text: """
  Helpers for dealing with vectorized environments.
  """
  
  from collections import OrderedDict
  
  import gym
  import numpy as np
  
  
  def copy_obs_dict(obs):
      """
      Deep-copy an observation dict.
      """
      return {k: np.copy(v) for k, v in obs.items()}
  
  
  def dict_to_obs(obs_dict):
      """
      Convert an observation dict into a raw array if the
      original observation space was not a Dict space.
      """
      if set(obs_dict.keys()) == {None}:
          return obs_dict[None]
      return obs_dict
  
  
  def obs_space_info(obs_space):
      """
      Get dict-structured information about a gym.Space.
  
      Returns:
        A tuple (keys, shapes, dtypes):
          keys: a list of dict keys.
          shapes: a dict mapping keys to shapes.
          dtypes: a dict mapping keys to dtypes.
      """
      if isinstance(obs_space, gym.spaces.Dict):
          assert isinstance(obs_space.spaces, OrderedDict)
          subspaces = obs_space.spaces
      else:
          subspaces = {None: obs_space}
      keys = []
      shapes = {}
      dtypes = {}
      for key, box in subspaces.items():
          keys.append(key)
          shapes[key] = box.shape
          dtypes[key] = box.dtype
      return keys, shapes, dtypes
  
  
  def obs_to_dict(obs):
      """
      Convert an observation into a dict.
      """
      if isinstance(obs, dict):
          return obs
      return {None: obs}
  