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

def PositionalEncoding(n_position, d_hid):
    def get_position_angle_vec(position, d_hid):
        return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]

    sinusoid_table = np.array([get_position_angle_vec(pos_i, d_hid) for pos_i in range(n_position)])
    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
    return torch.FloatTensor(sinusoid_table) # shape:(1, maxLen(n_position), d_hid)

class TrainablePositionalEncoding(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings.
    """
    def __init__(self, max_position_embeddings, hidden_size, dropout=0.1):
        super(TrainablePositionalEncoding, self).__init__()
        self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size)
        self.LayerNorm = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, input_feat):
        """
        Args:
            input_feat: (N, L, D)
        """
        bsz, seq_length = input_feat.shape[:2]
        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_feat.device)
        position_ids = position_ids.unsqueeze(0).repeat(bsz, 1)  # (N, L)

        position_embeddings = self.position_embeddings(position_ids)

        embeddings = self.LayerNorm(input_feat + position_embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


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. (To 1D sequences)
    """
    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):
        """
        Args:
            x: torch.tensor, (batch_size, L, d)
            mask: torch.tensor, (batch_size, L), with 1 as valid

        Returns:

        """
        assert mask is not None
        x_embed = mask.cumsum(1, dtype=torch.float32)  # (bsz, L)
        if self.normalize:
            eps = 1e-6
            x_embed = x_embed / (x_embed[:, -1:] + eps) * self.scale

        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        # import pdb; pdb.set_trace()
        # dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
        dim_t = self.temperature ** (2 * torch.div(dim_t, 2).int() / self.num_pos_feats)

        pos_x = x_embed[:, :, None] / dim_t  # (bsz, L, num_pos_feats)
        pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)  # (bsz, L, num_pos_feats*2)
        # import ipdb; ipdb.set_trace()
        return pos_x  # .permute(0, 2, 1)  # (bsz, num_pos_feats*2, L)


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, x, mask):
        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


def build_position_encoding(args):
    N_steps = args.hidden_dim
    if args.position_embedding in ('v2', 'sine'):
        # TODO find a better way of exposing other arguments
        position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
    # elif args.position_embedding in ('v3', 'learned'):
    #     position_embedding = PositionEmbeddingLearned(N_steps)
    else:
        raise ValueError(f"not supported {args.position_embedding}")

    txt_pos_embed = TrainablePositionalEncoding(
            max_position_embeddings=args.max_q_l,
            hidden_size=args.hidden_dim, dropout=args.input_dropout)
    return position_embedding, txt_pos_embed