import functools
import math
import os
import time
from tkinter import W

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load
import torch.nn.init as init
from scene.hexplane import HexPlaneField

class Deformation(nn.Module):
    def __init__(self, D=8, W=256, input_ch=27, input_ch_time=9, skips=[], args=None):
        super(Deformation, self).__init__()
        self.D = D
        self.W = W
        self.input_ch = input_ch
        self.input_ch_time = input_ch_time
        self.skips = skips

        self.no_grid = args.no_grid
        self.grid = HexPlaneField(args.bounds, args.kplanes_config, args.multires)
        self.pos_deform, self.scales_deform, self.rotations_deform, self.opacity_deform = self.create_net()
        self.args = args
    def create_net(self):
        
        mlp_out_dim = 0
        if self.no_grid:
            self.feature_out = [nn.Linear(4,self.W)]
        else:
            self.feature_out = [nn.Linear(mlp_out_dim + self.grid.feat_dim ,self.W)]
        
        for i in range(self.D-1):
            self.feature_out.append(nn.ReLU())
            self.feature_out.append(nn.Linear(self.W,self.W))
        self.feature_out = nn.Sequential(*self.feature_out)
        output_dim = self.W
        return  \
            nn.Sequential(nn.ReLU(),nn.Linear(self.W,self.W),nn.ReLU(),nn.Linear(self.W, 3)),\
            nn.Sequential(nn.ReLU(),nn.Linear(self.W,self.W),nn.ReLU(),nn.Linear(self.W, 3)),\
            nn.Sequential(nn.ReLU(),nn.Linear(self.W,self.W),nn.ReLU(),nn.Linear(self.W, 4)), \
            nn.Sequential(nn.ReLU(),nn.Linear(self.W,self.W),nn.ReLU(),nn.Linear(self.W, 1))
    
    def query_time(self, rays_pts_emb, scales_emb, rotations_emb, time_emb):

        if self.no_grid:
            h = torch.cat([rays_pts_emb[:,:3],time_emb[:,:1]],-1)
        else:
            grid_feature = self.grid(rays_pts_emb[:,:3], time_emb[:,:1])

            h = grid_feature
        
        h = self.feature_out(h)
  
        return h

    def forward(self, rays_pts_emb, scales_emb=None, rotations_emb=None, opacity = None, time_emb=None):
        if time_emb is None:
            return self.forward_static(rays_pts_emb[:,:3])
        else:
            return self.forward_dynamic(rays_pts_emb, scales_emb, rotations_emb, opacity, time_emb)

    def forward_static(self, rays_pts_emb):
        grid_feature = self.grid(rays_pts_emb[:,:3])
        dx = self.static_mlp(grid_feature)
        return rays_pts_emb[:, :3] + dx
    def forward_dynamic(self,rays_pts_emb, scales_emb, rotations_emb, opacity_emb, time_emb):
        hidden = self.query_time(rays_pts_emb, scales_emb, rotations_emb, time_emb).float()
        dx = self.pos_deform(hidden)
        pts = rays_pts_emb[:, :3] + dx
        if self.args.no_ds:
            scales = scales_emb[:,:3]
        else:
            ds = self.scales_deform(hidden)
            scales = scales_emb[:,:3] + ds
        if self.args.no_dr:
            rotations = rotations_emb[:,:4]
        else:
            dr = self.rotations_deform(hidden)
            rotations = rotations_emb[:,:4] + dr
        if self.args.no_do:
            opacity = opacity_emb[:,:1] 
        else:
            do = self.opacity_deform(hidden) 
            opacity = opacity_emb[:,:1] + do
        # + do
        # print("deformation value:","pts:",torch.abs(dx).mean(),"rotation:",torch.abs(dr).mean())

        return pts, scales, rotations, opacity
    def get_mlp_parameters(self):
        parameter_list = []
        for name, param in self.named_parameters():
            if  "grid" not in name:
                parameter_list.append(param)
        return parameter_list
    def get_grid_parameters(self):
        return list(self.grid.parameters() ) 
    # + list(self.timegrid.parameters())
class deform_network(nn.Module):
    def __init__(self, args) :
        super(deform_network, self).__init__()
        net_width = args.net_width
        timebase_pe = args.timebase_pe
        defor_depth= args.defor_depth
        posbase_pe= args.posebase_pe
        scale_rotation_pe = args.scale_rotation_pe
        opacity_pe = args.opacity_pe
        timenet_width = args.timenet_width
        timenet_output = args.timenet_output
        times_ch = 2*timebase_pe+1
        self.timenet = nn.Sequential(
        nn.Linear(times_ch, timenet_width), nn.ReLU(),
        nn.Linear(timenet_width, timenet_output))
        self.deformation_net = Deformation(W=net_width, D=defor_depth, input_ch=(4+3)+((4+3)*scale_rotation_pe)*2, input_ch_time=timenet_output, args=args)
        self.register_buffer('time_poc', torch.FloatTensor([(2**i) for i in range(timebase_pe)]))
        self.register_buffer('pos_poc', torch.FloatTensor([(2**i) for i in range(posbase_pe)]))
        self.register_buffer('rotation_scaling_poc', torch.FloatTensor([(2**i) for i in range(scale_rotation_pe)]))
        self.register_buffer('opacity_poc', torch.FloatTensor([(2**i) for i in range(opacity_pe)]))
        self.apply(initialize_weights)
        # print(self)

    def forward(self, point, scales=None, rotations=None, opacity=None, times_sel=None):
        if times_sel is not None:
            return self.forward_dynamic(point, scales, rotations, opacity, times_sel)
        else:
            return self.forward_static(point)

        
    def forward_static(self, points):
        points = self.deformation_net(points)
        return points
    def forward_dynamic(self, point, scales=None, rotations=None, opacity=None, times_sel=None):
        # times_emb = poc_fre(times_sel, self.time_poc)

        means3D, scales, rotations, opacity = self.deformation_net( point,
                                                  scales,
                                                rotations,
                                                opacity,
                                                # times_feature,
                                                times_sel)
        return means3D, scales, rotations, opacity
    def get_mlp_parameters(self):
        return self.deformation_net.get_mlp_parameters() + list(self.timenet.parameters())
    def get_grid_parameters(self):
        return self.deformation_net.get_grid_parameters()

def initialize_weights(m):
    if isinstance(m, nn.Linear):
        # init.constant_(m.weight, 0)
        init.xavier_uniform_(m.weight,gain=1)
        if m.bias is not None:
            init.xavier_uniform_(m.weight,gain=1)
            # init.constant_(m.bias, 0)