text: ###
  # From https://github.com/bentrevett/pytorch-sentiment-analysis
  ###
  import torch
  from torchtext import data
  from torchtext import datasets
  import torch.nn as nn
  import torch.optim as optim
  import time
  import random
  
  from nltk import FreqDist
  
  class LSTM(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout, pad_idx):
      super().__init__()
      self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)
      self.lstm = nn.LSTM(embedding_dim,
                          hidden_dim,
                          num_layers=n_layers,
                          bidirectional=bidirectional,
                          dropout=dropout)
      self.fc = nn.Linear(hidden_dim * 2, output_dim)
      self.dropout = nn.Dropout(dropout)
    
    def forward(self, text, text_lengths):
      embedded = self.dropout(self.embedding(text))
      packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths)
      packed_output, (hidden, cell) = self.lstm(packed_embedded)
      output, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_output)
      hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1))
      return self.fc(hidden.squeeze(0))
  
  
  SEED = 1234
  MAX_VOCAB_SIZE = 25_000
  BATCH_SIZE = 64
  
  torch.manual_seed(SEED)
  torch.backends.cudnn.deterministic = True
  
  TEXT = data.Field(tokenize = 'spacy', include_lengths = True)
  LABEL = data.LabelField(dtype = torch.float)
  
  train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)
  train_data, valid_data = train_data.split(random_state = random.seed(SEED))
  
  print(f'Number of training examples: {len(train_data)}')
  print(f'Number of validation examples:{len(valid_data)}')
  print(f'Number of testing examples: {len(test_data)}')
  
  print(vars(train_data.examples[0]))
  
  TEXT.build_vocab(train_data, 
                   max_size = MAX_VOCAB_SIZE)
                  #  vectors = 'glove.6B.100d',
                  #  unk_init = torch.Tensor.normal_)
  LABEL.build_vocab(train_data)
  
  print(f'Unique tokens in TEXT vocabulary: {len(TEXT.vocab)}')
  print(f'Unique tokens in LABEL vocabulary: {len(LABEL.vocab)}')
  
  print(TEXT.vocab.freqs.most_common(20))
  print(TEXT.vocab.itos[:10])
  print(LABEL.vocab.stoi)
  
  # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  
  # train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
  #   (train_data, valid_data, test_data),
  #   batch_size = BATCH_SIZE,
  #   sort_within_batch = True,
  #   device = device)
  
  # INPUT_DIM = len(TEXT.vocab)
  # EMBEDDING_DIM = 100
  # HIDDEN_DIM = 256
  # OUTPUT_DIM = 1
  # N_LAYERS = 2
  # BIDIRECTIONAL = False
  # DROPOUT = 0.5
  # PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]
  
  # model = LSTM(INPUT_DIM,
  #              EMBEDDING_DIM,
  #              HIDDEN_DIM,
  #              OUTPUT_DIM,
  #              N_LAYERS,
  #              BIDIRECTIONAL,
  #              DROPOUT,
  #              PAD_IDX)
  
  # def count_parameters(model):
  #   return sum(p.numel() for p in model.parameters() if p.requires_grad)
  
  # print(f'The model has {count_parameters(model):,} trainable parameters')
  
  # # pretrained_embeddings = TEXT.vocab.vectors
  # # print(pretrained_embeddings.shape)
  
  # # model.embedding.weight.data.copy_(pretrained_embeddings)
  
  # UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]
  # model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)
  # model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)
  
  # optimizer = optim.Adam(model.parameters())
  # criterion = nn.BCEWithLogitsLoss()
  
  # model = model.to(device)
  # criterion = criterion.to(device)
  
  # def binary_accuracy(preds, y):
  #   """
  #   Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8
  #   """
  
  #   #round predictions to the closes integer
  #   rounded_preds = torch.round(torch.sigmoid(preds))
  #   correct = (rounded_preds == y).float()
  #   acc = correct.sum() / len(correct)
  #   return acc
  
  # def train(model, iterator, optimizer, criterion):
    
  #   epoch_loss = 0
  #   epoch_acc = 0
  
  #   model.train()
  
  #   for batch in iterator:
  #     optimizer.zero_grad()
  #     text, text_lengths = batch.text
  #     predictions = model(text, text_lengths).squeeze(1)
  #     loss = criterion(predictions, batch.label)
  #     acc = binary_accuracy(predictions, batch.label)
  
  #     loss.backward()
  #     optimizer.step()
  
  #     epoch_loss += loss.item()
  #     epoch_acc  += acc.item()
    
  #   return epoch_loss / len(iterator), epoch_acc / len(iterator)
  
  # def evaluate(model, iterator, criterion):
  #   epoch_loss = 0
  #   epoch_acc = 0
  
  #   model.eval()
  
  #   with torch.no_grad():
  #     for batch in iterator:
  #       text, text_lengths = batch.text
  #       predictions = model(text, text_lengths).squeeze(1)
  #       loss = criterion(predictions, batch.label)
  #       acc = binary_accuracy(predictions, batch.label)
  
  #       epoch_loss += loss.item()
  #       epoch_acc += acc.item()
    
  #   return epoch_loss / len(iterator), epoch_acc / len(iterator)
  
  # def epoch_time(start_time, end_time):
  #   elapsed_time = end_time - start_time
  #   elapsed_mins = int(elapsed_time / 60)
  #   elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
  #   return elapsed_mins, elapsed_secs
  
  # N_EPOCHS = 5
  
  # best_valid_loss = float('inf')
  
  # print("="*10)
  # print("Train Start")
  # print("="*10)
  
  
  # for epoch in range(N_EPOCHS):
  #   start_time = time.time()
  #   train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
  #   valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
  
  #   end_time = time.time()
  
  #   epoch_mins, epoch_secs = epoch_time(start_time, end_time)
  
  #   if valid_loss < best_valid_loss:
  #     best_valid_loss = valid_loss
  #     torch.save(model.state_dict(), 'lstm-model2.pt')
    
  #   print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')
  #   print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
  #   print(f'\t Val. Loss: {valid_loss:.3f} | Val. Acc : {valid_acc*100:.2f}%')
  
  # print("="*10)
  # print("Train Finish")
  # print("="*10)
  
  # model.load_state_dict(torch.load('lstm-model2.pt'))
  # test_loss, test_acc = evaluate(model, test_iterator, criterion)
  # print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')
  