import numpy as np
from argparse import Namespace
from xuance.common import Optional, Union
from xuance.environment import DummyVecEnv, SubprocVecEnv
from xuance.mindspore import Module
from xuance.mindspore.utils import NormalizeFunctions, ActivationFunctions, InitializeFunctions
from xuance.mindspore.policies import REGISTRY_Policy
from xuance.mindspore.agents import OffPolicyAgent


class DDPG_Agent(OffPolicyAgent):
    """The implementation of DDPG agent.

    Args:
        config: the Namespace variable that provides hyper-parameters and other settings.
        envs: the vectorized environments.
    """

    def __init__(self,
                 config: Namespace,
                 envs: Union[DummyVecEnv, SubprocVecEnv]):
        super(DDPG_Agent, self).__init__(config, envs)
        self.start_noise, self.end_noise = config.start_noise, config.end_noise
        self.noise_scale = config.start_noise
        self.delta_noise = (self.start_noise - self.end_noise) / (config.running_steps / self.n_envs)

        self.policy = self._build_policy()  # build policy
        self.memory = self._build_memory()  # build memory
        self.learner = self._build_learner(self.config, self.policy)  # build learner

    def _build_policy(self) -> Module:
        normalize_fn = NormalizeFunctions[self.config.normalize] if hasattr(self.config, "normalize") else None
        initializer = InitializeFunctions[self.config.initialize] if hasattr(self.config, "initialize") else None
        activation = ActivationFunctions[self.config.activation]

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

        # build policy
        if self.config.policy == "DDPG_Policy":
            policy = REGISTRY_Policy["DDPG_Policy"](
                action_space=self.action_space, representation=representation,
                actor_hidden_size=self.config.actor_hidden_size, critic_hidden_size=self.config.critic_hidden_size,
                normalize=normalize_fn, initialize=initializer,
                activation=activation, activation_action=ActivationFunctions[self.config.activation_action])
        else:
            raise AttributeError(f"DDPG currently does not support the policy named {self.config.policy}.")

        return policy

    def action(self, observations: np.ndarray, test_mode: Optional[bool] = False):
        """Returns actions and values.

        Parameters:
            observations (np.ndarray): The observation.
            test_mode (Optional[bool]): True for testing without noises.

        Returns:
            actions: The actions to be executed.
        """
        _, actions_output = self.policy(observations)
        if test_mode:
            actions = actions_output.numpy()
        else:
            actions = self.exploration(actions_output.numpy())
        return {"actions": actions}
