import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from functools import partial
from einops.layers.torch import Rearrange, Reduce
from utils import *
from layers import *
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_
from timm.models.registry import register_model
from timm.models.layers.helpers import to_2tuple


class ConvE(torch.nn.Module):
    def __init__(self, params, ):
        super(ConvE, self).__init__()

        self.p = params

        self.ent_embed = torch.nn.Embedding(
            self.p.num_ent,   self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed.weight)

        self.rel_embed = torch.nn.Embedding(
            self.p.num_rel*2, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed.weight)

        self.in_channels = self.p.in_channels
        self.out_channels = self.p.out_channels

        self.bceloss = torch.nn.BCELoss()

        self.inp_drop = torch.nn.Dropout(self.p.inp_drop)
        self.hidden_drop = torch.nn.Dropout(self.p.hid_drop)
        self.feature_map_drop = torch.nn.Dropout2d(self.p.feat_drop)

        self.conv1 = torch.nn.Conv2d(
            self.in_channels, self.out_channels, (self.p.filt_h, self.p.filt_w), 1, 0, bias=True)
        self.bn0 = torch.nn.BatchNorm2d(self.in_channels)
        self.bn1 = torch.nn.BatchNorm2d(self.out_channels)
        self.bn2 = torch.nn.BatchNorm1d(self.p.embed_dim)

        self.register_parameter(
            'bias', torch.nn.Parameter(torch.zeros(self.p.num_ent)))
        fc_length = (20-self.p.filt_h+1)*(20-self.p.filt_w+1)*self.out_channels
        self.fc = torch.nn.Linear(fc_length, self.p.embed_dim)

    def loss(self, pred, true_label=None, sub_samp=None):
        label_pos = true_label[0]
        label_neg = true_label[1:]
        loss = self.bceloss(pred, true_label)
        return loss

    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
        sub_emb = self.ent_embed(sub).view(-1, 1, self.p.k_w, self.p.k_h)
        rel_emb = self.rel_embed(rel).view(-1, 1, self.p.k_w, self.p.k_h)

        x = torch.cat([sub_emb, rel_emb], 2)
        x = self.bn0(x)
        x = self.inp_drop(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.feature_map_drop(x)
        x = x.view(sub_emb.size(0), -1)
        x = self.fc(x)
        x = self.hidden_drop(x)
        x = self.bn2(x)
        x = F.relu(x)

        if strategy == 'one_to_n':
            x = torch.mm(x, self.ent_embed.weight.transpose(1, 0))
            x += self.bias.expand_as(x)
        else:
            x = torch.mul(x.unsqueeze(1), self.ent_embed(neg_ents)).sum(dim=-1)
            x += self.bias[neg_ents]

        pred = torch.sigmoid(x)

        return pred


class HypER(torch.nn.Module):
    def __init__(self, params, ):
        super(HypER, self).__init__()

        self.p = params

        self.ent_embed = torch.nn.Embedding(
            self.p.num_ent,   self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed.weight)

        self.rel_embed = torch.nn.Embedding(
            self.p.num_rel*2, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed.weight)

        self.in_channels = self.p.in_channels
        self.out_channels = self.p.out_channels

        self.bceloss = torch.nn.BCELoss()

        self.inp_drop = torch.nn.Dropout(self.p.inp_drop)
        self.hidden_drop = torch.nn.Dropout(self.p.hid_drop)
        self.feature_map_drop = torch.nn.Dropout2d(self.p.feat_drop)

        self.bn0 = torch.nn.BatchNorm2d(self.in_channels)
        self.bn1 = torch.nn.BatchNorm2d(self.out_channels)
        self.bn2 = torch.nn.BatchNorm1d(self.p.embed_dim)

        self.register_parameter(
            'bias', torch.nn.Parameter(torch.zeros(self.p.num_ent)))

        fc_length = (1-self.p.filt_h+1)*(self.p.embed_dim -
                                         self.p.filt_w+1)*self.out_channels
        self.fc = torch.nn.Linear(fc_length, self.p.embed_dim)
        fc1_length = self.in_channels*self.out_channels*self.p.filt_h*self.p.filt_w
        self.fc1 = torch.nn.Linear(self.p.embed_dim, fc1_length)

    def loss(self, pred, true_label=None, sub_samp=None):
        label_pos = true_label[0]
        label_neg = true_label[1:]
        loss = self.bceloss(pred, true_label)
        return loss

    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
        sub_emb = self.ent_embed(
            sub).view(-1, 1, 1, self.ent_embed.weight.size(1))
        rel_emb = self.rel_embed(rel)

        x = self.bn0(sub_emb)
        x = self.inp_drop(x)

        k = self.fc1(rel_emb)
        k = k.view(-1, self.in_channels, self.out_channels,
                   self.p.filt_h, self.p.filt_w)
        k = k.view(sub_emb.size(0)*self.in_channels *
                   self.out_channels, 1, self.p.filt_h, self.p.filt_w)

        x = x.permute(1, 0, 2, 3)

        x = F.conv2d(x, k, groups=sub_emb.size(0))
        x = x.view(sub_emb.size(0), 1, self.out_channels, 1 -
                   self.p.filt_h+1, sub_emb.size(3)-self.p.filt_w+1)
        x = x.permute(0, 3, 4, 1, 2)
        x = torch.sum(x, dim=3)
        x = x.permute(0, 3, 1, 2).contiguous()

        x = self.bn1(x)
        x = self.feature_map_drop(x)
        x = x.view(sub_emb.size(0), -1)
        x = self.fc(x)
        x = self.hidden_drop(x)
        x = self.bn2(x)
        x = F.relu(x)

        if strategy == 'one_to_n':
            x = torch.mm(x, self.ent_embed.weight.transpose(1, 0))
            x += self.bias.expand_as(x)
        else:
            x = torch.mul(x.unsqueeze(1), self.ent_embed(neg_ents)).sum(dim=-1)
            x += self.bias[neg_ents]

        pred = torch.sigmoid(x)

        return pred


class HypE(torch.nn.Module):
    def __init__(self, params, ):
        super(HypE, self).__init__()

        self.p = params

        self.ent_embed = torch.nn.Embedding(
            self.p.num_ent,   self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed.weight)

        self.rel_embed = torch.nn.Embedding(
            self.p.num_rel*2, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed.weight)

        self.in_channels = self.p.in_channels
        self.out_channels = self.p.out_channels

        self.bceloss = torch.nn.BCELoss()

        self.inp_drop = torch.nn.Dropout(self.p.inp_drop)
        self.hidden_drop = torch.nn.Dropout(self.p.hid_drop)
        self.feature_map_drop = torch.nn.Dropout2d(self.p.feat_drop)

        self.bn0 = torch.nn.BatchNorm2d(self.in_channels)
        self.bn1 = torch.nn.BatchNorm2d(self.out_channels)
        self.bn2 = torch.nn.BatchNorm1d(self.p.embed_dim)

        self.register_parameter(
            'bias', torch.nn.Parameter(torch.zeros(self.p.num_ent)))

        fc_length = (10-self.p.filt_h+1)*(20-self.p.filt_w+1)*self.out_channels
        self.fc = torch.nn.Linear(fc_length, self.p.embed_dim)

    def loss(self, pred, true_label=None, sub_samp=None):
        label_pos = true_label[0]
        label_neg = true_label[1:]
        loss = self.bceloss(pred, true_label)
        return loss

    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
        sub_emb = self.ent_embed(
            sub).view(-1, 1, 1, self.ent_embed.weight.size(1))
        rel_emb = self.rel_embed(rel)

        x = self.bn0(sub_emb)
        x = self.inp_drop(x)

        k = self.fc1(rel_emb)
        k = k.view(-1, self.in_channels, self.out_channels,
                   self.p.filt_h, self.p.filt_w)
        k = k.view(sub_emb.size(0)*self.in_channels *
                   self.out_channels, 1, self.p.filt_h, self.p.filt_w)

        x = x.permute(1, 0, 2, 3)

        x = F.conv2d(x, k, groups=sub_emb.size(0))
        x = x.view(sub_emb.size(0), 1, self.out_channels, 1 -
                   self.p.filt_h+1, sub_emb.size(3)-self.p.filt_w+1)
        x = x.permute(0, 3, 4, 1, 2)
        x = torch.sum(x, dim=3)
        x = x.permute(0, 3, 1, 2).contiguous()

        x = self.bn1(x)
        x = self.feature_map_drop(x)
        x = x.view(sub_emb.size(0), -1)
        x = self.fc(x)
        x = self.hidden_drop(x)
        x = self.bn2(x)
        x = F.relu(x)

        if strategy == 'one_to_n':
            x = torch.mm(x, self.ent_embed.weight.transpose(1, 0))
            x += self.bias.expand_as(x)
        else:
            x = torch.mul(x.unsqueeze(1), self.ent_embed(neg_ents)).sum(dim=-1)
            x += self.bias[neg_ents]

        pred = torch.sigmoid(x)

        return pred


class DistMult(torch.nn.Module):
    def __init__(self, params, ):
        super(DistMult, self).__init__()

        self.p = params

        self.ent_embed = torch.nn.Embedding(
            self.p.num_ent, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed.weight)

        self.rel_embed = torch.nn.Embedding(
            self.p.num_rel*2, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed.weight)

        self.bceloss = torch.nn.BCELoss()

        self.inp_drop = torch.nn.Dropout(self.p.inp_drop)
        self.bn0 = torch.nn.BatchNorm1d(self.p.embed_dim)

        self.register_parameter(
            'bias', torch.nn.Parameter(torch.zeros(self.p.num_ent)))

    def loss(self, pred, true_label=None, sub_samp=None):
        label_pos = true_label[0]
        label_neg = true_label[1:]
        loss = self.bceloss(pred, true_label)
        return loss

    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
        sub_emb = self.ent_embed(sub)
        rel_emb = self.rel_embed(rel)
        sub_emb = self.bn0(sub_emb)
        sub_emb = self.inp_drop(sub_emb)

        if strategy == 'one_to_n':
            x = torch.mm(sub_emb * rel_emb,
                         self.ent_embed.weight.transpose(1, 0))
            x += self.bias.expand_as(x)
        else:
            x = torch.mul((sub_emb * rel_emb).unsqueeze(1),
                          self.ent_embed(neg_ents)).sum(dim=-1)
            x += self.bias[neg_ents]

        pred = torch.sigmoid(x)

        return pred


class ComplEx(torch.nn.Module):
    def __init__(self, params, ):
        super(ComplEx, self).__init__()

        self.p = params

        self.ent_embed_real = torch.nn.Embedding(
            self.p.num_ent, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed_real.weight)

        self.ent_embed_imaginary = torch.nn.Embedding(
            self.p.num_ent, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed_imaginary.weight)

        self.rel_embed_real = torch.nn.Embedding(
            self.p.num_rel*2, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed_real.weight)

        self.rel_embed_imaginary = torch.nn.Embedding(
            self.p.num_rel*2, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed_imaginary.weight)

        self.bceloss = torch.nn.BCELoss()

        self.inp_drop = torch.nn.Dropout(self.p.inp_drop)
        self.bn0 = torch.nn.BatchNorm1d(self.p.embed_dim)
        self.bn1 = torch.nn.BatchNorm1d(self.p.embed_dim)

        self.register_parameter(
            'bias', torch.nn.Parameter(torch.zeros(self.p.num_ent)))

    def loss(self, pred, true_label=None, sub_samp=None):
        label_pos = true_label[0]
        label_neg = true_label[1:]
        loss = self.bceloss(pred, true_label)
        return loss

    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
        sub_emb_real = self.ent_embed_real(sub)
        sub_emb_imaginary = self.ent_embed_imaginary(sub)

        rel_emb_real = self.rel_embed_real(rel)
        rel_emb_imaginary = self.rel_embed_imaginary(rel)

        sub_emb_real = self.bn0(sub_emb_real)
        sub_emb_real = self.inp_drop(sub_emb_real)

        sub_emb_imaginary = self.bn0(sub_emb_imaginary)
        sub_emb_imaginary = self.inp_drop(sub_emb_imaginary)

        if strategy == 'one_to_n':
            x = torch.mm(sub_emb_real*rel_emb_real, self.ent_embed_real.weight.transpose(1, 0)) +\
                torch.mm(sub_emb_real*rel_emb_imaginary, self.ent_embed_imaginary.weight.transpose(1, 0)) +\
                torch.mm(sub_emb_imaginary*rel_emb_real, self.ent_embed_imaginary.weight.transpose(1, 0)) -\
                torch.mm(sub_emb_imaginary*rel_emb_imaginary,
                         self.ent_embed_real.weight.transpose(1, 0))
            x += self.bias.expand_as(x)
        else:
            neg_embs_real = self.ent_embed_real(neg_ents)
            neg_embs_imaginary = self.ent_embed_imaginary(neg_ents)

            x = (torch.mul((sub_emb_real*rel_emb_real).unsqueeze(1), neg_embs_real) +
                 torch.mul((sub_emb_real*rel_emb_imaginary).unsqueeze(1), neg_embs_imaginary) +
                 torch.mul((sub_emb_imaginary*rel_emb_real).unsqueeze(1), neg_embs_imaginary) -
                 torch.mul((sub_emb_imaginary*rel_emb_imaginary).unsqueeze(1), neg_embs_real)).sum(dim=-1)

            x += self.bias[neg_ents]

        pred = torch.sigmoid(x)

        return pred


class TuckER(torch.nn.Module):
    def __init__(self, params, ):
        super(TuckER, self).__init__()

        self.p = params

        self.ent_embed = torch.nn.Embedding(
            self.p.num_ent, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed.weight)

        self.rel_embed = torch.nn.Embedding(
            self.p.num_rel*2, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed.weight)

        self.core_W = torch.nn.Parameter(torch.tensor(np.random.uniform(-1, 1, (self.p.embed_dim,
                                         self.p.embed_dim, self.p.embed_dim)), dtype=torch.float, device="cuda", requires_grad=True))

        self.bceloss = torch.nn.BCELoss()

        self.inp_drop = torch.nn.Dropout(self.p.inp_drop)
        self.hidden_drop1 = torch.nn.Dropout(self.p.hid_drop)
        self.hidden_drop2 = torch.nn.Dropout(self.p.hid_drop)

        self.bn0 = torch.nn.BatchNorm1d(self.p.embed_dim)
        self.bn1 = torch.nn.BatchNorm1d(self.p.embed_dim)

        self.register_parameter(
            'bias', torch.nn.Parameter(torch.zeros(self.p.num_ent)))

    def loss(self, pred, true_label=None, sub_samp=None):
        label_pos = true_label[0]
        label_neg = true_label[1:]
        loss = self.bceloss(pred, true_label)
        return loss

    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
        sub_emb = self.ent_embed(sub)

        sub_emb = self.bn0(sub_emb)
        sub_emb = self.inp_drop(sub_emb)

        x = sub_emb.view(-1, 1, sub_emb.size(1))

        r = self.rel_embed(rel)

        W_mat = torch.mm(r, self.core_W.view(r.size(1), -1))
        W_mat = W_mat.view(-1, sub_emb.size(1), sub_emb.size(1))
        W_mat = self.hidden_drop1(W_mat)

        x = torch.bmm(x, W_mat)
        x = x.view(-1, sub_emb.size(1))
        x = self.bn1(x)
        x = self.hidden_drop2(x)

        if strategy == 'one_to_n':
            x = torch.mm(x, self.ent_embed.weight.transpose(1, 0))
            x += self.bias.expand_as(x)
        else:
            x = torch.mul(x.unsqueeze(1),
                          self.ent_embed(neg_ents)).sum(dim=-1)
            x += self.bias[neg_ents]

        pred = torch.sigmoid(x)

        return pred


class FouriER(torch.nn.Module):
    def __init__(self, params):
        super(FouriER, self).__init__()

        self.p = params

        image_h, image_w = self.p.image_h, self.p.image_w

        self.in_channels = self.p.in_channels
        self.out_channels = self.p.out_channels

        self.ent_embed = torch.nn.Embedding(
            self.p.num_ent, self.p.ent_vec_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed.weight)

        self.rel_embed = torch.nn.Embedding(
            self.p.num_rel*2, self.p.rel_vec_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed.weight)

        self.ent_fusion = torch.nn.Linear(
            self.p.ent_vec_dim, image_h*image_w)
        torch.nn.init.xavier_normal_(self.ent_fusion.weight)

        self.rel_fusion = torch.nn.Linear(
            self.p.rel_vec_dim, image_h*image_w)
        torch.nn.init.xavier_normal_(self.rel_fusion.weight)

        self.bceloss = torch.nn.BCELoss()
        
        channels = 2
        self.bn0 = torch.nn.BatchNorm2d(channels)
        self.bn1 = torch.nn.BatchNorm1d(self.p.embed_dim)
        self.hidden_drop = torch.nn.Dropout(self.p.hid_drop)

        self.register_parameter(
            'bias', torch.nn.Parameter(torch.zeros(self.p.num_ent)))

        patch_size = self.p.patch_size

        assert (image_h % patch_size) == 0 and (image_w %
                                                patch_size) == 0, 'image must be divisible by patch size'
        
        self.patch_embed = PatchEmbed(in_chans=channels, patch_size=self.p.patch_size, 
                                      embed_dim=self.p.embed_dim, stride=4, padding=2)
        network = []
        layers = [4, 4, 12, 4]
        embed_dims = [self.p.embed_dim, 128, 320, 128]
        mlp_ratios = [4, 4, 4, 4]
        downsamples = [True, True, True, True]
        pool_size=3
        act_layer=nn.GELU
        drop_rate=self.p.drop
        norm_layer=GroupNorm
        drop_path_rate=self.p.drop_path
        use_layer_scale=True
        layer_scale_init_value=1e-5
        down_patch_size=3
        down_stride=2
        down_pad=1
        num_classes=self.p.embed_dim
        for i in range(len(layers)):
            stage = basic_blocks(embed_dims[i], i, layers, 
                                 pool_size=pool_size, mlp_ratio=mlp_ratios[i],
                                 act_layer=act_layer, norm_layer=norm_layer, 
                                 drop_rate=drop_rate, 
                                 drop_path_rate=drop_path_rate,
                                 use_layer_scale=use_layer_scale, 
                                 layer_scale_init_value=layer_scale_init_value)
            network.append(stage)
            if i >= len(layers) - 1:
                break
            if downsamples[i] or embed_dims[i] != embed_dims[i+1]:
                # downsampling between two stages
                network.append(
                    PatchEmbed(
                        patch_size=down_patch_size, stride=down_stride, 
                        padding=down_pad, 
                        in_chans=embed_dims[i], embed_dim=embed_dims[i+1]
                        )
                    )

        self.network = nn.ModuleList(network)
        self.norm = norm_layer(embed_dims[-1])
        self.head = nn.Linear(
                embed_dims[-1], num_classes) if num_classes > 0 \
                else nn.Identity()
        

    def loss(self, pred, true_label=None, sub_samp=None):
        label_pos = true_label[0]
        label_neg = true_label[1:]
        loss = self.bceloss(pred, true_label)
        return loss
    
    def forward_embeddings(self, x):
        x = self.patch_embed(x)
        return x

    def forward_tokens(self, x):
        outs = []
        for idx, block in enumerate(self.network):
            x = block(x)
        # output only the features of last layer for image classification
        return x

    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
        sub_emb = self.ent_fusion(self.ent_embed(sub))
        rel_emb = self.rel_fusion(self.rel_embed(rel))
        comb_emb = torch.stack([sub_emb.view(-1, self.p.image_h, self.p.image_w), rel_emb.view(-1, self.p.image_h, self.p.image_w)], dim=1)
        y = comb_emb.view(-1, 2, self.p.image_h, self.p.image_w)
        y = self.bn0(y)
        z = self.forward_embeddings(y)
        z = self.forward_tokens(z)
        z = z.mean([-2, -1])
        z = self.norm(z)
        x = self.head(z)
        x = self.hidden_drop(x)
        x = self.bn1(x)
        x = F.relu(x)
        
        if strategy == 'one_to_n':
            x = torch.mm(x, self.ent_embed.weight.transpose(1, 0))
            x += self.bias.expand_as(x)
        else:
            x = torch.mul(x.unsqueeze(1),
                          self.ent_embed(neg_ents)).sum(dim=-1)
            x += self.bias[neg_ents]

        pred = torch.sigmoid(x)

        return pred


class InteractE(torch.nn.Module):
    """
    Proposed method in the paper. Refer Section 6 of the paper for mode details
    Parameters
    ----------
    params:        	Hyperparameters of the model
    chequer_perm:   Reshaping to be used by the model

    Returns
    -------
    The InteractE model instance

    """

    def __init__(self, params, chequer_perm):
        super(InteractE, self).__init__()

        self.p = params

        self.ent_embed = torch.nn.Embedding(
            self.p.num_ent,   self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.ent_embed.weight)

        self.rel_embed = torch.nn.Embedding(
            self.p.num_rel*2, self.p.embed_dim, padding_idx=None)
        torch.nn.init.xavier_normal_(self.rel_embed.weight)

        self.bceloss = torch.nn.BCELoss()

        self.hidden_drop = torch.nn.Dropout(self.p.hid_drop)
        self.feature_map_drop = torch.nn.Dropout2d(self.p.feat_drop)
        self.bn0 = torch.nn.BatchNorm2d(self.p.perm)

        flat_sz_h = self.p.k_h
        flat_sz_w = 2*self.p.k_w
        self.padding = 0

        self.bn1 = torch.nn.BatchNorm2d(self.p.num_filt*self.p.perm)
        self.flat_sz = flat_sz_h * flat_sz_w * self.p.num_filt*self.p.perm

        self.bn2 = torch.nn.BatchNorm1d(self.p.embed_dim)
        self.fc = torch.nn.Linear(self.flat_sz, self.p.embed_dim)
        self.chequer_perm = chequer_perm

        self.register_parameter(
            'bias', torch.nn.Parameter(torch.zeros(self.p.num_ent)))
        self.register_parameter('conv_filt', torch.nn.Parameter(
            torch.zeros(self.p.num_filt, 1, self.p.ker_sz,  self.p.ker_sz)))

        torch.nn.init.xavier_normal_(self.conv_filt)

    def loss(self, pred, true_label=None, sub_samp=None):
        label_pos = true_label[0]
        label_neg = true_label[1:]
        loss = self.bceloss(pred, true_label)
        return loss

    def circular_padding_chw(self, batch, padding):
        upper_pad = batch[..., -padding:, :]
        lower_pad = batch[..., :padding, :]
        temp = torch.cat([upper_pad, batch, lower_pad], dim=2)

        left_pad = temp[..., -padding:]
        right_pad = temp[..., :padding]
        padded = torch.cat([left_pad, temp, right_pad], dim=3)
        return padded

    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
        sub_emb = self.ent_embed(sub)
        rel_emb = self.rel_embed(rel)
        comb_emb = torch.cat([sub_emb, rel_emb], dim=1)
        chequer_perm = comb_emb[:, self.chequer_perm]
        stack_inp = chequer_perm.reshape(
            (-1, self.p.perm, 2*self.p.k_w, self.p.k_h))
        stack_inp = self.bn0(stack_inp)
        x = self.inp_drop(stack_inp)
        x = self.circular_padding_chw(x, self.p.ker_sz//2)
        x = F.conv2d(x, self.conv_filt.repeat(self.p.perm, 1, 1, 1),
                     padding=self.padding, groups=self.p.perm)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.feature_map_drop(x)
        x = x.view(-1, self.flat_sz)
        x = self.fc(x)
        x = self.hidden_drop(x)
        x = self.bn2(x)
        x = F.relu(x)

        if strategy == 'one_to_n':
            x = torch.mm(x, self.ent_embed.weight.transpose(1, 0))
            x += self.bias.expand_as(x)
        else:
            x = torch.mul(x.unsqueeze(1), self.ent_embed(neg_ents)).sum(dim=-1)
            x += self.bias[neg_ents]

        pred = torch.sigmoid(x)

        return pred

class GroupNorm(nn.GroupNorm):
    """
    Group Normalization with 1 group.
    Input: tensor in shape [B, C, H, W]
    """
    def __init__(self, num_channels, **kwargs):
        super().__init__(1, num_channels, **kwargs)
        
def basic_blocks(dim, index, layers, 
                 pool_size=3, mlp_ratio=4., 
                 act_layer=nn.GELU, norm_layer=GroupNorm, 
                 drop_rate=.0, drop_path_rate=0., 
                 use_layer_scale=True, layer_scale_init_value=1e-5):
    """
    generate PoolFormer blocks for a stage
    return: PoolFormer blocks 
    """
    blocks = []
    for block_idx in range(layers[index]):
        block_dpr = drop_path_rate * (
            block_idx + sum(layers[:index])) / (sum(layers) - 1)
        blocks.append(PoolFormerBlock(
            dim, pool_size=pool_size, mlp_ratio=mlp_ratio, 
            act_layer=act_layer, norm_layer=norm_layer, 
            drop=drop_rate, drop_path=block_dpr, 
            use_layer_scale=use_layer_scale, 
            layer_scale_init_value=layer_scale_init_value, 
            ))
    blocks = nn.Sequential(*blocks)

    return blocks


class PoolFormerBlock(nn.Module):
    """
    Implementation of one PoolFormer block.
    --dim: embedding dim
    --pool_size: pooling size
    --mlp_ratio: mlp expansion ratio
    --act_layer: activation
    --norm_layer: normalization
    --drop: dropout rate
    --drop path: Stochastic Depth, 
        refer to https://arxiv.org/abs/1603.09382
    --use_layer_scale, --layer_scale_init_value: LayerScale, 
        refer to https://arxiv.org/abs/2103.17239
    """
    def __init__(self, dim, pool_size=3, mlp_ratio=4., 
                 act_layer=nn.GELU, norm_layer=GroupNorm, 
                 drop=0., drop_path=0., 
                 use_layer_scale=True, layer_scale_init_value=1e-5):

        super().__init__()

        self.norm1 = norm_layer(dim)
        #self.token_mixer = Pooling(pool_size=pool_size)
        self.token_mixer = FNetBlock()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, 
                       act_layer=act_layer, drop=drop)

        # The following two techniques are useful to train deep PoolFormers.
        self.drop_path = DropPath(drop_path) if drop_path > 0. \
            else nn.Identity()
        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale_1 = nn.Parameter(
                layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            self.layer_scale_2 = nn.Parameter(
                layer_scale_init_value * torch.ones((dim)), requires_grad=True)

    def forward(self, x):
        if self.use_layer_scale:
            x = x + self.drop_path(
                self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)
                * self.token_mixer(self.norm1(x)))
            x = x + self.drop_path(
                self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
                * self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.token_mixer(self.norm1(x)))
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x
class PatchEmbed(nn.Module):
    """
    Patch Embedding that is implemented by a layer of conv. 
    Input: tensor in shape [B, C, H, W]
    Output: tensor in shape [B, C, H/stride, W/stride]
    """
    def __init__(self, patch_size=16, stride=16, padding=0, 
                 in_chans=3, embed_dim=768, norm_layer=None):
        super().__init__()
        patch_size = to_2tuple(patch_size)
        stride = to_2tuple(stride)
        padding = to_2tuple(padding)
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, 
                              stride=stride, padding=padding)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        x = self.proj(x)
        x = self.norm(x)
        return x

class Pooling(nn.Module):
    """
    Implementation of pooling for PoolFormer
    --pool_size: pooling size
    """
    def __init__(self, pool_size=3):
        super().__init__()
        self.pool = nn.AvgPool2d(
            pool_size, stride=1, padding=pool_size//2, count_include_pad=False)

    def forward(self, x):
        return self.pool(x) - x


class Mlp(nn.Module):
    """
    Implementation of MLP with 1*1 convolutions.
    Input: tensor with shape [B, C, H, W]
    """
    def __init__(self, in_features, hidden_features=None, 
                 out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
        self.act = act_layer()
        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
        self.drop = nn.Dropout(drop)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Conv2d):
            trunc_normal_(m.weight, std=.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x

class LayerNormChannel(nn.Module):
    """
    LayerNorm only for Channel Dimension.
    Input: tensor in shape [B, C, H, W]
    """
    def __init__(self, num_channels, eps=1e-05):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(num_channels))
        self.bias = nn.Parameter(torch.zeros(num_channels))
        self.eps = eps

    def forward(self, x):
        u = x.mean(1, keepdim=True)
        s = (x - u).pow(2).mean(1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.eps)
        x = self.weight.unsqueeze(-1).unsqueeze(-1) * x \
            + self.bias.unsqueeze(-1).unsqueeze(-1)
        return x

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x):
        return self.net(x)
      
class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn
    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)

class FNetBlock(nn.Module):
  def __init__(self):
    super().__init__()

  def forward(self, x):
    x = torch.fft.fft(torch.fft.fft(x, dim=-1), dim=-2).real
    return x

class FNet(nn.Module):
    def __init__(self, dim, depth, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, FNetBlock()),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))
    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x
        return x