#
# OtterTune - ddpg.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
# from: https://github.com/KqSMea8/CDBTune
# Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
# deep reinforcement learning." Proceedings of the 2019 International Conference
# on Management of Data. ACM, 2019

import pickle
import torch
import torch.nn as nn
import torch.optim as optimizer
from torch.autograd import Variable

from analysis.ddpg.ou_process import OUProcess
from analysis.ddpg.prioritized_replay_memory import PrioritizedReplayMemory
from analysis.util import get_analysis_logger

LOG = get_analysis_logger(__name__)


class Actor(nn.Module):

    def __init__(self, n_states, n_actions, hidden_sizes, use_default):
        super(Actor, self).__init__()
        if use_default:
            self.layers = nn.Sequential(
                nn.Linear(n_states, 128),
                nn.LeakyReLU(negative_slope=0.2),
                nn.BatchNorm1d(hidden_sizes[0]),
                nn.Linear(128, 128),
                nn.Tanh(),
                nn.Dropout(0.3),
                nn.Linear(128, 128),
                nn.Tanh(),
                nn.Linear(128, 64),
                nn.Linear(64, n_actions)
            )
        else:
            self.layers = nn.Sequential(
                nn.Linear(n_states, hidden_sizes[0]),
                nn.LeakyReLU(negative_slope=0.2),
                nn.BatchNorm1d(hidden_sizes[0]),
                nn.Linear(hidden_sizes[0], hidden_sizes[1]),
                nn.Tanh(),
                nn.Dropout(0.3),
                nn.BatchNorm1d(hidden_sizes[1]),
                nn.Linear(hidden_sizes[1], hidden_sizes[2]),
                nn.Tanh(),
                nn.Dropout(0.3),
                nn.BatchNorm1d(hidden_sizes[2]),
                nn.Linear(hidden_sizes[2], n_actions)
            )
        # This act layer maps the output to (0, 1)
        self.act = nn.Sigmoid()
        self._init_weights()

    def _init_weights(self):

        for m in self.layers:
            if isinstance(m, nn.Linear):
                m.weight.data.normal_(0.0, 1e-2)
                m.bias.data.uniform_(-0.1, 0.1)

    def forward(self, states):  # pylint: disable=arguments-differ

        actions = self.act(self.layers(states))
        return actions


class Critic(nn.Module):

    def __init__(self, n_states, n_actions, hidden_sizes, use_default):
        super(Critic, self).__init__()
        self.act = nn.Tanh()
        if use_default:
            self.state_input = nn.Linear(n_states, 128)
            self.action_input = nn.Linear(n_actions, 128)
            self.layers = nn.Sequential(
                nn.Linear(256, 256),
                nn.LeakyReLU(negative_slope=0.2),
                nn.BatchNorm1d(256),
                nn.Linear(256, 256),
                nn.Linear(256, 64),
                nn.Tanh(),
                nn.Dropout(0.3),
                nn.BatchNorm1d(64),
                nn.Linear(64, 1)
            )
        else:
            self.state_input = nn.Linear(n_states, hidden_sizes[0])
            self.action_input = nn.Linear(n_actions, hidden_sizes[0])
            self.layers = nn.Sequential(
                nn.Linear(hidden_sizes[0] * 2, hidden_sizes[1]),
                nn.LeakyReLU(negative_slope=0.2),
                nn.Dropout(0.3),
                nn.BatchNorm1d(hidden_sizes[1]),
                nn.Linear(hidden_sizes[1], hidden_sizes[2]),
                nn.Tanh(),
                nn.Dropout(0.3),
                nn.BatchNorm1d(hidden_sizes[2]),
                nn.Linear(hidden_sizes[2], 1)
            )
        self._init_weights()

    def _init_weights(self):
        self.state_input.weight.data.normal_(0.0, 1e-2)
        self.state_input.bias.data.uniform_(-0.1, 0.1)

        self.action_input.weight.data.normal_(0.0, 1e-2)
        self.action_input.bias.data.uniform_(-0.1, 0.1)

        for m in self.layers:
            if isinstance(m, nn.Linear):
                m.weight.data.normal_(0.0, 1e-2)
                m.bias.data.uniform_(-0.1, 0.1)

    def forward(self, states, actions):  # pylint: disable=arguments-differ
        states = self.act(self.state_input(states))
        actions = self.act(self.action_input(actions))

        _input = torch.cat([states, actions], dim=1)
        value = self.layers(_input)
        return value


class DDPG(object):

    def __init__(self, n_states, n_actions, model_name='', alr=0.001, clr=0.001,
                 gamma=0.9, batch_size=32, tau=0.002, shift=0, memory_size=100000,
                 a_hidden_sizes=[128, 128, 64], c_hidden_sizes=[128, 256, 64],
                 use_default=False):
        self.n_states = n_states
        self.n_actions = n_actions
        self.alr = alr
        self.clr = clr
        self.model_name = model_name
        self.batch_size = batch_size
        self.gamma = gamma
        self.tau = tau
        self.a_hidden_sizes = a_hidden_sizes
        self.c_hidden_sizes = c_hidden_sizes
        self.shift = shift
        self.use_default = use_default

        self._build_network()

        self.replay_memory = PrioritizedReplayMemory(capacity=memory_size)
        self.noise = OUProcess(n_actions)

    @staticmethod
    def totensor(x):
        return Variable(torch.FloatTensor(x))

    def _build_network(self):
        self.actor = Actor(self.n_states, self.n_actions, self.a_hidden_sizes, self.use_default)
        self.target_actor = Actor(self.n_states, self.n_actions, self.a_hidden_sizes,
                                  self.use_default)
        self.critic = Critic(self.n_states, self.n_actions, self.c_hidden_sizes, self.use_default)
        self.target_critic = Critic(self.n_states, self.n_actions, self.c_hidden_sizes,
                                    self.use_default)

        # Copy actor's parameters
        self._update_target(self.target_actor, self.actor, tau=1.0)

        # Copy critic's parameters
        self._update_target(self.target_critic, self.critic, tau=1.0)

        self.loss_criterion = nn.MSELoss()
        self.actor_optimizer = optimizer.Adam(lr=self.alr, params=self.actor.parameters(),
                                              weight_decay=1e-5)
        self.critic_optimizer = optimizer.Adam(lr=self.clr, params=self.critic.parameters(),
                                               weight_decay=1e-5)

    @staticmethod
    def _update_target(target, source, tau):
        for (target_param, param) in zip(target.parameters(), source.parameters()):
            target_param.data.copy_(
                target_param.data * (1 - tau) + param.data * tau
            )

    def reset(self, sigma, theta):
        self.noise.reset(sigma, theta)

    def _sample_batch(self):
        batch, idx = self.replay_memory.sample(self.batch_size)
        states = list(map(lambda x: x[0].tolist(), batch))  # pylint: disable=W0141
        actions = list(map(lambda x: x[1].tolist(), batch))  # pylint: disable=W0141
        rewards = list(map(lambda x: x[2], batch))  # pylint: disable=W0141
        next_states = list(map(lambda x: x[3].tolist(), batch))  # pylint: disable=W0141

        return idx, states, next_states, actions, rewards

    def add_sample(self, state, action, reward, next_state):
        self.critic.eval()
        self.actor.eval()
        self.target_critic.eval()
        self.target_actor.eval()
        batch_state = self.totensor([state.tolist()])
        batch_next_state = self.totensor([next_state.tolist()])
        current_value = self.critic(batch_state, self.totensor([action.tolist()]))
        target_action = self.target_actor(batch_next_state)
        target_value = self.totensor([reward]) \
            + self.target_critic(batch_next_state, target_action) * self.gamma
        error = float(torch.abs(current_value - target_value).data.numpy()[0])

        self.target_actor.train()
        self.actor.train()
        self.critic.train()
        self.target_critic.train()
        self.replay_memory.add(error, (state, action, reward, next_state))

    def update(self):
        idxs, states, next_states, actions, rewards = self._sample_batch()
        batch_states = self.totensor(states)
        batch_next_states = self.totensor(next_states)
        batch_actions = self.totensor(actions)
        batch_rewards = self.totensor(rewards)

        target_next_actions = self.target_actor(batch_next_states).detach()
        target_next_value = self.target_critic(batch_next_states, target_next_actions).detach()
        current_value = self.critic(batch_states, batch_actions)
        next_value = batch_rewards + target_next_value * self.gamma + self.shift

        # update prioritized memory
        if isinstance(self.replay_memory, PrioritizedReplayMemory):
            error = torch.abs(current_value - next_value).data.numpy()
            for i in range(self.batch_size):
                idx = idxs[i]
                self.replay_memory.update(idx, error[i][0])

        # Update Critic
        loss = self.loss_criterion(current_value, next_value)
        self.critic_optimizer.zero_grad()
        loss.backward()
        self.critic_optimizer.step()

        # Update Actor
        self.critic.eval()
        policy_loss = -self.critic(batch_states, self.actor(batch_states))
        policy_loss = policy_loss.mean()
        self.actor_optimizer.zero_grad()
        policy_loss.backward()

        self.actor_optimizer.step()
        self.critic.train()

        self._update_target(self.target_critic, self.critic, tau=self.tau)
        self._update_target(self.target_actor, self.actor, tau=self.tau)

        return loss.data, policy_loss.data

    def choose_action(self, states):
        self.actor.eval()
        act = self.actor(self.totensor([states.tolist()])).squeeze(0)
        self.actor.train()
        action = act.data.numpy()
        action += self.noise.noise()
        return action.clip(0, 1)

    def set_model(self, actor_dict, critic_dict):
        self.actor.load_state_dict(pickle.loads(actor_dict))
        self.critic.load_state_dict(pickle.loads(critic_dict))

    def get_model(self):
        return pickle.dumps(self.actor.state_dict()), pickle.dumps(self.critic.state_dict())
