##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, argparse, collections
from copy import deepcopy
import torch
import torch.nn as nn
from pathlib import Path
from collections import defaultdict
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from log_utils    import AverageMeter, time_string, convert_secs2time
from config_utils import load_config, dict2config
from datasets     import get_datasets
# NAS-Bench-102 related module or function
from models       import CellStructure, get_cell_based_tiny_net
from nas_102_api  import ArchResults, ResultsCount
from functions    import pure_evaluate



def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict):
  xresult     = ResultsCount(dataset, results['net_state_dict'], results['train_acc1es'], results['train_losses'], \
                               results['param'], results['flop'], arch_config, used_seed, results['total_epoch'], None)

  net_config = dict2config({'name': 'infer.tiny', 'C': arch_config['channel'], 'N': arch_config['num_cells'], 'genotype': CellStructure.str2structure(arch_config['arch_str']), 'num_classes':arch_config['class_num']}, None)
  network = get_cell_based_tiny_net(net_config)
  network.load_state_dict(xresult.get_net_param())
  if 'train_times' in results: # new version
    xresult.update_train_info(results['train_acc1es'], results['train_acc5es'], results['train_losses'], results['train_times'])
    xresult.update_eval(results['valid_acc1es'], results['valid_losses'], results['valid_times'])
  else:
    if dataset == 'cifar10-valid':
      xresult.update_OLD_eval('x-valid' , results['valid_acc1es'], results['valid_losses'])
      loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format('cifar10', 'test')], network.cuda())
      xresult.update_OLD_eval('ori-test', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
      xresult.update_latency(latencies)
    elif dataset == 'cifar10':
      xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
      loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
      xresult.update_latency(latencies)
    elif dataset == 'cifar100' or dataset == 'ImageNet16-120':
      xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
      loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'valid')], network.cuda())
      xresult.update_OLD_eval('x-valid', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
      loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset,  'test')], network.cuda())
      xresult.update_OLD_eval('x-test' , {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
      xresult.update_latency(latencies)
    else:
      raise ValueError('invalid dataset name : {:}'.format(dataset))
  return xresult
  


def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dict):
  information = ArchResults(arch_index, arch_str)

  for checkpoint_path in checkpoints:
    checkpoint = torch.load(checkpoint_path, map_location='cpu')
    used_seed  = checkpoint_path.name.split('-')[-1].split('.')[0]
    for dataset in datasets:
      assert dataset in checkpoint, 'Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path)
      results     = checkpoint[dataset]
      assert results['finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(arch_index, used_seed, dataset, checkpoint_path)
      arch_config = {'channel': results['channel'], 'num_cells': results['num_cells'], 'arch_str': arch_str, 'class_num': results['config']['class_num']}
      
      xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
      information.update(dataset, int(used_seed), xresult)
  return information



def GET_DataLoaders(workers):

  torch.set_num_threads(workers)

  root_dir  = (Path(__file__).parent / '..' / '..').resolve()
  torch_dir = Path(os.environ['TORCH_HOME'])
  # cifar
  cifar_config_path = root_dir / 'configs' / 'nas-benchmark' / 'CIFAR.config'
  cifar_config = load_config(cifar_config_path, None, None)
  print ('{:} Create data-loader for all datasets'.format(time_string()))
  print ('-'*200)
  TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets('cifar10', str(torch_dir/'cifar.python'), -1)
  print ('original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num))
  cifar10_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar-split.txt', None, None)
  assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [1, 2, 3, 4, 6, 8, 9, 10, 12, 14]
  temp_dataset = deepcopy(TRAIN_CIFAR10)
  temp_dataset.transform = VALID_CIFAR10.transform
  # data loader
  trainval_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
  train_cifar10_loader    = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train), num_workers=workers, pin_memory=True)
  valid_cifar10_loader    = torch.utils.data.DataLoader(temp_dataset , batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid), num_workers=workers, pin_memory=True)
  test__cifar10_loader    = torch.utils.data.DataLoader(VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
  print ('CIFAR-10  : trval-loader has {:3d} batch with {:} per batch'.format(len(trainval_cifar10_loader), cifar_config.batch_size))
  print ('CIFAR-10  : train-loader has {:3d} batch with {:} per batch'.format(len(train_cifar10_loader), cifar_config.batch_size))
  print ('CIFAR-10  : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_cifar10_loader), cifar_config.batch_size))
  print ('CIFAR-10  : test--loader has {:3d} batch with {:} per batch'.format(len(test__cifar10_loader), cifar_config.batch_size))
  print ('-'*200)
  # CIFAR-100
  TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets('cifar100', str(torch_dir/'cifar.python'), -1)
  print ('original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num))
  cifar100_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar100-test-split.txt', None, None)
  assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [0, 2, 6, 7, 9, 11, 12, 17, 20, 24]
  train_cifar100_loader = torch.utils.data.DataLoader(TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
  valid_cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True)
  test__cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest) , num_workers=workers, pin_memory=True)
  print ('CIFAR-100  : train-loader has {:3d} batch'.format(len(train_cifar100_loader)))
  print ('CIFAR-100  : valid-loader has {:3d} batch'.format(len(valid_cifar100_loader)))
  print ('CIFAR-100  : test--loader has {:3d} batch'.format(len(test__cifar100_loader)))
  print ('-'*200)

  imagenet16_config_path = 'configs/nas-benchmark/ImageNet-16.config'
  imagenet16_config = load_config(imagenet16_config_path, None, None)
  TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets('ImageNet16-120', str(torch_dir/'cifar.python'/'ImageNet16'), -1)
  print ('original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num))
  imagenet_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'imagenet-16-120-test-split.txt', None, None)
  assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [0, 4, 5, 10, 11, 13, 14, 15, 17, 20]
  train_imagenet_loader = torch.utils.data.DataLoader(TRAIN_ImageNet16_120, batch_size=imagenet16_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
  valid_imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid), num_workers=workers, pin_memory=True)
  test__imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest) , num_workers=workers, pin_memory=True)
  print ('ImageNet-16-120  : train-loader has {:3d} batch with {:} per batch'.format(len(train_imagenet_loader), imagenet16_config.batch_size))
  print ('ImageNet-16-120  : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_imagenet_loader), imagenet16_config.batch_size))
  print ('ImageNet-16-120  : test--loader has {:3d} batch with {:} per batch'.format(len(test__imagenet_loader), imagenet16_config.batch_size))

  # 'cifar10', 'cifar100', 'ImageNet16-120'
  loaders = {'cifar10@trainval': trainval_cifar10_loader,
             'cifar10@train'   : train_cifar10_loader,
             'cifar10@valid'   : valid_cifar10_loader,
             'cifar10@test'    : test__cifar10_loader,
             'cifar100@train'  : train_cifar100_loader,
             'cifar100@valid'  : valid_cifar100_loader,
             'cifar100@test'   : test__cifar100_loader,
             'ImageNet16-120@train': train_imagenet_loader,
             'ImageNet16-120@valid': valid_imagenet_loader,
             'ImageNet16-120@test' : test__imagenet_loader}
  return loaders



def simplify(save_dir, meta_file, basestr, target_dir):
  meta_infos     = torch.load(meta_file, map_location='cpu')
  meta_archs     = meta_infos['archs'] # a list of architecture strings
  meta_num_archs = meta_infos['total']
  meta_max_node  = meta_infos['max_node']
  assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))

  sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
  print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
  
  subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
  num_seeds = defaultdict(lambda: 0)
  for index, sub_dir in enumerate(sub_model_dirs):
    xcheckpoints = list(sub_dir.glob('arch-*-seed-*.pth'))
    arch_indexes = set()
    for checkpoint in xcheckpoints:
      temp_names = checkpoint.name.split('-')
      assert len(temp_names) == 4 and temp_names[0] == 'arch' and temp_names[2] == 'seed', 'invalid checkpoint name : {:}'.format(checkpoint.name)
      arch_indexes.add( temp_names[1] )
    subdir2archs[sub_dir] = sorted(list(arch_indexes))
    num_evaluated_arch   += len(arch_indexes)
    # count number of seeds for each architecture
    for arch_index in arch_indexes:
      num_seeds[ len(list(sub_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))) ] += 1
  print('{:} There are {:5d} architectures that have been evaluated ({:} in total).'.format(time_string(), num_evaluated_arch, meta_num_archs))
  for key in sorted( list( num_seeds.keys() ) ): print ('{:} There are {:5d} architectures that are evaluated {:} times.'.format(time_string(), num_seeds[key], key))

  dataloader_dict = GET_DataLoaders( 6 )

  to_save_simply = save_dir / 'simplifies'
  to_save_allarc = save_dir / 'simplifies' / 'architectures'
  if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
  if not to_save_allarc.exists(): to_save_allarc.mkdir(parents=True, exist_ok=True)

  assert (save_dir / target_dir) in subdir2archs, 'can not find {:}'.format(target_dir)
  arch2infos, datasets = {}, ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
  evaluated_indexes    = set()
  target_directory     = save_dir / target_dir
  target_less_dir      = save_dir / '{:}-LESS'.format(target_dir)
  arch_indexes         = subdir2archs[ target_directory ]
  num_seeds            = defaultdict(lambda: 0)
  end_time             = time.time()
  arch_time            = AverageMeter()
  for idx, arch_index in enumerate(arch_indexes):
    checkpoints = list(target_directory.glob('arch-{:}-seed-*.pth'.format(arch_index)))
    ckps_less   = list(target_less_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))
    # create the arch info for each architecture
    try:
      arch_info_full = account_one_arch(arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict)
      arch_info_less = account_one_arch(arch_index, meta_archs[int(arch_index)], ckps_less, ['cifar10-valid'], dataloader_dict)
      num_seeds[ len(checkpoints) ] += 1
    except:
      print('Loading {:} failed, : {:}'.format(arch_index, checkpoints))
      continue
    assert int(arch_index) not in evaluated_indexes, 'conflict arch-index : {:}'.format(arch_index)
    assert 0 <= int(arch_index) < len(meta_archs), 'invalid arch-index {:} (not found in meta_archs)'.format(arch_index)
    arch_info = {'full': arch_info_full, 'less': arch_info_less}
    evaluated_indexes.add( int(arch_index) )
    arch2infos[int(arch_index)] = arch_info
    torch.save({'full': arch_info_full.state_dict(),
                'less': arch_info_less.state_dict()}, to_save_allarc / '{:}-FULL.pth'.format(arch_index))
    arch_info['full'].clear_params()
    arch_info['less'].clear_params()
    torch.save({'full': arch_info_full.state_dict(),
                'less': arch_info_less.state_dict()}, to_save_allarc / '{:}-SIMPLE.pth'.format(arch_index))
    # measure elapsed time
    arch_time.update(time.time() - end_time)
    end_time  = time.time()
    need_time = '{:}'.format( convert_secs2time(arch_time.avg * (len(arch_indexes)-idx-1), True) )
    print('{:} {:} [{:03d}/{:03d}] : {:} still need {:}'.format(time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time))
  # measure time
  xstrs = ['{:}:{:03d}'.format(key, num_seeds[key]) for key in sorted( list( num_seeds.keys() ) ) ]
  print('{:} {:} done : {:}'.format(time_string(), target_dir, xstrs))
  final_infos = {'meta_archs' : meta_archs,
                 'total_archs': meta_num_archs,
                 'basestr'    : basestr,
                 'arch2infos' : arch2infos,
                 'evaluated_indexes': evaluated_indexes}
  save_file_name = to_save_simply / '{:}.pth'.format(target_dir)
  torch.save(final_infos, save_file_name)
  print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))



def merge_all(save_dir, meta_file, basestr):
  meta_infos     = torch.load(meta_file, map_location='cpu')
  meta_archs     = meta_infos['archs']
  meta_num_archs = meta_infos['total']
  meta_max_node  = meta_infos['max_node']
  assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))

  sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
  print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
  for index, sub_dir in enumerate(sub_model_dirs):
    arch_info_files = sorted( list(sub_dir.glob('arch-*-seed-*.pth') ) )
    print ('The {:02d}/{:02d}-th directory : {:} : {:} runs.'.format(index, len(sub_model_dirs), sub_dir, len(arch_info_files)))
  
  arch2infos, evaluated_indexes = dict(), set()
  for IDX, sub_dir in enumerate(sub_model_dirs):
    ckp_path = sub_dir.parent / 'simplifies' / '{:}.pth'.format(sub_dir.name)
    if ckp_path.exists():
      sub_ckps = torch.load(ckp_path, map_location='cpu')
      assert sub_ckps['total_archs'] == meta_num_archs and sub_ckps['basestr'] == basestr
      xarch2infos = sub_ckps['arch2infos']
      xevalindexs = sub_ckps['evaluated_indexes']
      for eval_index in xevalindexs:
        assert eval_index not in evaluated_indexes and eval_index not in arch2infos
        #arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
        arch2infos[eval_index] = {'full': xarch2infos[eval_index]['full'].state_dict(),
                                  'less': xarch2infos[eval_index]['less'].state_dict()}
        evaluated_indexes.add( eval_index )
      print ('{:} [{:03d}/{:03d}] merge data from {:} with {:} models.'.format(time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)))
    else:
      raise ValueError('Can not find {:}'.format(ckp_path))
      #print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))

  evaluated_indexes = sorted( list( evaluated_indexes ) )
  print ('Finally, there are {:} architectures that have been trained and evaluated.'.format(len(evaluated_indexes)))

  to_save_simply = save_dir / 'simplifies'
  if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
  final_infos = {'meta_archs' : meta_archs,
                 'total_archs': meta_num_archs,
                 'arch2infos' : arch2infos,
                 'evaluated_indexes': evaluated_indexes}
  save_file_name = to_save_simply / '{:}-final-infos.pth'.format(basestr)
  torch.save(final_infos, save_file_name)
  print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))



if __name__ == '__main__':

  parser = argparse.ArgumentParser(description='NAS-BENCH-102', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('--mode'         ,  type=str, choices=['cal', 'merge'],            help='The running mode for this script.')
  parser.add_argument('--base_save_dir',  type=str, default='./output/NAS-BENCH-102-4',  help='The base-name of folder to save checkpoints and log.')
  parser.add_argument('--target_dir'   ,  type=str,                                      help='The target directory.')
  parser.add_argument('--max_node'     ,  type=int, default=4,                           help='The maximum node in a cell.')
  parser.add_argument('--channel'      ,  type=int, default=16,                          help='The number of channels.')
  parser.add_argument('--num_cells'    ,  type=int, default=5,                           help='The number of cells in one stage.')
  args = parser.parse_args()
  
  save_dir  = Path( args.base_save_dir )
  meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
  assert save_dir.exists(),  'invalid save dir path : {:}'.format(save_dir)
  assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
  print ('start the statistics of our nas-benchmark from {:} using {:}.'.format(save_dir, args.target_dir))
  basestr   = 'C{:}-N{:}'.format(args.channel, args.num_cells)
  
  if args.mode == 'cal':
    simplify(save_dir, meta_path, basestr, args.target_dir)
  elif args.mode == 'merge':
    merge_all(save_dir, meta_path, basestr)
  else:
    raise ValueError('invalid mode : {:}'.format(args.mode))
