import pickle

import numpy as np
import torch as t
import torch
from torch.nn import functional as F
from torch import nn
import torchvision
from torch import nn

from .generate_anchors import generate_anchors
from .bbox_transform import bbox_transform_inv, clip_boxes 


class RegionProposalNetwork(nn.Module):
    def __init__(self, pre_nms_topN,post_nms_topN, nms_thresh, min_size, anchor_scales, feat_stride):
        super(RegionProposalNetwork, self).__init__()
        self._anchors = generate_anchors(scales=np.array(anchor_scales))
        self._num_anchors = self._anchors.shape[0]
        self._feat_stride = feat_stride
        self.pre_nms_topN = pre_nms_topN
        self.post_nms_topN = post_nms_topN
        self.nms_thresh = nms_thresh
        self.min_size = min_size
        self.anchor_scales = anchor_scales
        self.feat_stride = feat_stride


    def forward(self, rpn_cls_prob, rpn_bbox_pred, img_size):
        scores = rpn_cls_prob[:,self._num_anchors:, :, :]
        bbox_deltas = rpn_bbox_pred
        min_size = self.min_size

        pre_nms_topN = self.pre_nms_topN
        post_nms_topN = self.post_nms_topN
        nms_thresh = self.nms_thresh
        min_size = self.min_size
        anchor_scales = self.anchor_scales
        feat_stride = self.feat_stride

        n, _, hh, ww = scores.shape
        
        #n_anchor = anchor.shape[0] // (hh * ww)

        anchors = self._enumerate_shifted_anchor(self._anchors, self._feat_stride, hh, ww)
        bbox_deltas = bbox_deltas.permute((0, 2, 3, 1)).reshape((-1, 4))

        bbox_deltas = bbox_deltas.cpu().detach().numpy()
        proposals = bbox_transform_inv(anchors, bbox_deltas)
        scores = scores.permute((0, 2, 3, 1)).reshape((-1, 1))

        proposals = bbox_transform_inv(anchors, bbox_deltas)

        proposals = clip_boxes(proposals, img_size[:2])

        keep = _filter_boxes(proposals, min_size * img_size[2])
        proposals = proposals[keep, :]
        scores = scores[keep]

        order = scores.ravel().argsort(descending=True)
        proposals = t.FloatTensor(proposals, device = rpn_cls_prob.device)

        if pre_nms_topN > 0:
            order = order[:pre_nms_topN]

        proposals = proposals[order, :]
        scores = scores[order]

        keep = torchvision.ops.nms(proposals, scores.ravel(), nms_thresh)

        if post_nms_topN > 0:
            keep = keep[:post_nms_topN]
        proposals = proposals[keep, :]
        scores = scores[keep]

        batch_inds = t.zeros((proposals.shape[0], 1), dtype = proposals.dtype, device = proposals.device)
        rois = t.hstack([batch_inds, proposals])
       
        return rois

        #keep = nms(np.hstack((proposals, scores)), nms_thresh)
        #if post_nms_topN > 0:
        #    keep = keep[:post_nms_topN]
        #proposals = proposals[keep, :]
        #scores = scores[keep]
        
    def _enumerate_shifted_anchor(self, anchor_base, feat_stride, height, width):
        # Enumerate all shifted anchors:
        #
        # add A anchors (1, A, 4) to
        # cell K shifts (K, 1, 4) to get
        # shift anchors (K, A, 4)
        # reshape to (K*A, 4) shifted anchors
        # return (K*A, 4)

        # !TODO: add support for torch.CudaTensor
        # xp = cuda.get_array_module(anchor_base)
        # it seems that it can't be boosed using GPU
        import numpy as xp
        shift_y = xp.arange(0, height * feat_stride, feat_stride)
        shift_x = xp.arange(0, width * feat_stride, feat_stride)
        shift_x, shift_y = xp.meshgrid(shift_x, shift_y)
        shift = xp.stack((shift_x.ravel(), shift_y.ravel(),
                          shift_x.ravel(), shift_y.ravel()), axis=1)

        A = anchor_base.shape[0]
        K = shift.shape[0]
        anchor = anchor_base.reshape((1, A, 4)) + \
                 shift.reshape((1, K, 4)).transpose((1, 0, 2))
        anchor = anchor.reshape((K * A, 4)).astype(np.float32)

        return anchor

def _filter_boxes(boxes, min_size):
    """Remove all boxes with any side smaller than min_size."""
    ws = boxes[:, 2] - boxes[:, 0] + 1
    hs = boxes[:, 3] - boxes[:, 1] + 1
    keep = np.where((ws >= min_size) & (hs >= min_size))[0]
    return keep