import torch.nn as nn
import torch
from .GaussianChannel import GaussianChannel
from .NormalizePower import NormalizePower
from . import util


class ConstellationNet(nn.Module):
    """
    Autoencoder network to automatically shape a constellation of symbols for
    efficient communication over an optical fiber channel.
    """
    def __init__(
        self,
        order=2,
        encoder_layers=(),
        decoder_layers=(),
    ):
        """
        Create an autoencoder.

        :param order: Order of the constellation, i.e. the number of messages
        that are to be transmitted, or equivalently the number of symbols whose
        placements in the constellation have to be learned.
        :param encoder_layers: Shape of the encoder’s hidden layers. The
        size of this sequence is the number of hidden layers, with each element
        being a number which specifies the number of neurons in its channel.
        :param decoder_layers: Shape of the decoder’s hidden layers. Uses
        the same convention as `encoder_layers_sizes` above.
        """
        super().__init__()
        self.order = order

        # Build the encoder network taking a one-hot encoded message as input
        # and outputting an I/Q vector. The network additionally uses hidden
        # layers as specified in `encoder_layers`
        prev_layer_size = order
        encoder_layers_list = []

        for layer_size in encoder_layers:
            encoder_layers_list.append(nn.Linear(prev_layer_size, layer_size))
            encoder_layers_list.append(nn.ReLU())
            encoder_layers_list.append(nn.BatchNorm1d(layer_size))
            prev_layer_size = layer_size

        encoder_layers_list += [
            nn.Linear(prev_layer_size, 2),
            NormalizePower(),
        ]

        self.encoder = nn.Sequential(*encoder_layers_list)
        self.channel = GaussianChannel()

        # Build the decoder network taking the noisy I/Q vector received from
        # the channel as input and outputting a probability vector for each
        # original message. The network additionally uses hidden layers as
        # specified in `decoder_layers`
        prev_layer_size = 2
        decoder_layers_list = []

        for layer_size in decoder_layers:
            decoder_layers_list.append(nn.Linear(prev_layer_size, layer_size))
            encoder_layers_list.append(nn.ReLU())
            decoder_layers_list.append(nn.BatchNorm1d(layer_size))
            prev_layer_size = layer_size

        # Softmax is not used at the end of the network because the
        # CrossEntropyLoss criterion is used for training, which includes
        # LogSoftmax
        decoder_layers_list.append(nn.Linear(prev_layer_size, order))

        self.decoder = nn.Sequential(*decoder_layers_list)

    def forward(self, x):
        """
        Perform encoding and decoding of an input vector and compute its
        reconstructed vector.

        :param x: Original one-hot encoded data.
        :return: Reconstructed vector.
        """
        symbol = self.encoder(x)
        noisy_symbol = self.channel(symbol)
        return self.decoder(noisy_symbol)

    def get_constellation(self):
        """
        Extract the symbol constellation out of the trained encoder.

        :return: Matrix containing `order` rows with the nᵗʰ one being the I/Q
        vector that is the result of encoding the nᵗʰ message.
        """
        with torch.no_grad():
            return self.encoder(
                util.messages_to_onehot(
                    torch.arange(0, self.order),
                    self.order
                )
            )