoint = torch.load('./checkpoint/ckpt.pth')
    net.load_state_dict(checkpoint['net'])
    best_acc = checkpoint['acc']
    start_epoch = checkpoint['epoch']

criterion = nn.CrossEntropyLoss()
optimizer = apex.optimizers.NpuFusedSGD(net.parameters(), lr=args.lr,
                                             momentum=0.9, weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)
net, optimizer = amp.initialize(net, optimizer, opt_level='O2', combine_grad=True, loss_scale=128.)
net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[local_rank], broadcast_buffers=False)

# Training
def train(epoch):
    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)
        with amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward()
        optimizer.step()

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

        if torch.distributed.get_rank() == 0:
            avg_step_time = progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                     % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
        else: 
            avg_step_time = None

    print(f"Train: average_step_time: {avg_step_time} train_loss: {train_loss/(batch_idx+1)}", flush=True)


def 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 torch.distributed.get_rank() == 0:
                avg_step_time = progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                         % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
            else: 
                avg_step_time = None

    # Save checkpoint.
    acc = 100.*correct/total
    if acc > best_acc:
        print('Saving..')
        state = {
            'net': net.state_dict(),
            'acc': acc,
            'epoch': epoch,
        }
        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')
        torch.save(state, './checkpoint/ckpt.pth')
        best_acc = acc

    print(f"Val: average_step_time: {avg_step_time} val_loss: {test_loss/(batch_idx+1)} ",
        f"acc: {acc} best_acc: {best_acc}", flush=True)


for epoch in range(start_epoch, start_epoch+train_epoch):
    train(epoch)
    test(epoch)
    scheduler.step()