text: from utils.config import *
  # from models.GLMP_memory_using_kb_arr import *
  from models.GLMP import *
  
  '''
  Command:
  
  python myTest.py -ds= -path= 
  
  '''
  
  directory = args['path'].split("/")
  task = directory[2].split('HDD')[0]
  HDD = directory[2].split('HDD')[1].split('BSZ')[0]
  L = directory[2].split('L')[1].split('lr')[0].split("-")[0]
  decoder = directory[1].split('-')[0] 
  BSZ = int(directory[2].split('BSZ')[1].split('DR')[0])
  DS = 'kvr' if 'kvr' in directory[1].split('-')[1].lower() else 'babi'
  DS = 'kvr'
  
  # if DS=='kvr':
  #     from utils.utils_Ent_kvr import *
  if DS=='kvr':
      from utils.utils_Ent_kvr_new import *
  elif DS=='babi':
      from utils.utils_Ent_babi import *
  elif DS=='multiwoz':
      # from utils.utils_Ent_multiwoz_for_dialogue_reasoning_dataset import *
  	# from utils.utils_Ent_multiwoz_new_memory_using_kb_arr import *
      from utils.utils_Ent_multiwoz_new import *
  elif DS=='sgd':
      from utils.utils_Ent_sgd_new import *
  else:
      print("You need to provide the --dataset information")
  
  train, dev, test, testOOV, lang, max_resp_len = prepare_data_seq(task, batch_size=BSZ)
  
  model = globals()[decoder](
  	int(HDD), 
  	lang, 
  	max_resp_len, 
  	args['path'], 
  	"", 
  	lr=0.0, 
  	n_layers=int(L), 
  	dropout=0.0)
  
  acc_test = model.evaluate(test, 1e7) 
  if testOOV!=[]: 
  	acc_oov_test = model.evaluate(testOOV, 1e7)