text: """This file is a fork from a MIT-licensed project named OpenAI Baselines:
  https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
  """
  
  from collections import deque
  
  import gym
  import numpy as np
  
  from gym import spaces
  
  import pfrl
  
  
  try:
      import cv2
  
      cv2.ocl.setUseOpenCL(False)
      _is_cv2_available = True
  except Exception:
      _is_cv2_available = 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, info = self.env.step(self.noop_action)
              if done or info.get("needs_reset", False):
                  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 envs 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, info = self.env.step(1)
          if done or info.get("needs_reset", False):
              self.env.reset(**kwargs)
          obs, _, done, info = self.env.step(2)
          if done or info.get("needs_reset", False):
              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 end.
  
          Done by DeepMind for the DQN and co. since it helps value estimation.
          """
          gym.Wrapper.__init__(self, env)
          self.lives = 0
          self.needs_real_reset = True
  
      def step(self, action):
          obs, reward, done, info = self.env.step(action)
          self.needs_real_reset = done or info.get("needs_reset", False)
          # 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.needs_real_reset:
              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 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 or info.get("needs_reset", False):
                  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, channel_order="hwc"):
          """Warp frames to 84x84 as done in the Nature paper and later work.
  
          To use this wrapper, OpenCV-Python is required.
          """
          if not _is_cv2_available:
              raise RuntimeError(
                  "Cannot import cv2 module. Please install OpenCV-Python to use"
                  " WarpFrame."
              )
          gym.ObservationWrapper.__init__(self, env)
          self.width = 84
          self.height = 84
          shape = {
              "hwc": (self.height, self.width, 1),
              "chw": (1, self.height, self.width),
          }
          self.observation_space = spaces.Box(
              low=0, high=255, shape=shape[channel_order], 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.reshape(self.observation_space.low.shape)
  
  
  class FrameStack(gym.Wrapper):
      def __init__(self, env, k, channel_order="hwc"):
          """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)
          self.stack_axis = {"hwc": 2, "chw": 0}[channel_order]
          orig_obs_space = env.observation_space
          low = np.repeat(orig_obs_space.low, k, axis=self.stack_axis)
          high = np.repeat(orig_obs_space.high, k, axis=self.stack_axis)
          self.observation_space = spaces.Box(
              low=low, high=high, dtype=orig_obs_space.dtype
          )
  
      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), stack_axis=self.stack_axis)
  
  
  class ScaledFloatFrame(gym.ObservationWrapper):
      """Divide frame values by 255.0 and return them as np.float32.
  
      Especially, when the original env.observation_space is np.uint8,
      this wrapper converts frame values into [0.0, 1.0] of dtype np.float32.
      """
  
      def __init__(self, env):
          assert isinstance(env.observation_space, spaces.Box)
          gym.ObservationWrapper.__init__(self, env)
  
          self.scale = 255.0
  
          orig_obs_space = env.observation_space
          self.observation_space = spaces.Box(
              low=self.observation(orig_obs_space.low),
              high=self.observation(orig_obs_space.high),
              dtype=np.float32,
          )
  
      def observation(self, observation):
          # careful! This undoes the memory optimization, use
          # with smaller replay buffers only.
          return np.array(observation).astype(np.float32) / self.scale
  
  
  class LazyFrames(object):
      """Array-like object that lazily concat multiple 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.
      """
  
      def __init__(self, frames, stack_axis=2):
          self.stack_axis = stack_axis
          self._frames = frames
  
      def __array__(self, dtype=None):
          out = np.concatenate(self._frames, axis=self.stack_axis)
          if dtype is not None:
              out = out.astype(dtype)
          return out
  
  
  class FlickerFrame(gym.ObservationWrapper):
      """Stochastically flicker frames."""
  
      def __init__(self, env):
          gym.ObservationWrapper.__init__(self, env)
  
      def observation(self, observation):
          if self.unwrapped.np_random.rand() < 0.5:
              return np.zeros_like(observation)
          else:
              return observation
  
  
  def make_atari(env_id, max_frames=30 * 60 * 60):
      env = gym.make(env_id)
      assert "NoFrameskip" in env.spec.id
      assert isinstance(env, gym.wrappers.TimeLimit)
      # Unwrap TimeLimit wrapper because we use our own time limits
      env = env.env
      if max_frames:
          env = pfrl.wrappers.ContinuingTimeLimit(env, max_episode_steps=max_frames)
      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=True,
      scale=False,
      fire_reset=False,
      channel_order="chw",
      flicker=False,
  ):
      """Configure environment for DeepMind-style Atari."""
      if episode_life:
          env = EpisodicLifeEnv(env)
      if fire_reset and "FIRE" in env.unwrapped.get_action_meanings():
          env = FireResetEnv(env)
      env = WarpFrame(env, channel_order=channel_order)
      if scale:
          env = ScaledFloatFrame(env)
      if clip_rewards:
          env = ClipRewardEnv(env)
      if flicker:
          env = FlickerFrame(env)
      if frame_stack:
          env = FrameStack(env, 4, channel_order=channel_order)
      return env
  