from collections import namedtuple
import numpy as np


def convert(dictionary):
    return namedtuple('GenericDict', dictionary.keys())(**dictionary)


class MultiAgentEnv(object):

    def __init__(self, batch_size=None, **kwargs):
        # Unpack arguments from sacred
        args = kwargs["env_args"]
        if isinstance(args, dict):
            args = convert(args)
        self.args = args

        if getattr(args, "seed", None) is not None:
            self.seed = args.seed
            self.rs = np.random.RandomState(self.seed)  # initialise numpy random state

    def step(self, actions):
        """ Returns reward, terminated, info """
        raise NotImplementedError

    def get_obs(self):
        """ Returns all agent observations in a list """
        raise NotImplementedError

    def get_obs_agent(self, agent_id):
        """ Returns observation for agent_id """
        raise NotImplementedError

    def get_obs_size(self):
        """ Returns the shape of the observation """
        raise NotImplementedError

    def get_state(self):
        raise NotImplementedError

    def get_state_size(self):
        """ Returns the shape of the state"""
        raise NotImplementedError

    def get_avail_actions(self):
        raise NotImplementedError

    def get_avail_agent_actions(self, agent_id):
        """ Returns the available actions for agent_id """
        raise NotImplementedError

    def get_total_actions(self):
        """ Returns the total number of actions an agent could ever take """
        # TODO: This is only suitable for a discrete 1 dimensional action space for each agent
        raise NotImplementedError

    def get_stats(self):
        raise NotImplementedError

    # TODO: Temp hack
    def get_agg_stats(self, stats):
        return {}

    def reset(self):
        """ Returns initial observations and states"""
        raise NotImplementedError

    def render(self):
        raise NotImplementedError

    def close(self):
        raise NotImplementedError

    def seed(self, seed):
        raise NotImplementedError

    def get_env_info(self):
        env_info = {
            "state_shape": self.get_state_size(),
            "obs_shape": self.get_obs_size(),
            "n_actions": self.get_total_actions(),
            "n_agents": self.n_agents,
            "episode_limit": self.episode_limit
        }
        return env_info