from collections import deque

import numpy as np
import torch

import GEO_parameters
from Replay_memory import Memory, Transaction
from model import Net
import torch.nn.functional as f
import matplotlib.pyplot as plt
from GEO_parameters import GEO_Parameters


def dev0(a, b):
    a = a.detach().numpy()
    b = b.detach().numpy()
    with np.errstate(divide='ignore', invalid='ignore'):
        c = np.true_divide(a, b)
        c[~ np.isfinite(c)] = 0
    c = torch.from_numpy(c)
    return c


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


def hard_update(target, source):
    for target_param, param in zip(target.parameters(), source.parameters()):
        target_param.data.copy_(param.data)


class DeepQNetwork:
    def __init__(self, cap, n_actions, n_states, learning_rate, reward_decay, e_greedy, replace_target_iter, batch_size,e_greedy_increment=None):

        self.memory = Memory(cap)
        self.batch = None
        self.state_batch = batch_size
        self.action_batch = None
        self.reward_batch = None
        self.non_final_next_states = None
        self.expected_state_action_values = None
        self.state_action_values = None

        self.n_states = n_states
        self.n_actions = n_actions
        self.lr = learning_rate
        self.gamma = reward_decay
        self.epsilon_max = e_greedy
        self.replace_target_iter = replace_target_iter
        self.batch_size = batch_size
        self.epsilon_decay = e_greedy_increment
        self.epsilon = 0 if e_greedy_increment is None else self.epsilon_max


        self.learn_step_counter = 1

        self.counter = GEO_parameters.state_dim
        self.main_q_network = Net(n_states, n_actions) # 输入的nstates 应该是M_c+1
        self.target_q_network = Net(n_states, n_actions)

        self.optimizer = torch.optim.Adam(self.main_q_network.parameters(), self.lr)

        self.cost_his = []

        self.weight_dir = 'weight_DQN'

    def replay(self):
        if self.learn_step_counter % self.replace_target_iter == 0:
            self.update_target_q_network()

        if len(self.memory) <self.batch_size:
            return

        self.batch, self.state_batch, self.action_batch, self.reward_batch, self.non_final_next_states = self.make_mini_batch()
        self.expected_state_action_values = self.get_expected_state_action_values()
        self.update_main_q_network()
        self.epsilon *= 0.9998
        self.learn_step_counter += 1

    def make_mini_batch(self):
        transitions = self.memory.sample(self.batch_size)

        batch = Transaction(*zip(*transitions))
        state_batch = torch.cat(batch.state)
        action_batch = torch.cat(batch.action)
        reward_batch = torch.cat(batch.reward)
        non_final_next_states = torch.cat([s for s in batch.next_state if s is not None])
        return batch, state_batch, action_batch, reward_batch, non_final_next_states

    def get_expected_state_action_values(self):
        self.main_q_network.eval()
        self.target_q_network.eval()

        self.state_action_values = self.main_q_network

        non_final_mask = torch.ByteTensor(tuple(map(lambda x : x is not None, self.batch.next_state))).bool()

        next_state_values = torch.zeros(self.batch_size)

        a_m = torch.zeros(self.batch_size).type(torch.LongTensor)

        a_m[non_final_mask] = self.main_q_network(self.non_final_next_states).detach().max(1)[1]

        a_m_non_final_next_states = a_m[non_final_mask].view(-1, 1)

        next_state_values[non_final_mask] = self.target_q_network(
            self.non_final_next_states
        ).gather(1, a_m_non_final_next_states).detach().squeeze()

        expected_state_action_values = self.reward_batch + self.gamma * next_state_values.view(-1, 1)
        return expected_state_action_values

    def decide_action(self, state):
        # self.epsilon = 0.5 * (1 / (self.epsilon + 1))
        if self.epsilon < np.random.uniform():
            self.main_q_network.eval()
            with torch.no_grad():
                action = self.main_q_network(state).detach().max(1)[1]
        else:
            action = self.LongTenser([np.random.randint(0, self.n_actions)])
        return action.view(1, 1)

    def update_main_q_network(self):
        self.main_q_network.train()

        loss = f.mse_loss(self.state_action_values, self.expected_state_action_values, reduction = 'mean')
        self.cost_his.append(loss.item())
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def update_target_q_network(self):
        self.target_q_network.load_state_dict(self.main_q_network.state_dict())

    def plot_cost(self):
        plt.plot(np.arange(len(self.cost_his)), self.cost_his)
        plt.xlabel('training step')
        plt.ylabel('cost')
        plt.show()

    def binary(self, q):
        self.counter = GEO_Parameters.state_dim - 1
        t = torch.unsqueeze(q.max(1)[1], 1)
        temp = torch.zeros_like(t)
        temp_extend = temp.repeat(1, GEO_Parameters.state_dim)
        n = temp
        while torch.logical_not(torch.eq(n, t)):
            t1 = t % 2
            t2 = t >> 1
            numColumns = temp_extend.shape[1]
            new_tensor_left = temp_extend[:, :self.counter]
            new_tensor_right = temp_extend[:, self.counter+1:numColumns]
            temp_extend = torch.cat([new_tensor_left, new_tensor_right], 1)
            self.counter -= 1

        a1 = temp_extend
        a1 = a1.float()
        return a1

    def load_weight_from_pkl(self, file_name):
        checkpoint = torch.load(file_name)
        self.main_q_network.load_state_dict(checkpoint['main_q_network_state_dict'])
        self.target_q_network.load_state_dict(checkpoint['target_q_network_state_dict'])
        self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
        self.cost_his = checkpoint['loss']

    def save_weight_to_pkl(self, file_name):
        torch.save({
            # 'epoch': epochs,
            'main_q_network_state_dict': self.main_q_network.state_dict(),
            'target_q_network_state_dict': self.target_q_network.state_dict(),
            'optimizer_state_dict': self.optimizer.state_dict(),
            'loss': self.cost_his,
        }, file_name)

    def update_replay_buffer(self, state, action, reward, next_state):
        self.memory.push(state, action, reward, next_state)