# Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""

import torch
from torch import Tensor

from mmdet.core.bbox.assigners import AssignResult
from .sampling_result import SamplingResult


class MaskSamplingResult(SamplingResult):
    """Mask sampling result."""

    def __init__(self,
                 pos_inds: Tensor,
                 neg_inds: Tensor,
                 masks: Tensor,
                 gt_masks: Tensor,
                 assign_result: AssignResult,
                 gt_flags: Tensor,
                 avg_factor_with_neg: bool = True) -> None:
        self.pos_inds = pos_inds
        self.neg_inds = neg_inds
        self.num_pos = max(pos_inds.numel(), 1)
        self.num_neg = max(neg_inds.numel(), 1)
        self.avg_factor = self.num_pos + self.num_neg \
            if avg_factor_with_neg else self.num_pos

        self.pos_masks = masks[pos_inds]
        self.neg_masks = masks[neg_inds]
        self.pos_is_gt = gt_flags[pos_inds]

        self.num_gts = gt_masks.shape[0]
        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1

        if gt_masks.numel() == 0:
            # hack for index error case
            assert self.pos_assigned_gt_inds.numel() == 0
            self.pos_gt_masks = torch.empty_like(gt_masks)
        else:
            self.pos_gt_masks = gt_masks[self.pos_assigned_gt_inds, :]

    @property
    def masks(self) -> Tensor:
        """torch.Tensor: concatenated positive and negative masks."""
        return torch.cat([self.pos_masks, self.neg_masks])

    def __nice__(self) -> str:
        data = self.info.copy()
        data['pos_masks'] = data.pop('pos_masks').shape
        data['neg_masks'] = data.pop('neg_masks').shape
        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
        body = '    ' + ',\n    '.join(parts)
        return '{\n' + body + '\n}'

    @property
    def info(self) -> dict:
        """Returns a dictionary of info about the object."""
        return {
            'pos_inds': self.pos_inds,
            'neg_inds': self.neg_inds,
            'pos_masks': self.pos_masks,
            'neg_masks': self.neg_masks,
            'pos_is_gt': self.pos_is_gt,
            'num_gts': self.num_gts,
            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
        }

# Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""

import torch

from .sampling_result import SamplingResult


class MaskSamplingResult(SamplingResult):
    """Mask sampling result."""

    def __init__(self, pos_inds, neg_inds, masks, gt_masks, assign_result,
                 gt_flags):
        self.pos_inds = pos_inds
        self.neg_inds = neg_inds
        self.pos_masks = masks[pos_inds]
        self.neg_masks = masks[neg_inds]
        self.pos_is_gt = gt_flags[pos_inds]

        self.num_gts = gt_masks.shape[0]
        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1

        if gt_masks.numel() == 0:
            # hack for index error case
            assert self.pos_assigned_gt_inds.numel() == 0
            self.pos_gt_masks = torch.empty_like(gt_masks)
        else:
            self.pos_gt_masks = gt_masks[self.pos_assigned_gt_inds, :]

        if assign_result.labels is not None:
            self.pos_gt_labels = assign_result.labels[pos_inds]
        else:
            self.pos_gt_labels = None

    @property
    def masks(self):
        """torch.Tensor: concatenated positive and negative boxes"""
        return torch.cat([self.pos_masks, self.neg_masks])

    def __nice__(self):
        data = self.info.copy()
        data['pos_masks'] = data.pop('pos_masks').shape
        data['neg_masks'] = data.pop('neg_masks').shape
        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
        body = '    ' + ',\n    '.join(parts)
        return '{\n' + body + '\n}'

    @property
    def info(self):
        """Returns a dictionary of info about the object."""
        return {
            'pos_inds': self.pos_inds,
            'neg_inds': self.neg_inds,
            'pos_masks': self.pos_masks,
            'neg_masks': self.neg_masks,
            'pos_is_gt': self.pos_is_gt,
            'num_gts': self.num_gts,
            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
        }
