#!/usr/bin/env python

# -*- coding: utf-8 -*-

import tensorflow as tf

import numpy as np

params=np.random.normal(loc=0.0,scale=1.0,size=[10,10])

encoder_inputs=tf.placeholder(dtype=tf.int32,shape=[10,10])

decoder_inputs=tf.placeholder(dtype=tf.int32,shape=[10,10])

logits=tf.placeholder(dtype=tf.float32,shape=[10,10,10])

targets=tf.placeholder(dtype=tf.int32,shape=[10,10])

weights=tf.placeholder(dtype=tf.float32,shape=[10,10])

train_encoder_inputs=np.ones(shape=[10,10],dtype=np.int32)

train_decoder_inputs=np.ones(shape=[10,10],dtype=np.int32)

train_weights=np.ones(shape=[10,10],dtype=np.float32)

num_encoder_symbols=10

num_decoder_symbols=10

embedding_size=10

cell=tf.nn.rnn_cell.BasicLSTMCell(10)

def seq2seq(encoder_inputs,decoder_inputs,cell,num_encoder_symbols,num_decoder_symbols,embedding_size):

	encoder_inputs = tf.unstack(encoder_inputs, axis=0)

	decoder_inputs = tf.unstack(decoder_inputs, axis=0)

	results,states=tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(

    encoder_inputs,

    decoder_inputs,

    cell,

    num_encoder_symbols,

    num_decoder_symbols,

    embedding_size,

    output_projection=None,

    feed_previous=False,

    dtype=None,

    scope=None

)

	return results

def get_loss(logits,targets,weights):

	loss=tf.contrib.seq2seq.sequence_loss(

		logits,

		targets=targets,

		weights=weights

	)

	return loss

results=seq2seq(encoder_inputs,decoder_inputs,cell,num_encoder_symbols,num_decoder_symbols,embedding_size)

logits=tf.stack(results,axis=0)

print(logits)

loss=get_loss(logits,targets,weights)

with tf.Session() as sess:

	sess.run(tf.global_variables_initializer())

	results_value=sess.run(results,feed_dict={encoder_inputs:train_encoder_inputs,decoder_inputs:train_decoder_inputs})

	print(type(results_value[0]))

	print(len(results_value))

	cost = sess.run(loss, feed_dict={encoder_inputs: train_encoder_inputs, targets: train_decoder_inputs,

	                                 weights:train_weights,decoder_inputs:train_decoder_inputs})

	print(cost)

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