# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. 
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction, 
# disclosure or distribution of this material and related documentation 
# without an express license agreement from NVIDIA CORPORATION or 
# its affiliates is strictly prohibited.

import torch
# import tinycudann as tcnn
import numpy as np

#######################################################################################################################################################
# Small MLP using PyTorch primitives, internal helper class
#######################################################################################################################################################

class _MLP(torch.nn.Module):
    def __init__(self, cfg, loss_scale=1.0):
        super(_MLP, self).__init__()
        self.loss_scale = loss_scale
        net = (torch.nn.Linear(cfg['n_input_dims'], cfg['n_neurons'], bias=False), torch.nn.ReLU())
        for i in range(cfg['n_hidden_layers']-1):
            net = net + (torch.nn.Linear(cfg['n_neurons'], cfg['n_neurons'], bias=False), torch.nn.ReLU())
        net = net + (torch.nn.Linear(cfg['n_neurons'], cfg['n_output_dims'], bias=False),)
        self.net = torch.nn.Sequential(*net).cuda()
        
        self.net.apply(self._init_weights)
        
        if self.loss_scale != 1.0:
            self.net.register_full_backward_hook(lambda module, grad_i, grad_o: (grad_i[0] * self.loss_scale, ))

    def forward(self, x):
        return self.net(x.to(torch.float32))

    @staticmethod
    def _init_weights(m):
        if type(m) == torch.nn.Linear:
            torch.nn.init.kaiming_uniform_(m.weight, nonlinearity='relu')
            if hasattr(m.bias, 'data'):
                m.bias.data.fill_(0.0)

#######################################################################################################################################################
# Outward visible MLP class
#######################################################################################################################################################

class MLPTexture3D(torch.nn.Module):
    def __init__(self, AABB, channels=3, internal_dims=32, hidden=2, feat_dim=0, min_max=None, bsdf='diffuse', perturb_normal=False, symmetrize=False):
        super(MLPTexture3D, self).__init__()

        self.channels = channels
        self.feat_dim = feat_dim
        self.internal_dims = internal_dims
        self.AABB = AABB
        self.bsdf = bsdf
        self.perturb_normal = perturb_normal
        self.symmetrize = symmetrize
        if min_max is not None:
            self.register_buffer('min_max', min_max)
        else:
            self.min_max = None

        # Setup positional encoding, see https://github.com/NVlabs/tiny-cuda-nn for details.
        desired_resolution = 4096
        base_grid_resolution = 16
        num_levels = 16
        per_level_scale = np.exp(np.log(desired_resolution / base_grid_resolution) / (num_levels-1))

        enc_cfg =  {
            "otype": "HashGrid",
            "n_levels": num_levels,
            "n_features_per_level": 2,
            "log2_hashmap_size": 19,
            "base_resolution": base_grid_resolution,
            "per_level_scale" : per_level_scale
	    }

        # gradient_scaling = 128.0
        gradient_scaling = 1.0
        self.encoder = tcnn.Encoding(3, enc_cfg)
        self.encoder.register_full_backward_hook(lambda module, grad_i, grad_o: (grad_i[0] / gradient_scaling, ))

        # Setup MLP
        mlp_cfg = {
            "n_input_dims" : internal_dims + feat_dim,
            "n_output_dims" : self.channels,
            "n_hidden_layers" : hidden,
            "n_neurons" : self.internal_dims
        }
        self.linear = torch.nn.Linear(self.encoder.n_output_dims, internal_dims)
        self.net = _MLP(mlp_cfg, gradient_scaling)
        self.relu = torch.nn.ReLU(inplace=True)
        print("Encoder output: %d dims" % (self.encoder.n_output_dims))
    
    # Sample texture at a given location
    def sample(self, texc, feat=None):
        assert (feat is None and self.feat_dim == 0) or feat.shape[-1] == self.feat_dim

        if self.symmetrize:
            xs, ys, zs = texc.unbind(-1)
            texc = torch.stack([xs.abs(), ys, zs], -1)  # mirror -x to +x

        _texc = (texc.view(-1, 3) - self.AABB[0][None, ...]) / (self.AABB[1][None, ...] - self.AABB[0][None, ...])
        _texc = torch.clamp(_texc, min=0, max=1)

        _, image_h, image_w, _ = texc.shape
        p_enc = self.encoder(_texc.contiguous())
        x_in = self.linear(p_enc.type(texc.dtype))
        if feat is not None:
            feat_in = feat[:, None, None, :].repeat(1, image_h, image_w, 1).view(-1, self.feat_dim)
            x_in = torch.concat([x_in, feat_in], dim=-1)
        out = self.net(self.relu(x_in))

        # Sigmoid limit and scale to the allowed range
        out = torch.sigmoid(out)
        if self.min_max is not None:
            out = out * (self.min_max[1][None, :] - self.min_max[0][None, :]) + self.min_max[0][None, :]

        return out.view(*texc.shape[:-1], self.channels) # Remap to [n, h, w, c]

    # def cleanup(self):
    #     tcnn.free_temporary_memory()