code: # -*- coding: utf-8 -*-
  # @Author: Jie
  # @Date:   2017-06-14 17:34:32
  # @Last Modified by:   Jie Yang,     Contact: jieynlp@gmail.com
  # @Last Modified time: 2019-01-25 20:25:59
  from __future__ import print_function
  from __future__ import absolute_import
  import argparse
  import sys
  import os
  import torch
  os.chdir(sys.path[0])
  sys.path.append("../../")
  sys.path.append("../../../")
  sys.path.append("../")
  from utils.alphabet import Alphabet
  from utils.functions import *
  
  try:
      import cPickle as pickle
  except ImportError:
      import pickle as pickle
  
  
  START = "</s>"
  UNKNOWN = "</unk>"
  PADDING = "</pad>"
  
  class Data:
      def __init__(self):
          self.sentence_classification = False
          self.MAX_SENTENCE_LENGTH = 250
          self.MAX_WORD_LENGTH = -1
          self.number_normalized = True
          self.norm_word_emb = False
          self.norm_char_emb = False
          
          self.word_alphabet = Alphabet('word')
          self.char_alphabet = Alphabet('character')
          
          # self.word_alphabet_pos = Alphabet('word')
          # self.char_alphabet_pos = Alphabet('character')
          
          self.feature_name = []
          self.feature_alphabets = []
          self.feature_num = len(self.feature_alphabets)
          self.feat_config = None
  
  
          self.ner_label_alphabet = Alphabet('ner_label',True)
          self.pos_label_alphabet = Alphabet('pos_label',True)
          self.chunk_label_alphabet = Alphabet('chunk_label',True)
          self.tagScheme = "NoSeg" ## BMES/BIO
          self.split_token = ' ||| '
          self.seg = True
  
          ### I/O
          self.train_dir = None
          self.dev_dir = None
          self.test_dir = None
          self.pos_train_dir = None
          self.pos_dev_dir = None
          self.pos_test_dir = None
          self.raw_dir = None
  
          self.decode_dir = None
          self.dset_dir = None ## data vocabulary related file
          self.model_dir = None ## model save  file
          self.load_model_dir = None ## model load file
  
          self.word_emb_dir = None
          self.char_emb_dir = None
          self.feature_emb_dirs = []
  
          self.train_texts = []
          self.dev_texts = []
          self.test_texts = []
          self.raw_texts = []
  
          self.train_Ids = []
          self.dev_Ids = []
          self.test_Ids = []
          self.raw_Ids = []
  
          self.pretrain_word_embedding = None
          self.pretrain_char_embedding = None
          self.pretrain_feature_embeddings = []
  
          self.ner_label_size = 0
          self.pos_label_size = 0
          self.chunk_label_size = 0
          self.word_alphabet_size = 0
          self.char_alphabet_size = 0
          self.ner_label_alphabet_size = 0
          self.pos_label_alphabet_size = 0
          self.chunk_label_alphabet_size = 0
          self.feature_alphabet_sizes = []
          self.feature_emb_dims = []
          self.norm_feature_embs = []
          self.word_emb_dim = 50
          self.char_emb_dim = 30
  
          ###Networks
          self.word_feature_extractor = "LSTM" ## "LSTM"/"CNN"/"GRU"/
          self.use_char = True
          self.char_feature_extractor = "CNN" ## "LSTM"/"CNN"/"GRU"/None
          self.use_crf = True
          self.nbest = None
  
          ## Training
          self.average_batch_loss = False
          self.optimizer = "SGD" ## "SGD"/"AdaGrad"/"AdaDelta"/"RMSProp"/"Adam"
          self.status = "train"
          ### Hyperparameters
          self.HP_cnn_layer = 4
          self.HP_iteration = 100
          self.HP_batch_size = 10
          self.HP_char_hidden_dim = 50
          self.HP_hidden_dim = 200
          self.HP_dropout = 0.5
          self.HP_lstm_layer = 1
          self.HP_bilstm = True
  
          self.HP_gpu = False
          self.HP_lr = 0.015
          self.HP_lr_decay = 0.05
          self.HP_clip = None
          self.HP_momentum = 0
          self.HP_l2 = 1e-8
  
      def show_data_summary(self):
          
          print("++"*50)
          print("DATA SUMMARY START:")
          print(" I/O:")
          if self.sentence_classification:
              print("     Start Sentence Classification task...")
          else:
              print("     Start   Sequence   Laebling   task...")
          print("     Tag          scheme: %s"%(self.tagScheme))
          print("     Split         token: %s"%(self.split_token))
          print("     MAX SENTENCE LENGTH: %s"%(self.MAX_SENTENCE_LENGTH))
          print("     MAX   WORD   LENGTH: %s"%(self.MAX_WORD_LENGTH))
          print("     Number   normalized: %s"%(self.number_normalized))
          print("     Word  alphabet size: %s"%(self.word_alphabet_size))
          print("     Char  alphabet size: %s"%(self.char_alphabet_size))
          print("     Label alphabet size: %s"%(self.ner_label_alphabet_size))
          print("     Label alphabet size: %s"%(self.pos_label_alphabet_size))
          print("     Label alphabet size: %s"%(self.chunk_label_alphabet_size))
          print("     Word embedding  dir: %s"%(self.word_emb_dir))
          print("     Char embedding  dir: %s"%(self.char_emb_dir))
          print("     Word embedding size: %s"%(self.word_emb_dim))
          print("     Char embedding size: %s"%(self.char_emb_dim))
          print("     Norm   word     emb: %s"%(self.norm_word_emb))
          print("     Norm   char     emb: %s"%(self.norm_char_emb))
          print("     Train  file directory: %s"%(self.train_dir))
          print("     Dev    file directory: %s"%(self.dev_dir))
          print("     Test   file directory: %s"%(self.test_dir))
          print("     Raw    file directory: %s"%(self.raw_dir))
          print("     Dset   file directory: %s"%(self.dset_dir))
          print("     Model  file directory: %s"%(self.model_dir))
          print("     Loadmodel   directory: %s"%(self.load_model_dir))
          print("     Decode file directory: %s"%(self.decode_dir))
          print("     Train instance number: %s"%(len(self.train_texts)))
          print("     Dev   instance number: %s"%(len(self.dev_texts)))
          print("     Test  instance number: %s"%(len(self.test_texts)))
          print("     Raw   instance number: %s"%(len(self.raw_texts)))
          print("     FEATURE num: %s"%(self.feature_num))
          for idx in range(self.feature_num):
              print("         Fe: %s  alphabet  size: %s"%(self.feature_alphabets[idx].name, self.feature_alphabet_sizes[idx]))
              print("         Fe: %s  embedding  dir: %s"%(self.feature_alphabets[idx].name, self.feature_emb_dirs[idx]))
              print("         Fe: %s  embedding size: %s"%(self.feature_alphabets[idx].name, self.feature_emb_dims[idx]))
              print("         Fe: %s  norm       emb: %s"%(self.feature_alphabets[idx].name, self.norm_feature_embs[idx]))
          print(" "+"++"*20)
          print(" Model Network:")
          print("     Model        use_crf: %s"%(self.use_crf))
          print("     Model word extractor: %s"%(self.word_feature_extractor))
          print("     Model       use_char: %s"%(self.use_char))
          if self.use_char:
              print("     Model char extractor: %s"%(self.char_feature_extractor))
              print("     Model char_hidden_dim: %s"%(self.HP_char_hidden_dim))
          print(" "+"++"*20)
          print(" Training:")
          print("     Optimizer: %s"%(self.optimizer))
          print("     Iteration: %s"%(self.HP_iteration))
          print("     BatchSize: %s"%(self.HP_batch_size))
          print("     Average  batch   loss: %s"%(self.average_batch_loss))
  
          print(" "+"++"*20)
          print(" Hyperparameters:")
  
          print("     Hyper              lr: %s"%(self.HP_lr))
          print("     Hyper        lr_decay: %s"%(self.HP_lr_decay))
          print("     Hyper         HP_clip: %s"%(self.HP_clip))
          print("     Hyper        momentum: %s"%(self.HP_momentum))
          print("     Hyper              l2: %s"%(self.HP_l2))
          print("     Hyper      hidden_dim: %s"%(self.HP_hidden_dim))
          print("     Hyper         dropout: %s"%(self.HP_dropout))
          print("     Hyper      lstm_layer: %s"%(self.HP_lstm_layer))
          print("     Hyper          bilstm: %s"%(self.HP_bilstm))
          print("     Hyper             GPU: %s"%(self.HP_gpu))
          print("DATA SUMMARY END.")
          print("++"*50)
          sys.stdout.flush()
  
  
      def initial_feature_alphabets(self):
          # 
          if self.sentence_classification:
              ## if sentence classification data format, splited by '\t'
              items = open(self.train_dir,'r').readline().strip('\n').split('\t')
          else:
              ## if sequence labeling data format i.e. CoNLL 2003, split by ' '
              items = open(self.train_dir,'r').readline().strip('\n').split()
          total_column = len(items)
          if total_column > 2:
              for idx in range(1, total_column-1):
                  feature_prefix = items[idx].split(']',1)[0]+"]"
                  self.feature_alphabets.append(Alphabet(feature_prefix))
                  self.feature_name.append(feature_prefix)
                  print("Find feature: ", feature_prefix)
          self.feature_num = len(self.feature_alphabets)
          self.pretrain_feature_embeddings = [None]*self.feature_num
          self.feature_emb_dims = [20]*self.feature_num
          self.feature_emb_dirs = [None]*self.feature_num
          self.norm_feature_embs = [False]*self.feature_num
          self.feature_alphabet_sizes = [0]*self.feature_num
          if self.feat_config:
              for idx in range(self.feature_num):
                  if self.feature_name[idx] in self.feat_config:
                      self.feature_emb_dims[idx] = self.feat_config[self.feature_name[idx]]['emb_size']
                      self.feature_emb_dirs[idx] = self.feat_config[self.feature_name[idx]]['emb_dir']
                      self.norm_feature_embs[idx] = self.feat_config[self.feature_name[idx]]['emb_norm']
          # exit(0)
  
  
      def build_alphabet(self, input_file):
          in_lines = open(input_file,'r').readlines()
          for line in in_lines:
              if len(line) > 2:
                  ## if sequence labeling data format i.e. CoNLL 2003
                      pairs = line.strip().split()
                      word = pairs[0]
                      if word == '-DOCSTART-': 
                      # print("clear dirty data") # clear dirty data
                          continue
                      if sys.version_info[0] < 3:
                          word = word.decode('utf-8')
                      if self.number_normalized:
                          word = normalize_word(word)
                      ner_label = pairs[3]
                      pos_label = pairs[1]
                      chunk_label = pairs[2]
                      self.ner_label_alphabet.add(ner_label)
                      self.pos_label_alphabet.add(pos_label)
                      self.chunk_label_alphabet.add(chunk_label)
  
                      ## build feature alphabet
                      for idx in range(self.feature_num):
                          feat_idx = pairs[idx+1].split(']',1)[-1]
                          self.feature_alphabets[idx].add(feat_idx)
                      for char in word:
                          self.char_alphabet.add(char)
                      
          self.word_alphabet_size = self.word_alphabet.size()
          self.char_alphabet_size = self.char_alphabet.size()
          self.pos_label_alphabet_size = self.pos_label_alphabet.size()
          self.chunk_label_alphabet_size = self.chunk_label_alphabet.size()
          self.ner_label_alphabet_size = self.ner_label_alphabet.size()
          for idx in range(self.feature_num):
              self.feature_alphabet_sizes[idx] = self.feature_alphabets[idx].size()
          startS = False
          startB = False
          for label,_ in self.ner_label_alphabet.iteritems():
              if "S-" in label.upper():
                  startS = True
              elif "B-" in label.upper():
                  startB = True
          for label,_ in self.chunk_label_alphabet.iteritems():
              if "S-" in label.upper():
                  startS = True
              elif "B-" in label.upper():
                  startB = True
          if startB:
              if startS:
                  self.tagScheme = "BMES"
              else:
                  self.tagScheme = "BIO"
          if self.sentence_classification:
              self.tagScheme = "Not sequence labeling task"
  
  
      def fix_alphabet(self):
          self.word_alphabet.close()
          self.char_alphabet.close()
          self.ner_label_alphabet.close()
          self.chunk_label_alphabet.close()
          self.pos_label_alphabet.close()
          for idx in range(self.feature_num):
              self.feature_alphabets[idx].close()
  
  
      def build_pretrain_emb(self):
          if self.word_emb_dir:
              print("Load pretrained word embedding, norm: %s, dir: %s"%(self.norm_word_emb, self.word_emb_dir))
              self.pretrain_word_embedding, self.word_emb_dim = build_pretrain_embedding(self.word_emb_dir, self.word_alphabet, self.word_emb_dim, self.norm_word_emb)
              pass
          if self.char_emb_dir:
              print("Load pretrained char embedding, norm: %s, dir: %s"%(self.norm_char_emb, self.char_emb_dir))
              self.pretrain_char_embedding, self.char_emb_dim = build_pretrain_embedding(self.char_emb_dir, self.char_alphabet, self.char_emb_dim, self.norm_char_emb)
          for idx in range(self.feature_num):
              if self.feature_emb_dirs[idx]:
                  print("Load pretrained feature %s embedding:, norm: %s, dir: %s"%(self.feature_name[idx], self.norm_feature_embs[idx], self.feature_emb_dirs[idx]))
                  self.pretrain_feature_embeddings[idx], self.feature_emb_dims[idx] = build_pretrain_embedding(self.feature_emb_dirs[idx], self.feature_alphabets[idx], self.feature_emb_dims[idx], self.norm_feature_embs[idx])
  
  
      def generate_instance(self, name):
          self.fix_alphabet()
          if name == "train":
              self.train_texts, self.train_Ids = read_instance(self.train_dir, self.word_alphabet, self.char_alphabet, self.feature_alphabets, self.ner_label_alphabet,self.pos_label_alphabet,self.chunk_label_alphabet, self.number_normalized, self.MAX_SENTENCE_LENGTH, self.sentence_classification, self.split_token)
          elif name == "dev":
              self.dev_texts, self.dev_Ids = read_instance(self.dev_dir, self.word_alphabet, self.char_alphabet, self.feature_alphabets, self.ner_label_alphabet,self.pos_label_alphabet,self.chunk_label_alphabet, self.number_normalized, self.MAX_SENTENCE_LENGTH, self.sentence_classification, self.split_token)
          elif name == "test":
              self.test_texts, self.test_Ids = read_instance(self.test_dir, self.word_alphabet, self.char_alphabet, self.feature_alphabets, self.ner_label_alphabet,self.pos_label_alphabet,self.chunk_label_alphabet, self.number_normalized, self.MAX_SENTENCE_LENGTH, self.sentence_classification, self.split_token)
          elif name == "raw":
              self.raw_texts, self.raw_Ids = read_instance(self.raw_dir, self.word_alphabet, self.char_alphabet, self.feature_alphabets, self.ner_label_alphabet, self.pos_label_alphabet,self.chunk_label_alphabet,self.number_normalized, self.MAX_SENTENCE_LENGTH, self.sentence_classification, self.split_token)
          else:
              print("Error: you can only generate train/dev/test instance! Illegal input:%s"%(name))
  
  
      def write_decoded_results(self, predict_results, name):
          
          sent_num = len(predict_results)
          content_list = []
          if name == 'raw':
             content_list = self.raw_texts
          elif name == 'test':
              content_list = self.test_texts
          elif name == 'dev':
              content_list = self.dev_texts
          elif name == 'train':
              content_list = self.train_texts
          else:
              print("Error: illegal name during writing predict result, name should be within train/dev/test/raw !")
          assert(sent_num == len(content_list))
          fout = open(self.decode_dir,'w')
          for idx in range(sent_num):
              if self.sentence_classification:
                  fout.write(" ".join(content_list[idx][0])+"\t"+predict_results[idx]+ '\n')
              else:
                  sent_length = len(predict_results[idx])
                  for idy in range(sent_length):
                      ## content_list[idx] is a list with [word, char, label]
                      fout.write(content_list[idx][0][idy].encode('utf-8') + " " + predict_results[idx][idy] + '\n')
                  fout.write('\n')
          fout.close()
          print("Predict %s result has been written into file. %s"%(name, self.decode_dir))
  
  
      def load(self,data_file):
          f = open(data_file, 'rb')
          tmp_dict = pickle.load(f)
          f.close()
          self.__dict__.update(tmp_dict)
  
      def save(self,save_file):
          f = open(save_file, 'wb')
          pickle.dump(self.__dict__, f, 2)
          f.close()
  
  
  
      def write_nbest_decoded_results(self, predict_results, pred_scores, name):
          ## predict_results : [whole_sent_num, nbest, each_sent_length]
          ## pred_scores: [whole_sent_num, nbest]
          fout = open(self.decode_dir,'w')
          sent_num = len(predict_results)
          content_list = []
          if name == 'raw':
             content_list = self.raw_texts
          elif name == 'test':
              content_list = self.test_texts
          elif name == 'dev':
              content_list = self.dev_texts
          elif name == 'train':
              content_list = self.train_texts
          else:
              print("Error: illegal name during writing predict result, name should be within train/dev/test/raw !")
          assert(sent_num == len(content_list))
          assert(sent_num == len(pred_scores))
          for idx in range(sent_num):
              sent_length = len(predict_results[idx][0])
              nbest = len(predict_results[idx])
              score_string = "#"
              for idz in range(nbest):
                  score_string += format(pred_scores[idx][idz], '.4f')+" "
              fout.write(score_string.strip() + "\n")
  
              for idy in range(sent_length):
                  try:  # Will fail with python3
                      label_string = content_list[idx][0][idy].encode('utf-8') + " "
                  except:
                      label_string = content_list[idx][0][idy] + " "
                  for idz in range(nbest):
                      label_string += predict_results[idx][idz][idy]+" "
                  label_string = label_string.strip() + "\n"
                  fout.write(label_string)
              fout.write('\n')
          fout.close()
          print("Predict %s %s-best result has been written into file. %s"%(name,nbest, self.decode_dir))
  
  
      def read_config(self,config_file):
          config = config_file_to_dict(config_file)
          ## read data:
          the_item = 'train_dir'
          if the_item in config:
              self.train_dir = config[the_item]
          the_item = 'dev_dir'
          if the_item in config:
              self.dev_dir = config[the_item]
          the_item = 'test_dir'
          if the_item in config:
              self.test_dir = config[the_item]
          the_item = 'raw_dir'
          if the_item in config:
              self.raw_dir = config[the_item]
          the_item = 'decode_dir'
          if the_item in config:
              self.decode_dir = config[the_item]
          the_item = 'dset_dir'
          if the_item in config:
              self.dset_dir = config[the_item]
          the_item = 'model_dir'
          if the_item in config:
              self.model_dir = config[the_item]
          the_item = 'load_model_dir'
          if the_item in config:
              self.load_model_dir = config[the_item]
  
          the_item = 'word_emb_dir'
          if the_item in config:
              self.word_emb_dir = config[the_item]
          the_item = 'char_emb_dir'
          if the_item in config:
              self.char_emb_dir = config[the_item]
  
  
          the_item = 'MAX_SENTENCE_LENGTH'
          if the_item in config:
              self.MAX_SENTENCE_LENGTH = int(config[the_item])
          the_item = 'MAX_WORD_LENGTH'
          if the_item in config:
              self.MAX_WORD_LENGTH = int(config[the_item])
  
          the_item = 'norm_word_emb'
          if the_item in config:
              self.norm_word_emb = str2bool(config[the_item])
          the_item = 'norm_char_emb'
          if the_item in config:
              self.norm_char_emb = str2bool(config[the_item])
          the_item = 'number_normalized'
          if the_item in config:
              self.number_normalized = str2bool(config[the_item])
  
          the_item = 'sentence_classification'
          if the_item in config:
              self.sentence_classification = str2bool(config[the_item])
          the_item = 'seg'
          if the_item in config:
              self.seg = str2bool(config[the_item])
          the_item = 'word_emb_dim'
          if the_item in config:
              self.word_emb_dim = int(config[the_item])
          the_item = 'char_emb_dim'
          if the_item in config:
              self.char_emb_dim = int(config[the_item])
  
          ## read network:
          the_item = 'use_crf'
          if the_item in config:
              self.use_crf = str2bool(config[the_item])
          the_item = 'use_char'
          if the_item in config:
              self.use_char = str2bool(config[the_item])
          the_item = 'word_seq_feature'
          if the_item in config:
              self.word_feature_extractor = config[the_item]
          the_item = 'char_seq_feature'
          if the_item in config:
              self.char_feature_extractor = config[the_item]
          the_item = 'nbest'
          if the_item in config:
              self.nbest = int(config[the_item])
  
          the_item = 'feature'
          if the_item in config:
              self.feat_config = config[the_item] ## feat_config is a dict
  
  
          ## read training setting:
          the_item = 'optimizer'
          if the_item in config:
              self.optimizer = config[the_item]
          the_item = 'ave_batch_loss'
          if the_item in config:
              self.average_batch_loss = str2bool(config[the_item])
          the_item = 'status'
          if the_item in config:
              self.status = config[the_item]
  
          ## read Hyperparameters:
          the_item = 'cnn_layer'
          if the_item in config:
              self.HP_cnn_layer = int(config[the_item])
          the_item = 'iteration'
          if the_item in config:
              self.HP_iteration = int(config[the_item])
          the_item = 'batch_size'
          if the_item in config:
              self.HP_batch_size = int(config[the_item])
  
          the_item = 'char_hidden_dim'
          if the_item in config:
              self.HP_char_hidden_dim = int(config[the_item])
          the_item = 'hidden_dim'
          if the_item in config:
              self.HP_hidden_dim = int(config[the_item])
          the_item = 'dropout'
          if the_item in config:
              self.HP_dropout = float(config[the_item])
          the_item = 'lstm_layer'
          if the_item in config:
              self.HP_lstm_layer = int(config[the_item])
          the_item = 'bilstm'
          if the_item in config:
              self.HP_bilstm = str2bool(config[the_item])
  
          the_item = 'gpu'
          if the_item in config:
              self.HP_gpu = str2bool(config[the_item])
          the_item = 'learning_rate'
          if the_item in config:
              self.HP_lr = float(config[the_item])
          the_item = 'lr_decay'
          if the_item in config:
              self.HP_lr_decay = float(config[the_item])
          the_item = 'clip'
          if the_item in config:
              self.HP_clip = float(config[the_item])
          the_item = 'momentum'
          if the_item in config:
              self.HP_momentum = float(config[the_item])
          the_item = 'l2'
          if the_item in config:
              self.HP_l2 = float(config[the_item])
          ## no seg for sentence classification
          if self.sentence_classification:
              self.seg = False
              self.use_crf = False
  
  
  def config_file_to_dict(input_file):
      config = {}
      fins = open(input_file,'r').readlines()
      for line in fins:
          if len(line) > 0 and line[0] == "#":
              continue
          if "=" in line:
              pair = line.strip().split('#',1)[0].split('=',1)
              item = pair[0]
              if item=="feature":
                  if item not in config:
                      feat_dict = {}
                      config[item]= feat_dict
                  feat_dict = config[item]
                  new_pair = pair[-1].split()
                  feat_name = new_pair[0]
                  one_dict = {}
                  one_dict["emb_dir"] = None
                  one_dict["emb_size"] = 10
                  one_dict["emb_norm"] = False
                  if len(new_pair) > 1:
                      for idx in range(1,len(new_pair)):
                          conf_pair = new_pair[idx].split('=')
                          if conf_pair[0] == "emb_dir":
                              one_dict["emb_dir"]=conf_pair[-1]
                          elif conf_pair[0] == "emb_size":
                              one_dict["emb_size"]=int(conf_pair[-1])
                          elif conf_pair[0] == "emb_norm":
                              one_dict["emb_norm"]=str2bool(conf_pair[-1])
                  feat_dict[feat_name] = one_dict
                  # print "feat",feat_dict
              else:
                  if item in config:
                      print("Warning: duplicated config item found: %s, updated."%(pair[0]))
                  config[item] = pair[-1]
  
  
      return config
  
  
  def str2bool(string):
      if string == "True" or string == "true" or string == "TRUE":
          return True
      else:
          return False
  
  if __name__ == '__main__':
  
      parser = argparse.ArgumentParser(description='Tuning with NCRF++')
      # parser.add_argument('--status', choices=['train', 'decode'], help='update algorithm', default='train')
      parser.add_argument('--config',  help='Configuration File', default='None')
      parser.add_argument('--wordemb',  help='Embedding for words', default='None')
      parser.add_argument('--charemb',  help='Embedding for chars', default='None')
      parser.add_argument('--status', choices=['train', 'decode'], help='update algorithm', default='train')
      parser.add_argument('--savemodel', default="data/model/saved_model.lstmcrf.")
      parser.add_argument('--savedset', help='Dir of saved data setting')
      parser.add_argument('--train', default="data/conll03/train.bmes") 
      parser.add_argument('--dev', default="data/conll03/dev.bmes" )  
      parser.add_argument('--test', default="data/conll03/test.bmes") 
      parser.add_argument('--seg', default="True") 
      parser.add_argument('--raw') 
      parser.add_argument('--loadmodel')
      parser.add_argument('--output') 
  
      args = parser.parse_args()
      args.config = '../demo.train.config'
      data = Data()
      data.HP_gpu = torch.cuda.is_available()
      if args.config == 'None':
          data.train_dir = args.train 
          data.dev_dir = args.dev 
          data.test_dir = args.test
          data.model_dir = args.savemodel
          data.dset_dir = args.savedset
          print("Save dset directory:",data.dset_dir)
          save_model_dir = args.savemodel
          data.word_emb_dir = args.wordemb
          data.char_emb_dir = args.charemb
          if args.seg.lower() == 'true':
              data.seg = True
          else:
              data.seg = False
      else:
          data.read_config(args.config)
      # data.show_data_summary()
      status = data.status.lower()
  
      if status == 'train':
          print("MODEL: train")
          # data_initialization(data)
          data.initial_feature_alphabets()
          data.build_alphabet(data.train_dir)
          data.build_alphabet(data.dev_dir)
          data.build_alphabet(data.test_dir)
          data.fix_alphabet()
          data.generate_instance('train')
          data.generate_instance('dev')
          data.generate_instance('test')
          print(data.chunk_label_alphabet.instance2index)
          print(data.pos_label_alphabet.instance2index)
          print(data.ner_label_alphabet.instance2index)
          # data.build_pretrain_emb()
          train(data)
      elif status == 'decode':
          print("MODEL: decode")
          data.load(data.dset_dir)
          data.read_config(args.config)
          print(data.raw_dir)
          # exit(0)
          data.show_data_summary()
          # data.generate_instance('raw')
          print("nbest: %s"%(data.nbest))
          decode_results, pred_scores = load_model_decode(data, 'test')
          if data.nbest and not data.sentence_classification:
              data.write_nbest_decoded_results(decode_results, pred_scores, 'test')
          else:
              data.write_decoded_results(decode_results, 'test')
      else:
          print("Invalid argument! Please use valid arguments! (train/test/decode)")
  
    
apis: torch.cuda.is_available