import torch
from argparse import Namespace
from gymnasium.spaces import Space
from xuance.common import List, 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, QMIX_mixer
from xuance.torch.agents import OffPolicyMARLAgents


class QMIX_Agents(OffPolicyMARLAgents):
    """The implementation of QMIX 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: Optional[DummyVecMultiAgentEnv | SubprocVecMultiAgentEnv] = None,
            num_agents: Optional[int] = None,
            agent_keys: Optional[List[str]] = None,
            state_space: Optional[Space] = None,
            observation_space: Optional[Space] = None,
            action_space: Optional[Space] = None,
            callback: Optional[MultiAgentBaseCallback] = None
    ):
        super(QMIX_Agents, self).__init__(
            config, envs, num_agents, agent_keys, state_space, observation_space, action_space, callback
        )
        self.state_space = envs.state_space
        self.use_global_state = True

        self.start_greedy, self.end_greedy = config.start_greedy, config.end_greedy
        self.e_greedy = self.start_greedy
        self.delta_egreedy = (self.start_greedy - self.end_greedy) / (config.decay_step_greedy / self.n_envs)

        # build policy, optimizers, schedulers
        self.policy = self._build_policy()  # build policy
        self.memory = self._build_memory()  # build memory
        self.learner = self._build_learner(self.config, self.model_keys, self.agent_keys, self.policy, self.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]

        # build representations
        representation = self._build_representation(self.config.representation, self.observation_space, self.config)

        # build policies
        dim_state = self.state_space.shape[-1]
        mixer = QMIX_mixer(dim_state, self.config.hidden_dim_mixing_net,
                           self.config.hidden_dim_hyper_net, self.n_agents, self.device)
        if self.config.policy == "Mixing_Q_network":
            policy = REGISTRY_Policy["Mixing_Q_network"](
                action_space=self.action_space, n_agents=self.n_agents, representation=representation,
                mixer=mixer, hidden_size=self.config.q_hidden_size,
                normalize=normalize_fn, initialize=initializer, activation=activation,
                device=self.device, use_distributed_training=self.distributed_training,
                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"QMIX currently does not support the policy named {self.config.policy}.")

        return policy
