from model_train import tensorFromSentence, SOS_token, MAX_LENGTH, device
import pickle
from lstm_model import EncoderRNN, DecoderRNN
import sys
import torch

with open('data/pl_lang.pkl', 'rb') as input_file:
    input_lang = pickle.load(input_file)

with open('data/en_lang.pkl', 'rb') as out_file:
    output_lang = pickle.load(out_file)


def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):
    with torch.no_grad():
        input_tensor = tensorFromSentence(input_lang, sentence)
        encoder_hidden = encoder.initHidden()

        encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)

        encoder_output, encoder_hidden = encoder(input_tensor,encoder_hidden)

        encoder_outputs = encoder_output

        decoder_input = torch.tensor([[SOS_token]], device=device)

        decoder_hidden = encoder_hidden

        decoded_words = []

        for di in range(max_length):
            decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)
            topv, topi = decoder_output.topk(1)
            decoded_words.append(topi)
            decoder_input = topi.transpose(0, 1)
        out = torch.stack(decoded_words)
        return out

hidden_size = 256
encoder = EncoderRNN(input_lang.n_words, hidden_size).to(device)
decoder = DecoderRNN(hidden_size, output_lang.n_words).to(device)
encoder.load_state_dict(torch.load('encoder.dict'))
decoder.load_state_dict(torch.load('decoder.dict'))
encoder.eval()
decoder.eval()
for line in sys.stdin:
    line = line.rstrip()
    dec_words = evaluate(encoder, decoder, line, MAX_LENGTH)
    dec_words = dec_words.transpose(0, 1)
    for sen in dec_words:
        out = []
        for idx in sen:
            if idx == 0:
                break
            out.append(output_lang.index2word[idx.item()])
    print(' '.join(out))