# Path: meltingpot/python/substrate.py
# AVAILABLE_SUBSTRATES = substrate_configs.SUBSTRATES
# class SubstrateObservables:
# class Substrate(base.Wrapper):
#   def __init__(self, env: ...) -> None:
#   def reset(self) -> dm_env.TimeStep:
#   def step(self, action: Sequence[int]) -> dm_env.TimeStep:
#   def reward_spec(self) -> Sequence[dm_env.specs.Array]:
#   def observation_spec(self) -> Sequence[Mapping[str, dm_env.specs.Array]]:
#   def action_spec(self) -> Sequence[dm_env.specs.DiscreteArray]:
#   def close(self) -> None:
#   def observables(self) -> SubstrateObservables:
# def get_config(substrate_name: str) -> config_dict.ConfigDict:
# def build(config: config_dict.ConfigDict) -> Substrate:


import dataclasses
import dm_env
import numpy as np
from absl.testing import absltest
from absl.testing import parameterized
from meltingpot.python import substrate
# Copyright 2020 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.
"""Tests for substrate."""




REWARD_SPEC = dm_env.specs.Array(shape=[], dtype=np.float64, name='REWARD')
ACTION_SPEC = dm_env.specs.DiscreteArray(
    num_values=1, dtype=np.int32, name='action')


def _get_lua_randomization_map():
  """Replaces first row of walls with items randomized by Lua."""

# Path: meltingpot/python/substrate.py
# AVAILABLE_SUBSTRATES = substrate_configs.SUBSTRATES
# class SubstrateObservables:
# class Substrate(base.Wrapper):
#   def __init__(self, env: ...) -> None:
#   def reset(self) -> dm_env.TimeStep:
#   def step(self, action: Sequence[int]) -> dm_env.TimeStep:
#   def reward_spec(self) -> Sequence[dm_env.specs.Array]:
#   def observation_spec(self) -> Sequence[Mapping[str, dm_env.specs.Array]]:
#   def action_spec(self) -> Sequence[dm_env.specs.DiscreteArray]:
#   def close(self) -> None:
#   def observables(self) -> SubstrateObservables:
# def get_config(substrate_name: str) -> config_dict.ConfigDict:
# def build(config: config_dict.ConfigDict) -> Substrate:


# Path: meltingpot/python/substrate_test.py
import dataclasses
import dm_env
import numpy as np
from absl.testing import absltest
from absl.testing import parameterized
from meltingpot.python import substrate

# Copyright 2020 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.
"""Tests for substrate."""




REWARD_SPEC = dm_env.specs.Array(shape=[], dtype=np.float64, name='REWARD')
ACTION_SPEC = dm_env.specs.DiscreteArray(
    num_values=1, dtype=np.int32, name='action')


def _get_lua_randomization_map():
  """Replaces first row of walls with items randomized by Lua."""

  config = substrate.get_config('running_with_scissors_in_the_matrix')