text: import json
  from data_loader.utils import DD
  
  device = "cpu"
  
  save = False
  test_save = False
  toy = False
  do_gen = False
  
  save_strategy = "all"
  
  
  def get_parameters(opt):
      params = DD()
      params.net = DD()
  
      params.mle = 0
      params.dataset = opt.dataset
  
      params.net = get_net_parameters(opt)
      params.train = get_training_parameters(opt)
  
      params.model = params.net.model
      params.exp = opt.exp
  
      params.data = get_data_parameters(opt, params.exp, params.dataset)
      params.eval = get_eval_parameters(opt, params.data.get("categories", None))
  
      meta = DD()
  
      params.trainer = opt.trainer
  
      meta.iterations = int(opt.iterations)
      meta.cycle = opt.cycle
      params.cycle = opt.cycle
      params.iters = int(opt.iterations)
  
      global toy
      toy = opt.toy
  
      global do_gen
      do_gen = opt.do_gen
  
      global save
      save = opt.save
  
      global test_save
      test_save = opt.test_save
  
      global save_strategy
      save_strategy = opt.save_strategy
  
      print(params)
      return params, meta
  
  
  def get_eval_parameters(opt, force_categories=None):
      evaluate = DD()
  
      if opt.eval_sampler == "beam":
          evaluate.bs = opt.beam_size
      elif opt.eval_sampler == "greedy":
          evaluate.bs = 1
      elif opt.eval_sampler == "topk":
          evaluate.k = opt.topk_size
  
      evaluate.smax = opt.gen_seqlength
      evaluate.sample = opt.eval_sampler
  
      evaluate.numseq = opt.num_sequences
  
      evaluate.gs = opt.generate_sequences
      evaluate.es = opt.evaluate_sequences
  
      if opt.dataset == "atomic":
          if "eval_categories" in opt and force_categories is None:
              evaluate.categories = opt.eval_categories
          else:
              evaluate.categories = force_categories
  
      return evaluate
  
  
  def get_data_parameters(opt, experiment, dataset):
      data = DD()
      if dataset == "atomic":
          data.categories = sorted(opt.categories)
          # hard-coded
          data.maxe1 = 17
          data.maxe2 = 35
          data.maxr = 1
  
      elif dataset == "conceptnet":
          data.rel = opt.relation_format
          data.trainsize = opt.training_set_size
          data.devversion = opt.development_set_versions_to_use
          data.maxe1 = opt.max_event_1_size
          data.maxe2 = opt.max_event_2_size
          if data.rel == "language":
              # hard-coded
              data.maxr = 5
          else:
              # hard-coded
              data.maxr = 1
  
      return data
  
  
  def get_training_parameters(opt):
      train = DD()
      static = DD()
      static.exp = opt.exp
  
      static.seed = opt.random_seed
  
      # weight decay
      static.l2 = opt.l2
      static.vl2 = True
      static.lrsched = opt.learning_rate_schedule  # 'warmup_linear'
      static.lrwarm = opt.learning_rate_warmup  # 0.002
  
      # gradient clipping
      static.clip = opt.clip
  
      # what loss function to use
      static.loss = opt.loss
  
      dynamic = DD()
      dynamic.lr = opt.learning_rate  # learning rate
      dynamic.bs = opt.batch_size  # batch size
      # optimizer to use {adam, rmsprop, etc.}
      dynamic.optim = opt.optimizer
  
      # rmsprop
      # alpha is interpolation average
  
      static.update(opt[dynamic.optim])
  
      train.static = static
      train.dynamic = dynamic
  
      return train
  
  
  def get_net_parameters(opt):
      net = DD()
      net.model = opt.model
      net.nL = opt.num_layers
      net.nH = opt.num_heads
      net.hSize = opt.hidden_dim
      net.edpt = opt.embedding_dropout
      net.adpt = opt.attention_dropout
      net.rdpt = opt.residual_dropout
      net.odpt = opt.output_dropout
      net.pt = opt.pretrain
      net.afn = opt.activation
  
      # how to intialize parameters
      # format is gauss+{}+{}.format(mean, std)
      # n = the default initialization pytorch
      net.init = opt.init
  
      return net
  
  
  def read_config(file_):
      config = DD()
      print(file_)
      for k, v in file_.items():
          if v == "True" or v == "T" or v == "true":
              config[k] = True
          elif v == "False" or v == "F" or v == "false":
              config[k] = False
          elif type(v) == dict:
              config[k] = read_config(v)
          else:
              config[k] = v
  
      return config
  
  
  def load_config(name):
      with open(name, "r") as f:
          config = json.load(f)
      return config
  