import torch
from torch import nn
import PytorchBoot.namespace as namespace
import PytorchBoot.stereotype as stereotype
from PytorchBoot.factory.component_factory import ComponentFactory
from PytorchBoot.utils import Log

@stereotype.pipeline("nbv_reconstruction_pipeline_local")
class NBVReconstructionLocalPointsPipeline(nn.Module):
    def __init__(self, config):
        super(NBVReconstructionLocalPointsPipeline, self).__init__()
        self.config = config
        self.module_config = config["modules"]
        self.pts_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pts_encoder"])
        self.pose_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pose_encoder"])
        self.seq_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["seq_encoder"])
        self.view_finder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["view_finder"])
        self.eps = float(self.config["eps"])
        self.enable_global_scanned_feat = self.config["global_scanned_feat"]
        
    def forward(self, data):
        mode = data["mode"]
        
        if mode == namespace.Mode.TRAIN:
            return self.forward_train(data)
        elif mode == namespace.Mode.TEST:
            return self.forward_test(data)
        else:
            Log.error("Unknown mode: {}".format(mode), True)
    
    def pertube_data(self, gt_delta_9d):
        bs = gt_delta_9d.shape[0]
        random_t = torch.rand(bs, device=gt_delta_9d.device) * (1. - self.eps) + self.eps
        random_t = random_t.unsqueeze(-1)
        mu, std = self.view_finder.marginal_prob(gt_delta_9d, random_t)
        std = std.view(-1, 1)
        z = torch.randn_like(gt_delta_9d)
        perturbed_x = mu + z * std
        target_score = - z * std / (std ** 2)
        return perturbed_x, random_t, target_score, std
    
    def forward_train(self, data):
        main_feat = self.get_main_feat(data)
        ''' get std '''
        best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
        perturbed_x, random_t, target_score, std = self.pertube_data(best_to_world_pose_9d_batch)
        input_data = {
            "sampled_pose": perturbed_x,
            "t": random_t,
            "main_feat": main_feat,
        }
        estimated_score = self.view_finder(input_data)
        output = {
            "estimated_score": estimated_score,
            "target_score": target_score,
            "std": std
        }
        return output
    
    def forward_test(self,data):
        main_feat = self.get_main_feat(data)
        repeat_num = data.get("repeat_num", 50)
        main_feat = main_feat.repeat(repeat_num, 1)
        estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
            main_feat
        )
        result = {
            "pred_pose_9d": estimated_delta_rot_9d,
            "in_process_sample": in_process_sample,
        }
        return result
    
    
    def get_main_feat(self, data):
        scanned_pts_batch = data['scanned_pts']
        scanned_n_to_world_pose_9d_batch = data['scanned_n_to_world_pose_9d']
        device = next(self.parameters()).device
        feat_seq_list = []
    
        for scanned_pts,scanned_n_to_world_pose_9d in zip(scanned_pts_batch,scanned_n_to_world_pose_9d_batch):
            scanned_pts = scanned_pts.to(device)
            scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)
            pts_feat = self.pts_encoder.encode_points(scanned_pts)
            pose_feat = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)
            seq_feat = torch.cat([pts_feat, pose_feat], dim=-1)
            feat_seq_list.append(seq_feat)
        main_feat = self.seq_encoder.encode_sequence(feat_seq_list)
        
        if self.enable_global_scanned_feat:
            combined_scanned_pts_batch = data['combined_scanned_pts']   
            global_scanned_feat = self.pts_encoder.encode_points(combined_scanned_pts_batch)
            main_feat = torch.cat([main_feat, global_scanned_feat], dim=-1)
        
        
        if torch.isnan(main_feat).any():
            Log.error("nan in main_feat", True)
        
        return main_feat