"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn

from lib.utils.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.
    """
    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, tensor_list: NestedTensor):
        x = tensor_list.tensors
        mask = tensor_list.mask
        assert mask is not None
        not_mask = ~mask # (b,h,w)
        y_embed = not_mask.cumsum(1, dtype=torch.float32)  # cumulative sum along axis 1 (h axis) --> (b, h, w)
        x_embed = not_mask.cumsum(2, dtype=torch.float32)  # cumulative sum along axis 2 (w axis) --> (b, h, w)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale  # 2pi * (y / sigma(y))
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale  # 2pi * (x / sigma(x))

        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)  # (0,1,2,...,d/2)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)

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


class PositionEmbeddingLearned(nn.Module):
    """
    Absolute pos embedding, learned.
    """
    def __init__(self, num_pos_feats=256):
        super().__init__()
        self.row_embed = nn.Embedding(50, num_pos_feats)
        self.col_embed = nn.Embedding(50, num_pos_feats)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.uniform_(self.row_embed.weight)
        nn.init.uniform_(self.col_embed.weight)

    def forward(self, tensor_list: NestedTensor):
        x = tensor_list.tensors
        h, w = x.shape[-2:]
        i = torch.arange(w, device=x.device)
        j = torch.arange(h, device=x.device)
        x_emb = self.col_embed(i)
        y_emb = self.row_embed(j)
        pos = torch.cat([
            x_emb.unsqueeze(0).repeat(h, 1, 1),
            y_emb.unsqueeze(1).repeat(1, w, 1),
        ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)
        return pos  # (H,W,C) --> (C,H,W) --> (1,C,H,W) --> (B,C,H,W)


class PositionEmbeddingNone(nn.Module):
    """
    No positional encoding.
    """
    def __init__(self, num_pos_feats=256):
        super().__init__()
        self.n_dim = num_pos_feats * 2

    def forward(self, tensor_list: NestedTensor):
        x = tensor_list.tensors
        b, _, h, w = x.size()
        return torch.zeros((b, self.n_dim, h, w), device=x.device)  # (B, C, H, W)


def build_position_encoding(cfg):
    N_steps = cfg.MODEL.HIDDEN_DIM // 2
    if cfg.MODEL.POSITION_EMBEDDING in ('v2', 'sine'):
        # TODO find a better way of exposing other arguments
        position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
    elif cfg.MODEL.POSITION_EMBEDDING in ('v3', 'learned'):
        position_embedding = PositionEmbeddingLearned(N_steps)
    elif cfg.MODEL.POSITION_EMBEDDING in ('None', ):
        print("Not using positional encoding.")
        position_embedding = PositionEmbeddingNone(N_steps)
    else:
        raise ValueError(f"not supported {cfg.MODEL.POSITION_EMBEDDING}")

    return position_embedding


class PositionEmbeddingLearned_new(nn.Module):
    """
    Absolute pos embedding, learned. (allow users to specify the size)
    """
    def __init__(self, num_pos_feats=256, sz=20):
        super().__init__()
        self.sz = sz
        self.row_embed = nn.Embedding(sz, num_pos_feats)
        self.col_embed = nn.Embedding(sz, num_pos_feats)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.uniform_(self.row_embed.weight)
        nn.init.uniform_(self.col_embed.weight)

    def forward(self, bs):
        """bs: batch size"""
        h, w = self.sz, self.sz
        i = torch.arange(w, device=self.col_embed.weight.device)
        j = torch.arange(h, device=self.row_embed.weight.device)
        x_emb = self.col_embed(i)
        y_emb = self.row_embed(j)
        # pos = torch.cat([
        #     x_emb.unsqueeze(0).repeat(h, 1, 1),
        #     y_emb.unsqueeze(1).repeat(1, w, 1),
        # ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(bs, 1, 1, 1)

        pos = torch.cat([
            x_emb.unsqueeze(0).repeat(h, 1, 1),
            y_emb.unsqueeze(1).repeat(1, w, 1),
        ], dim=-1).repeat(1, bs, 1)
        # print(bs, pos)
        return pos  # (H,W,C) --> (C,H,W) --> (1,C,H,W) --> (B,C,H,W)


# def build_position_encoding_new(cfg, sz):
#     N_steps = cfg.MODEL.HIDDEN_DIM // 2
#     position_embedding = PositionEmbeddingLearned_new(N_steps, sz)
#     return position_embedding



def build_position_encoding(embed_dim, sz=256):
    N_steps = embed_dim // 2
    position_embedding = PositionEmbeddingLearned_new(N_steps, sz)
    return position_embedding