#
# Copyright (c) 2017 Intel Corporation 
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import tensorflow as tf
import numpy as np


class MiddlewareEmbedder(object):
    def __init__(self, activation_function=tf.nn.relu, name="middleware_embedder"):
        self.name = name
        self.input = None
        self.output = None
        self.activation_function = activation_function

    def __call__(self, input_layer):
        with tf.variable_scope(self.get_name()):
            self.input = input_layer
            self._build_module()

        return self.input, self.output

    def _build_module(self):
        pass

    def get_name(self):
        return self.name


class LSTM_Embedder(MiddlewareEmbedder):
    def _build_module(self):

        middleware = tf.layers.dense(self.input, 512, activation=self.activation_function)
        lstm_cell = tf.contrib.rnn.BasicLSTMCell(256, state_is_tuple=True)
        self.c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)
        self.h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)
        self.state_init = [self.c_init, self.h_init]
        self.c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
        self.h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
        self.state_in = (self.c_in, self.h_in)
        rnn_in = tf.expand_dims(middleware, [0])
        step_size = tf.shape(middleware)[:1]
        state_in = tf.contrib.rnn.LSTMStateTuple(self.c_in, self.h_in)
        lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
            lstm_cell, rnn_in, initial_state=state_in, sequence_length=step_size, time_major=False)
        lstm_c, lstm_h = lstm_state
        self.state_out = (lstm_c[:1, :], lstm_h[:1, :])
        self.output = tf.reshape(lstm_outputs, [-1, 256])


class FC_Embedder(MiddlewareEmbedder):
    def _build_module(self):
        self.output = tf.layers.dense(self.input, 512, activation=self.activation_function)