old_contents: # Copyright 2018 DeepMind Technologies Limited. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Atari wrapper using Opencv for pixel prepocessing.
  
  Note that the AtariWrapper comes with several options, including the legacy
  style that allows reproducing behavior of previous Acme versions.
  
  To reproduce accurate standard result, we recommend using the default
  configuration.
  """
  
  from typing import List
  
  from acme.wrappers import atari_wrapper
  # pytype: disable=import-error
  import cv2
  # pytype: enable=import-error
  import dm_env
  import numpy as np
  
  
  class AtariWrapperDopamine(atari_wrapper.BaseAtariWrapper):
    """Atari wrapper that matches exactly Dopamine's prepocessing.
  
    Wraning: using this wrapper requires that you have opencv and its dependencies
    installed. In general, opencv is not required for Acme.
    """
  
    def _preprocess_pixels(self, timestep_stack: List[dm_env.TimeStep]):
      """Preprocess Atari frames."""
  
      # 1. RBG to grayscale
      def rgb_to_grayscale(obs):
        if self._grayscaling:
          return np.tensordot(obs, [0.2989, 0.5870, 0.1140], (-1, 0))
        return obs
  
      # 2. Max pooling
      processed_pixels = np.max(
          np.stack([
              rgb_to_grayscale(s.observation[atari_wrapper.RGB_INDEX])
              for s in timestep_stack[-self._pooled_frames:]
          ]),
          axis=0)
  
      # 3. Resize
      processed_pixels = np.round(processed_pixels).astype(np.uint8)
      if self._scale_dims != processed_pixels.shape[:2]:
        processed_pixels = cv2.resize(
            processed_pixels, (self._width, self._height),
            interpolation=cv2.INTER_AREA)
  
        processed_pixels = np.round(processed_pixels).astype(np.uint8)
  
      return processed_pixels
  
new_contents: # Copyright 2018 DeepMind Technologies Limited. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Atari wrapper using Opencv for pixel prepocessing.
  
  Note that the default Atari wrapper in Acme does not use Opencv, and has
  slightly different operations (see docstrings). This is available to match
  the preprocessing from the Dopamine [1] library.
  
  To reproduce accurate standard result, we recommend using the default
  configuration using the wrapper defined in atari_wrapper.py.
  
  [1]: https://github.com/google/dopamine
  """
  
  from typing import List
  
  from acme.wrappers import atari_wrapper
  # pytype: disable=import-error
  import cv2
  # pytype: enable=import-error
  import dm_env
  import numpy as np
  
  
  class AtariWrapperDopamine(atari_wrapper.BaseAtariWrapper):
    """Atari wrapper that matches exactly Dopamine's prepocessing.
  
    Warning: using this wrapper requires that you have opencv and its dependencies
    installed. In general, opencv is not required for Acme.
    """
  
    def _preprocess_pixels(self, timestep_stack: List[dm_env.TimeStep]):
      """Preprocess Atari frames."""
  
      # 1. RBG to grayscale
      def rgb_to_grayscale(obs):
        if self._grayscaling:
          return np.tensordot(obs, [0.2989, 0.5870, 0.1140], (-1, 0))
        return obs
  
      # 2. Max pooling
      processed_pixels = np.max(
          np.stack([
              rgb_to_grayscale(s.observation[atari_wrapper.RGB_INDEX])
              for s in timestep_stack[-self._pooled_frames:]
          ]),
          axis=0)
  
      # 3. Resize
      processed_pixels = np.round(processed_pixels).astype(np.uint8)
      if self._scale_dims != processed_pixels.shape[:2]:
        processed_pixels = cv2.resize(
            processed_pixels, (self._width, self._height),
            interpolation=cv2.INTER_AREA)
  
        processed_pixels = np.round(processed_pixels).astype(np.uint8)
  
      return processed_pixels
  
subject: Fix comments.