##################################################
# NAS-Bench-102 ##################################
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, torch, random, argparse
from PIL     import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from copy    import deepcopy
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
from procedures   import save_checkpoint, copy_checkpoint
from procedures   import get_machine_info
from datasets     import get_datasets
from log_utils    import Logger, AverageMeter, time_string, convert_secs2time
from models       import CellStructure, CellArchitectures, get_search_spaces
from functions    import evaluate_for_seed


def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger):
  machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
  all_infos = {'info': machine_info}
  all_dataset_keys = []
  # look all the datasets
  for dataset, xpath, split in zip(datasets, xpaths, splits):
    # train valid data
    train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
    # load the configurature
    if dataset == 'cifar10' or dataset == 'cifar100':
      if use_less: config_path = 'configs/nas-benchmark/LESS.config'
      else       : config_path = 'configs/nas-benchmark/CIFAR.config'
      split_info  = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
    elif dataset.startswith('ImageNet16'):
      if use_less: config_path = 'configs/nas-benchmark/LESS.config'
      else       : config_path = 'configs/nas-benchmark/ImageNet-16.config'
      split_info  = load_config('configs/nas-benchmark/{:}-split.txt'.format(dataset), None, None)
    else:
      raise ValueError('invalid dataset : {:}'.format(dataset))
    config = load_config(config_path, \
                            {'class_num': class_num,
                             'xshape'   : xshape}, \
                            logger)
    # check whether use splited validation set
    if bool(split):
      assert dataset == 'cifar10'
      ValLoaders = {'ori-test': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)}
      assert len(train_data) == len(split_info.train) + len(split_info.valid), 'invalid length : {:} vs {:} + {:}'.format(len(train_data), len(split_info.train), len(split_info.valid))
      train_data_v2 = deepcopy(train_data)
      train_data_v2.transform = valid_data.transform
      valid_data = train_data_v2
      # data loader
      train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train), num_workers=workers, pin_memory=True)
      valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid), num_workers=workers, pin_memory=True)
      ValLoaders['x-valid'] = valid_loader
    else:
      # data loader
      train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
      valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
      if dataset == 'cifar10':
        ValLoaders = {'ori-test': valid_loader}
      elif dataset == 'cifar100':
        cifar100_splits = load_config('configs/nas-benchmark/cifar100-test-split.txt', None, None)
        ValLoaders = {'ori-test': valid_loader,
                      'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True),
                      'x-test'  : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest ), num_workers=workers, pin_memory=True)
                     }
      elif dataset == 'ImageNet16-120':
        imagenet16_splits = load_config('configs/nas-benchmark/imagenet-16-120-test-split.txt', None, None)
        ValLoaders = {'ori-test': valid_loader,
                      'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xvalid), num_workers=workers, pin_memory=True),
                      'x-test'  : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xtest ), num_workers=workers, pin_memory=True)
                     }
      else:
        raise ValueError('invalid dataset : {:}'.format(dataset))

    dataset_key = '{:}'.format(dataset)
    if bool(split): dataset_key = dataset_key + '-valid'
    logger.log('Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(dataset_key, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size))
    logger.log('Evaluate ||||||| {:10s} ||||||| Config={:}'.format(dataset_key, config))
    for key, value in ValLoaders.items():
      logger.log('Evaluate ---->>>> {:10s} with {:} batchs'.format(key, len(value)))
    results = evaluate_for_seed(arch_config, config, arch, train_loader, ValLoaders, seed, logger)
    all_infos[dataset_key] = results
    all_dataset_keys.append( dataset_key )
  all_infos['all_dataset_keys'] = all_dataset_keys
  return all_infos


def main(save_dir, workers, datasets, xpaths, splits, use_less, srange, arch_index, seeds, cover_mode, meta_info, arch_config):
  assert torch.cuda.is_available(), 'CUDA is not available.'
  torch.backends.cudnn.enabled   = True
  #torch.backends.cudnn.benchmark = True
  torch.backends.cudnn.deterministic = True
  torch.set_num_threads( workers )

  assert len(srange) == 2 and 0 <= srange[0] <= srange[1], 'invalid srange : {:}'.format(srange)
  
  if use_less:
    sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}-LESS'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
  else:
    sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
  logger  = Logger(str(sub_dir), 0, False)

  all_archs = meta_info['archs']
  assert srange[1] < meta_info['total'], 'invalid range : {:}-{:} vs. {:}'.format(srange[0], srange[1], meta_info['total'])
  assert arch_index == -1 or srange[0] <= arch_index <= srange[1], 'invalid range : {:} vs. {:} vs. {:}'.format(srange[0], arch_index, srange[1])
  if arch_index == -1:
    to_evaluate_indexes = list(range(srange[0], srange[1]+1))
  else:
    to_evaluate_indexes = [arch_index]
  logger.log('xargs : seeds      = {:}'.format(seeds))
  logger.log('xargs : arch_index = {:}'.format(arch_index))
  logger.log('xargs : cover_mode = {:}'.format(cover_mode))
  logger.log('-'*100)

  logger.log('Start evaluating range =: {:06d} vs. {:06d} vs. {:06d} / {:06d} with cover-mode={:}'.format(srange[0], arch_index, srange[1], meta_info['total'], cover_mode))
  for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
    logger.log('--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}'.format(i, len(datasets), dataset, xpath, split))
  logger.log('--->>> architecture config : {:}'.format(arch_config))
  

  start_time, epoch_time = time.time(), AverageMeter()
  for i, index in enumerate(to_evaluate_indexes):
    arch = all_archs[index]
    logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th architecture [seeds={:}] {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seeds, '-'*15))
    #logger.log('{:} {:} {:}'.format('-'*15, arch.tostr(), '-'*15))
    logger.log('{:} {:} {:}'.format('-'*15, arch, '-'*15))
  
    # test this arch on different datasets with different seeds
    has_continue = False
    for seed in seeds:
      to_save_name = sub_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
      if to_save_name.exists():
        if cover_mode:
          logger.log('Find existing file : {:}, remove it before evaluation'.format(to_save_name))
          os.remove(str(to_save_name))
        else         :
          logger.log('Find existing file : {:}, skip this evaluation'.format(to_save_name))
          has_continue = True
          continue
      results = evaluate_all_datasets(CellStructure.str2structure(arch), \
                                        datasets, xpaths, splits, use_less, seed, \
                                        arch_config, workers, logger)
      torch.save(results, to_save_name)
      logger.log('{:} --evaluate-- {:06d}/{:06d} ({:06d}/{:06d})-th seed={:} done, save into {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seed, to_save_name))
    # measure elapsed time
    if not has_continue: epoch_time.update(time.time() - start_time)
    start_time = time.time()
    need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes)-i-1), True) )
    logger.log('This arch costs : {:}'.format( convert_secs2time(epoch_time.val, True) ))
    logger.log('{:}'.format('*'*100))
    logger.log('{:}   {:74s}   {:}'.format('*'*10, '{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}'.format(i, len(to_evaluate_indexes), index, meta_info['total'], need_time), '*'*10))
    logger.log('{:}'.format('*'*100))

  logger.close()


def train_single_model(save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config):
  assert torch.cuda.is_available(), 'CUDA is not available.'
  torch.backends.cudnn.enabled   = True
  torch.backends.cudnn.deterministic = True
  #torch.backends.cudnn.benchmark = True
  torch.set_num_threads( workers )
  
  save_dir = Path(save_dir) / 'specifics' / '{:}-{:}-{:}-{:}'.format('LESS' if use_less else 'FULL', model_str, arch_config['channel'], arch_config['num_cells'])
  logger   = Logger(str(save_dir), 0, False)
  if model_str in CellArchitectures:
    arch   = CellArchitectures[model_str]
    logger.log('The model string is found in pre-defined architecture dict : {:}'.format(model_str))
  else:
    try:
      arch = CellStructure.str2structure(model_str)
    except:
      raise ValueError('Invalid model string : {:}. It can not be found or parsed.'.format(model_str))
  assert arch.check_valid_op(get_search_spaces('cell', 'full')), '{:} has the invalid op.'.format(arch)
  logger.log('Start train-evaluate {:}'.format(arch.tostr()))
  logger.log('arch_config : {:}'.format(arch_config))

  start_time, seed_time = time.time(), AverageMeter()
  for _is, seed in enumerate(seeds):
    logger.log('\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------'.format(_is, len(seeds), seed))
    to_save_name = save_dir / 'seed-{:04d}.pth'.format(seed)
    if to_save_name.exists():
      logger.log('Find the existing file {:}, directly load!'.format(to_save_name))
      checkpoint = torch.load(to_save_name)
    else:
      logger.log('Does not find the existing file {:}, train and evaluate!'.format(to_save_name))
      checkpoint = evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger)
      torch.save(checkpoint, to_save_name)
    # log information
    logger.log('{:}'.format(checkpoint['info']))
    all_dataset_keys = checkpoint['all_dataset_keys']
    for dataset_key in all_dataset_keys:
      logger.log('\n{:} dataset : {:} {:}'.format('-'*15, dataset_key, '-'*15))
      dataset_info = checkpoint[dataset_key]
      #logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
      logger.log('Flops = {:} MB, Params = {:} MB'.format(dataset_info['flop'], dataset_info['param']))
      logger.log('config : {:}'.format(dataset_info['config']))
      logger.log('Training State (finish) = {:}'.format(dataset_info['finish-train']))
      last_epoch = dataset_info['total_epoch'] - 1
      train_acc1es, train_acc5es = dataset_info['train_acc1es'], dataset_info['train_acc5es']
      valid_acc1es, valid_acc5es = dataset_info['valid_acc1es'], dataset_info['valid_acc5es']
      logger.log('Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%'.format(train_acc1es[last_epoch], train_acc5es[last_epoch], 100-train_acc1es[last_epoch], valid_acc1es[last_epoch], valid_acc5es[last_epoch], 100-valid_acc1es[last_epoch]))
    # measure elapsed time
    seed_time.update(time.time() - start_time)
    start_time = time.time()
    need_time = 'Time Left: {:}'.format( convert_secs2time(seed_time.avg * (len(seeds)-_is-1), True) )
    logger.log('\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}'.format(_is, len(seeds), seed, need_time))
  logger.close()


def generate_meta_info(save_dir, max_node, divide=40):
  aa_nas_bench_ss = get_search_spaces('cell', 'nas-bench-102')
  archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
  print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))

  random.seed( 88 ) # please do not change this line for reproducibility
  random.shuffle( archs )
  # to test fixed-random shuffle 
  #print ('arch [0] : {:}\n---->>>>   {:}'.format( archs[0], archs[0].tostr() ))
  #print ('arch [9] : {:}\n---->>>>   {:}'.format( archs[9], archs[9].tostr() ))
  assert archs[0  ].tostr() == '|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|', 'please check the 0-th architecture : {:}'.format(archs[0])
  assert archs[9  ].tostr() == '|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|', 'please check the 9-th architecture : {:}'.format(archs[9])
  assert archs[123].tostr() == '|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|', 'please check the 123-th architecture : {:}'.format(archs[123])
  total_arch = len(archs)
  
  num = 50000
  indexes_5W = list(range(num))
  random.seed( 1021 )
  random.shuffle( indexes_5W )
  train_split = sorted( list(set(indexes_5W[:num//2])) )
  valid_split = sorted( list(set(indexes_5W[num//2:])) )
  assert len(train_split) + len(valid_split) == num
  assert train_split[0] == 0 and train_split[10] == 26 and train_split[111] == 203 and valid_split[0] == 1 and valid_split[10] == 18 and valid_split[111] == 242, '{:} {:} {:} - {:} {:} {:}'.format(train_split[0], train_split[10], train_split[111], valid_split[0], valid_split[10], valid_split[111])
  splits = {num: {'train': train_split, 'valid': valid_split} }

  info = {'archs' : [x.tostr() for x in archs],
          'total' : total_arch,
          'max_node' : max_node,
          'splits': splits}

  save_dir = Path(save_dir)
  save_dir.mkdir(parents=True, exist_ok=True)
  save_name = save_dir / 'meta-node-{:}.pth'.format(max_node)
  assert not save_name.exists(), '{:} already exist'.format(save_name)
  torch.save(info, save_name)
  print ('save the meta file into {:}'.format(save_name))

  script_name_full = save_dir / 'BENCH-102-N{:}.opt-full.script'.format(max_node)
  script_name_less = save_dir / 'BENCH-102-N{:}.opt-less.script'.format(max_node)
  full_file = open(str(script_name_full), 'w')
  less_file = open(str(script_name_less), 'w')
  gaps = total_arch // divide
  for start in range(0, total_arch, gaps):
    xend = min(start+gaps, total_arch)
    full_file.write('bash ./scripts-search/NAS-Bench-102/train-models.sh 0 {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
    less_file.write('bash ./scripts-search/NAS-Bench-102/train-models.sh 1 {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
  print ('save the training script into {:} and {:}'.format(script_name_full, script_name_less))
  full_file.close()
  less_file.close()

  script_name = save_dir / 'meta-node-{:}.cal-script.txt'.format(max_node)
  macro = 'OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0'
  with open(str(script_name), 'w') as cfile:
    for start in range(0, total_arch, gaps):
      xend = min(start+gaps, total_arch)
      cfile.write('{:} python exps/NAS-Bench-102/statistics.py --mode cal --target_dir {:06d}-{:06d}-C16-N5\n'.format(macro, start, xend-1))
  print ('save the post-processing script into {:}'.format(script_name))


if __name__ == '__main__':
  #mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
  #parser = argparse.ArgumentParser(description='Algorithm-Agnostic NAS Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser = argparse.ArgumentParser(description='NAS-Bench-102', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('--mode'   ,     type=str,   required=True,  help='The script mode.')
  parser.add_argument('--save_dir',    type=str,                   help='Folder to save checkpoints and log.')
  parser.add_argument('--max_node',    type=int,                   help='The maximum node in a cell.')
  # use for train the model
  parser.add_argument('--workers',     type=int,   default=8,      help='number of data loading workers (default: 2)')
  parser.add_argument('--srange' ,     type=int,   nargs='+',      help='The range of models to be evaluated')
  parser.add_argument('--arch_index',  type=int,   default=-1,     help='The architecture index to be evaluated (cover mode).')
  parser.add_argument('--datasets',    type=str,   nargs='+',      help='The applied datasets.')
  parser.add_argument('--xpaths',      type=str,   nargs='+',      help='The root path for this dataset.')
  parser.add_argument('--splits',      type=int,   nargs='+',      help='The root path for this dataset.')
  parser.add_argument('--use_less',    type=int,   default=0, choices=[0,1], help='Using the less-training-epoch config.')
  parser.add_argument('--seeds'  ,     type=int,   nargs='+',      help='The range of models to be evaluated')
  parser.add_argument('--channel',     type=int,                   help='The number of channels.')
  parser.add_argument('--num_cells',   type=int,                   help='The number of cells in one stage.')
  args = parser.parse_args()

  assert args.mode in ['meta', 'new', 'cover'] or args.mode.startswith('specific-'), 'invalid mode : {:}'.format(args.mode)

  if args.mode == 'meta':
    generate_meta_info(args.save_dir, args.max_node)
  elif args.mode.startswith('specific'):
    assert len(args.mode.split('-')) == 2, 'invalid mode : {:}'.format(args.mode)
    model_str = args.mode.split('-')[1]
    train_single_model(args.save_dir, args.workers, args.datasets, args.xpaths, args.splits, args.use_less>0, \
                         tuple(args.seeds), model_str, {'channel': args.channel, 'num_cells': args.num_cells})
  else:
    meta_path = Path(args.save_dir) / 'meta-node-{:}.pth'.format(args.max_node)
    assert meta_path.exists(), '{:} does not exist.'.format(meta_path)
    meta_info = torch.load( meta_path )
    # check whether args is ok
    assert len(args.srange) == 2 and args.srange[0] <= args.srange[1], 'invalid length of srange args: {:}'.format(args.srange)
    assert len(args.seeds) > 0, 'invalid length of seeds args: {:}'.format(args.seeds)
    assert len(args.datasets) == len(args.xpaths) == len(args.splits), 'invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits))
    assert args.workers > 0, 'invalid number of workers : {:}'.format(args.workers)
  
    main(args.save_dir, args.workers, args.datasets, args.xpaths, args.splits, args.use_less>0, \
           tuple(args.srange), args.arch_index, tuple(args.seeds), \
           args.mode == 'cover', meta_info, \
           {'channel': args.channel, 'num_cells': args.num_cells})
