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def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_vocab_size, encoding_embedding_size):
 
 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: tuple (RNN output, RNN state)
 #RNN State: LSTM Tuple ( c(hidden_state of every layer) and h (output per input) )
 return outputs, state
 
 
 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
 
def decoding_layer_train(encoder_outputs, dec_cell, dec_embed_input, target_sequence_length, max_summary_length, 
 output_layer, keep_prob):
 
 dec_cell = tf.contrib.rnn.DropoutWrapper(dec_cell, output_keep_prob=keep_prob)
 
 attn_mech = tf.contrib.seq2seq.BahdanauAttention(num_units = rnn_size, memory = encoder_outputs,
 memory_sequence_length = target_sequence_length)
 
 attn_cell = tf.contrib.seq2seq.AttentionWrapper(cell = dec_cell, attention_mechanism = attn_mech,
 alignment_history=True)
 
 helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input,target_sequence_length)
 
 decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_outputs, output_layer)
 
 outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, impute_finished=True, 
 maximum_iterations=max_summary_length)
 return outputs
 
def decoding_layer_infer(encoder_outputs, dec_cell, dec_embeddings, start_of_sequence_id,end_of_sequence_id,
 max_target_sequence_length,vocab_size, output_layer, batch_size, keep_prob):
 
 #Creating an inference process in decoding layer 
 dec_cell = tf.contrib.rnn.DropoutWrapper(dec_cell, output_keep_prob=keep_prob)
 
 attn_mech = tf.contrib.seq2seq.BahdanauAttention(num_units = rnn_size, memory = ecnoder_outputs,
 memory_sequence_length = max_target_sequence_length)
 
 attn_cell = tf.contrib.seq2seq.AttentionWrapper(cell = dec_cell, attention_mechanism = attn_mech,
 alignment_history=True)
 
 helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, tf.fill([batch_size], start_of_sequence_id), 
 end_of_sequence_id)
 
 decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper,encoder_outputs,output_layer)
 outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, impute_finished=True, 
 maximum_iterations=max_target_sequence_length)
 return outputs
 
def decoding_layer(dec_input, encoder_state,target_sequence_length, max_target_sequence_length,
 rnn_size,num_layers, target_vocab_to_int, target_vocab_size,batch_size, keep_prob, decoding_embedding_size):

 target_vocab_size = len(target_vocab_to_int)
 dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size]))
 dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)
 
 cells = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.LSTMCell(rnn_size) for _ in range(num_layers)])
 
 with tf.variable_scope("decode"):
 output_layer = tf.layers.Dense(target_vocab_size)
 train_output = decoding_layer_train(encoder_state, cells, dec_embed_input, target_sequence_length, 
 max_target_sequence_length, output_layer, keep_prob)

 with tf.variable_scope("decode", reuse=True):
 infer_output = decoding_layer_infer(encoder_state, cells, dec_embeddings, target_vocab_to_int['<GO>'], 
 target_vocab_to_int['<EOS>'], max_target_sequence_length, target_vocab_size, 
 output_layer,batch_size,keep_prob)

 return (train_output, infer_output)
 
def seq2seq_model(input_data, target_data, keep_prob, batch_size,target_sequence_length, max_target_sentence_length,
 source_vocab_size, target_vocab_size, enc_embedding_size, dec_embedding_size, rnn_size, num_layers, 
 target_vocab_to_int):
 
 enc_states, enc_outputs = encoding_layer(input_data, rnn_size, num_layers, keep_prob, source_vocab_size, enc_embedding_size)
 
 dec_input = process_decoder_input(target_data, target_vocab_to_int, batch_size)
 
 train_output, infer_output = decoding_layer(dec_input, enc_states, target_sequence_length, max_target_sentence_length,
 rnn_size, num_layers,target_vocab_to_int, target_vocab_size,batch_size,
 keep_prob,dec_embedding_size)
 
 return train_output, infer_output,enc_outputs, enc_states
 
to, ifo, eno, ens = seq2seq_model((inputs), targets, keep_prob, batch_size,target_sequence_length, max_target_length,
 len(source_vocab_to_int), len(target_vocab_to_int), embed_size, dec_embed_size, rnn_size, num_layers, 
 target_vocab_to_int)