# Copyright 2019 DeepMind Technologies Limited.
#
# 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
#
#    https://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.
# ============================================================================
# python2 python3
"""Goal-Finding tasks testing for generalization to new shapes.

In this task there are target sprites of orange-green-ish color. In train mode
there is one target, while in test mode there are two. All target sprites must
be brought to the goal location, which is the center of the arena.  There are
always two distractor sprites, which are blue-purple-ish color and do not
contribute to the reward.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
from spriteworld import factor_distributions as distribs
from spriteworld import sprite_generators
from spriteworld import tasks
from spriteworld.configs.cobra import common

TERMINATE_DISTANCE = 0.075
NUM_DISTRACTORS = 2
MODES_NUM_TARGETS = {
    'train': 1,
    'test': 2,
}


def get_config(mode='train'):
  """Generate environment config.

  Args:
    mode: 'train' or 'test'.

  Returns:
    config: Dictionary defining task/environment configuration. Can be fed as
      kwargs to environment.Environment.
  """

  shared_factors = distribs.Product([
      distribs.Continuous('x', 0.1, 0.9),
      distribs.Continuous('y', 0.1, 0.9),
      distribs.Discrete('shape', ['square', 'triangle', 'circle']),
      distribs.Discrete('scale', [0.13]),
      distribs.Continuous('c1', 0.3, 1.),
      distribs.Continuous('c2', 0.9, 1.),
  ])
  target_hue = distribs.Continuous('c0', 0., 0.4)
  distractor_hue = distribs.Continuous('c0', 0.5, 0.9)
  target_factors = distribs.Product([
      target_hue,
      shared_factors,
  ])
  distractor_factors = distribs.Product([
      distractor_hue,
      shared_factors,
  ])

  target_sprite_gen = sprite_generators.generate_sprites(
      target_factors, num_sprites=MODES_NUM_TARGETS[mode])
  distractor_sprite_gen = sprite_generators.generate_sprites(
      distractor_factors, num_sprites=NUM_DISTRACTORS)
  sprite_gen = sprite_generators.chain_generators(target_sprite_gen,
                                                  distractor_sprite_gen)
  # Randomize sprite ordering to eliminate any task information from occlusions
  sprite_gen = sprite_generators.shuffle(sprite_gen)

  task = tasks.FindGoalPosition(
      filter_distrib=target_hue, terminate_distance=TERMINATE_DISTANCE)

  config = {
      'task': task,
      'action_space': common.action_space(),
      'renderers': common.renderers(),
      'init_sprites': sprite_gen,
      'max_episode_length': 20,
      'metadata': {
          'name': os.path.basename(__file__),
          'mode': mode
      }
  }
  return config
