text: import sys
  import os
  import torch
  import torch.autograd as autograd
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  import numpy as np
  import pickle
  
  from data import POSdata, TorchData
  from models import SequenceTagger
  
  from text_classifier_torch.t2i import T2I as text_vectorizer
  from text_classifier_torch.t2i import to_torch_long_tensor,torch_minibatched_2dim
  
  WARNING='\033[91m'
  END_WARNING='\033[0m'
  
  def predictions2text(predictions,label_vectorizer):
      text_labels=[]
      # unpack because predictions is packed sequence
      data, lens = torch.nn.utils.rnn.pad_packed_sequence(predictions)  # unpack
  #    print("*******",data.size())
      scores,predictions_=data.max(2)
      for pred in torch.transpose(predictions_,0,1):
          max_labels=[label_vectorizer.reverse(int(i)) for i in pred.data.cpu().numpy()]
          text_labels.append(max_labels)
      return text_labels
  
  
  def accuracy(data,model,label_vectorizer,targets,args,sentences,verbose=False):
      correct=0
      total=0
      _word_in,_char_in,_lengths=data
      number_of_batches=_word_in.size(1)
      for batch_id in range(number_of_batches):
          word_batch=autograd.Variable(_word_in[:,batch_id,:])
          char_batch=autograd.Variable(_char_in[:,batch_id,:])
          len_batch=torch.LongTensor(_lengths[batch_id*args.batch_size:batch_id*args.batch_size+args.batch_size])
          if args.cuda:
              word_batch=word_batch.cuda()
              char_batch=char_batch.cuda()
          model.eval()
          predictions=predictions2text(model(word_batch,char_batch,len_batch),label_vectorizer)
          target_batch=targets[batch_id*args.batch_size:batch_id*args.batch_size+args.batch_size]
          sentence_batch=sentences[batch_id*args.batch_size:batch_id*args.batch_size+args.batch_size]
          for psent,sent,sentence in zip(predictions,target_batch,sentence_batch):
              if verbose:
                  print("PRED:",psent)
                  print("GOLD:",sent)
                  print("INPUT:",sentence,"\n")
              for ptoken,token in zip(psent,sent):
                  if token=="<START>" or token=="<END>":
                      continue
                  if token=="__PADDING__":
                      break
                  if token==ptoken:
                      correct+=1
                  total+=1
      return correct/total*100 if total!= 0 else 0
  
  def minibatched_3dim(data,batch_size):
      seq_count,word_count,char_count=data.size()
      seq_count_mbatch_aligned=(seq_count//batch_size)*batch_size
      data_batched=data[:seq_count_mbatch_aligned].transpose(0,1).contiguous().view(word_count,seq_count//batch_size,-1,char_count)
      return data_batched
  
  
  def train(args):
  
      # data
      posdata=POSdata() # class to read part-of-speech tagging data from conllu
      train_sentences,train_labels=posdata.read_data(args.train_file, args.max_train, count_words=False)
  #    train_sentences=posdata.mask_rare_words(train_sentences, freq=args.word_freq_cutoff, mask_term="__UNK__", verbose=args.verbose)
  
      torchdata=TorchData() # class to turn text into torch style minibatches
      if len(args.pretrained_word_embeddings)>0:
          torchdata.init_vocab_from_pretrained(args.pretrained_word_embeddings)
      
      train_batches_word, train_batches_char, train_batches_label, train_sequence_lengths, train_sorting_indices, train_unsorting_indices=torchdata.prepare_torch_data(train_sentences, train_labels, args.batch_size, args.max_seq_len, args.max_seq_len_char, train=True, shuffle=args.shuffle_train, sort_batch=True) # sentences, labels, batch_size, seq_words, seq_chars, train=True, shuffle=False, sort_batch=False
  
      print("Train word input sizes:",train_batches_word.size())
      print("Train character input sizes:",train_batches_char.size())
      print("Train target sizes:",train_batches_label.size())
      print("Training examples:",len(train_sentences),"Unique words:",len(torchdata.word_vectorizer.idict),"Unique characters:",len(torchdata.char_vectorizer.idict),"Classes:",len(torchdata.label_vectorizer.idict))
  
      
  
      # model
      model=SequenceTagger(len(torchdata.word_vectorizer.idict), len(torchdata.char_vectorizer.idict), len(torchdata.label_vectorizer.idict), args, pretrained_size=torchdata.embedding_size)
  
      # copy weights from pretrained embeddings
      if len(args.pretrained_word_embeddings)>0:
          torchdata.load_pretrained_embeddings(args.pretrained_word_embeddings, model, model.pretrained_word_embeddings)
  
      if args.cuda:
          model.cuda()
      
      # remove frozen parameters
      trainable_parameters = filter(lambda p: p.requires_grad, model.parameters())
  
      loss_function=nn.CrossEntropyLoss()#ignore_index=0)
      optimizer=optim.SGD(trainable_parameters,lr=args.learning_rate)
  #    optimizer=optim.Adam(trainable_parameters,lr=args.learning_rate)
  
      number_of_batches=train_batches_word.size(1)
      
      
      print("Training batches",number_of_batches)
      print("Shuffling training data:",args.shuffle_train)
  
      # devel data
      devel_sentences,devel_labels=posdata.read_data(args.devel_file,args.max_devel)
  
      devel_batches_word, devel_batches_char, devel_batches_label, devel_sequence_lengths, devel_sorting_indices, devel_unsorting_indices=torchdata.prepare_torch_data(devel_sentences, devel_labels, args.batch_size, args.max_seq_len, args.max_seq_len_char, train=False, shuffle=False, sort_batch=True)
      devel_sentences_sorted=list(np.array(devel_sentences)[devel_sorting_indices])
      devel_labels_sorted=list(np.array(devel_labels)[devel_sorting_indices])
  
      print("Devel word input sizes:",devel_batches_word.size())
      print("Devel character input sizes:",devel_batches_char.size())
      print("Devel target sizes:",devel_batches_label.size())
  
      # TRAINING
      for epoch in range(args.epochs):
          if epoch is not 0:
              acc=accuracy((devel_batches_word, devel_batches_char, devel_sequence_lengths), model, torchdata.label_vectorizer, devel_labels_sorted, args, devel_sentences_sorted, args.verbose)
              print("EPOCH:", epoch, "LOSS:", loss.data[0], "ACCURACY:", acc, flush=True)
  
          # shuffle batches
          idxs=[i for i in range(number_of_batches)]
          np.random.shuffle(idxs)
  
  
          # shuffle training data (...and create new batches)
          if args.shuffle_train:
              train_batches_word, train_batches_char, train_batches_label, train_sequence_lengths, train_sorting_indices, train_unsorting_indices=torchdata.prepare_torch_data(train_sentences, train_labels, args.batch_size, args.max_seq_len, args.max_seq_len_char, train=True, shuffle=True, sort_batch=True)
  
  
  #        for batch_id in range(number_of_batches):
          for batch_id in idxs:
  
              word_batch=autograd.Variable(train_batches_word[:,batch_id,:])
              char_batch=autograd.Variable(train_batches_char[:,batch_id,:])
              targets=autograd.Variable(train_batches_label[:,batch_id,:])
              lengths=torch.LongTensor(train_sequence_lengths[batch_id*args.batch_size:batch_id*args.batch_size+args.batch_size])
      
              if args.cuda:
                  word_batch=word_batch.cuda()
                  char_batch=char_batch.cuda()
                  targets=targets.cuda()
                  lengths=lengths.cuda()
  
              optimizer.zero_grad()
              model.train()
              outputs=model(word_batch, char_batch, lengths)
  
              # pack targets
              targets_packed=torch.nn.utils.rnn.pack_padded_sequence(targets, list(lengths))
  
  #            loss=loss_function(outputs.contiguous().view(outputs.size(0)*outputs.size(1),-1), targets_packed.data.contiguous().view(outputs.size(0)*outputs.size(1)))
              loss=loss_function(outputs.data, targets_packed.data)
  
              loss.backward()
              optimizer.step()
      acc=accuracy((devel_batches_word, devel_batches_char, devel_sequence_lengths), model, torchdata.label_vectorizer, devel_labels_sorted, args, devel_sentences_sorted, args.verbose)
      print("EPOCH:", epoch, "LOSS:", loss.data[0], "ACCURACY:", acc, flush=True)
  
      if len(args.save_directory)>0:
          print("Saving model into",args.save_directory)
  
          if not os.path.exists(args.save_directory):
              os.makedirs(args.save_directory)
          print("Model:",os.path.join(args.save_directory,"model.pt"))
          torch.save(model, os.path.join(args.save_directory,"model.pt"))
          print("Vectorizers:",os.path.join(args.save_directory,"torchdata_vectorizers.pkl"))
          with open(os.path.join(args.save_directory,"torchdata_vectorizers.pkl"),"wb") as f:
              pickle.dump(torchdata,f)
          print("Saving ready.")
          
      return acc
  
  
  
  def callable(inputs):
      # inputs is a list of hyperparameters (batch_size, word_embedding_size, char_embedding_size, recurrent_size, recurrent_dropout, learning_rate)
      # returns final accuracy
  
      from collections import namedtuple
      Arguments = namedtuple('Arguments', ['train_file', 'devel_file', 'max_train', 'max_devel', 'cuda', 'verbose', 'shuffle_train', 'epochs', 'encoder_layers', 'max_seq_len', 'max_seq_len_char', 'word_freq_cutoff', 'pretrained_word_embeddings', 'batch_size', 'word_embedding_size', 'char_embedding_size', 'recurrent_size', 'recurrent_dropout', 'learning_rate'])
  
      args=Arguments("../UD_Finnish/fi-ud-train.conllu", "../UD_Finnish/fi-ud-dev.conllu", 1000000, 1000000, True, False, True, 10, 2, 50, 20, 5, "", int(inputs[0])**2, int(inputs[1])*100, int(inputs[2])*100, int(inputs[3])*100, float(inputs[4])/10, float(inputs[5])/10000)
  
      print(args)
  
      accuracy=train(args)
  
      return 100-accuracy
      
  
  
  if __name__=="__main__":
  
      import argparse
  
      parser = argparse.ArgumentParser(description='')
      g=parser.add_argument_group("Reguired arguments")
      
      g.add_argument('--train_file', type=str, help='Input training file name')
      g.add_argument('--devel_file', type=str, help='Input development file name')
      g.add_argument('--max_train', type=int, default=1000000, help='Maximum number of sentences used in training')
      g.add_argument('--max_devel', type=int, default=1000000, help='Maximum number of sentences used in training eval')
      g.add_argument('--cpu', dest='cuda', default=True, action="store_false", help='Use cpu.')
      g.add_argument('--batch_size', type=int, default=64, help='Minibatch size')
      g.add_argument('--word_embedding_size', type=int, default=200, help='Size of word embeddings')
      g.add_argument('--char_embedding_size', type=int, default=200, help='Size of word embeddings')
      g.add_argument('--recurrent_size', type=int, default=500, help='Size of recurrent layers')
      g.add_argument('--char_recurrent_size', type=int, default=300, help='Size of recurrent layers in character rnn')
      g.add_argument('--encoder_layers', type=int, default=2, help='Number of recurrent layer in the endocer')
      g.add_argument('--recurrent_dropout', type=float, default=0.0, help='Dropout in the recurrent layers')
      g.add_argument('--max_seq_len', type=int, default=100, help='Max sentence len (words in sentence)')
      g.add_argument('--max_seq_len_char', type=int, default=30, help='Max word len (characters in word)')
      g.add_argument('--learning_rate', type=float, default=1.0, help='Learning rate')
      g.add_argument('--shuffle_train', default=False, action='store_true', help='Shuffle training data between epochs')
      g.add_argument('--word_freq_cutoff', type=int, default=2, help='Cutoff frequency for words, use unknown in training if less than this')
      g.add_argument('--epochs', type=int, default=100, help='Number of epochs.')
      g.add_argument('--pretrained_word_embeddings', type=str, default="", help='Pretrained word embeddings file (.bin or .vectors)')
      g.add_argument('--verbose', default=False, action='store_true', help='Verbose prints during training')
      g.add_argument('--save_directory', type=str, default="", help='Directory to save the model and vectorizers. Model will be .pt file and vectorizer .pkl files. Default="" --> do not save.')
      
      args = parser.parse_args()
  
      if len(args.save_directory)==0:
          print(WARNING, "Warning! Model will not be saved, use --save_directory argument to save the model into a directory.", END_WARNING)
      if os.path.exists(args.save_directory):
          print(WARNING, "Warning! Model saving directory already exists, old files may be overwritten when the training ends.", END_WARNING)
  
      accuracy=train(args)
  