text: import multiprocessing
  from collections import OrderedDict
  
  import gym
  import numpy as np
  
  from stable_baselines.common.vec_env import VecEnv, CloudpickleWrapper
  from stable_baselines.common.tile_images import tile_images
  
  
  def _worker(remote, parent_remote, env_fn_wrapper):
      parent_remote.close()
      env = env_fn_wrapper.var()
      while True:
          try:
              cmd, data = remote.recv()
              if cmd == 'step':
                  observation, reward, done, info = env.step(data)
                  if done:
                      # save final observation where user can get it, then reset
                      info['terminal_observation'] = observation
                      observation = env.reset()
                  remote.send((observation, reward, done, info))
              elif cmd == 'reset':
                  observation = env.reset()
                  remote.send(observation)
              elif cmd == 'render':
                  remote.send(env.render(*data[0], **data[1]))
              elif cmd == 'close':
                  env.close()
                  remote.close()
                  break
              elif cmd == 'get_spaces':
                  remote.send((env.observation_space, env.action_space))
              elif cmd == 'env_method':
                  method = getattr(env, data[0])
                  remote.send(method(*data[1], **data[2]))
              elif cmd == 'get_attr':
                  remote.send(getattr(env, data))
              elif cmd == 'set_attr':
                  remote.send(setattr(env, data[0], data[1]))
              else:
                  raise NotImplementedError
          except EOFError:
              break
  
  
  class SubprocVecEnv(VecEnv):
      """
      Creates a multiprocess vectorized wrapper for multiple environments, distributing each environment to its own
      process, allowing significant speed up when the environment is computationally complex.
  
      For performance reasons, if your environment is not IO bound, the number of environments should not exceed the
      number of logical cores on your CPU.
  
      .. warning::
  
          Only 'forkserver' and 'spawn' start methods are thread-safe,
          which is important when TensorFlow sessions or other non thread-safe
          libraries are used in the parent (see issue #217). However, compared to
          'fork' they incur a small start-up cost and have restrictions on
          global variables. With those methods, users must wrap the code in an
          ``if __name__ == "__main__":`` block.
          For more information, see the multiprocessing documentation.
  
      :param env_fns: ([Gym Environment]) Environments to run in subprocesses
      :param start_method: (str) method used to start the subprocesses.
             Must be one of the methods returned by multiprocessing.get_all_start_methods().
             Defaults to 'forkserver' on available platforms, and 'spawn' otherwise.
      """
  
      def __init__(self, env_fns, start_method=None):
          self.waiting = False
          self.closed = False
          n_envs = len(env_fns)
  
          if start_method is None:
              # Fork is not a thread safe method (see issue #217)
              # but is more user friendly (does not require to wrap the code in
              # a `if __name__ == "__main__":`)
              forkserver_available = 'forkserver' in multiprocessing.get_all_start_methods()
              start_method = 'forkserver' if forkserver_available else 'spawn'
          ctx = multiprocessing.get_context(start_method)
  
          self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(n_envs)])
          self.processes = []
          for work_remote, remote, env_fn in zip(self.work_remotes, self.remotes, env_fns):
              args = (work_remote, remote, CloudpickleWrapper(env_fn))
              # daemon=True: if the main process crashes, we should not cause things to hang
              process = ctx.Process(target=_worker, args=args, daemon=True)
              process.start()
              self.processes.append(process)
              work_remote.close()
  
          self.remotes[0].send(('get_spaces', None))
          observation_space, action_space = self.remotes[0].recv()
          VecEnv.__init__(self, len(env_fns), observation_space, action_space)
  
      def step_async(self, actions):
          for remote, action in zip(self.remotes, actions):
              remote.send(('step', action))
          self.waiting = True
  
      def step_wait(self):
          results = [remote.recv() for remote in self.remotes]
          self.waiting = False
          obs, rews, dones, infos = zip(*results)
          return _flatten_obs(obs, self.observation_space), np.stack(rews), np.stack(dones), infos
  
      def reset(self):
          for remote in self.remotes:
              remote.send(('reset', None))
          obs = [remote.recv() for remote in self.remotes]
          return _flatten_obs(obs, self.observation_space)
  
      def close(self):
          if self.closed:
              return
          if self.waiting:
              for remote in self.remotes:
                  remote.recv()
          for remote in self.remotes:
              remote.send(('close', None))
          for process in self.processes:
              process.join()
          self.closed = True
  
      def render(self, mode='human', *args, **kwargs):
          for pipe in self.remotes:
              # gather images from subprocesses
              # `mode` will be taken into account later
              pipe.send(('render', (args, {'mode': 'rgb_array', **kwargs})))
          imgs = [pipe.recv() for pipe in self.remotes]
          # Create a big image by tiling images from subprocesses
          bigimg = tile_images(imgs)
          if mode == 'human':
              import cv2
              cv2.imshow('vecenv', bigimg[:, :, ::-1])
              cv2.waitKey(1)
          elif mode == 'rgb_array':
              return bigimg
          else:
              raise NotImplementedError
  
      def get_images(self):
          for pipe in self.remotes:
              pipe.send(('render', {"mode": 'rgb_array'}))
          imgs = [pipe.recv() for pipe in self.remotes]
          return imgs
  
      def get_attr(self, attr_name, indices=None):
          """Return attribute from vectorized environment (see base class)."""
          target_remotes = self._get_target_remotes(indices)
          for remote in target_remotes:
              remote.send(('get_attr', attr_name))
          return [remote.recv() for remote in target_remotes]
  
      def set_attr(self, attr_name, value, indices=None):
          """Set attribute inside vectorized environments (see base class)."""
          target_remotes = self._get_target_remotes(indices)
          for remote in target_remotes:
              remote.send(('set_attr', (attr_name, value)))
          for remote in target_remotes:
              remote.recv()
  
      def env_method(self, method_name, *method_args, indices=None, **method_kwargs):
          """Call instance methods of vectorized environments."""
          target_remotes = self._get_target_remotes(indices)
          for remote in target_remotes:
              remote.send(('env_method', (method_name, method_args, method_kwargs)))
          return [remote.recv() for remote in target_remotes]
  
      def _get_target_remotes(self, indices):
          """
          Get the connection object needed to communicate with the wanted
          envs that are in subprocesses.
  
          :param indices: (None,int,Iterable) refers to indices of envs.
          :return: ([multiprocessing.Connection]) Connection object to communicate between processes.
          """
          indices = self._get_indices(indices)
          return [self.remotes[i] for i in indices]
  
  
  def _flatten_obs(obs, space):
      """
      Flatten observations, depending on the observation space.
  
      :param obs: (list<X> or tuple<X> where X is dict<ndarray>, tuple<ndarray> or ndarray) observations.
                  A list or tuple of observations, one per environment.
                  Each environment observation may be a NumPy array, or a dict or tuple of NumPy arrays.
      :return (OrderedDict<ndarray>, tuple<ndarray> or ndarray) flattened observations.
              A flattened NumPy array or an OrderedDict or tuple of flattened numpy arrays.
              Each NumPy array has the environment index as its first axis.
      """
      assert isinstance(obs, (list, tuple)), "expected list or tuple of observations per environment"
      assert len(obs) > 0, "need observations from at least one environment"
  
      if isinstance(space, gym.spaces.Dict):
          assert isinstance(space.spaces, OrderedDict), "Dict space must have ordered subspaces"
          assert isinstance(obs[0], dict), "non-dict observation for environment with Dict observation space"
          return OrderedDict([(k, np.stack([o[k] for o in obs])) for k in space.spaces.keys()])
      elif isinstance(space, gym.spaces.Tuple):
          assert isinstance(obs[0], tuple), "non-tuple observation for environment with Tuple observation space"
          obs_len = len(space.spaces)
          return tuple((np.stack([o[i] for o in obs]) for i in range(obs_len)))
      else:
          return np.stack(obs)
  