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

#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

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

class Network(nn.Module):
    def __init__(self, input_dim, hidden_in_dim, hidden_out_dim, output_dim, actor=False):
        super(Network, self).__init__()

        """self.input_norm = nn.BatchNorm1d(input_dim)
        self.input_norm.weight.data.fill_(1)
        self.input_norm.bias.data.fill_(0)"""

        self.fc1 = nn.Linear(input_dim,hidden_in_dim)
        self.fc2 = nn.Linear(hidden_in_dim,hidden_out_dim)
        self.fc3 = nn.Linear(hidden_out_dim,output_dim)
        self.nonlin = f.leaky_relu
        self.actor = actor
        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_(-1e-3, 1e-3)

    def forward(self, x):
        if self.actor:
            # return a vector of the force
            h1 = self.nonlin(self.fc1(x))
            h2 = self.nonlin(self.fc2(h1))
            h3 = f.tanh(self.fc3(h2))
            return h3

        else:
            # critic network simply outputs a number
            h1 = self.nonlin(self.fc1(x))
            h2 = self.nonlin(self.fc2(h1))
            h3 = (self.fc3(h2))
            return h3

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

    def __init__(self, state_size, action_size, seed, fc1_units=256, fc2_units=128):
        """Initialize parameters and build model.
        Params
        ======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
            fc1_units (int): Number of nodes in first hidden layer
            fc2_units (int): Number of nodes in second hidden layer
        """
        super(Actor, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(state_size, fc1_units)
        self.fc2 = nn.Linear(fc1_units, fc2_units)
        self.fc3 = nn.Linear(fc2_units, action_size)
        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):
        """Build an actor (policy) network that maps states -> actions."""
        x = f.relu(self.fc1(state))
        x = f.relu(self.fc2(x))
        return torch.tanh(self.fc3(x))


class Critic(nn.Module):
    """Critic (Value) Model."""

    def __init__(self, state_size, action_size, seed, fcs1_units=256, fc2_units=128):
        """Initialize parameters and build model.
        Params
        ======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
            fcs1_units (int): Number of nodes in the first hidden layer
            fc2_units (int): Number of nodes in the second hidden layer
        """
        super(Critic, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fcs1 = nn.Linear(state_size, fcs1_units)
        self.fc2 = nn.Linear(fcs1_units+(action_size), fc2_units)
        self.fc3 = nn.Linear(fc2_units, 1)
        self.reset_parameters()

    def reset_parameters(self):
        self.fcs1.weight.data.uniform_(*hidden_init(self.fcs1))
        self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
        self.fc3.weight.data.uniform_(-3e-3, 3e-3)

    def forward(self, state, action):
        """Build a critic (value) network that maps (state, action) pairs -> Q-values."""
        xs = f.relu(self.fcs1(state))
        x = torch.cat((xs, action), dim=1)
        x = f.relu(self.fc2(x))
        return self.fc3(x)
