text: from dep2label.encodings import * 
  from dep2label.postprocessing import *
  import codecs
  
  
  class Labeler:
  
      def __init__(self):   
          self.index_of_sentence = 1
          self.index_to_decode = 1
  
      def encode(self, file2encode, output, enc, mtl):
          sentence = {}
          encoded_sentences ={}
         
          with codecs.open(file2encode) as f2label:
              lines = f2label.readlines()
  
          for line in lines:
              if not line == '\n':
                  dummy_root = {"id": 0, "word": "ROOT",
                           "lemma": "ROOT","pos":"ROOT", "head": 0, "deprel": "root"}
                  sentence.update({0: dummy_root})
                  field = line.split("\t")
                  if "." in field[0] or "-" in field[0]:
                      continue
                  elif field[0].isdigit():
                      word_id = int(field[0])
                      if enc == "zero":
                          word = {"id":int(field[0]), "word": field[1], "lemma":field[2], "pos":field[3], "head":field[6], "deprel":field[7]}
                      else:
                          word = {"id":int(field[0]), "word": field[1], "lemma":field[2], "pos":field[3], "head":int(field[6]), "deprel":field[7]}
                      sentence.update({word_id: word})
              else:
                  words_with_labels = {}
                  if enc == "rel-pos":
                      if mtl == None:
                          task="2-task-combined"
                      else:
                          task = mtl
                      rel = RelPosEncoding()
                      words_with_labels = rel.encode(sentence, task)
                  elif enc == "1-planar-brackets":
                      if mtl == None:
                          task="2-task"
                      else:
                          task = mtl
                      brackets = Bracketing1pEncoding()
                      words_with_labels = brackets.encode(sentence, task)
                  elif enc =="2-planar-brackets-greedy":
                      if mtl == None:
                          task="3-task"
                      else:
                          task = mtl
                      brackets_2p = Bracketing2pGreedyEncoding()
                      words_with_labels = brackets_2p.encode(sentence, task)
                  elif enc == "2-planar-brackets-propagation":
                      if mtl == None:
                          task="3-task"
                      else:
                          task = mtl
                      brackets_2p = Bracketing2pPropagationEncoding()
                      words_with_labels = brackets_2p.encode(sentence, task)
                  elif enc == "arc-standard":
                      if mtl == None:
                          task="2-task"
                      else:
                          task = mtl
                      transition = ArcStandardEncoding()
                      words_with_labels = transition.encode(sentence, task)
                  elif enc == "arc-eager":
                      if mtl == None:
                          task="2-task"
                      else:
                          task = mtl
                      transition = ArcEagerEncoding()
                      words_with_labels = transition.encode(sentence, task)
                  elif enc == "arc-hybrid":
                      if mtl == None:
                          task="2-task"
                      else:
                          task = mtl
                      transition = ArcHybridEncoding()
                      words_with_labels = transition.encode(sentence, task)
                  elif enc == "covington":
                      if mtl == None:
                          task="2-task"
                      else:
                          task = mtl
                      transition = CovingtonEncoding()
                      words_with_labels = transition.encode(sentence, task)
                  elif enc =="zero":
                      if mtl == None:
                          task="2-task-combined"
                      else:
                          task = mtl
                      z = ZeroEncoding()
                      words_with_labels = z.encode(sentence, task)
                  else:
                      print("Invalid encoding")
                  
                  encoded_sentences.update({self.index_of_sentence: words_with_labels})
                  self.index_of_sentence += 1
                  sentence = {}        
          f2label.close()
          p = CoNLLPostProcessor()
          p.write_to_file(encoded_sentences, output)
  
  
      def decode(self, file2decode, output, enc, conllu):
          p = CoNLLPostProcessor()
          l = LabelPostProcessor()
          sentences = l.separate_labels(file2decode)
          decoded_sentences = {}
          nb_of_sentence = 1
          
          for sent_id in sentences:
              word_index = 1
              sentence = sentences.get(sent_id)
              sentence_with_sep_labels = {}
              sentence_with_sep_labels[0] = ["ROOT", "ROOT", 0, "root", "ROOT"]
              
              for word in sentence:
                  sentence_with_sep_labels.update({word_index: word})
                  word_index += 1
          
              if enc == "rel-pos":
                  rel = RelPosEncoding()
                  decoded_words, headless_words = rel.decode(sentence_with_sep_labels)
              elif enc == "1-planar-brackets":
                  bracketing = Bracketing1pEncoding()
                  decoded_words, headless_words = bracketing.decode(sentence_with_sep_labels)
              elif enc == "2-planar-brackets-greedy":
                  brackets_2p = Bracketing2pGreedyEncoding()
                  decoded_words, headless_words = brackets_2p.decode(sentence_with_sep_labels)
              elif enc == "2-planar-brackets-propagation":
                  brackets_2p = Bracketing2pPropagationEncoding()
                  decoded_words, headless_words = brackets_2p.decode(sentence_with_sep_labels)
              elif enc== "arc-standard":
                  transition = ArcStandardEncoding()
                  decoded_words, headless_words = transition.decode(sentence_with_sep_labels)
              elif enc== "arc-eager":
                  transition = ArcEagerEncoding()
                  decoded_words, headless_words = transition.decode(sentence_with_sep_labels)
              elif enc== "arc-hybrid":
                  transition = ArcHybridEncoding()
                  decoded_words, headless_words = transition.decode(sentence_with_sep_labels)
              elif enc== "covington":
                  transition = CovingtonEncoding()
                  decoded_words, headless_words = transition.decode(sentence_with_sep_labels)
  
              
              # POSTPROCESSING
              try:
                  if not l.has_root(decoded_words):
                      l.find_candidates_for_root(decoded_words, headless_words)
                  if not l.has_root(decoded_words):
                      l.assign_root_index_one(decoded_words, headless_words)
                  if l.has_multiple_roots(decoded_words):
                      l.choose_root_from_multiple_candidates(decoded_words)
                  l.assign_headless_words_to_root(decoded_words, headless_words)
                  l.find_cycles(decoded_words)
              except: # Finding root failed, probably due to no decoded words (happens with too long sentences)
                  decoded_words = [['-',]*6,]*len(sentence)
  
              p.convert_labels2conllu(decoded_words)
              decoded_sentences.update({nb_of_sentence: decoded_words})
              nb_of_sentence += 1
  
          if conllu == None:
              p.write_to_file(decoded_sentences, output)
          else:
              lookup = p.dump_into_lookup(conllu) 
              p.write_to_file(decoded_sentences, output)
              p.merge_lookup(output, lookup)
  