text: #
  # Copyright (c) 2017 Intel Corporation
  #
  # 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.
  #
  
  
  from enum import Enum
  from typing import Union, List
  
  import numpy as np
  
  from rl_coach.filters.observation.observation_move_axis_filter import ObservationMoveAxisFilter
  
  try:
      from pysc2 import maps
      from pysc2.env import sc2_env
      from pysc2.env import available_actions_printer
      from pysc2.lib import actions
      from pysc2.lib import features
      from pysc2.env import environment
      from absl import app
      from absl import flags
  except ImportError:
      from rl_coach.logger import failed_imports
      failed_imports.append("PySc2")
  
  from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
  from rl_coach.base_parameters import VisualizationParameters
  from rl_coach.spaces import BoxActionSpace, VectorObservationSpace, PlanarMapsObservationSpace, StateSpace, CompoundActionSpace, \
      DiscreteActionSpace
  from rl_coach.filters.filter import InputFilter, OutputFilter
  from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
  from rl_coach.filters.action.linear_box_to_box_map import LinearBoxToBoxMap
  from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
  
  FLAGS = flags.FLAGS
  FLAGS(['coach.py'])
  
  SCREEN_SIZE = 84  # will also impact the action space size
  
  # Starcraft Constants
  _NOOP = actions.FUNCTIONS.no_op.id
  _MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id
  _SELECT_ARMY = actions.FUNCTIONS.select_army.id
  _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index
  _NOT_QUEUED = [0]
  _SELECT_ALL = [0]
  
  
  class StarcraftObservationType(Enum):
      Features = 0
      RGB = 1
  
  
  StarcraftInputFilter = InputFilter(is_a_reference_filter=True)
  StarcraftInputFilter.add_observation_filter('screen', 'move_axis', ObservationMoveAxisFilter(0, -1))
  StarcraftInputFilter.add_observation_filter('screen', 'rescaling',
                                              ObservationRescaleToSizeFilter(
                                                  PlanarMapsObservationSpace(np.array([84, 84, 1]),
                                                                             low=0, high=255, channels_axis=-1)))
  StarcraftInputFilter.add_observation_filter('screen', 'to_uint8', ObservationToUInt8Filter(0, 255))
  
  StarcraftInputFilter.add_observation_filter('minimap', 'move_axis', ObservationMoveAxisFilter(0, -1))
  StarcraftInputFilter.add_observation_filter('minimap', 'rescaling',
                                              ObservationRescaleToSizeFilter(
                                                  PlanarMapsObservationSpace(np.array([64, 64, 1]),
                                                                             low=0, high=255, channels_axis=-1)))
  StarcraftInputFilter.add_observation_filter('minimap', 'to_uint8', ObservationToUInt8Filter(0, 255))
  
  
  StarcraftNormalizingOutputFilter = OutputFilter(is_a_reference_filter=True)
  StarcraftNormalizingOutputFilter.add_action_filter(
      'normalization', LinearBoxToBoxMap(input_space_low=-SCREEN_SIZE / 2, input_space_high=SCREEN_SIZE / 2 - 1))
  
  
  class StarCraft2EnvironmentParameters(EnvironmentParameters):
      def __init__(self, level=None):
          super().__init__(level=level)
          self.screen_size = 84
          self.minimap_size = 64
          self.feature_minimap_maps_to_use = range(7)
          self.feature_screen_maps_to_use = range(17)
          self.observation_type = StarcraftObservationType.Features
          self.disable_fog = False
          self.auto_select_all_army = True
          self.default_input_filter = StarcraftInputFilter
          self.default_output_filter = StarcraftNormalizingOutputFilter
          self.use_full_action_space = False
  
  
      @property
      def path(self):
          return 'rl_coach.environments.starcraft2_environment:StarCraft2Environment'
  
  
  # Environment
  class StarCraft2Environment(Environment):
      def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters,
                   target_success_rate: float=1.0, seed: Union[None, int]=None, human_control: bool=False,
                   custom_reward_threshold: Union[int, float]=None,
                   screen_size: int=84, minimap_size: int=64,
                   feature_minimap_maps_to_use: List=range(7), feature_screen_maps_to_use: List=range(17),
                   observation_type: StarcraftObservationType=StarcraftObservationType.Features,
                   disable_fog: bool=False, auto_select_all_army: bool=True,
                   use_full_action_space: bool=False, **kwargs):
          super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters,
                           target_success_rate)
  
          self.screen_size = screen_size
          self.minimap_size = minimap_size
          self.feature_minimap_maps_to_use = feature_minimap_maps_to_use
          self.feature_screen_maps_to_use = feature_screen_maps_to_use
          self.observation_type = observation_type
          self.features_screen_size = None
          self.feature_minimap_size = None
          self.rgb_screen_size = None
          self.rgb_minimap_size = None
          if self.observation_type == StarcraftObservationType.Features:
              self.features_screen_size = screen_size
              self.feature_minimap_size = minimap_size
          elif self.observation_type == StarcraftObservationType.RGB:
              self.rgb_screen_size = screen_size
              self.rgb_minimap_size = minimap_size
          self.disable_fog = disable_fog
          self.auto_select_all_army = auto_select_all_army
          self.use_full_action_space = use_full_action_space
  
          # step_mul is the equivalent to frame skipping. Not sure if it repeats actions in between or not though.
          self.env = sc2_env.SC2Env(map_name=self.env_id, step_mul=frame_skip,
                                    visualize=self.is_rendered,
                                    agent_interface_format=sc2_env.AgentInterfaceFormat(
                                        feature_dimensions=sc2_env.Dimensions(
                                            screen=self.features_screen_size,
                                            minimap=self.feature_minimap_size
                                        )
                                        # rgb_dimensions=sc2_env.Dimensions(
                                        #     screen=self.rgb_screen_size,
                                        #     minimap=self.rgb_screen_size
                                        # )
                                    ),
                                    # feature_screen_size=self.features_screen_size,
                                    # feature_minimap_size=self.feature_minimap_size,
                                    # rgb_screen_size=self.rgb_screen_size,
                                    # rgb_minimap_size=self.rgb_screen_size,
                                    disable_fog=disable_fog,
                                    random_seed=self.seed
                                    )
  
          # print all the available actions
          # self.env = available_actions_printer.AvailableActionsPrinter(self.env)
  
          self.reset_internal_state(True)
  
          """
          feature_screen:  [height_map, visibility_map, creep, power, player_id, player_relative, unit_type, selected,
                            unit_hit_points, unit_hit_points_ratio, unit_energy, unit_energy_ratio, unit_shields,
                            unit_shields_ratio, unit_density, unit_density_aa, effects]
          feature_minimap: [height_map, visibility_map, creep, camera, player_id, player_relative, selecte
          d]
          player:          [player_id, minerals, vespene, food_cap, food_army, food_workers, idle_worker_dount,
                            army_count, warp_gate_count, larva_count]
          """
          self.screen_shape = np.array(self.env.observation_spec()[0]['feature_screen'])
          self.screen_shape[0] = len(self.feature_screen_maps_to_use)
          self.minimap_shape = np.array(self.env.observation_spec()[0]['feature_minimap'])
          self.minimap_shape[0] = len(self.feature_minimap_maps_to_use)
          self.state_space = StateSpace({
              "screen": PlanarMapsObservationSpace(shape=self.screen_shape, low=0, high=255, channels_axis=0),
              "minimap": PlanarMapsObservationSpace(shape=self.minimap_shape, low=0, high=255, channels_axis=0),
              "measurements": VectorObservationSpace(self.env.observation_spec()[0]["player"][0])
          })
          if self.use_full_action_space:
              action_identifiers = list(self.env.action_spec()[0].functions)
              num_action_identifiers = len(action_identifiers)
              action_arguments = [(arg.name, arg.sizes) for arg in self.env.action_spec()[0].types]
              sub_action_spaces = [DiscreteActionSpace(num_action_identifiers)]
              for argument in action_arguments:
                  for dimension in argument[1]:
                      sub_action_spaces.append(DiscreteActionSpace(dimension))
              self.action_space = CompoundActionSpace(sub_action_spaces)
          else:
              self.action_space = BoxActionSpace(2, 0, self.screen_size - 1, ["X-Axis, Y-Axis"],
                                                 default_action=np.array([self.screen_size/2, self.screen_size/2]))
  
          self.target_success_rate = target_success_rate
  
      def _update_state(self):
          timestep = 0
          self.screen = self.last_result[timestep].observation.feature_screen
          # extract only the requested segmentation maps from the observation
          self.screen = np.take(self.screen, self.feature_screen_maps_to_use, axis=0)
          self.minimap = self.last_result[timestep].observation.feature_minimap
          self.measurements = self.last_result[timestep].observation.player
          self.reward = self.last_result[timestep].reward
          self.done = self.last_result[timestep].step_type == environment.StepType.LAST
          self.state = {
              'screen': self.screen,
              'minimap': self.minimap,
              'measurements': self.measurements
          }
  
      def _take_action(self, action):
          if self.use_full_action_space:
              action_identifier = action[0]
              action_arguments = action[1:]
              action = actions.FunctionCall(action_identifier, action_arguments)
          else:
              coord = np.array(action[0:2])
              noop = False
              coord = coord.round()
              coord = np.clip(coord, 0, SCREEN_SIZE - 1)
              self.last_action_idx = coord
  
              if noop:
                  action = actions.FunctionCall(_NOOP, [])
              else:
                  action = actions.FunctionCall(_MOVE_SCREEN, [_NOT_QUEUED, coord])
  
          self.last_result = self.env.step(actions=[action])
  
      def _restart_environment_episode(self, force_environment_reset=False):
          # reset the environment
          self.last_result = self.env.reset()
  
          # select all the units on the screen
          if self.auto_select_all_army:
              self.env.step(actions=[actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL])])
  
      def get_rendered_image(self):
          screen = np.squeeze(np.tile(np.expand_dims(self.screen, -1), (1, 1, 3)))
          screen = screen / np.max(screen) * 255
          return screen.astype('uint8')
  
      def dump_video_of_last_episode(self):
          from rl_coach.logger import experiment_path
          self.env._run_config.replay_dir = experiment_path
          self.env.save_replay('replays')
          super().dump_video_of_last_episode()
  
      def get_target_success_rate(self):
          return self.target_success_rate
  