# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import math
from functools import partial

import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trunc_normal_


def get_abs_pos(abs_pos, tgt_size):
    # abs_pos: L, C
    # tgt_size: (H, W)
    # return: M, C
    src_size = int(math.sqrt(abs_pos.size(0)))
    # tgt_size = int(math.sqrt(tgt_size))
    dtype = abs_pos.dtype

    return (
        F.interpolate(
            abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
            size=(tgt_size[0], tgt_size[1]),
            mode="bicubic",
            align_corners=False,
        )
        .permute(0, 2, 3, 1)
        .flatten(0, 2)
        .to(dtype=dtype)
    )


# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    if isinstance(grid_size, int):
        grid_h_size, grid_w_size = grid_size, grid_size
    else:
        grid_h_size, grid_w_size = grid_size[0], grid_size[1]

    grid_h = np.arange(grid_h_size, dtype=np.float32)
    grid_w = np.arange(grid_w_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token:
        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float32)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb


class Resampler(nn.Module):
    """
    A 2D perceiver-resampler network with one cross attention layers by
        (grid_size**2) learnable queries and 2d sincos pos_emb
    Outputs:
        A tensor with the shape of (grid_size**2, embed_dim)
    """

    def __init__(
        self,
        grid_size,
        embed_dim,
        num_heads,
        kv_dim=None,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        adaptive=False,
    ):
        super().__init__()
        self.num_queries = grid_size**2
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.adaptive = adaptive

        self.pos_embed = nn.Parameter(
            torch.from_numpy(get_2d_sincos_pos_embed(embed_dim, grid_size)).float()
        ).requires_grad_(False)

        self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
        trunc_normal_(self.query, std=0.02)

        if kv_dim is not None and kv_dim != embed_dim:
            self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
        else:
            self.kv_proj = nn.Identity()

        self.attn = nn.MultiheadAttention(embed_dim, num_heads)
        self.ln_q = norm_layer(embed_dim)
        self.ln_kv = norm_layer(embed_dim)

        self.ln_post = norm_layer(embed_dim)
        self.proj = nn.Parameter((embed_dim**-0.5) * torch.randn(embed_dim, embed_dim))

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def forward(self, x, tgt_size=None, attn_mask=None):
        if self.adaptive:
            # print("adaptive")
            # raise Exception
            pos_embed = (
                torch.Tensor(get_2d_sincos_pos_embed(self.embed_dim, tgt_size))
                .float()
                .to(device=x.device, dtype=x.dtype)
            )
        else:
            pos_embed = get_abs_pos(self.pos_embed, tgt_size)

        x = self.kv_proj(x)
        x = self.ln_kv(x).permute(1, 0, 2)

        N = x.shape[1]
        q = self.ln_q(self.query)
        out = self.attn(
            self._repeat(q, N) + self.pos_embed.unsqueeze(1),
            x + pos_embed.unsqueeze(1),
            x,
            attn_mask=attn_mask,
        )[0]
        x = out.permute(1, 0, 2)

        x = self.ln_post(x)
        x = x @ self.proj
        return x

    def _repeat(self, query, N: int):
        return query.unsqueeze(1).repeat(1, N, 1)
