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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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# python ./exps/algos-v2/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
# python ./exps/algos-v2/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
# python ./exps/algos-v2/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed 777
####
# python ./exps/algos-v2/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
# python ./exps/algos-v2/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
# python ./exps/algos-v2/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo fbv2 --rand_seed 777
####
# python ./exps/algos-v2/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777 --use_api 0
# python ./exps/algos-v2/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777
# python ./exps/algos-v2/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tunas --arch_weight_decay 0 --rand_seed 777
######################################################################################
import os, sys, time, random, argparse
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from datasets     import get_datasets, get_nas_search_loaders
from procedures   import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
from utils        import count_parameters_in_MB, obtain_accuracy
from log_utils    import AverageMeter, time_string, convert_secs2time
from models       import get_cell_based_tiny_net, get_search_spaces
from nats_bench   import create


# Ad-hoc for TuNAS
class ExponentialMovingAverage(object):
  """Class that maintains an exponential moving average."""

  def __init__(self, momentum):
    self._numerator   = 0
    self._denominator = 0
    self._momentum    = momentum

  def update(self, value):
    self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value
    self._denominator = self._momentum * self._denominator + (1 - self._momentum)

  @property
  def value(self):
    """Return the current value of the moving average"""
    return self._numerator / self._denominator

RL_BASELINE_EMA = ExponentialMovingAverage(0.95)


def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, algo, epoch_str, print_freq, logger):
  data_time, batch_time = AverageMeter(), AverageMeter()
  base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
  arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
  end = time.time()
  network.train()
  for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
    scheduler.update(None, 1.0 * step / len(xloader))
    base_inputs = base_inputs.cuda(non_blocking=True)
    arch_inputs = arch_inputs.cuda(non_blocking=True)
    base_targets = base_targets.cuda(non_blocking=True)
    arch_targets = arch_targets.cuda(non_blocking=True)
    # measure data loading time
    data_time.update(time.time() - end)
    
    # Update the weights
    network.zero_grad()
    _, logits, _ = network(base_inputs)
    base_loss = criterion(logits, base_targets)
    base_loss.backward()
    w_optimizer.step()
    # record
    base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
    base_losses.update(base_loss.item(),  base_inputs.size(0))
    base_top1.update  (base_prec1.item(), base_inputs.size(0))
    base_top5.update  (base_prec5.item(), base_inputs.size(0))

    # update the architecture-weight
    network.zero_grad()
    _, logits, log_probs = network(arch_inputs)
    arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
    if algo == 'tunas':
      with torch.no_grad():
        RL_BASELINE_EMA.update(arch_prec1.item())
        rl_advantage = arch_prec1 - RL_BASELINE_EMA.value
      rl_log_prob = sum(log_probs)
      arch_loss = - rl_advantage * rl_log_prob
    elif algo == 'tas' or algo == 'fbv2':
      arch_loss = criterion(logits, arch_targets)
    else:
      raise ValueError('invalid algorightm name: {:}'.format(algo))
    arch_loss.backward()
    a_optimizer.step()
    # record
    arch_losses.update(arch_loss.item(),  arch_inputs.size(0))
    arch_top1.update  (arch_prec1.item(), arch_inputs.size(0))
    arch_top5.update  (arch_prec5.item(), arch_inputs.size(0))

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

    if step % print_freq == 0 or step + 1 == len(xloader):
      Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader))
      Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
      Wstr = 'Base [Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=base_losses, top1=base_top1, top5=base_top5)
      Astr = 'Arch [Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=arch_losses, top1=arch_top1, top5=arch_top5)
      logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
  return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg


def valid_func(xloader, network, criterion, logger):
  data_time, batch_time = AverageMeter(), AverageMeter()
  arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
  end = time.time()
  with torch.no_grad():
    network.eval()
    for step, (arch_inputs, arch_targets) in enumerate(xloader):
      arch_targets = arch_targets.cuda(non_blocking=True)
      # measure data loading time
      data_time.update(time.time() - end)
      # prediction
      _, logits, _ = network(arch_inputs.cuda(non_blocking=True))
      arch_loss = criterion(logits, arch_targets)
      # record
      arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
      arch_losses.update(arch_loss.item(),  arch_inputs.size(0))
      arch_top1.update  (arch_prec1.item(), arch_inputs.size(0))
      arch_top5.update  (arch_prec5.item(), arch_inputs.size(0))
      # measure elapsed time
      batch_time.update(time.time() - end)
      end = time.time()
  return arch_losses.avg, arch_top1.avg, arch_top5.avg


def main(xargs):
  assert torch.cuda.is_available(), 'CUDA is not available.'
  torch.backends.cudnn.enabled   = True
  torch.backends.cudnn.benchmark = False
  torch.backends.cudnn.deterministic = True
  torch.set_num_threads( xargs.workers )
  prepare_seed(xargs.rand_seed)
  logger = prepare_logger(args)

  train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
  if xargs.overwite_epochs is None:
    extra_info = {'class_num': class_num, 'xshape': xshape}
  else:
    extra_info = {'class_num': class_num, 'xshape': xshape, 'epochs': xargs.overwite_epochs}
  config = load_config(xargs.config_path, extra_info, logger)
  search_loader, train_loader, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', (config.batch_size, config.test_batch_size), xargs.workers)
  logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
  logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))

  search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
  
  model_config = dict2config(
      dict(name='generic', super_type='search-shape', candidate_Cs=search_space['candidates'], max_num_Cs=search_space['numbers'], num_classes=class_num,
           genotype=args.genotype, affine=bool(xargs.affine), track_running_stats=bool(xargs.track_running_stats)), None)
  logger.log('search space : {:}'.format(search_space))
  logger.log('model config : {:}'.format(model_config))
  search_model = get_cell_based_tiny_net(model_config)
  search_model.set_algo(xargs.algo)
  logger.log('{:}'.format(search_model))

  w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config)
  a_optimizer = torch.optim.Adam(search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay, eps=xargs.arch_eps)
  logger.log('w-optimizer : {:}'.format(w_optimizer))
  logger.log('a-optimizer : {:}'.format(a_optimizer))
  logger.log('w-scheduler : {:}'.format(w_scheduler))
  logger.log('criterion   : {:}'.format(criterion))
  params = count_parameters_in_MB(search_model)
  logger.log('The parameters of the search model = {:.2f} MB'.format(params))
  logger.log('search-space : {:}'.format(search_space))
  if bool(xargs.use_api):
    api = create(None, 'size', verbose=False)
  else:
    api = None
  logger.log('{:} create API = {:} done'.format(time_string(), api))

  last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
  network, criterion = search_model.cuda(), criterion.cuda()  # use a single GPU

  last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')

  if last_info.exists(): # automatically resume from previous checkpoint
    logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
    last_info   = torch.load(last_info)
    start_epoch = last_info['epoch']
    checkpoint  = torch.load(last_info['last_checkpoint'])
    genotypes   = checkpoint['genotypes']
    valid_accuracies = checkpoint['valid_accuracies']
    search_model.load_state_dict( checkpoint['search_model'] )
    w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )
    w_optimizer.load_state_dict ( checkpoint['w_optimizer'] )
    a_optimizer.load_state_dict ( checkpoint['a_optimizer'] )
    logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
  else:
    logger.log("=> do not find the last-info file : {:}".format(last_info))
    start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: network.random}

  # start training
  start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
  for epoch in range(start_epoch, total_epoch):
    w_scheduler.update(epoch, 0.0)
    need_time = 'Time Left: {:}'.format(convert_secs2time(epoch_time.val * (total_epoch-epoch), True))
    epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
    logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))

    if xargs.algo == 'fbv2' or xargs.algo == 'tas':
      network.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )
      logger.log('[RESET tau as : {:}]'.format(network.tau))
    search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
                = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, xargs.algo, epoch_str, xargs.print_freq, logger)
    search_time.update(time.time() - start_time)
    logger.log('[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))
    logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))

    genotype = network.genotype
    logger.log('[{:}] - [get_best_arch] : {:}'.format(epoch_str, genotype))
    valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion, logger)
    logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
    valid_accuracies[epoch] = valid_a_top1

    genotypes[epoch] = genotype
    logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
    # save checkpoint
    save_path = save_checkpoint({'epoch' : epoch + 1,
                'args'  : deepcopy(xargs),
                'search_model': search_model.state_dict(),
                'w_optimizer' : w_optimizer.state_dict(),
                'a_optimizer' : a_optimizer.state_dict(),
                'w_scheduler' : w_scheduler.state_dict(),
                'genotypes'   : genotypes,
                'valid_accuracies' : valid_accuracies},
                model_base_path, logger)
    last_info = save_checkpoint({
          'epoch': epoch + 1,
          'args' : deepcopy(args),
          'last_checkpoint': save_path,
          }, logger.path('info'), logger)
    with torch.no_grad():
      logger.log('{:}'.format(search_model.show_alphas()))
    if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch], '90')))
    # measure elapsed time
    epoch_time.update(time.time() - start_time)
    start_time = time.time()

  # the final post procedure : count the time
  start_time = time.time()
  genotype = network.genotype
  search_time.update(time.time() - start_time)

  valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
  logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1))

  logger.log('\n' + '-'*100)
  # check the performance from the architecture dataset
  logger.log('[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(xargs.algo, total_epoch, search_time.sum, genotype))
  if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '90') ))
  logger.close()
  

if __name__ == '__main__':
  parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.")
  parser.add_argument('--data_path'   ,       type=str,   help='Path to dataset')
  parser.add_argument('--dataset'     ,       type=str,   choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
  parser.add_argument('--search_space',       type=str,   default='sss', choices=['sss'], help='The search space name.')
  parser.add_argument('--algo'        ,       type=str,   choices=['tas', 'fbv2', 'tunas'], help='The search space name.')
  parser.add_argument('--genotype'    ,       type=str,   default='|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|', help='The genotype.')
  parser.add_argument('--use_api'     ,       type=int,   default=1, choices=[0,1], help='Whether use API or not (which will cost much memory).')
  # FOR GDAS
  parser.add_argument('--tau_min',            type=float, default=0.1,  help='The minimum tau for Gumbel Softmax.')
  parser.add_argument('--tau_max',            type=float, default=10,   help='The maximum tau for Gumbel Softmax.')
  #
  parser.add_argument('--track_running_stats',type=int,   default=0, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
  parser.add_argument('--affine'      ,       type=int,   default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.')
  parser.add_argument('--config_path' ,       type=str,   default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.')
  parser.add_argument('--overwite_epochs',    type=int,   help='The number of epochs to overwrite that value in config files.')
  # architecture leraning rate
  parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
  parser.add_argument('--arch_weight_decay' , type=float, default=1e-3, help='weight decay for arch encoding')
  parser.add_argument('--arch_eps'          , type=float, default=1e-8, help='weight decay for arch encoding')
  # log
  parser.add_argument('--workers',            type=int,   default=2,    help='number of data loading workers (default: 2)')
  parser.add_argument('--save_dir',           type=str,   default='./output/search', help='Folder to save checkpoints and log.')
  parser.add_argument('--print_freq',         type=int,   default=200,  help='print frequency (default: 200)')
  parser.add_argument('--rand_seed',          type=int,   help='manual seed')
  args = parser.parse_args()
  if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
  dirname = '{:}-affine{:}_BN{:}-AWD{:}'.format(args.algo, args.affine, args.track_running_stats, args.arch_weight_decay)
  if args.overwite_epochs is not None:
    dirname = dirname + '-E{:}'.format(args.overwite_epochs)
  args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, dirname)

  main(args)