# Copyright 2018 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.

"""Shared helpers for different experiment flavours."""

from typing import Mapping, Sequence, List

from acme import specs
from acme import types
from acme.tf import networks
from acme.tf import utils as tf2_utils

import numpy as np
import sonnet as snt


def make_default_networks(
    agent_number: int,
    action_spec: specs.BoundedArray,
    policy_layer_sizes: Sequence[int] = (256, 256, 256),
    critic_layer_sizes: Sequence[int] = (512, 512, 256),
    vmin: float = -150.,
    vmax: float = 150.,
    num_atoms: int = 51,
) -> List[Mapping[str, types.TensorTransformation]]:
    
    agent_network_list = []
    for _ in range(agent_number):
        """Creates networks used by the agent."""

        # Get total number of action dimensions from action spec.
        num_dimensions = np.prod(action_spec.shape, dtype=int)

        # Create the shared observation network; here simply a state-less operation.
        observation_network = tf2_utils.batch_concat

        # Create the policy network.
        policy_network = snt.Sequential([
            networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
            networks.NearZeroInitializedLinear(num_dimensions),
            networks.TanhToSpec(action_spec),
        ])

        # Create the critic network.
        critic_network = snt.Sequential([
            # The multiplexer concatenates the observations/actions.
            networks.CriticMultiplexer(),
            networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
            networks.DiscreteValuedHead(vmin, vmax, num_atoms),
        ])
        from Agents.MAD4PG.agent import D4PGNetworks
        agent_network_list.append(
            D4PGNetworks(
                policy_network=policy_network,
                critic_network=critic_network,
                observation_network=observation_network,
            )
        )

    return agent_network_list