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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
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import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical


class Controller(nn.Module):
    # we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py
    def __init__(self, num_edge, num_ops, lstm_size=32, lstm_num_layers=2, tanh_constant=2.5, temperature=5.0):
        super(Controller, self).__init__()
        # assign the attributes
        self.num_edge = num_edge
        self.num_ops = num_ops
        self.lstm_size = lstm_size
        self.lstm_N = lstm_num_layers
        self.tanh_constant = tanh_constant
        self.temperature = temperature
        # create parameters
        self.register_parameter('input_vars', nn.Parameter(torch.Tensor(1, 1, lstm_size)))
        self.w_lstm = nn.LSTM(input_size=self.lstm_size, hidden_size=self.lstm_size, num_layers=self.lstm_N)
        self.w_embd = nn.Embedding(self.num_ops, self.lstm_size)
        self.w_pred = nn.Linear(self.lstm_size, self.num_ops)

        nn.init.uniform_(self.input_vars, -0.1, 0.1)
        nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)
        nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)
        nn.init.uniform_(self.w_embd.weight, -0.1, 0.1)
        nn.init.uniform_(self.w_pred.weight, -0.1, 0.1)

    def forward(self):
        inputs, h0 = self.input_vars, None
        log_probs, entropys, sampled_arch = [], [], []
        for iedge in range(self.num_edge):
            outputs, h0 = self.w_lstm(inputs, h0)

            logits = self.w_pred(outputs)
            logits = logits / self.temperature
            logits = self.tanh_constant * torch.tanh(logits)
            # distribution
            op_distribution = Categorical(logits=logits)
            op_index = op_distribution.sample()
            sampled_arch.append(op_index.item())

            op_log_prob = op_distribution.log_prob(op_index)
            log_probs.append(op_log_prob.view(-1))
            op_entropy = op_distribution.entropy()
            entropys.append(op_entropy.view(-1))

            # obtain the input embedding for the next step
            inputs = self.w_embd(op_index)
        return torch.sum(torch.cat(log_probs)), torch.sum(torch.cat(entropys)), sampled_arch
