import torch
import pytorch_lightning as pl
from torch.nn import Module, Linear, ReLU, Tanh, Sigmoid, Dropout, GRU, AvgPool2d

from abc import ABC, abstractmethod

#######################
# Abstract NN Class

class AbstractNeuralNetwork(Module):

    @property
    def name(self):
        return self.__class__.__name__

    def __init__(self):
        super(AbstractNeuralNetwork, self).__init__()

    def forward(self, batch):
        pass


######################
# Abstract Network class following the Lightning Syntax
class LightningModule(pl.LightningModule, ABC):

    def __init__(self):
        super(LightningModule, self).__init__()

    @abstractmethod
    def forward(self, x):
        raise NotImplementedError

    @abstractmethod
    def training_step(self, batch, batch_nb):
        # REQUIRED
        raise NotImplementedError

    def validation_step(self, batch, batch_nb):
        # OPTIONAL
        pass

    def validation_end(self, outputs):
        # OPTIONAL
        pass

    @abstractmethod
    def configure_optimizers(self):
        # REQUIRED
        raise NotImplementedError

    @pl.data_loader
    def tng_dataloader(self):
        # REQUIRED
        raise NotImplementedError
        # return DataLoader(MNIST(os.getcwd(), train=True, download=True,
        # transform=transforms.ToTensor()), batch_size=32)

    @pl.data_loader
    def val_dataloader(self):
        # OPTIONAL
        pass

    @pl.data_loader
    def test_dataloader(self):
        # OPTIONAL
        pass


#######################
# Utility Modules
class TimeDistributed(Module):
    def __init__(self, module, batch_first=True):
        super(TimeDistributed, self).__init__()
        self.module = module
        self.batch_first = batch_first

    def forward(self, x):

        if len(x.size()) <= 2:
            return self.module(x)

        # Squash samples and timesteps into a single axis
        x_reshape = x.contiguous().view(-1, x.size(-1))  # (samples * timesteps, input_size)

        y = self.module(x_reshape)

        # We have to reshape Y
        if self.batch_first:
            y = y.contiguous().view(x.size(0), -1, y.size(-1))  # (samples, timesteps, output_size)
        else:
            y = y.view(-1, x.size(1), y.size(-1))  # (timesteps, samples, output_size)

        return y


class Repeater(Module):
    def __init__(self, shape):
        super(Repeater, self).__init__()
        self.shape = shape

    def forward(self, x: torch.Tensor):
        x = x.unsqueeze(-2)
        return x.expand(self.shape)


class RNNOutputFilter(Module):

    def __init__(self, return_output=True, only_last=False):
        super(RNNOutputFilter, self).__init__()
        self.only_last = only_last
        self.return_output = return_output

    def forward(self, x: tuple):
        outputs, hidden = x
        out = outputs if self.return_output else hidden
        return out if not self.only_last else out[:, -1, :]


class AvgDimPool(Module):

    def __init__(self):
        super(AvgDimPool, self).__init__()

    def forward(self, x):
        return x.mean(-2)


#######################
# Network Modules
# Generators, Decoders, Encoders, Discriminators
class Discriminator(Module):

    def __init__(self, latent_dim, features, dropout=.0, activation=ReLU):
        super(Discriminator, self).__init__()
        self.features = features
        self.latent_dim = latent_dim
        self.l1 = Linear(self.latent_dim, self.features * 10)
        self.l2 = Linear(self.features * 10, self.features * 20)
        self.lout = Linear(self.features * 20, 1)
        self.dropout = Dropout(dropout)
        self.activation = activation()
        self.sigmoid = Sigmoid()

    def forward(self, x, **kwargs):
        tensor = self.l1(x)
        tensor = self.dropout(self.activation(tensor))
        tensor = self.l2(tensor)
        tensor = self.dropout(self.activation(tensor))
        tensor = self.lout(tensor)
        tensor = self.sigmoid(tensor)
        return tensor


class DecoderLinearStack(Module):

    def __init__(self, out_shape):
        super(DecoderLinearStack, self).__init__()
        self.l1 = Linear(10, 100, bias=True)
        self.l2 = Linear(100, out_shape, bias=True)
        self.activation = ReLU()
        self.activation_out = Tanh()

    def forward(self, x):
        tensor = self.l1(x)
        tensor = self.activation(tensor)
        tensor = self.l2(tensor)
        tensor = self.activation_out(tensor)
        return tensor


class EncoderLinearStack(Module):

    def __init__(self):
        super(EncoderLinearStack, self).__init__()
        # FixMe: Get Hardcoded shit out of here
        self.l1 = Linear(6, 100, bias=True)
        self.l2 = Linear(100, 10, bias=True)
        self.activation = ReLU()

    def forward(self, x):
        tensor = self.l1(x)
        tensor = self.activation(tensor)
        tensor = self.l2(tensor)
        tensor = self.activation(tensor)
        return tensor


class Encoder(Module):

    def __init__(self, lat_dim, variational=False):
        self.lat_dim = lat_dim
        self.variational = variational

        super(Encoder, self).__init__()
        self.l_stack = TimeDistributed(EncoderLinearStack())
        self.gru = GRU(10, 10, batch_first=True)
        self.filter = RNNOutputFilter(only_last=True)
        if variational:
            self.mu = Linear(10, self.lat_dim)
            self.logvar = Linear(10, self.lat_dim)
        else:
            self.lat_dim_layer = Linear(10, self.lat_dim)

    def forward(self, x):
        tensor = self.l_stack(x)
        tensor = self.gru(tensor)
        tensor = self.filter(tensor)
        if self.variational:
            tensor = self.mu(tensor), self.logvar(tensor)
        else:
            tensor = self.lat_dim_layer(tensor)
        return tensor


class PoolingEncoder(Module):

    def __init__(self, lat_dim, variational=False):
        self.lat_dim = lat_dim
        self.variational = variational

        super(PoolingEncoder, self).__init__()
        self.p = AvgDimPool()
        self.l = EncoderLinearStack()
        if variational:
            self.mu = Linear(10, self.lat_dim)
            self.logvar = Linear(10, self.lat_dim)
        else:
            self.lat_dim_layer = Linear(10, self.lat_dim)

    def forward(self, x):
        tensor = self.p(x)
        tensor = self.l(tensor)
        if self.variational:
            tensor = self.mu(tensor), self.logvar(tensor)
        else:
            tensor = self.lat_dim_layer(tensor)
        return tensor


class Decoder(Module):

    def __init__(self, latent_dim, *args, variational=False):
        self.variational = variational
        super(Decoder, self).__init__()
        self.g = GRU(latent_dim, 10, batch_first=True)
        self.filter = RNNOutputFilter()
        self.l_stack = TimeDistributed(DecoderLinearStack(*args))
        pass

    def forward(self, x):
        tensor = self.g(x)
        tensor = self.filter(tensor)
        tensor = self.l_stack(tensor)
        return tensor


if __name__ == '__main__':
    raise PermissionError('Get out of here - never run this module')