# The implementation is adopted from Mask DINO, made publicly available under the Apache License,
# Version 2.0 at https://github.com/IDEA-Research/MaskDINO
# Part of implementation is borrowed from Mask2Former,
# https://github.com/facebookresearch/Mask2Former, under MIT license.

import math

import torch
from torch import nn


class PositionEmbeddingSine(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """

    def __init__(self,
                 num_pos_feats=64,
                 temperature=10000,
                 normalize=False,
                 scale=None):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError('normalize should be True if scale is passed')
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, x, mask=None):
        if mask is None:
            mask = torch.zeros((x.size(0), x.size(2), x.size(3)),
                               device=x.device,
                               dtype=torch.bool)
        not_mask = ~mask
        y_embed = not_mask.cumsum(1, dtype=torch.float32)
        x_embed = not_mask.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(
            self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature**(2 * (dim_t // 2) / self.num_pos_feats)

        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack(
            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
            dim=4).flatten(3)
        pos_y = torch.stack(
            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
            dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos

    def __repr__(self, _repr_indent=4):
        head = 'Positional encoding ' + self.__class__.__name__
        body = [
            'num_pos_feats: {}'.format(self.num_pos_feats),
            'temperature: {}'.format(self.temperature),
            'normalize: {}'.format(self.normalize),
            'scale: {}'.format(self.scale),
        ]
        # _repr_indent = 4
        lines = [head] + [' ' * _repr_indent + line for line in body]
        return '\n'.join(lines)