hunk.code_window: import unittest
   import numpy as np
   from gym.spaces import Box, Discrete, MultiDiscrete, MultiBinary, Tuple, Dict
   from ray.rllib.utils.spaces.space_utils import convert_element_to_space_type
   class TestSpaceUtils(unittest.TestCase):
  
hunk.labels: keep keep keep keep replace keep keep keep
hunk.after_edit: from ray.rllib.utils.spaces.space_utils import (
       convert_element_to_space_type,
       get_base_struct_from_space,
       unsquash_action,
   )
  
hunk.file_path: rllib/utils/spaces/tests/test_space_utils.py
hunk.type: replace
file: """
  DeepMind Control Suite Wrapper directly sourced from:
  https://github.com/denisyarats/dmc2gym
  
  MIT License
  
  Copyright (c) 2020 Denis Yarats
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.
  """
  from gym import core, spaces
  
  try:
      from dm_env import specs
  except ImportError:
      specs = None
  try:
      # Suppress MuJoCo warning (dm_control uses absl logging).
      import absl.logging
  
      absl.logging.set_verbosity("error")
      from dm_control import suite
  except (ImportError, OSError):
      suite = None
  import numpy as np
  
  
  def _spec_to_box(spec):
      def extract_min_max(s):
          assert s.dtype == np.float64 or s.dtype == np.float32
          dim = np.int(np.prod(s.shape))
          if type(s) == specs.Array:
              bound = np.inf * np.ones(dim, dtype=np.float32)
              return -bound, bound
          elif type(s) == specs.BoundedArray:
              zeros = np.zeros(dim, dtype=np.float32)
              return s.minimum + zeros, s.maximum + zeros
  
      mins, maxs = [], []
      for s in spec:
          mn, mx = extract_min_max(s)
          mins.append(mn)
          maxs.append(mx)
      low = np.concatenate(mins, axis=0)
      high = np.concatenate(maxs, axis=0)
      assert low.shape == high.shape
      return spaces.Box(low, high, dtype=np.float32)
  
  
  def _flatten_obs(obs):
      obs_pieces = []
      for v in obs.values():
          flat = np.array([v]) if np.isscalar(v) else v.ravel()
          obs_pieces.append(flat)
      return np.concatenate(obs_pieces, axis=0)
  
  
  class DMCEnv(core.Env):
      def __init__(
          self,
          domain_name,
          task_name,
          task_kwargs=None,
          visualize_reward=False,
          from_pixels=False,
          height=64,
          width=64,
          camera_id=0,
          frame_skip=2,
          environment_kwargs=None,
          channels_first=True,
          preprocess=True,
      ):
          self._from_pixels = from_pixels
          self._height = height
          self._width = width
          self._camera_id = camera_id
          self._frame_skip = frame_skip
          self._channels_first = channels_first
          self.preprocess = preprocess
  
          if specs is None:
              raise RuntimeError(
                  (
                      "The `specs` module from `dm_env` was not imported. Make sure "
                      "`dm_env` is installed and visible in the current python "
                      "environment."
                  )
              )
          if suite is None:
              raise RuntimeError(
                  (
                      "The `suite` module from `dm_control` was not imported. Make "
                      "sure `dm_control` is installed and visible in the current "
                      "python enviornment."
                  )
              )
  
          # create task
          self._env = suite.load(
              domain_name=domain_name,
              task_name=task_name,
              task_kwargs=task_kwargs,
              visualize_reward=visualize_reward,
              environment_kwargs=environment_kwargs,
          )
  
          # true and normalized action spaces
          self._true_action_space = _spec_to_box([self._env.action_spec()])
          self._norm_action_space = spaces.Box(
              low=-1.0, high=1.0, shape=self._true_action_space.shape, dtype=np.float32
          )
  
          # create observation space
          if from_pixels:
              shape = [3, height, width] if channels_first else [height, width, 3]
              self._observation_space = spaces.Box(
                  low=0, high=255, shape=shape, dtype=np.uint8
              )
              if preprocess:
                  self._observation_space = spaces.Box(
                      low=-0.5, high=0.5, shape=shape, dtype=np.float32
                  )
          else:
              self._observation_space = _spec_to_box(
                  self._env.observation_spec().values()
              )
  
          self._state_space = _spec_to_box(self._env.observation_spec().values())
  
          self.current_state = None
  
      def __getattr__(self, name):
          return getattr(self._env, name)
  
      def _get_obs(self, time_step):
          if self._from_pixels:
              obs = self.render(
                  height=self._height, width=self._width, camera_id=self._camera_id
              )
              if self._channels_first:
                  obs = obs.transpose(2, 0, 1).copy()
              if self.preprocess:
                  obs = obs / 255.0 - 0.5
          else:
              obs = _flatten_obs(time_step.observation)
          return obs
  
      def _convert_action(self, action):
          action = action.astype(np.float64)
          true_delta = self._true_action_space.high - self._true_action_space.low
          norm_delta = self._norm_action_space.high - self._norm_action_space.low
          action = (action - self._norm_action_space.low) / norm_delta
          action = action * true_delta + self._true_action_space.low
          action = action.astype(np.float32)
          return action
  
      @property
      def observation_space(self):
          return self._observation_space
  
      @property
      def state_space(self):
          return self._state_space
  
      @property
      def action_space(self):
          return self._norm_action_space
  
      def step(self, action):
          assert self._norm_action_space.contains(action)
          action = self._convert_action(action)
          assert self._true_action_space.contains(action)
          reward = 0
          extra = {"internal_state": self._env.physics.get_state().copy()}
  
          for _ in range(self._frame_skip):
              time_step = self._env.step(action)
              reward += time_step.reward or 0
              done = time_step.last()
              if done:
                  break
          obs = self._get_obs(time_step)
          self.current_state = _flatten_obs(time_step.observation)
          extra["discount"] = time_step.discount
          return obs, reward, done, extra
  
      def reset(self):
          time_step = self._env.reset()
          self.current_state = _flatten_obs(time_step.observation)
          obs = self._get_obs(time_step)
          return obs
  
      def render(self, mode="rgb_array", height=None, width=None, camera_id=0):
          assert mode == "rgb_array", "only support for rgb_array mode"
          height = height or self._height
          width = width or self._width
          camera_id = camera_id or self._camera_id
          return self._env.physics.render(height=height, width=width, camera_id=camera_id)
  