import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from utils.networks import TorchRunningMeanStd, to_np, soft_update_params


from agent.critic import DoubleQCritic
from agent.actor import DiagGaussianActor

def compute_state_entropy(obs, full_obs, k):
    batch_size = 500
    with torch.no_grad():
        dists = []
        for idx in range(len(full_obs) // batch_size + 1):
            start = idx * batch_size
            end = (idx + 1) * batch_size
            dist = torch.norm(
                obs[:, None, :] - full_obs[None, start:end, :], dim=-1, p=2
            )
            dists.append(dist)

        dists = torch.cat(dists, dim=1)
        knn_dists = torch.kthvalue(dists, k=k + 1, dim=1).values
        state_entropy = knn_dists
    return state_entropy.unsqueeze(1)

class SACAgent():
    """SAC algorithm."""
    def __init__(self, 
                obs_dim, 
                action_dim, 
                action_range, 
                device, 
                hidden_dim,
                hidden_depth,
                log_std_bounds,
                discount, 
                init_temperature, 
                alpha_lr, 
                alpha_betas,
                actor_lr, 
                actor_betas, 
                actor_update_frequency, 
                critic_lr,
                critic_betas, 
                critic_tau, 
                critic_target_update_frequency,
                batch_size, 
                learnable_temperature,
                normalize_state_entropy=True):
        super().__init__()
        self.obs_dim = obs_dim
        self.action_dim = action_dim
        self.hidden_dim = hidden_dim
        self.hidden_depth = hidden_depth
        self.action_range = action_range
        self.log_std_bounds=log_std_bounds
        self.device = torch.device(device)
        self.discount = discount
        self.critic_tau = critic_tau
        self.actor_update_frequency = actor_update_frequency
        self.critic_target_update_frequency = critic_target_update_frequency
        self.batch_size = batch_size
        self.learnable_temperature = learnable_temperature
        self.critic_lr = critic_lr
        self.critic_betas = critic_betas
        self.s_ent_stats = TorchRunningMeanStd(shape=[1], device=device)
        self.normalize_state_entropy = normalize_state_entropy
        self.init_temperature = init_temperature
        self.alpha_lr = alpha_lr
        self.alpha_betas = alpha_betas
        self.actor_betas = actor_betas
        self.alpha_lr = alpha_lr
        
        self.critic = DoubleQCritic(obs_dim, action_dim, hidden_dim, hidden_depth).to(self.device)
        self.critic_target = DoubleQCritic(obs_dim, action_dim, hidden_dim, hidden_depth).to(self.device)
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.actor = DiagGaussianActor(obs_dim, action_dim, hidden_dim, hidden_depth,
                 log_std_bounds).to(self.device)
        self.log_alpha = torch.tensor(np.log(init_temperature)).to(self.device)
        self.log_alpha.requires_grad = True
        
        # set target entropy to -|A|
        self.target_entropy = -action_dim

        # optimizers
        self.actor_optimizer = torch.optim.Adam(
            self.actor.parameters(),
            lr=actor_lr,
            betas=actor_betas)
        self.critic_optimizer = torch.optim.Adam(
            self.critic.parameters(),
            lr=critic_lr,
            betas=critic_betas)
        self.log_alpha_optimizer = torch.optim.Adam(
            [self.log_alpha],
            lr=alpha_lr,
            betas=alpha_betas)
        
        # change mode
        self.train()
        self.critic_target.train()
    
    def reset_critic(self):
        self.critic = DoubleQCritic(self.obs_dim, self.action_dim, self.hidden_dim, self.hidden_depth).to(self.device)
        self.critic_target = DoubleQCritic(self.obs_dim, self.action_dim, self.hidden_dim, self.hidden_depth).to(self.device)
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.critic_optimizer = torch.optim.Adam(
            self.critic.parameters(),
            lr=self.critic_lr,
            betas=self.critic_betas)
    
    def reset_actor(self):
        # reset log_alpha
        self.log_alpha = torch.tensor(np.log(self.init_temperature)).to(self.device)
        self.log_alpha.requires_grad = True
        self.log_alpha_optimizer = torch.optim.Adam(
            [self.log_alpha],
            lr=self.alpha_lr,
            betas=self.alpha_betas)
        
        # reset actor
        self.actor = DiagGaussianActor(self.obs_dim, self.action_dim, self.hidden_dim, self.hidden_depth,
                self.log_std_bounds).to(self.device)
        self.actor_optimizer = torch.optim.Adam(
            self.actor.parameters(),
            lr=self.actor_lr,
            betas=self.actor_betas)
        
    def train(self, training=True):
        self.training = training
        self.actor.train(training)
        self.critic.train(training)

    @property
    def alpha(self):
        return self.log_alpha.exp()

    def act(self, obs, sample=False):
        obs = torch.FloatTensor(obs).to(self.device)
        obs = obs.unsqueeze(0)
        dist = self.actor(obs)
        action = dist.sample() if sample else dist.mean
        action = action.clamp(*self.action_range)
        assert action.ndim == 2 and action.shape[0] == 1
        return to_np(action[0])

    def save(self, model_dir, step):
        torch.save(
            self.actor.state_dict(), '%s/actor_%s.pt' % (model_dir, step)
        )
        torch.save(
            self.critic.state_dict(), '%s/critic_%s.pt' % (model_dir, step)
        )
        torch.save(
            self.critic_target.state_dict(), '%s/critic_target_%s.pt' % (model_dir, step)
        )
        
    def load(self, model_dir, step):
        self.actor.load_state_dict(
            torch.load('%s/actor_%s.pt' % (model_dir, step))
        )
        self.critic.load_state_dict(
            torch.load('%s/critic_%s.pt' % (model_dir, step))
        )
        self.critic_target.load_state_dict(
            torch.load('%s/critic_target_%s.pt' % (model_dir, step))
        )
    
    def update_critic(self, obs, action, reward, next_obs, 
                      not_done, logger, timestep, print_flag=True):
        
        dist = self.actor(next_obs)
        next_action = dist.rsample()
        log_prob = dist.log_prob(next_action).sum(-1, keepdim=True)
        target_Q1, target_Q2 = self.critic_target(next_obs, next_action)
        target_V = torch.min(target_Q1,
                             target_Q2) - self.alpha.detach() * log_prob
        target_Q = reward + (not_done * self.discount * target_V)
        target_Q = target_Q.detach()

        # get current Q estimates
        current_Q1, current_Q2 = self.critic(obs, action)
        critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(
            current_Q2, target_Q)
        


        # Optimize the critic
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        #logger.store_data([{'sac/critic_loss' : critic_loss.detach().cpu().numpy()}])
        #logger.log('sac/critic_loss', timestep)
        
    def update_critic_state_ent(
        self, obs, full_obs, action, next_obs, not_done,
        timestep, logger, K=5, print_flag=True):
        
        dist = self.actor(next_obs)
        next_action = dist.rsample()
        log_prob = dist.log_prob(next_action).sum(-1, keepdim=True)
        target_Q1, target_Q2 = self.critic_target(next_obs, next_action)
        target_V = torch.min(target_Q1, target_Q2) - self.alpha.detach() * log_prob
        
        # compute state entropy
        state_entropy = compute_state_entropy(obs, full_obs, k=K)
        
        self.s_ent_stats.update(state_entropy)
        norm_state_entropy = state_entropy / self.s_ent_stats.std
        

        if self.normalize_state_entropy:
            state_entropy = norm_state_entropy
        
        target_Q = state_entropy + (not_done * self.discount * target_V)
        target_Q = target_Q.detach()

        # get current Q estimates
        current_Q1, current_Q2 = self.critic(obs, action)
        critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(
            current_Q2, target_Q)
        


        # Optimize the critic
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

    

    def update_actor_and_alpha(self, obs, timestep,logger, print_flag=False):
        dist = self.actor(obs)
        action = dist.rsample()
        log_prob = dist.log_prob(action).sum(-1, keepdim=True)
        actor_Q1, actor_Q2 = self.critic(obs, action)

        actor_Q = torch.min(actor_Q1, actor_Q2)
        actor_loss = (self.alpha.detach() * log_prob - actor_Q).mean()


        # optimize the actor
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()
        if self.learnable_temperature:
            self.log_alpha_optimizer.zero_grad()
            alpha_loss = (self.alpha *
                          (-log_prob - self.target_entropy).detach()).mean()

            alpha_loss.backward()
            self.log_alpha_optimizer.step()


    def update(self, replay_buffer, timestep, logger, gradient_update=1):
        for index in range(gradient_update):
            batch = replay_buffer.sample(self.batch_size)

            print_flag = False
            if index == gradient_update -1:
                print_flag = True
                
            self.update_critic(batch['obs'], batch['action'], batch['reward'], batch['next_obs'], batch['not_done_no_max'], logger,
                               timestep, print_flag)

            
            if timestep % self.actor_update_frequency == 0:
                self.update_actor_and_alpha(batch['obs'], timestep, logger, print_flag)

        if timestep % self.critic_target_update_frequency == 0:
            soft_update_params(self.critic, self.critic_target,
                                     self.critic_tau)
            
    def update_after_reset(self, replay_buffer, logger, timestep, gradient_update=1, policy_update=True):
        for index in range(gradient_update):
            batch = replay_buffer.sample(self.batch_size)

            print_flag = False
            if index == gradient_update -1:
                
                print_flag = True
                
            self.update_critic(batch['obs'], batch['action'], batch['reward'], batch['next_obs'], batch['not_done_no_max'], logger,
                               timestep, print_flag)

            if index % self.actor_update_frequency == 0 and policy_update:
                self.update_actor_and_alpha(batch['obs'], timestep, logger, print_flag)

            if index % self.critic_target_update_frequency == 0:
                soft_update_params(self.critic, self.critic_target,
                                         self.critic_tau)
            
    def update_state_ent(self, replay_buffer, timestep, logger, gradient_update=1, K=5):
        for index in range(gradient_update):
            obs, full_obs, action, reward, next_obs, not_done, not_done_no_max = replay_buffer.sample_state_ent(
                self.batch_size)

            print_flag = False
                
            self.update_critic_state_ent(
                obs, full_obs, action, next_obs, not_done_no_max, logger,
                timestep, K=K, print_flag=print_flag)

            if timestep % self.actor_update_frequency == 0:
                self.update_actor_and_alpha(obs, timestep, logger, print_flag)

        if timestep % self.critic_target_update_frequency == 0:
            soft_update_params(self.critic, self.critic_target,
                                     self.critic_tau)