import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.normal import Normal


class ObservationEncoder(nn.Module):
    def __init__(self, obs_shape, state_size, num_layers=4, num_filters=32, stride=None):
        super().__init__()  

        assert len(obs_shape) == 3

        self.state_size = state_size

        layers = []
        for i in range(num_layers):
            input_channels = obs_shape[0] if i == 0 else output_channels
            output_channels = num_filters * (2 ** i)
            layers.append(nn.Conv2d(in_channels=input_channels, out_channels= output_channels, kernel_size=4, stride=2))
            layers.append(nn.ReLU())

        self.convs = nn.Sequential(*layers)

        self.fc = nn.Linear(256 * 3 * 3, 2 * state_size)

    def forward(self, x):
        x = self.convs(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        
        # Mean and standard deviation
        mean, std = torch.chunk(x, 2, dim=-1)
        std = F.softplus(std)
        std = torch.clamp(std, min=0.0, max=1e5)

        # Normal Distribution
        dist = self.get_dist(mean, std)

        # Sampling via reparameterization Trick
        x = self.reparameterize(mean, std)

        encoded_output = {"sample": x, "distribution": dist}
        return encoded_output
    
    def reparameterize(self, mu, std):
        eps = torch.randn_like(std)
        return mu + eps * std
    
    def get_dist(self, mean, std):
        distribution = torch.distributions.Normal(mean, std)
        distribution = torch.distributions.independent.Independent(distribution, 1)
        return distribution


class ObservationDecoder(nn.Module):
    def __init__(self, state_size, output_shape):
        super().__init__()

        self.state_size = state_size
        self.output_shape = output_shape
        self.input_size = 256 * 3 * 3
        self.in_channels = [self.input_size, 256, 128, 64]
        self.out_channels = [256, 128, 64, 3]

        if output_shape[1] == 84:
            self.kernels = [5, 7, 5, 6]
            self.output_padding = [1, 1, 1, 0]
        elif output_shape[1] == 64:
            self.kernels = [5, 5, 6, 6]
            self.output_padding = [0, 0, 0, 0]

        self.dense = nn.Linear(state_size, self.input_size)
        
        layers = []
        for i in range(len(self.kernels)):
            layers.append(nn.ConvTranspose2d(in_channels=self.in_channels[i], out_channels=self.out_channels[i], 
                                             kernel_size=self.kernels[i], stride=2, output_padding=self.output_padding[i]))
            if i!=len(self.kernels)-1:
                layers.append(nn.ReLU())

        self.convtranspose = nn.Sequential(*layers)

    def forward(self, features):
        out_batch_shape = features.shape[:-1]
        out = self.dense(features)
        out = torch.reshape(out, [-1, self.input_size, 1, 1])
        out = self.convtranspose(out)
        
        mean = torch.reshape(out, (*out_batch_shape, *self.output_shape))
        out_dist = torch.distributions.independent.Independent(torch.distributions.Normal(mean, 1), len(self.output_shape))
        return out_dist


class ActionDecoder(nn.Module):
    def __init__(self, state_size, hidden_size, action_size, num_layers=5):
        super().__init__()
        self.state_size = state_size    
        self.hidden_size = hidden_size
        self.action_size = action_size
        self.num_layers = num_layers

        self._min_std=torch.Tensor([1e-4])[0]
        self._init_std=torch.Tensor([5])[0]
        self._mean_scale=torch.Tensor([5])[0]

        layers = []
        for i in range(self.num_layers):
            input_channels = state_size if i == 0 else self.hidden_size
            output_channels = self.hidden_size if i!= self.num_layers-1 else 2*action_size
            layers.append(nn.Linear(input_channels, output_channels))
            layers.append(nn.ReLU())
        self.action_model = nn.Sequential(*layers)

    def get_dist(self, mean, std):
        distribution = torch.distributions.Normal(mean, std)
        distribution = torch.distributions.transformed_distribution.TransformedDistribution(distribution, TanhBijector())
        distribution = torch.distributions.independent.Independent(distribution, 1)
        return distribution
    
    def forward(self, features):
        out = self.action_model(features)
        mean, std = torch.chunk(out, 2, dim=-1) 
        
        raw_init_std = torch.log(torch.exp(self._init_std) - 1)
        action_mean = self._mean_scale * torch.tanh(mean / self._mean_scale)
        action_std = F.softplus(std + raw_init_std) + self._min_std

        dist = self.get_dist(action_mean, action_std)
        sample = dist.rsample()
        return sample
        

class ValueModel(nn.Module):
    def __init__(self, state_size, hidden_size, num_layers=4):
        super().__init__()
        self.state_size = state_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers

        layers = []
        for i in range(self.num_layers):
            input_channels = state_size if i == 0 else self.hidden_size
            output_channels = self.hidden_size if i!= self.num_layers-1 else 1
            layers.append(nn.Linear(input_channels, output_channels))
            layers.append(nn.ReLU())
        self.value_model = nn.Sequential(*layers) 

    def forward(self, state):
        value = self.value_model(state)
        return value
    
        
class TransitionModel(nn.Module):
    def __init__(self, state_size, hidden_size, action_size, history_size):
        super().__init__()

        self.state_size = state_size
        self.hidden_size = hidden_size
        self.action_size = action_size
        self.history_size = history_size
        self.act_fn = nn.ReLU()

        self.fc_state_action = nn.Linear(state_size + action_size, hidden_size)
        self.history_cell = nn.GRUCell(hidden_size + history_size, history_size)
        self.fc_state_prior  = nn.Linear(history_size + state_size + action_size, 2 * state_size)
        self.fc_state_posterior = nn.Linear(history_size + state_size + action_size, 2 * state_size)

    def init_states(self, batch_size, device):
        self.prev_state = torch.zeros(batch_size, self.state_size).to(device)
        self.prev_action = torch.zeros(batch_size, self.action_size).to(device)
        self.prev_history = torch.zeros(batch_size, self.history_size).to(device)
            
    def get_dist(self, mean, std):
        distribution = torch.distributions.Normal(mean, std)
        distribution = torch.distributions.independent.Independent(distribution, 1)
        return distribution

    def imagine_step(self, prev_state, prev_action, prev_history):
        state_action = self.act_fn(self.fc_state_action(torch.cat([prev_state, prev_action], dim=-1)))
        history = self.history_cell(torch.cat([state_action, prev_history], dim=-1), prev_history)

        state_prior = self.fc_state_prior(torch.cat([history, prev_state, prev_action], dim=-1))
        state_prior_mean, state_prior_std = torch.chunk(state_prior, 2, dim=-1)
        state_prior_std = F.softplus(state_prior_std)

        # Normal Distribution
        state_prior_dist = self.get_dist(state_prior_mean, state_prior_std)

        # Sampling via reparameterization Trick
        sample_state_prior = self.reparemeterize(state_prior_mean, state_prior_std)
        prior = {"mean": state_prior_mean, "std": state_prior_std, "sample": sample_state_prior, "history": history, "distribution": state_prior_dist}
        return prior
    
    def reparemeterize(self, mean, std):
        eps = torch.randn_like(std)
        return mean + eps * std


class TanhBijector(torch.distributions.Transform):
    def __init__(self):
        super().__init__()
        self.bijective = True
        self.domain = torch.distributions.constraints.real
        self.codomain = torch.distributions.constraints.interval(-1.0, 1.0)

    @property
    def sign(self): return 1.

    def _call(self, x): return torch.tanh(x)

    def atanh(self, x):
        return 0.5 * torch.log((1 + x) / (1 - x))

    def _inverse(self, y: torch.Tensor):
        y = torch.where(
            (torch.abs(y) <= 1.),
            torch.clamp(y, -0.99999997, 0.99999997),
            y)
        y = self.atanh(y)
        return y

    def log_abs_det_jacobian(self, x, y):
        #return 2. * (np.log(2) - x - F.softplus(-2. * x))
        return 2.0 * (torch.log(torch.tensor([2.0])) - x - F.softplus(-2.0 * x))
     

class CLUBSample(nn.Module):  # Sampled version of the CLUB estimator
    def __init__(self, x_dim, y_dim, hidden_size):
        super(CLUBSample, self).__init__()
        self.p_mu = nn.Sequential(
                    nn.Linear(x_dim, hidden_size//2),
                    nn.ReLU(),
                    nn.Linear(hidden_size//2, hidden_size//2),
                    nn.ReLU(),
                    nn.Linear(hidden_size//2, y_dim)
                    )

        self.p_logvar = nn.Sequential(
                        nn.Linear(x_dim, hidden_size//2),
                        nn.ReLU(),
                        nn.Linear(hidden_size//2, hidden_size//2),
                        nn.ReLU(),
                        nn.Linear(hidden_size//2, y_dim),
                        nn.Tanh()
                        )

    def get_mu_logvar(self, x_samples):
        mu = self.p_mu(x_samples)
        logvar = self.p_logvar(x_samples)
        return mu, logvar
    
    def loglikeli(self, x_samples, y_samples):
        mu, logvar = self.get_mu_logvar(x_samples)
        return (-(mu - y_samples)**2 /logvar.exp()-logvar).sum(dim=1).mean(dim=0)

    def forward(self, x_samples, y_samples):
        mu, logvar = self.get_mu_logvar(x_samples)
        return - self.loglikeli(x_samples, y_samples)