# Copyright 2021 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.
# ============================================================================

import argparse
import os
import random
import shutil
import time
import warnings

import apex
from apex import amp

import numpy as np
import torch
import torch.npu
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import collections
from models.nasnet_mobile import nasnetamobile

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate pretrainedmodels on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained pretrainedmodels')
parser.add_argument('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')

parser.add_argument('--device', default='npu', type=str, help='npu or gpu')
parser.add_argument('--addr', default='10.136.181.115',
                    type=str, help='master addr')
parser.add_argument('--device-list', default='0,1,2,3,4,5,6,7', type=str, help='device id list')
parser.add_argument('--amp', default=False, action='store_true',
                    help='use amp to train the pretrainedmodels')
parser.add_argument('--loss-scale', default=1024., type=float,
                    help='loss scale using in amp, default -1 means dynamic')
parser.add_argument('--opt-level', default='O2', type=str,
                    help='loss scale using in amp, default -1 means dynamic')
parser.add_argument('--prof', default=False, action='store_true',
                    help='use profiling to evaluate the performance of pretrainedmodels')

parser.add_argument('--do-not-preserve-aspect-ratio',
                    dest='preserve_aspect_ratio',
                    help='do not preserve the aspect ratio when resizing an image',
                    action='store_false')
parser.set_defaults(preserve_aspect_ratio=True)

parser.add_argument('--label-smoothing',
                    default=0.0,
                    type=float,
                    metavar='S',
                    help='label smoothing')
parser.add_argument('--warm_up_epochs', default=0, type=int,
                    help='warm up')

best_acc1 = 0

def proc_node_module(checkpoint, AttrName):
    new_state_dict = collections.OrderedDict()
    for k, v in checkpoint[AttrName].items():
        if(k[0:7] == "module."):
            name = k[7:]
        else:
            name = k[0:]
        new_state_dict[name] = v
    return new_state_dict


def device_id_to_process_device_map(device_list):
    devices = device_list.split(",")
    devices = [int(x) for x in devices]
    devices.sort()

    process_device_map = dict()
    for process_id, device_id in enumerate(devices):
        process_device_map[process_id] = device_id

    return process_device_map


def main():
    args = parser.parse_args()

    os.environ['MASTER_ADDR'] = args.addr
    os.environ['MASTER_PORT'] = '29688'

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed

    args.process_device_map = device_id_to_process_device_map(args.device_list)

    if args.device == 'npu':
        ngpus_per_node = len(args.process_device_map)
    else:
        ngpus_per_node = torch.cuda.device_count()
    # ngpus_per_node = torch.cuda.device_count()
    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
    global best_acc1
    # args.gpu = gpu
    args.gpu = args.process_device_map[gpu]

    if args.gpu is not None:
        print("Use NPU: {} for training".format(args.gpu))

    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * ngpus_per_node + gpu
        ###### modify 8 ######
        if args.device == 'npu':
            print(args.dist_backend, args.world_size, args.rank)
            dist.init_process_group(backend=args.dist_backend,  # init_method=args.dist_url,
                                    world_size=args.world_size, rank=args.rank)
        else:
            dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                    world_size=args.world_size, rank=args.rank)

        ###### modify 8 end ######

    # create pretrainedmodels
    if args.pretrained:
        print("=> using pre-trained pretrainedmodel '{}'".format("nasnetamobile"))
        model = nasnetamobile(num_classes=1000, pretrained=True)
    else:
        print("=> creating model '{}'".format("nasnetamobile"))
        model = nasnetamobile(num_classes=1000)

    ############## npu modify begin #############
    loc = 'npu:{}'.format(args.gpu)
    torch.npu.set_device(loc)
    args.batch_size = int(args.batch_size / ngpus_per_node)
    args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
    ############## npu modify end #############


    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')
    normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
                                     std=[0.5, 0.5, 0.5])

    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    
    val_dataset = datasets.ImageFolder(
        valdir,
        transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ]))

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_sampler = None
    ###### modify 7 ######
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=False, sampler=train_sampler, drop_last=True)

    val_loader = torch.utils.data.DataLoader(
        val_dataset, batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    model = model.to(loc)
    # define loss function (criterion) and optimizer
    loss = nn.CrossEntropyLoss().to(loc)
    if args.label_smoothing > 0.0:
        loss = LabelSmoothing(loc, args.label_smoothing)
    criterion = loss
    optimizer = apex.optimizers.NpuFusedSGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                nesterov=True,
                                weight_decay=args.weight_decay)

    if args.amp:
        model, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level,
                        loss_scale=args.loss_scale, combine_grad=True)
    
    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume, map_location=loc)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            
            checkpoint['state_dict'] = proc_node_module(checkpoint, 'state_dict')
            model.load_state_dict(checkpoint['state_dict'])
            print("model over")
            optimizer.load_state_dict(checkpoint['optimizer'])
            if args.amp:
                amp.load_state_dict(checkpoint['amp'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], broadcast_buffers=False)

    cudnn.benchmark = True

    if args.evaluate:
        validate(val_loader, model, criterion, args, ngpus_per_node)
        return
    ###### modify 3 ######
    if args.prof:
        profiling(train_loader, model, criterion, optimizer, args)
        return
    ###### modify 3 end ######

    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, epoch, args)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, args, ngpus_per_node)

        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, args, ngpus_per_node)

        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                and args.rank % ngpus_per_node == 0):
            if args.amp:
                save_checkpoint({
                    'epoch': epoch + 5,
                    'arch': "nasnetamobile",
                    'state_dict': model.state_dict(),
                    'best_acc1': best_acc1,
                    'optimizer': optimizer.state_dict(),
                    'amp': amp.state_dict(),
                }, is_best)
            else:
                save_checkpoint({
                    'epoch': epoch + 5,
                    'arch': "nasnetamobile",
                    'state_dict': model.state_dict(),
                    'best_acc1': best_acc1,
                    'optimizer': optimizer.state_dict(),
                }, is_best)


def profiling(data_loader, model, criterion, optimizer, args):
    # switch to train mode
    model.train()

    def update(model, images, target, optimizer):
        output = model(images)
        loss = criterion(output, target)
        if args.amp:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            loss.backward()
        optimizer.zero_grad()
        optimizer.step()

    for step, (images, target) in enumerate(data_loader):
        if args.device == 'npu':
            loc = 'npu:{}'.format(args.gpu)
            # loc = CALCULATE_DEVICE
            images = images.to(loc, non_blocking=True).to(torch.float)
            target = target.to(torch.int32).to(loc, non_blocking=True)
        else:
            images = images.cuda(args.gpu, non_blocking=True)
            target = target.cuda(args.gpu, non_blocking=True)

        if step < 5:
            update(model, images, target, optimizer)
        else:
            if args.device == 'npu':
                with torch.autograd.profiler.profile(use_npu=True) as prof:
                    update(model, images, target, optimizer)
            else:
                with torch.autograd.profiler.profile(use_cuda=True) as prof:
                    update(model, images, target, optimizer)
            break

    prof.export_chrome_trace("output.prof")


def train(train_loader, model, criterion, optimizer, epoch, args, ngpus_per_node):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        loc = 'npu:{}'.format(args.gpu)
        images, target = images.to(loc, non_blocking=False), target.to(loc, non_blocking=False)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        ######  modify 2 ######
        if args.amp:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            loss.backward()

        ######  modify 2 end ######
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

    ###### modify 4 ######
        if i % args.print_freq == 0:
            if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                        and args.rank % ngpus_per_node == 0):
                progress.display(i)

    if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                and args.rank % ngpus_per_node == 0):
        if batch_time.avg:
            print("[npu id:", args.gpu, "]", "batch_size:", args.world_size * args.batch_size,
                  'Time: {:.3f}'.format(batch_time.avg), '* FPS@all {:.3f}'.format(
                    args.batch_size * args.world_size / batch_time.avg))
    ###### modify 4 end ######


def validate(val_loader, model, criterion, args, ngpus_per_node):
    ###### modify 5 ######
    batch_time = AverageMeter('Time', ':6.3f', start_count_index= 5)
    ###### modify 5 end ######
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            loc = 'npu:{}'.format(args.gpu)
            images, target = images.to(loc, non_blocking=False), target.to(loc, non_blocking=False)

            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                            and args.rank % ngpus_per_node == 0):
                    progress.display(i)

        # log
        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                    and args.rank % ngpus_per_node == 0):
            print("[npu id:", args.gpu, "]", '[AVG-ACC] * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
                  .format(top1=top1, top5=top5))

    return top1.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=':f', start_count_index=2):
        self.name = name
        self.fmt = fmt
        self.reset()
        self.start_count_index = start_count_index

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        if self.count == 0:
            self.N = n

        self.val = val
        self.count += n
        if self.count > (self.start_count_index * self.N):
            self.sum += val * n
            self.avg = self.sum / (self.count - self.start_count_index * self.N)

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    if args.warm_up_epochs > 0 and epoch < args.warm_up_epochs:
        lr = args.lr * ((epoch + 1) / (args.warm_up_epochs + 1))
    else:
        alpha = 0
        cosine_decay = 0.5 * (
                1 + np.cos(np.pi * (epoch - args.warm_up_epochs) / (args.epochs - args.warm_up_epochs)))
        decayed = (1 - alpha) * cosine_decay + alpha
        lr = args.lr * decayed

    print("=> Epoch[%d] Setting lr: %.4f" % (epoch, lr))

    for param_group in optimizer.param_groups:
        param_group['lr'] = lr
    return lr


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


class LabelSmoothing(nn.Module):
    """
    NLL loss with label smoothing.
    """
    def __init__(self, loc, smoothing=0.0):
        """
        Constructor for the LabelSmoothing module.

        :param smoothing: label smoothing factor
        """
        super(LabelSmoothing, self).__init__()
        self.confidence = 1.0 - smoothing
        self.smoothing = smoothing
        self.device = loc

    def forward(self, x, target):
        target = target.to(torch.int64)

        logprobs = torch.nn.functional.log_softmax(x, dim=-1)
        nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1).to(torch.int64))
        nll_loss = nll_loss.squeeze(1)
        smooth_loss = -logprobs.mean(dim=-1)
        loss = self.confidence * nll_loss + self.smoothing * smooth_loss
        return loss.mean()

if __name__ == '__main__':
    main()