from ou_noise import OUNoise

from helper import *

import opensim as osim

from osim.http.client import Client

from osim.env import *

import threading

import numpy as np

import tensorflow as tf

import tensorflow.contrib.slim as slim

from helper import *

from time import sleep

from time import time

from time import gmtime, strftime

import multiprocessing

from multiprocessing import Process, Pipe

from osim.env import *

# [Hacked] the memory might always be leaking, here's a solution #58

# https://github.com/stanfordnmbl/osim-rl/issues/58 

# separate process that holds a separate RunEnv instance.

# This has to be done since RunEnv() in the same process result in interleaved running of simulations.

def floatify(np):

    return [float(np[i]) for i in range(len(np))]

def standalone_headless_isolated(conn,vis):

    e = RunEnv(visualize=vis)

    while True:

        try:

            msg = conn.recv()

            # messages should be tuples,

            # msg[0] should be string

            if msg[0] == 'reset':

                o = e.reset(difficulty=2)

                conn.send(o)

            elif msg[0] == 'step':

                ordi = e.step(msg[1])

                conn.send(ordi)

            else:

                conn.close()

                del e

                return

        except:

            conn.close()

            del e

            raise

# class that manages the interprocess communication and expose itself as a RunEnv.

class ei: # Environment Instance

    def __init__(self,vis):

        self.pc, self.cc = Pipe()

        self.p = Process(

            target = standalone_headless_isolated,

            args=(self.cc,vis,)

        )

        self.p.daemon = True

        self.p.start()

    def reset(self):

        self.pc.send(('reset',))

        return self.pc.recv()

    def step(self,actions):

        self.pc.send(('step',actions,))

        try:

        return self.pc.recv()

    except EOFError:  

            return None

    def __del__(self):

        self.pc.send(('exit',))

        #print('(ei)waiting for join...')

        self.p.join()

# Added by Andrew Liao

# for NoisyNet-DQN (using Factorised Gaussian noise)

# modified from ```dense``` function

def sample_noise(shape):

    noise = np.random.normal(size=shape).astype(np.float32)

    #noise = np.ones(size=shape).astype(np.float32) # whenever not in training, simply return a matrix of ones. 

    return noise

global_p_a = 0.

global_q_a = 0.

global_p_v = 0.

global_q_v = 0.

def noisy_dense(x, size, name, bias=True, activation_fn=tf.identity, factorized=False):

    global global_p_a

    global global_q_a

    global global_p_v

    global global_q_v

    # https://arxiv.org/pdf/1706.10295.pdf page 4

    # the function used in eq.7,8 : f(x)=sgn(x)*sqrt(|x|)

    def f(x):

        return tf.multiply(tf.sign(x), tf.pow(tf.abs(x), 0.5))

    # Initializer of \mu and \sigma 

    mu_init = tf.random_uniform_initializer(minval=-1*1/np.power(x.get_shape().as_list()[1], 0.5),     

                                                maxval=1*1/np.power(x.get_shape().as_list()[1], 0.5))

    sigma_init = tf.constant_initializer(0.4/np.power(x.get_shape().as_list()[1], 0.5))

    # Sample noise from gaussian

    if name == 'global':

        if size == 18: # check condition

            p = sample_noise([128, 18]) # 256 is rnn_size

            global_p_a = p

            q = sample_noise([1, 18]) # 3 is action size

            global_q_a = q

        else:

            p = sample_noise([128, 1]) # 256 is rnn_size

            global_p_v = p

            q = sample_noise([1, 1]) # 1 is value size

            global_q_v = q

    else: # for actors, copy p & q from the global network

        if size == 3: # check condition

            p = global_p_a

            q = global_q_a

        else:

            p = global_p_v

            q = global_q_v

    f_p = f(p); f_q = f(q)

    w_epsilon = f_p*f_q; b_epsilon = tf.squeeze(f_q)

    if not factorized: # just resample the noisy matrix to get independent guassian noise

        w_epsilon = tf.identity(sample_noise(w_epsilon.get_shape().as_list()))

    # w = w_mu + w_sigma*w_epsilon

    options = {18:'action',1:'value'}

    w_mu = tf.get_variable(name + "/w_mu" + options[size], [x.get_shape()[1], size], initializer=mu_init)

    w_sigma = tf.get_variable(name + "/w_sigma" + options[size], [x.get_shape()[1], size], initializer=sigma_init)

    w = w_mu + tf.multiply(w_sigma, w_epsilon)

    ret = tf.matmul(x, w)

    if bias:

        # b = b_mu + b_sigma*b_epsilon

        b_mu = tf.get_variable(name + "/b_mu" + options[size], [size], initializer=mu_init)

        b_sigma = tf.get_variable(name + "/b_sigma" + options[size], [size], initializer=sigma_init)

        b = b_mu + tf.multiply(b_sigma, b_epsilon)

        return activation_fn(ret + b)

    else:

        return activation_fn(ret)

# ================================================================

# Model components

# ================================================================

# Actor Network------------------------------------------------------------------------------------------------------------

class AC_Network():

    def __init__(self,s_size,a_size,scope,trainer,noisy):

        with tf.variable_scope(scope):

            #Input and visual encoding layers

            self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32)

            self.imageIn = tf.reshape(self.inputs,shape=[-1,s_size,1])

            # Create the model, use the default arg scope to configure the batch norm parameters.

             '''   conv1 = tf.nn.elu(tf.nn.conv1d(self.imageIn,tf.truncated_normal([2,1,8],stddev=0.1),2,padding='VALID'))

                conv2 = tf.nn.elu(tf.nn.conv1d(conv1,tf.truncated_normal([3,8,16],stddev=0.05),1,padding='VALID'))

            hidden = slim.fully_connected(slim.flatten(conv2),200,activation_fn=tf.nn.elu)'''

            hidden = slim.fully_connected(slim.flatten(self.imageIn),300,activation_fn=tf.elu)

            #Recurrent network for temporal dependencies

            lstm_cell = tf.contrib.rnn.LayerNormBasicLSTMCell(128,dropout_keep_prob=0.8)

            #lstm_cell = tf.contrib.rnn.DropoutWrapper(lstm_cell,output_keep_prob=0.5)

            c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)

            h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)

            self.state_init = [c_init, h_init]

            c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])

            h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])

            self.state_in = (c_in, h_in)

            rnn_in = tf.expand_dims(hidden, [0])

            step_size = tf.shape(self.imageIn)[:1]

            state_in = tf.contrib.rnn.LSTMStateTuple(c_in, 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, :])

            rnn_out = tf.reshape(lstm_outputs, [-1, 128])

            if noisy:

                # Apply noisy network on fully connected layers

                # ref: https://arxiv.org/abs/1706.10295

                self.policy = tf.clip_by_value(noisy_dense(rnn_out,name=scope, size=a_size, activation_fn=tf.nn.relu),0.0,1.0)

                self.value = noisy_dense(rnn_out,name=scope, size=1) # default activation_fn=tf.identity

            else:

                #Output layers for policy and value estimations

                mu = slim.fully_connected(rnn_out,a_size,activation_fn=tf.nn.elu,weights_initializer=normalized_columns_initializer(0.01),biases_initializer=None)

                #var = slim.fully_connected(rnn_out,a_size,activation_fn=tf.nn.softplus,weights_initializer=normalized_columns_initializer(0.01),biases_initializer=None)

                self.normal_dist = tf.contrib.distributions.Normal(mu, 0.05)

                self.policy = tf.clip_by_value(self.normal_dist.sample(1),0.0,1.0) # self.normal_dist.sample(1)

                self.value = slim.fully_connected(rnn_out,1,

                    activation_fn=None,

                    weights_initializer=normalized_columns_initializer(1.0),

                    biases_initializer=None)

            #Only the worker network need ops for loss functions and gradient updating.

            if scope != 'global':

                self.actions = tf.placeholder(shape=[None,a_size],dtype=tf.float32)

                self.target_v = tf.placeholder(shape=[None],dtype=tf.float32)

                self.advantages = tf.placeholder(shape=[None],dtype=tf.float32)

                #Loss functions

                self.value_loss = 0.5 * tf.reduce_sum(tf.square(self.target_v - tf.reshape(self.value,[-1])))

                self.log_prob = tf.reduce_sum(self.normal_dist.log_prob(self.actions),axis=1)

                self.entropy = tf.reduce_sum(self.normal_dist.entropy(),axis=1)  # encourage exploration

                self.entropy = tf.reduce_sum(self.entropy,axis=0)

                self.policy_loss = -tf.reduce_sum(self.log_prob*self.advantages,axis=0)

                if noisy:

                    self.loss = 0.5 * self.value_loss + self.policy_loss

                else:

                    self.loss = 0.5 * self.value_loss + self.policy_loss #- 0.01 * self.entropy

                #Get gradients from local network using local losses

                local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)

                self.gradients = tf.gradients(self.loss,local_vars)

                self.var_norms = tf.global_norm(local_vars)

                grads,self.grad_norms = tf.clip_by_global_norm(self.gradients,40.0)

                #Apply local gradients to global network

                #Comment these two lines out to stop training

                global_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'global')

                self.apply_grads = trainer.apply_gradients(zip(grads,global_vars))

# Learning to run Worker------------------------------------------------------------------------------------------------------------

class Worker():

    def __init__(self,name,s_size,a_size,trainer,model_path,global_episodes,noisy,is_training):

        self.name = "worker_" + str(name)

        self.number = name        

        self.model_path = model_path

        self.trainer = trainer

        self.global_episodes = global_episodes

        self.increment = self.global_episodes.assign_add(1)

        self.episode_rewards = []

        self.episode_lengths = []

        self.episode_mean_values = []

        self.summary_writer = tf.summary.FileWriter("train_"+str(self.number))

        self.noisy = noisy

        self.is_training = is_training

        #Create the local copy of the network and the tensorflow op to copy global paramters to local network

        self.local_AC = AC_Network(s_size,a_size,self.name,trainer,noisy)

        #self.local_AC_target = AC_Network(s_size,a_size,self.name+'/target',trainer,noisy)

        self.update_local_ops = update_target_graph('global',self.name)

        #self.update_local_ops_target = update_target_graph('global/target',self.name+'/target')

        #self.update_global_target = update_target_network(self.name,'global/target')           

        # Initialize a random process the Ornstein-Uhlenbeck process for action exploration

        self.exploration_noise = OUNoise(a_size)

    def train(self,rollout,sess,gamma,bootstrap_value):

        rollout = np.array(rollout)

        observations = rollout[:,0]

        actions = rollout[:,1]

        rewards = rollout[:,2]

        # reward clipping:  scale and clip the values of the rewards to the range -1,+1

        #rewards = (rewards - np.mean(rewards)) / np.max(abs(rewards))

        next_observations = rollout[:,3] # Aug 1st, notice next observation is never used

        values = rollout[:,5]

        # Here we take the rewards and values from the rollout, and use them to 

        # generate the advantage and discounted returns. 

        # The advantage function uses "Generalized Advantage Estimation"

        self.rewards_plus = np.asarray(rewards.tolist() + [bootstrap_value])

        discounted_rewards = discount(self.rewards_plus,gamma)[:-1]

        self.value_plus = np.asarray(values.tolist() + [bootstrap_value])

        advantages = rewards + gamma * self.value_plus[1:] - self.value_plus[:-1]

        advantages = discount(advantages,gamma)

        # Update the global network using gradients from loss

        # Generate network statistics to periodically save

        rnn_state = self.local_AC.state_init

        feed_dict = {self.local_AC.target_v:discounted_rewards,

            self.local_AC.inputs:np.vstack(observations),

            self.local_AC.actions:np.vstack(actions),

            self.local_AC.advantages:advantages,

            self.local_AC.state_in[0]:rnn_state[0],

            self.local_AC.state_in[1]:rnn_state[1]}

        l,v_l,p_l,e_l,g_n,v_n,_ = sess.run([self.local_AC.loss,self.local_AC.value_loss,

            self.local_AC.policy_loss,

            self.local_AC.entropy,

            self.local_AC.grad_norms,

            self.local_AC.var_norms,

            self.local_AC.apply_grads],

            feed_dict=feed_dict)

        return l / len(rollout), v_l / len(rollout),p_l / len(rollout),e_l / len(rollout), g_n,v_n

    def work(self,max_episode_length,gamma,sess,coord,saver):

        if self.is_training:

            episode_count = sess.run(self.global_episodes)

        else:

            episode_count = 0

        wining_episode_count = 0

        total_steps = 0

        print ("Starting worker " + str(self.number))

        with open('result.txt','w') as f:

            f.write(strftime("Starting time: %a, %d %b %Y %H:%M:%S\n", gmtime()))

        explore = 1000

        if self.name == 'worker_1':

            self.env = ei(vis=False)#RunEnv(visualize=True)

        else:

            self.env = ei(vis=False)#RunEnv(visualize=False)

        with sess.as_default(), sess.graph.as_default():

            #not_start_training_yet = True

            while not coord.should_stop():

                # start the env (in the thread) every 50 eps to prevent memory leak

                if episode_count % 50 == 0:

                    if self.env != None:

                        del self.env

                    if self.name == 'worker_1':

                        self.env = ei(vis=True)#RunEnv(visualize=True)

                    else:

                        self.env = ei(vis=False)#RunEnv(visualize=False)

                self.setting=2

                sess.run(self.update_local_ops)

                #sess.run(self.update_local_ops_target)

                episode_buffer = []

                episode_values = []

                episode_reward = 0

                episode_step_count = 0

                done = False

                seed = np.random.rand()

                self.env.reset()

                # engineered initial input to make agent's life easier

                a=engineered_action(seed)

                ob = self.env.step(a)[0]

                s = ob

                ob = self.env.step(a)[0]

                s1 = ob

                s = process_state(s,s1)

                rnn_state = self.local_AC.state_init

                explore -= 1

                #st = time()

                chese=0

                while done == False:

                    #Take an action using probabilities from policy network output.

                    action,v,rnn_state = sess.run([self.local_AC.policy,self.local_AC.value,self.local_AC.state_out], 

                                feed_dict={self.local_AC.inputs:[s],

                                self.local_AC.state_in[0]:rnn_state[0],

                                self.local_AC.state_in[1]:rnn_state[1]})

                    if not (episode_count % 5 == 0 and self.name == 'worker_1') and self.is_training:

                        if explore > 0: # > 0 turn on OU_noise # test the agent every 2 eps

                            a = np.clip(action[0,0]+self.exploration_noise.noise(),0.0,1.0)

                        else:

                            a = action[0,0]

                        if chese < 60 and episode_count < 250:

                            a=engineered_action(seed)

                            chese += 1

                    else:

                        a = action[0,0]

                    ob,r,done,_ = self.env.step(a)

                    '''

                    if self.name == 'worker_0':

                        ct = time()

                        print(ct-st)

                        st = ct

                    '''

                    if done == False:

                        s2 = ob

                    else:

                        s2 = s1

                    s1 = process_state(s1,s2)

                    #print(s1)    

                    episode_buffer.append([s,a,r,s1,done,v[0,0]])

                    episode_values.append(v[0,0])

                    episode_reward += r

                    s = s1

                    s1 = s2

                    total_steps += 1

                    episode_step_count += 1

                    # If the episode hasn't ended, but the experience buffer is full, then we

                    # make an update step using that experience rollout.

                    '''        

                    if len(episode_buffer) == 120 and done != True and episode_step_count != max_episode_length - 1: # change pisode length to 5, and try to modify Worker.train() function to utilize the next frame to train imagined frame.

                        # Since we don't know what the true final return is, we "bootstrap" from our current

                        # value estimation.

                        if self.is_training:

                            v1 = sess.run(self.local_AC.value, 

                            feed_dict={self.local_AC.inputs:[s],

                            self.local_AC.state_in[0]:rnn_state[0],

                            self.local_AC.state_in[1]:rnn_state[1]})[0,0]

                            l,v_l,p_l,e_l,g_n,v_n = self.train(episode_buffer,sess,gamma,v1)

                            sess.run(self.update_local_ops)

                            episode_buffer = []

                    ''' 

                    if done == True:

                        break

                self.episode_rewards.append(episode_reward)

                self.episode_lengths.append(episode_step_count)

                self.episode_mean_values.append(np.mean(episode_values))

                # Update the network using the experience buffer at the end of the episode.

                if len(episode_buffer) != 0:

                    if self.is_training:

                        l,v_l,p_l,e_l,g_n,v_n = self.train(episode_buffer,sess,gamma,0.0)

                        #print(l,v_l,p_l,e_l,g_n,v_n)

                        #sess.run(self.update_global_target)

                # Periodically save gifs of episodes, model parameters, and summary statistics.

                if episode_count % 5 == 0 and episode_count != 0:

                    mean_reward = np.mean(self.episode_rewards[-5:])

                    mean_length = np.mean(self.episode_lengths[-5:])

                    mean_value = np.mean(self.episode_mean_values[-5:])

                    summary = tf.Summary()

                    summary.value.add(tag='Perf/Reward', simple_value=float(mean_reward))

                    summary.value.add(tag='Perf/Length', simple_value=float(mean_length))

                    summary.value.add(tag='Perf/Value', simple_value=float(mean_value))

                    if self.is_training:

                        summary.value.add(tag='Losses/Value Loss', simple_value=float(v_l))

                        summary.value.add(tag='Losses/Policy Loss', simple_value=float(p_l))

                        summary.value.add(tag='Losses/Entropy', simple_value=float(e_l))

                    self.summary_writer.add_summary(summary, episode_count)

                    self.summary_writer.flush()

                    if self.name == 'worker_1':

            with open('result.txt','a') as f:

                            f.write("Episode "+str(episode_count)+" reward (testing): %.2f\n" % episode_reward)

                    if self.name == 'worker_0':

            with open('result.txt','a') as f:

                f.write("Episodes "+str(episode_count)+" mean reward (training): %.2f\n" % mean_reward)

                        if episode_count % 100 == 0:

                            saver.save(sess,self.model_path+'/model-'+str(episode_count)+'.cptk')

                            with open('result.txt','a') as f:

                    f.write("Saved Model at episode: "+str(episode_count)+"\n")

                if self.name == 'worker_0' and self.is_training:

                    sess.run(self.increment)

                episode_count += 1

                if self.name == "worker_1" and episode_reward > 2.:

                    wining_episode_count += 1

                    print('Worker_1 is stepping forward in Episode {}! Reward: {:.2f}. Total percentage of success is: {}%'.format(episode_count, episode_reward, int(wining_episode_count / episode_count * 100)))

                    with open('result.txt','a') as f:

            f.wirte('Worker_1 is stepping forward in Episode {}! Reward: {:.2f}. Total percentage of success is: {}%\n'.format(episode_count, episode_reward, int(wining_episode_count / episode_count * 100)))

        # All done Stop trail

        # Confirm exit

        print('Exit/Done '+self.name)