import copy
from train.replay_buffer import EpisodeBatch
from agents.net import QMix,VDN
from envs.utils.marl_utils import build_td_lambda_targets, build_q_lambda_targets,get_parameters_num
import torch as th
from torch.optim import RMSprop, Adam
import numpy as np

class AgentQMix:
    def __init__(self, mac, scheme, logger, args):
        self.args = args
        self.mac = mac
        self.logger = logger
        
        self.last_target_update_episode = 0
        self.device = th.device('cuda' if args.use_cuda  else 'cpu')
        self.params = list(mac.parameters())
        self.mixer = QMix(args)

        self.target_mixer = copy.deepcopy(self.mixer)
        self.params += list(self.mixer.parameters())

        print('Mixer Size: ')
        print(get_parameters_num(self.mixer.parameters()))

        if self.args.optimizer == 'adam':
            self.optimiser = Adam(params=self.params,  lr=args.lr, weight_decay=getattr(args, "weight_decay", 0))
        else:
            self.optimiser = RMSprop(params=self.params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps)

        self.target_mac = copy.deepcopy(mac)
        self.log_stats_t = -self.args.learner_log_interval - 1
        self.train_t = 0

        self.use_per = getattr(self.args, 'use_per', False)
        self.return_priority = getattr(self.args, "return_priority", False)
        if self.use_per:
            self.priority_max = float('-inf')
            self.priority_min = float('inf')
        
    def train(self, batch: EpisodeBatch, t_env: int, episode_num: int, per_weight=None):
        rewards = batch["reward"][:, :-1]
        actions = batch["actions"][:, :-1]
        terminated = batch["terminated"][:, :-1].float()
        mask = batch["filled"][:, :-1].float()
        mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
        avail_actions = batch["avail_actions"]
        
        self.mac.agent.train()
        mac_out = []
        self.mac.init_hidden(batch.batch_size)
        for t in range(batch.max_seq_length):
            agent_outs = self.mac.forward(batch, t=t)
            mac_out.append(agent_outs)
        mac_out = th.stack(mac_out, dim=1)  

        chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(3)  # Remove the last dim
        chosen_action_qvals_ = chosen_action_qvals

        with th.no_grad():
            self.target_mac.agent.train()
            target_mac_out = []
            self.target_mac.init_hidden(batch.batch_size)
            for t in range(batch.max_seq_length):
                target_agent_outs = self.target_mac.forward(batch, t=t)
                target_mac_out.append(target_agent_outs)

            target_mac_out = th.stack(target_mac_out, dim=1)  # Concat across time

            mac_out_detach = mac_out.clone().detach()
            mac_out_detach[avail_actions == 0] = -9999999
            cur_max_actions = mac_out_detach.max(dim=3, keepdim=True)[1]
            target_max_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3)
            
            target_max_qvals = self.target_mixer(target_max_qvals, batch["state"])

            if getattr(self.args, 'q_lambda', False):
                qvals = th.gather(target_mac_out, 3, batch["actions"]).squeeze(3)
                qvals = self.target_mixer(qvals, batch["state"])

                targets = build_q_lambda_targets(rewards, terminated, mask, target_max_qvals, qvals,
                                    self.args.gamma, self.args.td_lambda)
            else:
                targets = build_td_lambda_targets(rewards, terminated, mask, target_max_qvals, 
                                                    self.args.n_agents, self.args.gamma, self.args.td_lambda)

        chosen_action_qvals = self.mixer(chosen_action_qvals, batch["state"][:, :-1])

        td_error = (chosen_action_qvals - targets.detach())
        td_error2 = 0.5 * td_error.pow(2)

        mask = mask.expand_as(td_error2)
        masked_td_error = td_error2 * mask


        if self.use_per:
            per_weight = th.from_numpy(per_weight).unsqueeze(-1).to(device=self.device)
            masked_td_error = masked_td_error.sum(1) * per_weight

        loss = L_td = masked_td_error.sum() / mask.sum()


        self.optimiser.zero_grad()
        loss.backward()
        grad_norm = th.nn.utils.clip_grad_norm_(self.params, self.args.grad_norm_clip)
        self.optimiser.step()

        if (episode_num - self.last_target_update_episode) / self.args.target_update_interval >= 1.0:
            self._update_targets()
            self.last_target_update_episode = episode_num

        if t_env - self.log_stats_t >= self.args.learner_log_interval:
            self.logger.log_stat("loss_td", L_td.item(), t_env)
            self.logger.log_stat("grad_norm", grad_norm, t_env)
            mask_elems = mask.sum().item()
            self.logger.log_stat("td_error_abs", (masked_td_error.abs().sum().item()/mask_elems), t_env)
            self.logger.log_stat("q_taken_mean", (chosen_action_qvals * mask).sum().item()/(mask_elems * self.args.n_agents), t_env)
            self.logger.log_stat("target_mean", (targets * mask).sum().item()/(mask_elems * self.args.n_agents), t_env)
            self.log_stats_t = t_env

        info = {}
        if self.use_per:
            if self.return_priority:
                info["td_errors_abs"] = rewards.sum(1).detach().to('cpu')
                self.priority_max = max(th.max(info["td_errors_abs"]).item(), self.priority_max)
                self.priority_min = min(th.min(info["td_errors_abs"]).item(), self.priority_min)
                info["td_errors_abs"] = (info["td_errors_abs"] - self.priority_min) \
                                / (self.priority_max - self.priority_min + 1e-5)
            else:
                info["td_errors_abs"] = ((td_error.abs() * mask).sum(1) \
                                / th.sqrt(mask.sum(1))).detach().to('cpu')
        return info

    def _update_targets(self):
        self.target_mac.load_state(self.mac)
        if self.mixer is not None:
            self.target_mixer.load_state_dict(self.mixer.state_dict())
        self.logger.console_logger.info("Updated target network")

    def cuda(self):
        self.mac.cuda()
        self.target_mac.cuda()
        if self.mixer is not None:
            self.mixer.cuda()
            self.target_mixer.cuda()
            
    def save_models(self, path):
        self.mac.save_models(path)
        if self.mixer is not None:
            th.save(self.mixer.state_dict(), "{}/mixer.th".format(path))
        th.save(self.optimiser.state_dict(), "{}/opt.th".format(path))

    def load_models(self, path):
        self.mac.load_models(path)
        self.target_mac.load_models(path)
        if self.mixer is not None:
            self.mixer.load_state_dict(th.load("{}/mixer.th".format(path), map_location=lambda storage, loc: storage))
        self.optimiser.load_state_dict(th.load("{}/opt.th".format(path), map_location=lambda storage, loc: storage))
