# import copy

# import torch as th
# from torch.optim import Adam

# from components.episode_buffer import EpisodeBatch
# from components.standarize_stream import RunningMeanStd
# from modules.mixers.vdn import VDNMixer
# from modules.mixers.qmix import QMixer


# class QLearner:
#     def __init__(self, mac, scheme, logger, args):
#         self.args = args
#         self.n_agents = args.n_agents
#         self.mac = mac
#         self.logger = logger

#         self.params = list(mac.parameters())
#         self.last_target_update_episode = 0

#         self.mixer = None
#         if args.mixer is not None:
#             if args.mixer == "vdn":
#                 assert args.common_reward, "VDN only supports common reward setting"
#                 self.mixer = VDNMixer()
#             elif args.mixer == "qmix":
#                 assert args.common_reward, "QMIX only supports common reward setting"
#                 self.mixer = QMixer(args)
#             else:
#                 raise ValueError("Mixer {} not recognised.".format(args.mixer))
#             self.params += list(self.mixer.parameters())
#             self.target_mixer = copy.deepcopy(self.mixer)

#         self.optimiser = Adam(params=self.params, lr=args.lr)

#         # a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
#         self.target_mac = copy.deepcopy(mac)

#         self.training_steps = 0
#         self.last_target_update_step = 0
#         self.log_stats_t = -self.args.learner_log_interval - 1

#         device = "cuda" if th.cuda.is_available() else "cpu" #"cuda" if args.use_cuda else "cpu"
#         if self.args.standardise_returns:
#             self.ret_ms = RunningMeanStd(shape=(self.n_agents,), device=device)
#         if self.args.standardise_rewards:
#             rew_shape = (1,) if self.args.common_reward else (self.n_agents,)
#             self.rew_ms = RunningMeanStd(shape=rew_shape, device=device)

#     def train(self, batch: EpisodeBatch, t_env: int, episode_num: int):
#         # Get the relevant quantities
#         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"]

#         if self.args.standardise_rewards:
#             self.rew_ms.update(rewards)
#             rewards = (rewards - self.rew_ms.mean) / th.sqrt(self.rew_ms.var)

#         if self.args.common_reward:
#             assert (
#                 rewards.size(2) == 1
#             ), "Expected singular agent dimension for common rewards"
#             # reshape rewards to be of shape (batch_size, episode_length, n_agents)
#             rewards = rewards.expand(-1, -1, self.n_agents)

#         # Calculate estimated Q-Values
#         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)  # Concat over time
#         # Pick the Q-Values for the actions taken by each agent
#         chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(
#             3
#         )  # Remove the last dim

#         # Calculate the Q-Values necessary for the target
#         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)

#         # We don't need the first timesteps Q-Value estimate for calculating targets
#         target_mac_out = th.stack(target_mac_out[1:], dim=1)  # Concat across time

#         # Mask out unavailable actions
#         target_mac_out[avail_actions[:, 1:] == 0] = -9999999

#         # Max over target Q-Values
#         if self.args.double_q:
#             # Get actions that maximise live Q (for double q-learning)
#             mac_out_detach = mac_out.clone().detach()
#             mac_out_detach[avail_actions == 0] = -9999999
#             cur_max_actions = mac_out_detach[:, 1:].max(dim=3, keepdim=True)[1]
#             target_max_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3)
#         else:
#             target_max_qvals = target_mac_out.max(dim=3)[0]

#         # Mix
#         if self.mixer is not None:
#             chosen_action_qvals = self.mixer(
#                 chosen_action_qvals, batch["state"][:, :-1]
#             )
#             target_max_qvals = self.target_mixer(
#                 target_max_qvals, batch["state"][:, 1:]
#             )

#         if self.args.standardise_returns:
#             target_max_qvals = (
#                 target_max_qvals * th.sqrt(self.ret_ms.var) + self.ret_ms.mean
#             )

#         # Calculate 1-step Q-Learning targets
#         targets = (
#             rewards + self.args.gamma * (1 - terminated) * target_max_qvals.detach()
#         )
        
#         if self.args.standardise_returns:
#             self.ret_ms.update(targets)
#             targets = (targets - self.ret_ms.mean) / th.sqrt(self.ret_ms.var)

#         # Td-error
#         td_error = chosen_action_qvals - targets.detach()

#         mask = mask.expand_as(td_error)

#         # 0-out the targets that came from padded data
#         masked_td_error = td_error * mask

#         # Normal L2 loss, take mean over actual data
#         loss = (masked_td_error**2).sum() / mask.sum()

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

#         self.training_steps += 1
#         if (
#             self.args.target_update_interval_or_tau > 1
#             and (self.training_steps - self.last_target_update_step)
#             / self.args.target_update_interval_or_tau
#             >= 1.0
#         ):
#             self._update_targets_hard()
#             self.last_target_update_step = self.training_steps
#         elif self.args.target_update_interval_or_tau <= 1.0:
#             self._update_targets_soft(self.args.target_update_interval_or_tau)

#         if t_env - self.log_stats_t >= self.args.learner_log_interval:
#             self.logger.log_stat("loss", loss.item(), t_env)
#             self.logger.log_stat("grad_norm", grad_norm.item(), 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

#     def _update_targets_hard(self):
#         self.target_mac.load_state(self.mac)
#         if self.mixer is not None:
#             self.target_mixer.load_state_dict(self.mixer.state_dict())

#     def _update_targets_soft(self, tau):
#         for target_param, param in zip(
#             self.target_mac.parameters(), self.mac.parameters()
#         ):
#             target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau)
#         if self.mixer is not None:
#             for target_param, param in zip(
#                 self.target_mixer.parameters(), self.mixer.parameters()
#             ):
#                 target_param.data.copy_(
#                     target_param.data * (1.0 - tau) + param.data * tau
#                 )

#     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)
#         # Not quite right but I don't want to save target networks
#         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)
#         )


import copy
from components.episode_buffer import EpisodeBatch
from modules.mixers.vdn import VDNMixer
from modules.mixers.qmix import QMixer
import torch as th
from torch.optim import RMSprop


class QLearner:
    def __init__(self, mac, scheme, logger, args):
        self.args = args
        self.mac = mac
        self.logger = logger

        self.params = list(mac.parameters())

        self.last_target_update_episode = 0

        self.mixer = None
        if args.mixer is not None:
            if args.mixer == "vdn":
                self.mixer = VDNMixer()
            elif args.mixer == "qmix":
                self.mixer = QMixer(args)
            else:
                raise ValueError("Mixer {} not recognised.".format(args.mixer))
            self.params += list(self.mixer.parameters())
            self.target_mixer = copy.deepcopy(self.mixer)

        self.optimiser = RMSprop(params=self.params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps)

        # a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
        self.target_mac = copy.deepcopy(mac)

        self.log_stats_t = -self.args.learner_log_interval - 1

    def train(self, batch: EpisodeBatch, t_env: int, episode_num: int):
        # Get the relevant quantities
        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"]

        # Calculate estimated Q-Values
        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)  # Concat over time

        # Pick the Q-Values for the actions taken by each agent
        chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(3)  # Remove the last dim

        # Calculate the Q-Values necessary for the target
        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)

        # We don't need the first timesteps Q-Value estimate for calculating targets
        target_mac_out = th.stack(target_mac_out[1:], dim=1)  # Concat across time

        # Mask out unavailable actions
        target_mac_out[avail_actions[:, 1:] == 0] = -9999999

        # Max over target Q-Values
        if self.args.double_q:
            # Get actions that maximise live Q (for double q-learning)
            mac_out_detach = mac_out.clone().detach()
            mac_out_detach[avail_actions == 0] = -9999999
            cur_max_actions = mac_out_detach[:, 1:].max(dim=3, keepdim=True)[1]
            target_max_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3)
        else:
            target_max_qvals = target_mac_out.max(dim=3)[0]

        # Mix
        if self.mixer is not None:
            chosen_action_qvals = self.mixer(chosen_action_qvals, batch["state"][:, :-1])
            target_max_qvals = self.target_mixer(target_max_qvals, batch["state"][:, 1:])

        # Calculate 1-step Q-Learning targets
        targets = rewards + self.args.gamma * (1 - terminated) * target_max_qvals

        # Td-error
        td_error = (chosen_action_qvals - targets.detach())

        mask = mask.expand_as(td_error)

        # 0-out the targets that came from padded data
        masked_td_error = td_error * mask

        # Normal L2 loss, take mean over actual data
        loss = (masked_td_error ** 2).sum() / mask.sum()

        # Optimise
        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", loss.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

    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)
        # Not quite right but I don't want to save target networks
        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))