# Modified from MMDetection (https://github.com/open-mmlab/mmdetection)
# Original license: Apache License 2.0
import math

import torch
from mmdet.utils import OptMultiConfig
from mmengine.model import BaseModule
from torch import Tensor


class SinePositionalEncoding(BaseModule):
    """Position encoding with sine and cosine functions.

    See `End-to-End Object Detection with Transformers
    <https://arxiv.org/pdf/2005.12872>`_ for details.

    Args:
        num_feats (int): The feature dimension for each position
            along x-axis or y-axis. Note the final returned dimension
            for each position is 2 times of this value.
        temperature (int, optional): The temperature used for scaling
            the position embedding. Defaults to 10000.
        normalize (bool, optional): Whether to normalize the position
            embedding. Defaults to False.
        scale (float, optional): A scale factor that scales the position
            embedding. The scale will be used only when `normalize` is True.
            Defaults to 2*pi.
        eps (float, optional): A value added to the denominator for
            numerical stability. Defaults to 1e-6.
        offset (float): offset add to embed when do the normalization.
            Defaults to 0.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Defaults to None
    """

    def __init__(
        self,
        num_feats: int,
        temperature: int = 10000,
        normalize: bool = False,
        scale: float = 2 * math.pi,
        eps: float = 1e-6,
        offset: float = 0.0,
        init_cfg: OptMultiConfig = None,
    ) -> None:
        super().__init__(init_cfg=init_cfg)
        if normalize:
            assert isinstance(scale, (float, int)), (
                "when normalize is set," "scale should be provided and in float or int type, " f"found {type(scale)}"
            )
        self.num_feats = num_feats
        self.temperature = temperature
        self.normalize = normalize
        self.scale = scale
        self.eps = eps
        self.offset = offset

    def forward(self, mask: Tensor, dtype=torch.float32) -> Tensor:
        """Forward function for `SinePositionalEncoding`.

        Args:
            mask (Tensor): ByteTensor mask. Non-zero values representing
                ignored positions, while zero values means valid positions
                for this image. Shape [bs, h, w].

        Returns:
            pos (Tensor): Returned position embedding with shape
                [bs, num_feats*2, h, w].
        """

        B, H, W = mask.size()
        device = mask.device
        # For convenience of exporting to ONNX,
        # it's required to convert
        # `masks` from bool to int.
        mask = mask.to(torch.int)
        not_mask = 1 - mask  # logical_not
        y_embed = not_mask.cumsum(1, dtype=dtype)
        x_embed = not_mask.cumsum(2, dtype=dtype)

        if self.normalize:
            y_embed = (y_embed + self.offset) / (y_embed[:, -1:, :] + self.eps) * self.scale
            x_embed = (x_embed + self.offset) / (x_embed[:, :, -1:] + self.eps) * self.scale
        dim_t = torch.arange(self.num_feats, dtype=dtype, device=device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_feats)
        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        # use `view` instead of `flatten` for dynamically exporting to ONNX

        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1)
        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos

    def __repr__(self) -> str:
        """str: a string that describes the module"""
        repr_str = self.__class__.__name__
        repr_str += f"(num_feats={self.num_feats}, "
        repr_str += f"temperature={self.temperature}, "
        repr_str += f"normalize={self.normalize}, "
        repr_str += f"scale={self.scale}, "
        repr_str += f"eps={self.eps})"
        return repr_str
