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def enc_dec_model_inputs():
    inputs = tf.placeholder(tf.int32, [None, None], name='input')
    targets = tf.placeholder(tf.int32, [None, None], name='target')
    
    target_sequence_length = tf.placeholder(tf.int32, [None], name='target_sequence_length')
    max_target_len = tf.reduce_max(target_sequence_length)
    
    return inputs, targets, target_sequence_length, max_target_len


    

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def process_decoder_input(target_data, target_vocab_to_int, batch_size):
    # get '<GO>' id
    go_id = target_vocab_to_int['<GO>']
    
    after_slice = tf.strided_slice(target_data, [0, 0], [batch_size,-1], [1, 1])
    after_concat = tf.concat([tf.fill([batch_size, 1], go_id), after_slice], 1)
    
    return after_concat


    

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def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob,
                  source_vocab_size, encoding_embedding_size):
    """
    :return: tuple (RNN output, RNN state)
    """
    embed = tf.contrib.layers.embed_sequence(rnn_inputs,
                                            vocab_size=source_vocab_size,
                                            embed_dim=encoding_embedding_size)
    
    stacked_cells = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.LSTMCell(rnn_size), keep_prob) for _ in range(num_layers)])
    
    outputs, state = tf.nn.dynamic_rnn(stacked_cells,
                                      embed,
                                      dtype=tf.float32)
    return outputs, state