import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from tqdm import trange
from time import sleep
from sklearn.preprocessing import OneHotEncoder, LabelEncoder

class Net(nn.Module):
    Image2Vector CNN which takes image of dimension (28x28x3) and return column vector length 64

    def sub_block(self, in_channels, out_channels=64, kernel_size=3):
        block = torch.nn.Sequential(
            torch.nn.Conv2d(kernel_size=kernel_size, in_channels=in_channels,
                            out_channels=out_channels, padding=1),
        return block

    def __init__(self):
        super(Net, self).__init__()
        self.convnet1 = self.sub_block(3)
        self.convnet2 = self.sub_block(64)
        self.convnet3 = self.sub_block(64)
        self.convnet4 = self.sub_block(64)

    def forward(self, x):
        x = self.convnet1(x)
        x = self.convnet2(x)
        x = self.convnet3(x)
        x = self.convnet4(x)
        x = torch.flatten(x, start_dim=1)
        return x

class PrototypicalNet(nn.Module):
    def __init__(self, use_gpu=False):
        super(PrototypicalNet, self).__init__()
        self.f = Net()
        self.gpu = use_gpu
        if self.gpu:
            self.f = self.f.cuda()

    def forward(self, datax, datay, Ns, Nc, Nq, total_classes):
        Implementation of one episode in Prototypical Net
        datax: Training images
        datay: Corresponding labels of datax
        Nc: Number  of classes per episode
        Ns: Number of support data per class
        Nq:  Number of query data per class
        total_classes: Total classes in training set
        k = total_classes.shape[0]
        K = np.random.choice(total_classes, Nc, replace=False)
        Query_x = torch.Tensor()
            Query_x = Query_x.cuda()
        Query_y = []
        Query_y_count = []
        centroid_per_class = {}
        class_label = {}
        label_encoding = 0
        for cls in K:
            S_cls, Q_cls = self.random_sample_cls(datax, datay, Ns, Nq, cls)
            centroid_per_class[cls] = self.get_centroid(S_cls, Nc)
            class_label[cls] = label_encoding
            label_encoding += 1
            # Joining all the query set together
            Query_x =, Q_cls), 0)
            Query_y += [cls]
            Query_y_count += [Q_cls.shape[0]]
        Query_y, Query_y_labels = self.get_query_y(
            Query_y, Query_y_count, class_label)
        Query_x = self.get_query_x(Query_x, centroid_per_class, Query_y_labels)
        return Query_x, Query_y

    def random_sample_cls(self, datax, datay, Ns, Nq, cls):
        Randomly samples Ns examples as support set and Nq as Query set
        data = datax[(datay == cls).nonzero()]
        perm = torch.randperm(data.shape[0])
        idx = perm[:Ns]
        S_cls = data[idx]
        idx = perm[Ns: Ns+Nq]
        Q_cls = data[idx]
        if self.gpu:
            S_cls = S_cls.cuda()
            Q_cls = Q_cls.cuda()
        return S_cls, Q_cls

    def get_centroid(self, S_cls, Nc):
        Returns a centroid vector of support set for a class
        return torch.sum(self.f(S_cls), 0).unsqueeze(1).transpose(0, 1) / Nc

    def get_query_y(self, Qy, Qyc, class_label):
        Returns labeled representation of classes of Query set and a list of labels.
        labels = []
        m = len(Qy)
        for i in range(m):
            labels += [Qy[i]] * Qyc[i]
        labels = np.array(labels).reshape(len(labels), 1)
        label_encoder = LabelEncoder()
        Query_y = torch.Tensor(
        if self.gpu:
            Query_y = Query_y.cuda()
        Query_y_labels = np.unique(labels)
        return Query_y, Query_y_labels

    def get_centroid_matrix(self, centroid_per_class, Query_y_labels):
        Returns the centroid matrix where each column is a centroid of a class.
        centroid_matrix = torch.Tensor()
            centroid_matrix = centroid_matrix.cuda()
        for label in Query_y_labels:
            centroid_matrix =
                (centroid_matrix, centroid_per_class[label]))
        if self.gpu:
            centroid_matrix = centroid_matrix.cuda()
        return centroid_matrix

    def get_query_x(self, Query_x, centroid_per_class, Query_y_labels):
        Returns distance matrix from each Query image to each centroid.
        centroid_matrix = self.get_centroid_matrix(
            centroid_per_class, Query_y_labels)
        Query_x = self.f(Query_x)
        m = Query_x.size(0)
        n = centroid_matrix.size(0)
        # The below expressions expand both the matrices such that they become compatible to each other in order to caclulate L2 distance.
        # Expanding centroid matrix to "m".
        centroid_matrix = centroid_matrix.expand(
            m, centroid_matrix.size(0), centroid_matrix.size(1))
        Query_matrix = Query_x.expand(n, Query_x.size(0), Query_x.size(
            1)).transpose(0, 1)  # Expanding Query matrix "n" times
        Qx = torch.pairwise_distance(centroid_matrix.transpose(
            1, 2), Query_matrix.transpose(1, 2))
        return Qx

def train_step(protonet, datax, datay, Ns, Nc, Nq):
    Qx, Qy = protonet(datax, datay, Ns, Nc, Nq, np.unique(datay))
    pred = torch.log_softmax(Qx, dim=-1)
    loss = F.nll_loss(pred, Qy)
    acc = torch.mean((torch.argmax(pred, 1) == Qy).float())
    return loss, acc

def test_step(protonet, datax, datay, Ns, Nc, Nq):
    Qx, Qy = protonet(datax, datay, Ns, Nc, Nq, np.unique(datay))
    pred = torch.log_softmax(Qx, dim=-1)
    loss = F.nll_loss(pred, Qy)
    acc = torch.mean((torch.argmax(pred, 1) == Qy).float())
    return loss, acc

def load_weights(filename, protonet, use_gpu):
    if use_gpu:
        protonet.load_state_dict(torch.load(filename), map_location='cpu')
    return protonet