import torch
import torch.nn as nn
import torch.nn.functional as F

import math

from models.model_utils import set_attributes_from_args

class MAB(nn.Module):
    def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
        super(MAB, self).__init__()
        self.dim_V = dim_V
        self.num_heads = num_heads
        self.fc_q = nn.Linear(dim_Q, dim_V)
        self.fc_k = nn.Linear(dim_K, dim_V)
        self.fc_v = nn.Linear(dim_K, dim_V)
        if ln:
            self.ln0 = nn.LayerNorm(dim_V)
            self.ln1 = nn.LayerNorm(dim_V)
        self.fc_o = nn.Linear(dim_V, dim_V)

    def forward(self, Q, K):
        Q = self.fc_q(Q)
        K, V = self.fc_k(K), self.fc_v(K)

        dim_split = self.dim_V // self.num_heads
        Q_ = torch.cat(Q.split(dim_split, 2), 0)
        K_ = torch.cat(K.split(dim_split, 2), 0)
        V_ = torch.cat(V.split(dim_split, 2), 0)

        A = torch.softmax(Q_.bmm(K_.transpose(1,2))/math.sqrt(self.dim_V), 2)
        O = torch.cat((Q_ + A.bmm(V_)).split(Q.size(0), 0), 2)
        O = O if getattr(self, 'ln0', None) is None else self.ln0(O)
        O = O + F.relu(self.fc_o(O))
        O = O if getattr(self, 'ln1', None) is None else self.ln1(O)
        return O

class SAB(nn.Module):
    def __init__(self, dim_in, dim_out, num_heads, ln=False):
        super(SAB, self).__init__()
        self.mab = MAB(dim_in, dim_in, dim_out, num_heads, ln=ln)

    def forward(self, X):
        return self.mab(X, X)

class ISAB(nn.Module):
    def __init__(self, dim_in, dim_out, num_heads, num_inds, ln=False):
        super(ISAB, self).__init__()
        self.I = nn.Parameter(torch.Tensor(1, num_inds, dim_out))
        nn.init.xavier_uniform_(self.I)
        self.mab0 = MAB(dim_out, dim_in, dim_out, num_heads, ln=ln)
        self.mab1 = MAB(dim_in, dim_out, dim_out, num_heads, ln=ln)

    def forward(self, X):
        H = self.mab0(self.I.repeat(X.size(0), 1, 1), X)
        return self.mab1(X, H)

class PMA(nn.Module):
    def __init__(self, dim, num_heads, num_seeds, ln=False):
        super(PMA, self).__init__()
        self.S = nn.Parameter(torch.Tensor(1, num_seeds, dim))
        nn.init.xavier_uniform_(self.S)
        self.mab = MAB(dim, dim, dim, num_heads, ln=ln)

    def forward(self, X):
        return self.mab(self.S.repeat(X.size(0), 1, 1), X)


class SetTransformer(nn.Module):
    def __init__(self, **kwargs):
        DEFAULT_CONFIG = {
            # Required, no default
            'input_len': None,
            'output_len': None,
            'set_len': None,
            'device': None,

            'optimizer': 0,

            'dim_hidden': 128,
            'num_heads': 4,
            'num_inds': 32,
            'ln': False,
        }
        super(SetTransformer, self).__init__()
        set_attributes_from_args(self, DEFAULT_CONFIG, kwargs)
        
        self.enc = nn.Sequential(
                ISAB(self.input_len, self.dim_hidden, self.num_heads, self.num_inds, ln=self.ln),
                ISAB(self.dim_hidden, self.dim_hidden, self.num_heads, self.num_inds, ln=self.ln)).to(self.device)
        self.dec = nn.Sequential(
                PMA(self.dim_hidden, self.num_heads, 1, ln=self.ln),
                SAB(self.dim_hidden, self.dim_hidden, self.num_heads, ln=self.ln),
                SAB(self.dim_hidden, self.dim_hidden, self.num_heads, ln=self.ln),
                nn.Linear(self.dim_hidden, self.output_len)).to(self.device)

        for param in self.enc.parameters():
            param.optimizer = self.optimizer
        for param in self.dec.parameters():
            param.optimizer = self.optimizer

    def forward(self, input):
        input = input.reshape(-1, self.set_len, self.input_len)
        return self.dec(self.enc(input)).squeeze(1)
