# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn

from util.misc import NestedTensor


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.
    Modified for 3dims (+Temporal) based on VisTR implementations (https://github.com/YuqingWang1029/VisTR/blob/master/models/position_encoding.py).
    """

    def __init__(self, num_pos_feats=64, num_frames = 200, temperature=10000, normalize=False, scale=None):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        self.frames = num_frames
        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, tensor_list: NestedTensor):
        x = tensor_list.tensors
        mask = tensor_list.mask
        n,h,w = mask.shape
        mask = mask.reshape(n//self.frames, self.frames,h,w)
        assert mask is not None
        not_mask = ~mask
        z_embed = not_mask.cumsum(1, dtype=torch.float32)
        y_embed = not_mask.cumsum(2, dtype=torch.float32)
        x_embed = not_mask.cumsum(3, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            z_embed = z_embed / (z_embed[:, -1:, :, :] + eps) * self.scale
            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_z = z_embed[:, :, :, :, None] / dim_t
        pos_x = torch.stack((pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()), dim=5).flatten(4)
        pos_y = torch.stack((pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()), dim=5).flatten(4)
        pos_z = torch.stack((pos_z[:, :, :, :, 0::2].sin(), pos_z[:, :, :, :, 1::2].cos()), dim=5).flatten(4)
        pos = torch.cat((pos_z, pos_y, pos_x), dim=4).permute(0, 1, 4, 2, 3)
        
        return pos



def build_position_encoding(args):

    # Modify from 2 dimensions to 3 dimensions (+ Temporal)
    N_steps = args.hidden_dim // 3
    if args.position_embedding in ('v2', 'sine'):
        # TODO find a better way of exposing other arguments
        position_embedding = PositionEmbeddingSine(N_steps, num_frames = args.max_process_window_length, normalize=True)

    else:
        raise ValueError(f"not supported {args.position_embedding}")

    return position_embedding