import torch
import torch.nn as nn

from ..builder import DETECTORS
from .two_stage import TwoStageDetector
from ..utils.post_processing import boundary_choose, batched_nms, convert_to_seconds


@DETECTORS.register_module()
class BMN(TwoStageDetector):
    def __init__(
        self,
        projection,
        rpn_head,
        roi_head,
        neck=None,
        backbone=None,
    ):
        super(BMN, self).__init__(
            backbone=backbone,
            neck=neck,
            projection=projection,
            rpn_head=rpn_head,
            roi_head=roi_head,
        )

        self.reset_params()

    def reset_params(self):
        for m in self.modules():
            if isinstance(m, (nn.Conv1d, nn.Conv2d)):
                nn.init.xavier_uniform_(m.weight)
                nn.init.constant_(m.bias, 0)

    def forward_test(self, inputs, masks, metas=None, infer_cfg=None):
        if self.with_backbone:
            x = self.backbone(inputs, masks)
        else:
            x = inputs

        if self.with_projection:
            x, masks = self.projection(x, masks)

        if self.with_neck:
            x, masks = self.neck(x, masks)

        # tem score
        tem_score = self.rpn_head.forward_test(x, masks)  # [B,2,T]

        # iou score
        pred_iou_map = self.roi_head.forward_test(x)  # [D,T,2]

        # pack all
        predictions = tem_score, pred_iou_map
        return predictions

    def post_processing(self, predictions, metas, post_cfg, ext_cls, **kwargs):
        assert ext_cls != None  #  BMN is a proposal generation network

        score_type = getattr(post_cfg, "score_type", "iou")  # otherwise, "iou*s*e"
        proposal_post = getattr(post_cfg, "proposal", False)

        tem_score, pred_iou_map = predictions
        tem_score = tem_score.sigmoid()
        pred_iou_map = pred_iou_map.sigmoid()

        dscale = self.roi_head.proposal_generator.dscale
        tscale = self.roi_head.proposal_generator.tscale

        # here is a nice matrix implementation for post processing
        ds = torch.arange(0, dscale).to(pred_iou_map.device)
        ts = torch.arange(0, tscale).to(pred_iou_map.device)
        ds_mesh, ts_mesh = torch.meshgrid(ds, ts, indexing="ij")
        start_end_index = torch.stack((ts_mesh, ts_mesh + ds_mesh), dim=-1)
        valid_mask = start_end_index[:, :, 1] < tscale
        start_end_index = start_end_index.clamp(max=tscale - 1).float()

        start_mask = boundary_choose(tem_score[:, 0, :])
        start_mask[:, 0] = True
        end_mask = boundary_choose(tem_score[:, 1, :])
        end_mask[:, -1] = True
        start_end_map = start_mask.unsqueeze(2) * end_mask.unsqueeze(1)
        pred_iou_map = pred_iou_map[:, 0, :, :] * pred_iou_map[:, 1, :, :]

        results = {}
        for i in range(len(metas)):  # processing each video
            start_end_mask = start_end_map[i][
                start_end_index[:, :, 0].view(-1).long(),
                start_end_index[:, :, 1].view(-1).long(),
            ]
            start_end_mask = start_end_mask.reshape(dscale, tscale) * valid_mask

            segments_start = start_end_index[start_end_mask][:, 0]
            segments_end = start_end_index[start_end_mask][:, 1] + 1
            segments = torch.stack((segments_start, segments_end), dim=-1).detach().cpu()

            # score
            scores_iou = pred_iou_map[i][start_end_mask].detach().cpu()
            if score_type == "iou":
                scores = scores_iou
            elif score_type == "iou*s*e":
                score_start = tem_score[i, 0, start_end_index[start_end_mask][:, 0].long()].detach().cpu()
                score_end = tem_score[i, 1, start_end_index[start_end_mask][:, 1].long()].detach().cpu()
                scores = score_start * score_end * scores_iou
            else:
                raise f"score type should be iou or iou*s*e, but get {score_type}"

            # nms
            labels = torch.zeros_like(scores_iou)  # pseudo labels
            if proposal_post:
                segments = segments / tscale  # convert to 0~1, since recall need a different NMS
                segments, scores, labels = batched_nms(segments, scores, labels, **post_cfg.nms)
                segments = segments * tscale  # convert back
            else:
                segments, scores, labels = batched_nms(segments, scores, labels, **post_cfg.nms)

            video_id = metas[i]["video_name"]

            # convert segments to seconds
            segments = convert_to_seconds(segments, metas[i])

            # merge with external classifier
            segments, labels, scores = ext_cls(video_id, segments, scores)

            results_per_video = []
            for segment, label, score in zip(segments, labels, scores):
                # convert to python scalars
                results_per_video.append(
                    dict(
                        segment=[round(seg.item(), 2) for seg in segment],
                        label=label,
                        score=round(score.item(), 4),
                    )
                )

            if video_id in results.keys():
                results[video_id].extend(results_per_video)
            else:
                results[video_id] = results_per_video

        return results

    def get_optim_groups(self, cfg):
        BASE_weight = []
        TEM_weight = []
        PEM_weight = []

        for name, p in self.named_parameters():
            # exclude the backbone
            if name.startswith("backbone"):
                continue

            if "projection" in name:
                BASE_weight.append(p)
            elif "rpn_head" in name:
                TEM_weight.append(p)
            elif "roi_head" in name:
                PEM_weight.append(p)
            else:
                print(name, "is not in the optimizer")

        # create the pytorch optimizer object
        optim_groups = [
            {"params": BASE_weight, "weight_decay": cfg["weight_decay"] * 10},
            {"params": TEM_weight, "weight_decay": cfg["weight_decay"]},
            {"params": PEM_weight, "weight_decay": cfg["weight_decay"]},
        ]
        return optim_groups