# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
import torch 
from loguru import logger 
import torch.nn as nn 
import torch.nn.functional as F
from .latent_points_ada import PVCNN2Unet 
from .utils import mask_inactive_variables 

# diffusion model for latent points 
class PVCNN2Prior(PVCNN2Unet): 
    sa_blocks = [ # conv_configs, sa_configs
        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
        ((128, 3, 8), (64, 0.4, 32, (128, 128))),
        (None, (16, 0.8, 32, (128, 128, 128))), 
    ]
    fp_blocks = [
        ((128, 128), (128, 3, 8)), # fp_configs, conv_configs
        ((128, 128), (128, 3, 8)),
        ((128, 128), (128, 2, 16)),
        ((128, 128, 64), (64, 2, 32)),
    ]

    def __init__(self, args, num_input_channels, cfg):

        # only cfg is used 
        self.clip_forge_enable = cfg.clipforge.enable
        clip_forge_dim = cfg.clipforge.feat_dim
        num_input_channels = num_classes = cfg.shapelatent.latent_dim + cfg.ddpm.input_dim 
        self.num_classes = num_classes 
        embed_dim = cfg.ddpm.time_dim 
        use_att = True 
        extra_feature_channels = cfg.shapelatent.latent_dim 
        self.num_points = cfg.data.tr_max_sample_points 
        dropout = cfg.ddpm.dropout 
        time_emb_scales = cfg.sde.embedding_scale  # 1k default 
        logger.info('[Build Prior Model] nclass={}, embed_dim={}, use_att={},'
                'extra_feature_channels={}, dropout={}, time_emb_scales={} num_point={}',
                num_classes, embed_dim, use_att, extra_feature_channels, dropout, time_emb_scales,
                self.num_points)
        # Attention: we are not using time_emb_scales here, but the embedding_scale
        super().__init__(
                num_classes, embed_dim, use_att, dropout=dropout,
                input_dim=cfg.ddpm.input_dim,
                extra_feature_channels=extra_feature_channels, 
                time_emb_scales=time_emb_scales,
                verbose=True,
                condition_input=False, 
                cfg=cfg,
                sa_blocks=self.sa_blocks,
                fp_blocks=self.fp_blocks, 
                clip_forge_enable=self.clip_forge_enable, clip_forge_dim=clip_forge_dim) 
        # init mixing logit 
        self.mixed_prediction = cfg.sde.mixed_prediction  # This enables mixed prediction
        if self.mixed_prediction:
            logger.info('init-mixing_logit = {}, after sigmoid = {}', 
                    cfg.sde.mixing_logit_init, torch.sigmoid(torch.tensor(cfg.sde.mixing_logit_init))
                    )
            init = cfg.sde.mixing_logit_init * torch.ones(size=[1, num_input_channels*self.num_points, 1, 1])
            self.mixing_logit = torch.nn.Parameter(init, requires_grad=True)
            self.is_active = None
        else: # no mixing_logit
          self.mixing_logit = None
          self.is_active = None

    def forward(self, x, t, *args, **kwargs): #x0=None):
        # Input: x: B,ND or B,ND,1,1   
        # require shape for x: B,C,N
        ## CHECKEQ(x.shape[-1], self.num_classes)
        assert('condition_input' in kwargs), 'require condition_input'
        if self.mixed_prediction and self.is_active is not None:
            x = mask_inactive_variables(x, self.is_active)
        input_shape = x.shape 
        x = x.view(-1,self.num_points,self.num_classes).permute(0,2,1).contiguous()
        B = x.shape[0] 
        out = super().forward(x, t=t, style=kwargs['condition_input'].squeeze(-1).squeeze(-1), clip_feat=kwargs.get('clip_feat', None))
        return out.permute(0,2,1).contiguous().view(input_shape)  
        # -1,self.num_classes) # BDN -> BND -> BN,D