text: import argparse
  import numpy as np
  import random
  import json
  import sys
  from env import make_env
  import time
  from vae.vae import ConvVAE
  from rnn.rnn import hps_sample,MDNRNN,rnn_init_state,rnn_next_state,rnn_output,rnn_output_size
  import ZMQUtils
  import zmq
  import tensorflow as tf
  render_mode=True
  MODE_ZCH=0
  MODE_ZC=1
  MODE_Z=2
  MODE_Z_HIDDEN=3 
  MODE_ZH=4
  EXP_MODE=MODE_ZH
  def make_model(load_model=True):
   model=Model(load_model=load_model)
   return model
  def sigmoid(x):
   return 1/(1+np.exp(-x))
  def relu(x):
   return np.maximum(x,0)
  def clip(x,lo=0.0,hi=1.0):
   return np.minimum(np.maximum(x,lo),hi)
  def passthru(x):
   return x
  def softmax(x):
   e_x=np.exp(x-np.max(x))
   return e_x/e_x.sum(axis=0)
  def sample(p):
   return np.argmax(np.random.multinomial(1,p))
  class Model:
   def __init__(self,load_model=True):
    self.env_name="carracing"
    self.vae=ConvVAE(batch_size=1,gpu_mode=False,is_training=False,reuse=True)
    self.rnn=MDNRNN(hps_sample,gpu_mode=False,reuse=True)
    if load_model:
     self.vae.load_json('vae/vae.json')
     self.rnn.load_json('rnn/rnn.json')
    self.state=rnn_init_state(self.rnn)
    self.rnn_mode=True
    self.input_size=rnn_output_size(EXP_MODE)
    self.z_size=32
    if EXP_MODE==MODE_Z_HIDDEN:
     self.hidden_size=40
     self.weight_hidden=np.random.randn(self.input_size,self.hidden_size)
     self.bias_hidden=np.random.randn(self.hidden_size)
     self.weight_output=np.random.randn(self.hidden_size,3)
     self.bias_output=np.random.randn(3)
     self.param_count=((self.input_size+1)*self.hidden_size)+(self.hidden_size*3+3)
    else:
     self.weight=np.random.randn(self.input_size,3)
     self.bias=np.random.randn(3)
     self.param_count=(self.input_size)*3+3
    self.render_mode=False
   def make_env(self,seed=-1,render_mode=False,full_episode=False):
    self.render_mode=render_mode
    self.env=make_env(self.env_name,seed=seed,render_mode=render_mode,full_episode=full_episode)
   def reset(self):
    self.state=rnn_init_state(self.rnn)
   def encode_obs(self,obs):
    result=np.copy(obs).astype(np.float)/255.0
    result=result.reshape(1,64,64,3)
    mu,logvar=self.vae.encode_mu_logvar(result)
    mu=mu[0]
    logvar=logvar[0]
    s=logvar.shape
    z=mu+np.exp(logvar/2.0)*np.random.randn(*s)
    return z,mu,logvar
   def get_action(self,z):
    h=rnn_output(self.state,z,EXP_MODE)
    if EXP_MODE==MODE_Z_HIDDEN:
     h=np.tanh(np.dot(h,self.weight_hidden)+self.bias_hidden)
     action=np.tanh(np.dot(h,self.weight_output)+self.bias_output)
    else:
     action=np.tanh(np.dot(h,self.weight)+self.bias)
    action[1]=(action[1]+1.0)/2.0
    action[2]=clip(action[2])
    self.state=rnn_next_state(self.rnn,z,action,self.state)
    return action
   def set_model_params(self,model_params):
    if EXP_MODE==MODE_Z_HIDDEN:
     params=np.array(model_params)
     cut_off=(self.input_size+1)*self.hidden_size
     params_1=params[:cut_off]
     params_2=params[cut_off:]
     self.bias_hidden=params_1[:self.hidden_size]
     self.weight_hidden=params_1[self.hidden_size:].reshape(self.input_size,self.hidden_size)
     self.bias_output=params_2[:3]
     self.weight_output=params_2[3:].reshape(self.hidden_size,3)
    else:
     self.bias=np.array(model_params[:3])
     self.weight=np.array(model_params[3:]).reshape(self.input_size,3)
   def load_model(self,filename):
    with open(filename)as f: 
     data=json.load(f)
    print('loading file %s'%(filename))
    self.data=data
    model_params=np.array(data[0])
    self.set_model_params(model_params)
   def get_random_model_params(self,stdev=0.1):
    return np.random.standard_cauchy(self.param_count)*stdev 
   def init_random_model_params(self,stdev=0.1):
    params=self.get_random_model_params(stdev=stdev)
    self.set_model_params(params)
    vae_params=self.vae.get_random_model_params(stdev=stdev)
    self.vae.set_model_params(vae_params)
    rnn_params=self.rnn.get_random_model_params(stdev=stdev)
    self.rnn.set_model_params(rnn_params)
  def simulate(model,train_mode=False,render_mode=True,num_episode=5,seed=-1,max_len=-1):
   reward_list=[]
   t_list=[]
   max_episode_length=1000
   recording_mode=False
   penalize_turning=False
   if train_mode and max_len>0:
    max_episode_length=max_len
   if(seed>=0):
    random.seed(seed)
    np.random.seed(seed)
    model.env.seed(seed)
   for episode in range(num_episode):
    model.reset()
    obs=model.env.reset()
    if obs is None:
     return np.inf,np.inf
    total_reward=0.0
    random_generated_int=np.random.randint(2**31-1)
    filename="record/"+str(random_generated_int)+".npz"
    recording_mu=[]
    recording_logvar=[]
    recording_action=[]
    recording_reward=[0]
    for t in range(max_episode_length):
     if render_mode:
      model.env.render("human")
     else:
      model.env.render('rgb_array')
     z,mu,logvar=model.encode_obs(obs)
     action=model.get_action(z)
     recording_mu.append(mu)
     recording_logvar.append(logvar)
     recording_action.append(action)
     obs,reward,done,info=model.env.step(action)
     extra_reward=0.0 
     if train_mode and penalize_turning:
      extra_reward-=np.abs(action[0])/10.0
      reward+=extra_reward
     recording_reward.append(reward)
     total_reward+=reward
     if done:
      break
    z,mu,logvar=model.encode_obs(obs)
    action=model.get_action(z)
    recording_mu.append(mu)
    recording_logvar.append(logvar)
    recording_action.append(action)
    recording_mu=np.array(recording_mu,dtype=np.float16)
    recording_logvar=np.array(recording_logvar,dtype=np.float16)
    recording_action=np.array(recording_action,dtype=np.float16)
    recording_reward=np.array(recording_reward,dtype=np.float16)
    if not render_mode:
     if recording_mode:
      np.savez_compressed(filename,mu=recording_mu,logvar=recording_logvar,action=recording_action,reward=recording_reward)
    if render_mode:
     print("total reward",total_reward,"timesteps",t)
    reward_list.append(total_reward)
    t_list.append(t)
   return total_reward,t_list
  parser=argparse.ArgumentParser()
  parser.add_argument('--source_worker_port',type=int)
  parser.add_argument('--worker_sink_port',type=int)
  parser.add_argument('--source_worker_direct_port',type=int)
  args=parser.parse_args()
  render_mode=False
  filename='log/carracing.cma.16.64.best.json'
  the_seed=123 
  np.random.seed(the_seed)
  model=make_model()
  print('model size',model.param_count)
  model.make_env(render_mode=render_mode,seed=the_seed)
  model.load_model(filename)
  source_worker_socket,worker_sink_socket,source_worker_direct_socket,context=ZMQUtils.openSearchSocket(args.source_worker_port,args.worker_sink_port,args.source_worker_direct_port)
  source_worker_direct_socket.recv()
  source_worker_direct_socket.send(b"Ack")
  while True:
   try:
    s=source_worker_direct_socket.recv(flags=zmq.NOBLOCK)
    source_worker_direct_socket.send(b"Ack")
    assert(s==b"end")
    break
   except zmq.Again as e:
    pass
   try:
    wholething=ZMQUtils.recv_array(source_worker_socket,flags=zmq.NOBLOCK)
   except zmq.Again as e:
    continue
   sample=wholething[:-1].reshape((12,2))
   index=wholething[-1]
   model.env.noise=sample
   np.random.seed(the_seed)
   tf.set_random_seed(the_seed)
   reward,steps_taken=simulate(model,train_mode=False,seed=the_seed,render_mode=render_mode,num_episode=1)
   temp=np.asarray([reward,index])
   ZMQUtils.send_array(worker_sink_socket,temp)
  # Created by pyminifier (https://github.com/liftoff/pyminifier)
  
  