import torch
from argparse import Namespace
from xuance.common import Union, Optional, MultiAgentBaseCallback
from xuance.environment import DummyVecMultiAgentEnv, SubprocVecMultiAgentEnv
from xuance.torch import Module
from xuance.torch.utils import NormalizeFunctions, ActivationFunctions
from xuance.torch.policies import REGISTRY_Policy
from xuance.torch.agents.multi_agent_rl.iddpg_agents import IDDPG_Agents


class MADDPG_Agents(IDDPG_Agents):
    """The implementation of MADDPG agents.

    Args:
        config: The Namespace variable that provides hyperparameters and other settings.
        envs: the vectorized environments.
        callback: A user-defined callback function object to inject custom logic during training.
    """
    def __init__(self,
                 config: Namespace,
                 envs: Union[DummyVecMultiAgentEnv, SubprocVecMultiAgentEnv],
                 callback: Optional[MultiAgentBaseCallback] = None):
        super(MADDPG_Agents, self).__init__(config, envs, callback)

    def _build_policy(self) -> Module:
        """
        Build representation(s) and policy(ies) for agent(s)

        Returns:
            policy (torch.nn.Module): A dict of policies.
        """
        normalize_fn = NormalizeFunctions[self.config.normalize] if hasattr(self.config, "normalize") else None
        initializer = torch.nn.init.orthogonal_
        activation = ActivationFunctions[self.config.activation]
        device = self.device

        # build representations
        A_representation = self._build_representation(self.config.representation, self.observation_space, self.config)
        critic_in = [sum(self.observation_space[k].shape) + sum(self.action_space[k].shape) for k in self.agent_keys]
        space_critic_in = {k: (sum(critic_in), ) for k in self.agent_keys}
        C_representation = self._build_representation(self.config.representation, space_critic_in, self.config)

        # build policies
        if self.config.policy == "MADDPG_Policy":
            policy = REGISTRY_Policy["MADDPG_Policy"](
                action_space=self.action_space, n_agents=self.n_agents,
                actor_representation=A_representation, critic_representation=C_representation,
                actor_hidden_size=self.config.actor_hidden_size,
                critic_hidden_size=self.config.critic_hidden_size,
                normalize=normalize_fn, initialize=initializer, activation=activation, device=device,
                use_distributed_training=self.distributed_training,
                activation_action=ActivationFunctions[self.config.activation_action],
                use_parameter_sharing=self.use_parameter_sharing, model_keys=self.model_keys,
                use_rnn=self.use_rnn, rnn=self.config.rnn if self.use_rnn else None)
        else:
            raise AttributeError(f"MADDPG currently does not support the policy named {self.config.policy}.")

        return policy
