import copy
import torch.nn as nn
from torch.autograd import Variable

from models.simple import SimpleNet


class RNNModel(SimpleNet):
    """Container module with an encoder, a recurrent module, and a decoder."""

    def __init__(
        self,
        name,
        created_time,
        rnn_type,
        ntoken,
        ninp,
        nhid,
        nlayers,
        dropout=0.5,
        tie_weights=False,
    ):
        super(RNNModel, self).__init__(name=name, created_time=created_time)
        self.drop = nn.Dropout(dropout)
        self.encoder = nn.Embedding(ntoken, ninp)
        if rnn_type in ['LSTM', 'GRU']:
            self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, dropout=dropout)
        else:
            try:
                nonlinearity = {'RNN_TANH': 'tanh', 'RNN_RELU': 'relu'}[rnn_type]
            except KeyError:
                raise ValueError(
                    """An invalid option for `--model` was supplied,
                                 options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']"""
                )
            self.rnn = nn.RNN(
                ninp, nhid, nlayers, nonlinearity=nonlinearity, dropout=dropout
            )
        self.decoder = nn.Linear(nhid, ntoken)


        if tie_weights:
            if nhid != ninp:
                raise ValueError(
                    'When using the tied flag, nhid must be equal to emsize'
                )
            self.decoder.weight = self.encoder.weight

        self.init_weights()

        self.rnn_type = rnn_type
        self.nhid = nhid
        self.nlayers = nlayers

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.bias.data.fill_(0)
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, input, hidden):
        emb = self.drop(self.encoder(input))
        output, hidden = self.rnn(emb, hidden)
        output = self.drop(output)
        decoded = self.decoder(
            output.view(output.size(0) * output.size(1), output.size(2))
        )
        return decoded.view(output.size(0), output.size(1), decoded.size(1)), hidden

    def init_hidden(self, bsz):
        weight = next(self.parameters()).data
        if self.rnn_type == 'LSTM':
            return (
                Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()),
                Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()),
            )
        else:
            return Variable(weight.new(self.nlayers, bsz, self.nhid).zero_())
