text:  import numpy as np
  from collections import deque
  import gym
  from gym import spaces
  import cv2
  cv2.ocl.setUseOpenCL(False)
  
  class NoopResetEnv(gym.Wrapper):
      def __init__(self, env, noop_max=30):
          """Sample initial states by taking random number of no-ops on reset.
          No-op is assumed to be action 0.
          """
          gym.Wrapper.__init__(self, env)
          self.noop_max = noop_max
          self.override_num_noops = None
          self.noop_action = 0
          assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
  
      def reset(self, **kwargs):
          """ Do no-op action for a number of steps in [1, noop_max]."""
          self.env.reset(**kwargs)
          if self.override_num_noops is not None:
              noops = self.override_num_noops
          else:
              noops = self.unwrapped.np_random.randint(1, self.noop_max + 1) #pylint: disable=E1101
          assert noops > 0
          obs = None
          for _ in range(noops):
              obs, _, done, _ = self.env.step(self.noop_action)
              if done:
                  obs = self.env.reset(**kwargs)
          return obs
  
      def step(self, ac):
          return self.env.step(ac)
  
  class FireResetEnv(gym.Wrapper):
      def __init__(self, env):
          """Take action on reset for environments that are fixed until firing."""
          gym.Wrapper.__init__(self, env)
          assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
          assert len(env.unwrapped.get_action_meanings()) >= 3
  
      def reset(self, **kwargs):
          self.env.reset(**kwargs)
          obs, _, done, _ = self.env.step(1)
          if done:
              self.env.reset(**kwargs)
          obs, _, done, _ = self.env.step(2)
          if done:
              self.env.reset(**kwargs)
          return obs
  
      def step(self, ac):
          return self.env.step(ac)
  
  class EpisodicLifeEnv(gym.Wrapper):
      def __init__(self, env):
          """Make end-of-life == end-of-episode, but only reset on true game over.
          Done by DeepMind for the DQN and co. since it helps value estimation.
          """
          gym.Wrapper.__init__(self, env)
          self.lives = 0
          self.was_real_done  = True
  
      def step(self, action):
          obs, reward, done, info = self.env.step(action)
          self.was_real_done = done
          # check current lives, make loss of life terminal,
          # then update lives to handle bonus lives
          lives = self.env.unwrapped.ale.lives()
          if lives < self.lives and lives > 0:
              # for Qbert sometimes we stay in lives == 0 condtion for a few frames
              # so its important to keep lives > 0, so that we only reset once
              # the environment advertises done.
              done = True
          self.lives = lives
          return obs, reward, done, info
  
      def reset(self, **kwargs):
          """Reset only when lives are exhausted.
          This way all states are still reachable even though lives are episodic,
          and the learner need not know about any of this behind-the-scenes.
          """
          if self.was_real_done:
              obs = self.env.reset(**kwargs)
          else:
              # no-op step to advance from terminal/lost life state
              obs, _, _, _ = self.env.step(0)
          self.lives = self.env.unwrapped.ale.lives()
          return obs
  
  class MaxAndSkipEnv(gym.Wrapper):
      def __init__(self, env, skip=4):
          """Return only every `skip`-th frame"""
          gym.Wrapper.__init__(self, env)
          # most recent raw observations (for max pooling across time steps)
          self._obs_buffer = np.zeros((2,)+env.observation_space.shape, dtype=np.uint8)
          self._skip       = skip
  
      def reset(self):
          return self.env.reset()
  
      def step(self, action):
          """Repeat action, sum reward, and max over last observations."""
          total_reward = 0.0
          done = None
          for i in range(self._skip):
              obs, reward, done, info = self.env.step(action)
              if i == self._skip - 2: self._obs_buffer[0] = obs
              if i == self._skip - 1: self._obs_buffer[1] = obs
              total_reward += reward
              if done:
                  break
          # Note that the observation on the done=True frame
          # doesn't matter
          max_frame = self._obs_buffer.max(axis=0)
  
          return max_frame, total_reward, done, info
  
      def reset(self, **kwargs):
          return self.env.reset(**kwargs)
  
  class ClipRewardEnv(gym.RewardWrapper):
      def __init__(self, env):
          gym.RewardWrapper.__init__(self, env)
  
      def reward(self, reward):
          """Bin reward to {+1, 0, -1} by its sign."""
          return np.sign(reward)
  
  class WarpFrame(gym.ObservationWrapper):
      def __init__(self, env):
          """Warp frames to 84x84 as done in the Nature paper and later work."""
          gym.ObservationWrapper.__init__(self, env)
          self.width = 84
          self.height = 84
          self.observation_space = spaces.Box(low=0, high=255,
              shape=(self.height, self.width, 1), dtype=np.uint8)
  
      def observation(self, frame):
          frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
          frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
          return frame[:, :, None]
  
  class FrameStack(gym.Wrapper):
      def __init__(self, env, k):
          """Stack k last frames.
          Returns lazy array, which is much more memory efficient.
          See Also
          --------
          baselines.common.atari_wrappers.LazyFrames
          """
          gym.Wrapper.__init__(self, env)
          self.k = k
          self.frames = deque([], maxlen=k)
          shp = env.observation_space.shape
          self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k), dtype=np.uint8)
  
      def reset(self):
          ob = self.env.reset()
          for _ in range(self.k):
              self.frames.append(ob)
          return self._get_ob()
  
      def step(self, action):
          ob, reward, done, info = self.env.step(action)
          self.frames.append(ob)
          return self._get_ob(), reward, done, info
  
      def _get_ob(self):
          assert len(self.frames) == self.k
          return LazyFrames(list(self.frames))
  
  class ScaledFloatFrame(gym.ObservationWrapper):
      def __init__(self, env):
          gym.ObservationWrapper.__init__(self, env)
  
      def observation(self, observation):
          # careful! This undoes the memory optimization, use
          # with smaller replay buffers only.
          return np.array(observation).astype(np.float32) / 255.0
  
  class LazyFrames(object):
      def __init__(self, frames):
          """This object ensures that common frames between the observations are only stored once.
          It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
          buffers.
          This object should only be converted to numpy array before being passed to the model.
          You'd not believe how complex the previous solution was."""
          self._frames = frames
          self._out = None
  
      def _force(self):
          if self._out is None:
              self._out = np.concatenate(self._frames, axis=2)
              self._frames = None
          return self._out
  
      def __array__(self, dtype=None):
          out = self._force()
          if dtype is not None:
              out = out.astype(dtype)
          return out
  
      def __len__(self):
          return len(self._force())
  
      def __getitem__(self, i):
          return self._force()[i]
  
  def make_atari(env_id):
      env = gym.make(env_id)
      # assert 'NoFrameskip' in env.spec.id
      env = NoopResetEnv(env, noop_max=30)
      env = MaxAndSkipEnv(env, skip=4)
      return env
  
  def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
      """Configure environment for DeepMind-style Atari.
      """
      if episode_life:
          env = EpisodicLifeEnv(env)
      if 'FIRE' in env.unwrapped.get_action_meanings():
          env = FireResetEnv(env)
      env = WarpFrame(env)
      if scale:
          env = ScaledFloatFrame(env)
      if clip_rewards:
          env = ClipRewardEnv(env)
      if frame_stack:
          env = FrameStack(env, 4)
      return env
  
  
  
  class ImageToPyTorch(gym.ObservationWrapper):
      """
      Image shape to num_channels x weight x height
      """
      def __init__(self, env):
          super(ImageToPyTorch, self).__init__(env)
          old_shape = self.observation_space.shape
          self.observation_space = gym.spaces.Box(low=0.0, high=1.0, shape=(old_shape[-1], old_shape[0], old_shape[1]), dtype=np.uint8)
  
      def observation(self, observation):
          return observation.transpose((2, 0, 1))
  
  def wrap_pytorch(env):
      return ImageToPyTorch(env)