"""
The MIT License (MIT)

Copyright (c) 2016 Flood Sung

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

This library is used and modified in General Artificial Intelligence for Game Playing project by Jan Kluj.
See the original license above.
"""

import tensorflow as tf
from tensorflow.contrib.layers.python.layers import batch_norm as batch_norm
import numpy as np
import math

# Hyper Parameters
LAYER1_SIZE = 400
LAYER2_SIZE = 300
LEARNING_RATE = 1e-4
TAU = 0.001


class ActorNetwork:
    """docstring for ActorNetwork"""

    def __init__(self, sess, state_dim, action_dim):
        self.sess = sess
        self.state_dim = state_dim
        self.action_dim = action_dim
        # create actor network
        self.state_input, self.action_output, self.net, self.is_training = self.create_network(state_dim, action_dim)

        # create target actor network
        self.target_state_input, self.target_action_output, self.target_update, self.target_is_training = self.create_target_network(
            state_dim, action_dim, self.net)

        # define training rules
        self.create_training_method()

        self.sess.run(tf.global_variables_initializer())

        self.update_target()

        # self.load_network()

    def get_parameters(self):
        data = {}
        data["layers"] = [LAYER1_SIZE, LAYER2_SIZE]
        data["learning_rate"] = LEARNING_RATE
        data["tau"] = TAU
        return data

    def create_training_method(self):
        self.q_gradient_input = tf.placeholder("float", [None, self.action_dim])
        self.parameters_gradients = tf.gradients(self.action_output, self.net, -self.q_gradient_input)
        self.optimizer = tf.train.AdamOptimizer(LEARNING_RATE).apply_gradients(zip(self.parameters_gradients, self.net))

    def create_network(self, state_dim, action_dim):
        layer1_size = LAYER1_SIZE
        layer2_size = LAYER2_SIZE

        state_input = tf.placeholder("float", [None, state_dim])
        is_training = tf.placeholder(tf.bool)

        W1 = self.variable([state_dim, layer1_size], state_dim)
        b1 = self.variable([layer1_size], state_dim)
        W2 = self.variable([layer1_size, layer2_size], layer1_size)
        b2 = self.variable([layer2_size], layer1_size)
        W3 = tf.Variable(tf.random_uniform([layer2_size, action_dim], -3e-3, 3e-3))
        b3 = tf.Variable(tf.random_uniform([action_dim], -3e-3, 3e-3))

        layer0_bn = self.batch_norm_layer(state_input, training_phase=is_training, scope_bn='batch_norm_0',
                                          activation=tf.identity)
        layer1 = tf.matmul(layer0_bn, W1) + b1
        layer1_bn = self.batch_norm_layer(layer1, training_phase=is_training, scope_bn='batch_norm_1',
                                          activation=tf.nn.relu)
        layer2 = tf.matmul(layer1_bn, W2) + b2
        layer2_bn = self.batch_norm_layer(layer2, training_phase=is_training, scope_bn='batch_norm_2',
                                          activation=tf.nn.relu)

        action_output = tf.tanh(tf.matmul(layer2_bn, W3) + b3)

        return state_input, action_output, [W1, b1, W2, b2, W3, b3], is_training

    def create_target_network(self, state_dim, action_dim, net):
        state_input = tf.placeholder("float", [None, state_dim])
        is_training = tf.placeholder(tf.bool)
        ema = tf.train.ExponentialMovingAverage(decay=1 - TAU)
        target_update = ema.apply(net)
        target_net = [ema.average(x) for x in net]

        layer0_bn = self.batch_norm_layer(state_input, training_phase=is_training, scope_bn='target_batch_norm_0',
                                          activation=tf.identity)

        layer1 = tf.matmul(layer0_bn, target_net[0]) + target_net[1]
        layer1_bn = self.batch_norm_layer(layer1, training_phase=is_training, scope_bn='target_batch_norm_1',
                                          activation=tf.nn.relu)
        layer2 = tf.matmul(layer1_bn, target_net[2]) + target_net[3]
        layer2_bn = self.batch_norm_layer(layer2, training_phase=is_training, scope_bn='target_batch_norm_2',
                                          activation=tf.nn.relu)

        action_output = tf.tanh(tf.matmul(layer2_bn, target_net[4]) + target_net[5])

        return state_input, action_output, target_update, is_training

    def update_target(self):
        self.sess.run(self.target_update)

    def train(self, q_gradient_batch, state_batch):
        self.sess.run(self.optimizer, feed_dict={
            self.q_gradient_input: q_gradient_batch,
            self.state_input: state_batch,
            self.is_training: True
        })

    def actions(self, state_batch):
        return self.sess.run(self.action_output, feed_dict={
            self.state_input: state_batch,
            self.is_training: True
        })

    def action(self, state):
        return self.sess.run(self.action_output, feed_dict={
            self.state_input: [state],
            self.is_training: False
        })[0]

    def target_actions(self, state_batch):
        return self.sess.run(self.target_action_output, feed_dict={
            self.target_state_input: state_batch,
            self.target_is_training: True
        })

    # f fan-in size
    def variable(self, shape, f):
        return tf.Variable(tf.random_uniform(shape, -1 / math.sqrt(f), 1 / math.sqrt(f)))

    def batch_norm_layer(self, x, training_phase, scope_bn, activation=None):
        return tf.cond(training_phase,
                       lambda: tf.contrib.layers.batch_norm(x, activation_fn=activation, center=True, scale=True,
                                                            updates_collections=None, is_training=True, reuse=None,
                                                            scope=scope_bn, decay=0.9, epsilon=1e-5),
                       lambda: tf.contrib.layers.batch_norm(x, activation_fn=activation, center=True, scale=True,
                                                            updates_collections=None, is_training=False, reuse=True,
                                                            scope=scope_bn, decay=0.9, epsilon=1e-5))


'''
	def load_network(self):
		self.saver = tf.train.Saver()
		checkpoint = tf.train.get_checkpoint_state("saved_actor_networks")
		if checkpoint and checkpoint.model_checkpoint_path:
			self.saver.restore(self.sess, checkpoint.model_checkpoint_path)
			print "Successfully loaded:", checkpoint.model_checkpoint_path
		else:
			print "Could not find old network weights"
	def save_network(self,time_step):
		print 'save actor-network...',time_step
		self.saver.save(self.sess, 'saved_actor_networks/' + 'actor-network', global_step = time_step)

'''
