import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


def hidden_init(layer):
    fan_in = layer.weight.data.size()[0]
    lim = 1. / np.sqrt(fan_in)
    return (-lim, lim)


class Actor(nn.Module):
    """Actor (Policy) Model."""

    def __init__(self, state_size, action_size, seed, fc1_units=256):
        """Initialize parameters and build model.
        Params
        ======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
        """
        super(Actor, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(state_size, fc1_units)
        self.fc2 = nn.Linear(fc1_units, action_size)
        self.b2 = nn.BatchNorm1d(action_size)
        self.tanh = nn.Tanh()
        self.reset_parameters()

    def reset_parameters(self):
        self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
        self.fc2.weight.data.uniform_(-3e-3, 3e-3)

    def forward(self, state):
        x = F.leaky_relu(self.fc1(state))
        x = F.leaky_relu(self.fc2(x))
        x = self.b2(x)
        x = self.tanh(x)

        return x


class CriticD4PG(nn.Module):
    """Critic (distribution) Model."""

    def __init__(self, state_size, action_size, seed, n_atoms, v_min, v_max, fc1_units=128, fc2_units=128):
        """Initialize parameters and build model.
        Params
        ======
            state_size (int): Dimension of each state
            action_size (int): Dimesion of each action
            seed (int): Random seed
        """
        super(CriticD4PG, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(state_size, fc1_units)
        self.fc2 = nn.Linear(fc1_units + action_size, fc2_units)
        self.fc3 = nn.Linear(fc2_units, n_atoms)
        delta = (v_max - v_min) / (n_atoms - 1)
        self.register_buffer("supports", torch.arange(v_min, v_max + delta, delta))
        self.reset_parameters()

    def reset_parameters(self):
        self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
        self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
        self.fc3.weight.data.uniform_(-3e-3, 3e-3)

    def forward(self, state, action):
        xs = F.leaky_relu(self.fc1(state))
        x = torch.cat((xs, action), dim=1)
        x = F.leaky_relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def distr_to_q(self, distr):
        weights = F.softmax(distr, dim=1) * self.supports
        res = weights.sum(dim=1)
        return res.unsqueeze(dim=-1)
