text: # pylint: disable=g-bad-file-header
  # Copyright 2019 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.
  # ============================================================================
  """bsuite adapter for OpenAI gym run-loops."""
  
  # Import all packages
  
  import dm_env
  from dm_env import specs
  import gym
  from gym import spaces
  import numpy as np
  from typing import Any, Dict, Optional, Text, Tuple, Union
  
  # OpenAI gym step format = obs, reward, is_finished, other_info
  _GymTimestep = Tuple[np.ndarray, float, bool, Dict[Text, Any]]
  
  
  class GymFromDMEnv(gym.Env):
    """A wrapper that converts a dm_env.Environment to an OpenAI gym.Env."""
  
    metadata = {'render.modes': ['human', 'rgb_array']}
  
    def __init__(self, env: dm_env.Environment):
      self._env = env  # type: dm_env.Environment
      self._last_observation = None  # type: Optional[np.ndarray]
      self.viewer = None
  
    def step(self, action: int) -> _GymTimestep:
      timestep = self._env.step(action)
      self._last_observation = timestep.observation
      reward = timestep.reward or 0.
      return timestep.observation, reward, timestep.last(), {}
  
    def reset(self) -> np.ndarray:
      timestep = self._env.reset()
      self._last_observation = timestep.observation
      return timestep.observation
  
    def render(self, mode: Text = 'rgb_array') -> Union[np.ndarray, bool]:
      if self._last_observation is None:
        raise ValueError('Environment not ready to render. Call reset() first.')
  
      if mode == 'rgb_array':
        return self._last_observation
  
      if mode == 'human':
        if self.viewer is None:
          from gym.envs.classic_control import rendering  # pylint: disable=g-import-not-at-top
          self.viewer = rendering.SimpleImageViewer()
        self.viewer.imshow(self._last_observation)
        return self.viewer.isopen
  
    @property
    def action_space(self) -> spaces.Discrete:
      action_spec = self._env.action_spec()  # type: specs.DiscreteArray
      return spaces.Discrete(action_spec.num_values)
  
    @property
    def observation_space(self) -> spaces.Box:
      obs_spec = self._env.observation_spec()  # type: specs.Array
      if isinstance(obs_spec, specs.BoundedArray):
        return spaces.Box(
            low=float(obs_spec.minimum),
            high=float(obs_spec.maximum),
            shape=obs_spec.shape,
            dtype=obs_spec.dtype)
      return spaces.Box(
          low=-float('inf'),
          high=float('inf'),
          shape=obs_spec.shape,
          dtype=obs_spec.dtype)
  
    @property
    def reward_range(self) -> Tuple[float, float]:
      reward_spec = self._env.reward_spec()
      if isinstance(reward_spec, specs.BoundedArray):
        return reward_spec.minimum, reward_spec.maximum
      return -float('inf'), float('inf')
  
    def __getattr__(self, attr):
      """Delegate attribute access to underlying environment."""
      return getattr(self._env, attr)
  
  
  def space2spec(space: gym.Space, name: Text = None):
    """Converts an OpenAI Gym space to a dm_env spec or nested structure of specs.
  
    Box, MultiBinary and MultiDiscrete Gym spaces are converted to BoundedArray
    specs. Discrete OpenAI spaces are converted to DiscreteArray specs. Tuple and
    Dict spaces are recursively converted to tuples and dictionaries of specs.
  
    Args:
      space: The Gym space to convert.
      name: Optional name to apply to all return spec(s).
  
    Returns:
      A dm_env spec or nested structure of specs, corresponding to the input
      space.
    """
    if isinstance(space, spaces.Discrete):
      return specs.DiscreteArray(num_values=space.n, dtype=space.dtype, name=name)
  
    elif isinstance(space, spaces.Box):
      return specs.BoundedArray(shape=space.shape, dtype=space.dtype,
                                minimum=space.low, maximum=space.high, name=name)
  
    elif isinstance(space, spaces.MultiBinary):
      return specs.BoundedArray(shape=space.shape, dtype=space.dtype, minimum=0.0,
                                maximum=1.0, name=name)
  
    elif isinstance(space, spaces.MultiDiscrete):
      return specs.BoundedArray(shape=space.shape, dtype=space.dtype,
                                minimum=np.zeros(space.shape),
                                maximum=space.nvec, name=name)
  
    elif isinstance(space, spaces.Tuple):
      return tuple(space2spec(s, name) for s in space.spaces)
  
    elif isinstance(space, spaces.Dict):
      return {key: space2spec(value, name) for key, value in space.items()}
  
    else:
      raise ValueError('Unexpected gym space: {}'.format(space))
  
  
  class DMEnvFromGym(dm_env.Environment):
    """A wrapper to convert an OpenAI Gym environment to a dm_env.Environment."""
  
    def __init__(self, gym_env: gym.Env):
      self.gym_env = gym_env
      # Convert gym action and observation spaces to dm_env specs.
      self._observation_spec = space2spec(self.gym_env.observation_space,
                                          name='observations')
      self._action_spec = space2spec(self.gym_env.action_space, name='actions')
      self._reset_next_step = True
  
    def reset(self) -> dm_env.TimeStep:
      self._reset_next_step = False
      observation = self.gym_env.reset()
      return dm_env.restart(observation)
  
    def step(self, action: int) -> dm_env.TimeStep:
      if self._reset_next_step:
        return self.reset()
  
      # Convert the gym step result to a dm_env TimeStep.
      obs, reward, done, _ = self.gym_env.step(action)
  
      if done:
        self._reset_next_step = True
        return dm_env.termination(reward, obs)
      else:
        return dm_env.transition(reward, obs)
  
    def close(self):
      self.gym_env.close()
  
    def observation_spec(self):
      return self._observation_spec
  
    def action_spec(self):
      return self._action_spec
  