text: import os
  
  
  from model.general_utils import get_logger
  from model.data_utils import get_trimmed_glove_vectors, load_vocab, \
          get_processing_word
  
  DEBUG_MODE = False
  
  class Config():
      def __init__(self, load=True):
          """Initialize hyperparameters and load vocabs
  
          Args:
              load_embeddings: (bool) if True, load embeddings into
                  np array, else None
  
          """
          # directory for training outputs
          if not os.path.exists(self.dir_output):
              os.makedirs(self.dir_output)
  
          # create instance of logger
          self.logger = get_logger(self.path_log)
  
          # load if requested (default)
          if load:
              self.load()
  
  
      def load(self):
          """Loads vocabulary, processing functions and embeddings
  
          Supposes that build_data.py has been run successfully and that
          the corresponding files have been created (vocab and trimmed GloVe
          vectors)
  
          """
          # 1. vocabulary
          self.vocab_words = load_vocab(self.filename_words)
          self.vocab_tags  = load_vocab(self.filename_tags)
          self.vocab_chars = load_vocab(self.filename_chars)
  
          self.nwords     = len(self.vocab_words)
          self.nchars     = len(self.vocab_chars)
          self.ntags      = len(self.vocab_tags)
  
          # 2. get processing functions that map str -> id
          self.processing_word = get_processing_word(self.vocab_words,
                  self.vocab_chars, lowercase=True, chars=self.use_chars)
          self.processing_tag  = get_processing_word(self.vocab_tags,
                  lowercase=False, allow_unk=False)
  
          # 3. get pre-trained embeddings
          self.embeddings = (get_trimmed_glove_vectors(self.filename_trimmed)
                  if self.use_pretrained else None)
  
  
      # general config
      file_name = os.path.basename(__file__)
      dir_output = "results/test-resume/" + "20-notraining-quickresume" + "/"
      dir_model  = dir_output + "model.weights/"
      path_log   = dir_output + "log.txt"
  
      # embeddings
      dim_word = 300
      dim_char = 100
  
      # glove files
      filename_glove = "data/glove.6B/glove.6B.{}d.txt".format(dim_word)
      # trimmed embeddings (created from glove_filename with build_data.py)
      filename_trimmed = "data/glove.6B.{}d.trimmed.npz".format(dim_word)
      use_pretrained = True
  
      # dataset
      if DEBUG_MODE:
          filename_dev = "data/dev.eval.small"
          filename_test = "data/dev.eval.small"
          filename_train = "data/train.eval.small"
      else:
          filename_dev = "data/dev.eval"
          filename_test = "data/dev.eval"
          filename_train = "data/train.eval"
  
  #filename_dev = filename_test = filename_train = "data/test.txt" # test
  
      max_iter = None # if not None, max number of examples in Dataset
  
      # vocab (created from dataset with build_data.py)
      filename_words = "data/words.txt"
      filename_tags = "data/tags.txt"
      filename_chars = "data/chars.txt"
  
      # training
      train_embeddings = False
      nepochs          = 1000
      dropout          = 0.5
      batch_size       = 20
      lr_method        = "adam"
      lr               = 0.00001
      lr_decay         = 0.9
      clip             = -1 # if negative, no clipping
      nepoch_no_imprv  = 12
  
      # model hyperparameters
      hidden_size_char = 100 # lstm on chars
      hidden_size_lstm = 300 # lstm on word embeddings
  
      # NOTE: if both chars and crf, only 1.6x slower on GPU
      use_crf = True # if crf, training is 1.7x slower on CPU
      use_chars = True # if char embedding, training is 3.5x slower on CPU
  