"""
Various positional encodings for the transformer.
"""
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 * 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
    
    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)


class PositionEmbeddingSine1D(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):
        """
        Args:
            x (Tensor): [T, Q, B, C]
        Output: temporal positional embedding with the same shape of x.
        """
        if mask is None:
            mask = torch.zeros((x.size(0), x.size(1), x.size(2)), device=x.device, dtype=torch.bool)
        not_mask = ~mask
        z_embed = not_mask.cumsum(0, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            z_embed = z_embed / (z_embed[-1:, :, :] + eps) * self.scale

        dim_t_z = torch.arange((self.num_pos_feats * 2), dtype=torch.float32, device=x.device)
        dim_t_z = self.temperature ** (2 * torch.div(dim_t_z, 2, rounding_mode="floor") / (self.num_pos_feats * 2))

        pos_z = z_embed[:, :, :, None] / dim_t_z
        pos_z = torch.stack((pos_z[:, :, :, 0::2].sin(), pos_z[:, :, :, 1::2].cos()), dim=4).flatten(3)

        pos = pos_z
        return pos