

# NOTE THIS CODE IS TAKEN FROM https://github.com/tensorflow/models/tree/master/research/efficient-hrl/environments
# and is not my code.



from ant_environments.ant_maze_env import AntMazeEnv
from ant_environments.point_maze_env import PointMazeEnv

import tensorflow as tf
import gin.tf
from tf_agents.environments import gym_wrapper
from tf_agents.environments import tf_py_environment


@gin.configurable
def create_maze_env(env_name=None, top_down_view=False):
  n_bins = 0
  manual_collision = False
  if env_name.startswith('Ego'):
    n_bins = 8
    env_name = env_name[3:]
  if env_name.startswith('Ant'):
    cls = AntMazeEnv
    env_name = env_name[3:]
    maze_size_scaling = 8
  elif env_name.startswith('Point'):
    cls = PointMazeEnv
    manual_collision = True
    env_name = env_name[5:]
    maze_size_scaling = 4
  else:
    assert False, 'unknown env %s' % env_name

  maze_id = None
  observe_blocks = False
  put_spin_near_agent = False
  if env_name == 'Maze':
    maze_id = 'Maze'
  elif env_name == 'Push':
    maze_id = 'Push'
  elif env_name == 'Fall':
    maze_id = 'Fall'
  elif env_name == 'Block':
    maze_id = 'Block'
    put_spin_near_agent = True
    observe_blocks = True
  elif env_name == 'BlockMaze':
    maze_id = 'BlockMaze'
    put_spin_near_agent = True
    observe_blocks = True
  else:
    raise ValueError('Unknown maze environment %s' % env_name)

  gym_mujoco_kwargs = {
      'maze_id': maze_id,
      'n_bins': n_bins,
      'observe_blocks': observe_blocks,
      'put_spin_near_agent': put_spin_near_agent,
      'top_down_view': top_down_view,
      'manual_collision': manual_collision,
      'maze_size_scaling': maze_size_scaling
  }
  gym_env = cls(**gym_mujoco_kwargs)
  gym_env.reset()
  wrapped_env = gym_wrapper.GymWrapper(gym_env)
  return wrapped_env


class TFPyEnvironment(tf_py_environment.TFPyEnvironment):

  def __init__(self, *args, **kwargs):
    super(TFPyEnvironment, self).__init__(*args, **kwargs)

  def start_collect(self):
    pass

  def current_obs(self):
    time_step = self.current_time_step()
    return time_step.observation[0]  # For some reason, there is an extra dim.

  def step(self, actions):
    actions = tf.expand_dims(actions, 0)
    next_step = super(TFPyEnvironment, self).step(actions)
    return next_step.is_last()[0], next_step.reward[0], next_step.discount[0]

  def reset(self):
    return super(TFPyEnvironment, self).reset()
