import os
import math
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
import time
import pybullet as p
from . import simpleHumanoid
import random

class SimpleHumanoidGymEnv(gym.Env):
  metadata = {
      'render.modes': ['human', 'rgb_array'],
      'video.frames_per_second' : 50
  }

  def __init__(self,
               urdfRoot="",
               actionRepeat=50,
               isEnableSelfCollision=True,
               renders=True):
    print("init")
    self._timeStep = 0.01
    self._urdfRoot = urdfRoot
    self._actionRepeat = actionRepeat
    self._isEnableSelfCollision = isEnableSelfCollision
    self._observation = []
    self._envStepCounter = 0
    self._renders = renders
    self._p = p
    if self._renders:
      p.connect(p.GUI)
    else:
      p.connect(p.DIRECT)
    self._seed()
    self.reset()
    observationDim = len(self.getExtendedObservation())
    #print("observationDim")
    #print(observationDim)
    
    observation_high = np.array([np.finfo(np.float32).max] * observationDim)    
    self.action_space = spaces.Discrete(9)
    self.observation_space = spaces.Box(-observation_high, observation_high)
    self.viewer = None

  def _reset(self):
    p.resetSimulation()
    #p.setPhysicsEngineParameter(numSolverIterations=300)
    p.setTimeStep(self._timeStep)
    p.loadURDF(os.path.join(os.path.dirname(__file__),"../data","plane.urdf"))
    
    dist = 5 +2.*random.random()
    ang = 2.*3.1415925438*random.random()
    
    ballx = dist * math.sin(ang)
    bally = dist * math.cos(ang)
    ballz = 1
        
    p.setGravity(0,0,-10)
    self._humanoid = simpleHumanoid.SimpleHumanoid(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
    self._envStepCounter = 0
    p.stepSimulation()
    self._observation = self.getExtendedObservation()
    return np.array(self._observation)

  def __del__(self):
    p.disconnect()

  def _seed(self, seed=None):
    self.np_random, seed = seeding.np_random(seed)
    return [seed]

  def getExtendedObservation(self):
     self._observation = self._humanoid.getObservation()
     return self._observation
     
  def _step(self, action):
    self._humanoid.applyAction(action)
    for i in range(self._actionRepeat):
      p.stepSimulation()
      if self._renders:
        time.sleep(self._timeStep)
      self._observation = self.getExtendedObservation()
      if self._termination():
        break
      self._envStepCounter += 1
    reward = self._reward()
    done = self._termination()
    #print("len=%r" % len(self._observation))
    
    return np.array(self._observation), reward, done, {}

  def _render(self, mode='human', close=False):
      return

  def _termination(self):
    return self._envStepCounter>1000
    
  def _reward(self):
    reward=self._humanoid.distance
    print(reward)
    return reward