__author__ = 'zhenhang.sun@gmail.com'
__version__ = '1.0.0'

import gym
import math
import random
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


class Actor(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Actor, self).__init__()
        self.linear1 = nn.Linear(input_size, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.linear3 = nn.Linear(hidden_size, output_size)

    def forward(self, s):
        x = F.relu(self.linear1(s))
        x = F.relu(self.linear2(x))
        x = torch.tanh(self.linear3(x))

        return x


class Critic(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = nn.Linear(input_size, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.linear3 = nn.Linear(hidden_size, output_size)

    def forward(self, s, a):
        x = torch.cat([s, a], 1)
        # print(x)
        x = F.relu(self.linear1(x))
        # print(x)
        x = F.relu(self.linear2(x))
        x = self.linear3(x)

        return x


class Agent(object):
    def __init__(self, **kwargs):
        for key, value in kwargs.items():
            setattr(self, key, value)

        s_dim = self.env.observation_space.shape[0]
        a_dim = self.env.action_space.shape[0]
        print(s_dim, a_dim)

        self.actor = Actor(s_dim, 256, a_dim)
        self.actor_target = Actor(s_dim, 256, a_dim)
        self.critic = Critic(s_dim + a_dim, 256, a_dim)
        self.critic_target = Critic(s_dim + a_dim, 256, a_dim)
        self.actor_optim = optim.Adam(self.actor.parameters(), lr=self.actor_lr)
        self.critic_optim = optim.Adam(self.critic.parameters(), lr=self.critic_lr)
        self.buffer = []

        self.actor_target.load_state_dict(self.actor.state_dict())
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.learn_steps = 0

    def act(self, s0):
        s0 = torch.tensor(s0, dtype=torch.float).unsqueeze(0)
        a0 = self.actor(s0).squeeze(0).detach().numpy()
        # print(a0)
        return a0

    def put(self, *transition):
        if len(self.buffer) == self.capacity:
            self.buffer.pop(0)
        self.buffer.append(transition)

    def learn(self):
        self.learn_steps += 1
        if len(self.buffer) < self.batch_size:
            return
        samples = random.sample(self.buffer, self.batch_size)
        # print(samples)

        s0, a0, r1, s1 = zip(*samples)
        s0 = torch.tensor(s0, dtype=torch.float)
        a0 = torch.tensor(a0, dtype=torch.float)
        r1 = torch.tensor(r1, dtype=torch.float).view(self.batch_size, -1)
        s1 = torch.tensor(s1, dtype=torch.float)

        def critic_learn():
            a1 = self.actor_target(s1).detach()
            # print(a1)
            # print(self.critic_target(s1, a1))
            y_true = r1 + self.gamma * self.critic_target(s1, a1).detach()

            y_pred = self.critic(s0, a0)

            loss_fn = nn.MSELoss()
            loss = loss_fn(y_pred, y_true)
            self.critic_optim.zero_grad()
            loss.backward()
            self.critic_optim.step()

        def actor_learn():
            loss = -torch.mean(self.critic(s0, self.actor(s0)))
            self.actor_optim.zero_grad()
            loss.backward()
            self.actor_optim.step()

        def soft_update(net_target, net, tau):
            for target_param, param in zip(net_target.parameters(), net.parameters()):
                target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau)

        critic_learn()
        actor_learn()
        soft_update(self.critic_target, self.critic, self.tau)
        soft_update(self.actor_target, self.actor, self.tau)