# -*- coding: utf-8 -*-
'''

Train CIFAR10 with PyTorch and Vision Transformers!
written by @kentaroy47, @arutema47

'''

from __future__ import print_function

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 numpy as np

import torchvision
import torchvision.transforms as transforms

import os
import argparse
import pandas as pd
#import csv
import time

import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
dist.init_process_group(backend="nccl", init_method="env://")
local_rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])

from models import *
from utils import progress_bar
from randomaug import RandAugment
from models.vit import ViT
from models.convmixer import ConvMixer

def write_pid_file(pid_file_path):
    '''Write pid file for watching the process later.
       In each round of case, we will write the current pid in the same path.
    '''
    if os.path.exists(pid_file_path):
        os.remove(pid_file_path)
    file_d=open(pid_file_path,"w")
    file_d.write("%s\n" % os.getpid())
    file_d.close()

# parsers
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=1e-4, type=float, help='learning rate') # resnets.. 1e-3, Vit..1e-4
parser.add_argument('--opt', default="adam")
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
parser.add_argument('--noaug', action='store_true', help='disable use randomaug')
parser.add_argument('--noamp', action='store_true', help='disable mixed precision training. for older pytorch versions')
#parser.add_argument('--nowandb', action='store_true', help='disable wandb')
parser.add_argument('--mixup', action='store_true', help='add mixup augumentations')
parser.add_argument('--net', default='vit')
parser.add_argument('--bs', default='512')
parser.add_argument('--size', default="32")
parser.add_argument('--n_epochs', type=int, default='200')
parser.add_argument('--patch', default='4', type=int, help="patch for ViT")
parser.add_argument('--dimhead', default="512", type=int)
parser.add_argument('--convkernel', default='8', type=int, help="parameter for convmixer")
parser.add_argument("--log_dir",
                    type=str,
                    default="/data/flagperf/training/result/",
                    help="Log directory in container.")

args = parser.parse_args()
if dist.get_rank() == 0:
    write_pid_file(args.log_dir)

# take in args
#usewandb = ~args.nowandb
#if usewandb:
#    import wandb
#    watermark = "{}_lr{}".format(args.net, args.lr)
#    wandb.init(project="cifar10-challange",
#            name=watermark)
#    wandb.config.update(args)

bs = int(args.bs)
bs = int(bs / world_size)
imsize = int(args.size)

use_amp = bool(~args.noamp)
aug = args.noaug

#device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = torch.device('cuda', local_rank)
torch.cuda.set_device(device)
best_acc = 0  # best test accuracy
start_epoch = 0  # start from epoch 0 or last checkpoint epoch
global_steps = 0
target_acc = 84.49
final_acc = 0
num_trained_samples = 0

log_file_name = f'rank{local_rank}.out.log'
log_file = open(log_file_name, 'w')

# Data
#print('==> Preparing data..')
if args.net=="vit_timm":
    size = 384
else:
    size = imsize

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.Resize(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(size),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

# Add RandAugment with N, M(hyperparameter)
if aug:  
    N = 2; M = 14;
    transform_train.transforms.insert(0, RandAugment(N, M))

# Prepare dataset
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=False, transform=transform_train)
train_sampler = torch.utils.data.distributed.DistributedSampler(trainset, num_replicas=world_size, rank=local_rank, shuffle=True)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=bs, sampler=train_sampler, num_workers=8)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=False, transform=transform_test)
test_sampler = torch.utils.data.distributed.DistributedSampler(testset)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, sampler=test_sampler,shuffle=False, num_workers=8)

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

# Model factory..
#print('==> Building model..')
# net = VGG('VGG19')
if args.net=='res18':
    net = ResNet18()
elif args.net=='vgg':
    net = VGG('VGG19')
elif args.net=='res34':
    net = ResNet34()
elif args.net=='res50':
    net = ResNet50()
elif args.net=='res101':
    net = ResNet101()
elif args.net=="convmixer":
    # from paper, accuracy >96%. you can tune the depth and dim to scale accuracy and speed.
    net = ConvMixer(256, 16, kernel_size=args.convkernel, patch_size=1, n_classes=10)
elif args.net=="mlpmixer":
    from models.mlpmixer import MLPMixer
    net = MLPMixer(
    image_size = 32,
    channels = 3,
    patch_size = args.patch,
    dim = 512,
    depth = 6,
    num_classes = 10
)
elif args.net=="vit_small":
    from models.vit_small import ViT
    net = ViT(
    image_size = size,
    patch_size = args.patch,
    num_classes = 10,
    dim = int(args.dimhead),
    depth = 6,
    heads = 8,
    mlp_dim = 512,
    dropout = 0.1,
    emb_dropout = 0.1
)
elif args.net=="vit_tiny":
    from models.vit_small import ViT
    net = ViT(
    image_size = size,
    patch_size = args.patch,
    num_classes = 10,
    dim = int(args.dimhead),
    depth = 4,
    heads = 6,
    mlp_dim = 256,
    dropout = 0.1,
    emb_dropout = 0.1
)
elif args.net=="simplevit":
    from models.simplevit import SimpleViT
    net = SimpleViT(
    image_size = size,
    patch_size = args.patch,
    num_classes = 10,
    dim = int(args.dimhead),
    depth = 6,
    heads = 8,
    mlp_dim = 512
)
elif args.net=="vit":
    # ViT for cifar10
    net = ViT(
    image_size = size,
    patch_size = args.patch,
    num_classes = 10,
    dim = int(args.dimhead),
    depth = 6,
    heads = 8,
    mlp_dim = 512,
    dropout = 0.1,
    emb_dropout = 0.1
)
elif args.net=="vit_timm":
    import timm
    net = timm.create_model("vit_base_patch16_384", pretrained=True)
    net.head = nn.Linear(net.head.in_features, 10)
elif args.net=="cait":
    from models.cait import CaiT
    net = CaiT(
    image_size = size,
    patch_size = args.patch,
    num_classes = 10,
    dim = int(args.dimhead),
    depth = 6,   # depth of transformer for patch to patch attention only
    cls_depth=2, # depth of cross attention of CLS tokens to patch
    heads = 8,
    mlp_dim = 512,
    dropout = 0.1,
    emb_dropout = 0.1,
    layer_dropout = 0.05
)
elif args.net=="cait_small":
    from models.cait import CaiT
    net = CaiT(
    image_size = size,
    patch_size = args.patch,
    num_classes = 10,
    dim = int(args.dimhead),
    depth = 6,   # depth of transformer for patch to patch attention only
    cls_depth=2, # depth of cross attention of CLS tokens to patch
    heads = 6,
    mlp_dim = 256,
    dropout = 0.1,
    emb_dropout = 0.1,
    layer_dropout = 0.05
)
elif args.net=="swin":
    from models.swin import swin_t
    net = swin_t(window_size=args.patch,
                num_classes=10,
                downscaling_factors=(2,2,2,1))

# For Multi-GPU
#if 'cuda' in device:
    #print(device)
    #print("using data parallel")
    #net = torch.nn.DataParallel(net) # make parallel
net = net.to(device)
net = DistributedDataParallel(net, device_ids=[device])
cudnn.benchmark = True

if args.resume:
    # Load checkpoint.
    #print('==> Resuming from checkpoint..')
    assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
    checkpoint = torch.load('./checkpoint/{}-ckpt.t7'.format(args.net))
    net.load_state_dict(checkpoint['net'])
    best_acc = checkpoint['acc']
    start_epoch = checkpoint['epoch']

# Loss is CE
criterion = nn.CrossEntropyLoss()

if args.opt == "adam":
    optimizer = optim.Adam(net.parameters(), lr=args.lr)
elif args.opt == "sgd":
    optimizer = optim.SGD(net.parameters(), lr=args.lr)  
    
# use cosine scheduling
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.n_epochs)

##### Training
scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
def train(epoch):
    global num_trained_samples, global_steps
    #print('\nEpoch: %d' % epoch)
    train_sampler.set_epoch(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)
        # Train with amp
        with torch.cuda.amp.autocast(enabled=use_amp):
            outputs = net(inputs)
            loss = criterion(outputs, targets)
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
        optimizer.zero_grad()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()
        num_trained_samples += targets.size(0)
        global_steps += 1
        learning_rate = f'{optimizer.param_groups[0]["lr"]:.9f}'
        loss_str = "%.4f" % (train_loss/(batch_idx+1))
        acc = 100.*correct/total
        step_output = f'[PerfLog] {{"event": "STEP_END", "value": {{"epoch": {epoch+1}, "global_steps": {global_steps},"loss": {loss_str},"accuracy":{acc:.4f},"num_trained_samples": {num_trained_samples}, "learning_rate": {learning_rate}}}}}'
        log_file.write(step_output + '\n')
        print(f'rank {local_rank}: ' + step_output)

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

##### Validation
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()

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

    torch.cuda.synchronize()
    t = torch.tensor([total, correct], device='cuda')
    dist.all_reduce(t)
    total = t[0]
    correct = t[1]
    
    # Save checkpoint.
    acc = 100.*correct/total
    if acc > best_acc:
        if dist.get_rank() == 0:
            #print('Saving..')
            state = {"model": net.state_dict(),
                  "optimizer": optimizer.state_dict(),
                  "scaler": scaler.state_dict()}
            if not os.path.isdir('checkpoint'):
                os.mkdir('checkpoint')
            torch.save(state, './checkpoint/'+args.net+'-{}-ckpt.t7'.format(args.patch))
        best_acc = acc
    
    #os.makedirs("log", exist_ok=True)
    #content = time.ctime() + ' ' + f'Epoch {epoch}, lr: {optimizer.param_groups[0]["lr"]:.7f}, val loss: {test_loss:.5f}, acc: {(acc):.5f}'
    #print(content)
    #with open(f'log/log_{args.net}_patch{args.patch}.txt', 'a') as appender:
    #    appender.write(content + "\n")
    return test_loss, acc, total

#list_loss = []
#list_acc = []

#if usewandb:
#    wandb.watch(net)

training_start = time.time()
training_only = 0
    
net.cuda()
for epoch in range(start_epoch, args.n_epochs):
    start = time.time()
    trainloss = train(epoch)
    epoch_time = time.time() - start
    training_only += epoch_time
    start = time.time()
    val_loss, acc, total= test(epoch)
    eval_time = time.time() - start

    eval_output = f'[PerfLog] {{"event": "EVALUATE_END", "value": {{"global_steps": {global_steps},"eval_loss": {val_loss:.4f},"eval_mlm_accuracy":{acc:.4f},"eval_time": {eval_time:.4f},"epoch_time":{epoch_time:.4f},"num_eval_samples":{total}}}}}'
    log_file.write(eval_output + '\n')
    print(f'rank {local_rank}: ' + eval_output)
    
    if acc >= target_acc:
        final_acc = acc
        break
    
    scheduler.step(epoch-1) # step cosine scheduling
    
    #list_loss.append(val_loss)
    #list_acc.append(acc)
    
    # Log training..
    #if usewandb:
    #    wandb.log({'epoch': epoch, 'train_loss': trainloss, 'val_loss': val_loss, "val_acc": acc, "lr": optimizer.param_groups[0]["lr"],
    #    "epoch_time": time.time()-start})

    # Write out csv..
    #with open(f'log/log_{args.net}_patch{args.patch}.csv', 'w') as f:
    #    writer = csv.writer(f, lineterminator='\n')
    #    writer.writerow(list_loss) 
    #    writer.writerow(list_acc) 
    #print(list_loss)

train_time = time.time() - training_start
samples_sec = num_trained_samples / training_only
train_output = f'[PerfLog] {{"event": "TRAIN_END", "value": {{"accuracy":{final_acc:.4f},"train_time":{train_time:.4f},"samples/sec: {samples_sec:.4f}","num_trained_samples":{num_trained_samples}}}}}'
log_file.write(train_output + '\n')
print(f'rank {local_rank}: ' + train_output)
log_file.close()

# writeout wandb
#if usewandb:
#    wandb.save("wandb_{}.h5".format(args.net))