import math

import torch
from torch import Tensor, 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.

    Adapted from https://github.com/shannanyinxiang/SPTS.
    """

    def __init__(self,
                 num_pos_feats=64,
                 temperature=10000,
                 normalize=True,
                 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, mask: Tensor):
        assert mask is not None
        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=mask.device)
        dim_t = self.temperature**(2 *
                                   torch.div(dim_t, 2, rounding_mode='floor') /
                                   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