text: import numpy as np
  from gym.spaces import Box
  
  from metaworld.envs.asset_path_utils import full_v2_path_for
  from metaworld.envs.mujoco.sawyer_xyz.sawyer_xyz_env import SawyerXYZEnv, _assert_task_is_set
  
  
  class SawyerDrawerOpenEnvV2(SawyerXYZEnv):
      def __init__(self):
  
          hand_low = (-0.5, 0.40, 0.05)
          hand_high = (0.5, 1, 0.5)
          obj_low = (-0.1, 0.9, 0.0)
          obj_high = (0.1, 0.9, 0.0)
  
          super().__init__(
              self.model_name,
              hand_low=hand_low,
              hand_high=hand_high,
          )
  
          self.init_config = {
              'obj_init_angle': np.array([0.3, ], dtype=np.float32),
              'obj_init_pos': np.array([0., 0.9, 0.0], dtype=np.float32),
              'hand_init_pos': np.array([0, 0.6, 0.2], dtype=np.float32),
          }
          self.obj_init_pos = self.init_config['obj_init_pos']
          self.obj_init_angle = self.init_config['obj_init_angle']
          self.hand_init_pos = self.init_config['hand_init_pos']
  
          goal_low = self.hand_low
          goal_high = self.hand_high
  
          self.max_path_length = 150
  
          self._random_reset_space = Box(
              np.array(obj_low),
              np.array(obj_high),
          )
          self.goal_space = Box(np.array(goal_low), np.array(goal_high))
  
          self.maxDist = 0.2
          self.target_reward = 1000 * self.maxDist + 1000 * 2
  
      @property
      def model_name(self):
          return full_v2_path_for('sawyer_xyz/sawyer_drawer.xml')
  
      @_assert_task_is_set
      def step(self, action):
          ob = super().step(action)
          reward, reachDist, pullDist = self.compute_reward(action, ob)
          self.curr_path_length += 1
          info = {
              'reachDist': reachDist,
              'goalDist': pullDist,
              'epRew': reward,
              'pickRew': None,
              'success': float(pullDist <= 0.03),
          }
  
          return ob, reward, False, info
  
      def _get_pos_objects(self):
          return self.get_body_com('drawer_link') + np.array([.0, -.16, .05])
  
      def reset_model(self):
          self._reset_hand()
  
          # Compute nightstand position
          self.obj_init_pos = self._get_state_rand_vec() if self.random_init \
              else self.init_config['obj_init_pos']
          # Set mujoco body to computed position
          self.sim.model.body_pos[self.model.body_name2id(
              'drawer'
          )] = self.obj_init_pos
          # Set _target_pos to current drawer position (closed) minus an offset
          self._target_pos = self.obj_init_pos + np.array([.0, -.16 - self.maxDist, .09])
  
          return self._get_obs()
  
      def _reset_hand(self):
          super()._reset_hand()
          self.reachCompleted = False
  
      def compute_reward(self, actions, obs):
          del actions
  
          objPos = obs[3:6]
          rightFinger, leftFinger = self._get_site_pos('rightEndEffector'), self._get_site_pos('leftEndEffector')
          fingerCOM  =  (rightFinger + leftFinger)/2
          pullGoal = self._target_pos
          pullDist = np.abs(objPos[1] - pullGoal[1])
          reachDist = np.linalg.norm(objPos - fingerCOM)
          reachRew = -reachDist
  
          self.reachCompleted = reachDist < 0.05
  
          def pullReward():
              c1 = 1000
              c2 = 0.01
              c3 = 0.001
  
              if self.reachCompleted:
                  pullRew = 1000 * (self.maxDist - pullDist) + c1 * (np.exp(-(pullDist ** 2) / c2) + np.exp(-(pullDist ** 2) / c3))
                  pullRew = max(pullRew,0)
                  return pullRew
              else:
                  return 0
  
              pullRew = max(pullRew,0)
  
              return pullRew
  
  
          pullRew = pullReward()
          reward = reachRew + pullRew
  
          return [reward, reachDist, pullDist]
  