# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import torch
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable

import MultiScaleDeformableAttention as MSDA


class MSDeformAttnFunction(Function):
    @staticmethod
    def forward(ctx, value, value_spatial_shapes, value_level_start_index,
                sampling_locations, attention_weights, im2col_step):
        ctx.im2col_step = im2col_step
        output = MSDA.ms_deform_attn_forward(value, value_spatial_shapes,
                                             value_level_start_index,
                                             sampling_locations,
                                             attention_weights,
                                             ctx.im2col_step)
        ctx.save_for_backward(value, value_spatial_shapes,
                              value_level_start_index, sampling_locations,
                              attention_weights)
        return output

    @staticmethod
    @once_differentiable
    def backward(ctx, grad_output):
        value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors
        grad_value, grad_sampling_loc, grad_attn_weight = \
            MSDA.ms_deform_attn_backward(
                value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)

        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None


def ms_deform_attn_core_pytorch(value, value_spatial_shapes,
                                sampling_locations, attention_weights):

    N_, S_, M_, D_ = value.shape
    _, Lq_, M_, L_, P_, _ = sampling_locations.shape
    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes],
                             dim=1)
    sampling_grids = 2 * sampling_locations - 1
    sampling_value_list = []
    for lid_, (H_, W_) in enumerate(value_spatial_shapes):
        value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(
            N_ * M_, D_, H_, W_)
        sampling_grid_l_ = sampling_grids[:, :, :,
                                          lid_].transpose(1, 2).flatten(0, 1)
        sampling_value_l_ = F.grid_sample(value_l_,
                                          sampling_grid_l_,
                                          mode='bilinear',
                                          padding_mode='zeros',
                                          align_corners=False)
        sampling_value_list.append(sampling_value_l_)
    attention_weights = attention_weights.transpose(1, 2).reshape(
        N_ * M_, 1, Lq_, L_ * P_)
    output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) *
              attention_weights).sum(-1).view(N_, M_ * D_, Lq_)
    return output.transpose(1, 2).contiguous()