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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.

# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import normal_
from torch.amp import autocast

from dinov3.eval.segmentation.models.utils.batch_norm import get_norm
from dinov3.eval.segmentation.models.utils.position_encoding import PositionEmbeddingSine
from dinov3.eval.segmentation.models.utils.transformer import _get_clones, _get_activation_fn
from dinov3.eval.segmentation.models.utils.ms_deform_attn import MSDeformAttn


def c2_xavier_fill(module: nn.Module) -> None:
    """
    Initialize `module.weight` using the "XavierFill" implemented in Caffe2.
    Also initializes `module.bias` to 0.
    Args:
        module (torch.nn.Module): module to initialize.
    """
    # Caffe2 implementation of XavierFill in fact
    # corresponds to kaiming_uniform_ in PyTorch
    # pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Module, Tensor]`.
    nn.init.kaiming_uniform_(module.weight, a=1)
    if module.bias is not None:
        # pyre-fixme[6]: Expected `Tensor` for 1st param but got `Union[nn.Module,
        #  torch.Tensor]`.
        nn.init.constant_(module.bias, 0)


class Conv2d(torch.nn.Conv2d):
    """
    A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
    """

    def __init__(self, *args, **kwargs):
        """
        Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:
        Args:
            norm (nn.Module, optional): a normalization layer
            activation (callable(Tensor) -> Tensor): a callable activation function
        It assumes that norm layer is used before activation.
        """
        norm = kwargs.pop("norm", None)
        activation = kwargs.pop("activation", None)
        super().__init__(*args, **kwargs)

        self.norm = norm
        self.activation = activation

    def forward(self, x):
        # torchscript does not support SyncBatchNorm yet
        # https://github.com/pytorch/pytorch/issues/40507
        # and we skip these codes in torchscript since:
        # 1. currently we only support torchscript in evaluation mode
        # 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or
        # later version, `Conv2d` in these PyTorch versions has already supported empty inputs.
        # if not torch.jit.is_scripting():
        #     # Dynamo doesn't support context managers yet
        #     is_dynamo_compiling = check_if_dynamo_compiling()
        #     if not is_dynamo_compiling:
        #         with warnings.catch_warnings(record=True):
        #             if x.numel() == 0 and self.training:
        #                 # https://github.com/pytorch/pytorch/issues/12013
        #                 assert not isinstance(
        #                     self.norm, torch.nn.SyncBatchNorm
        #                 ), "SyncBatchNorm does not support empty inputs!"

        x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
        if self.norm is not None:
            x = self.norm(x)
        if self.activation is not None:
            x = self.activation(x)
        return x


# MSDeformAttn Transformer encoder in deformable detr
class MSDeformAttnTransformerEncoderOnly(nn.Module):
    def __init__(
        self,
        d_model=256,
        nhead=8,
        num_encoder_layers=6,
        dim_feedforward=1024,
        dropout=0.1,
        activation="relu",
        num_feature_levels=4,
        enc_n_points=4,
    ):
        super().__init__()

        self.d_model = d_model
        self.nhead = nhead

        encoder_layer = MSDeformAttnTransformerEncoderLayer(
            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
        )
        self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)

        self.level_encoding = nn.Parameter(torch.Tensor(num_feature_levels, d_model))

        self._reset_parameters()

    def _reset_parameters(self):
        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)
        for m in self.modules():
            if isinstance(m, MSDeformAttn):
                m._reset_parameters()
        normal_(self.level_encoding)

    def get_valid_ratio(self, mask):
        _, H, W = mask.shape
        valid_H = torch.sum(~mask[:, :, 0], 1)
        valid_W = torch.sum(~mask[:, 0, :], 1)
        valid_ratio_h = valid_H.float() / H
        valid_ratio_w = valid_W.float() / W
        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
        return valid_ratio

    def forward(self, srcs, pos_embeds):
        masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]
        # prepare input for encoder
        src_flatten = []
        mask_flatten = []
        lvl_pos_embed_flatten = []
        spatial_shapes = []
        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
            bs, c, h, w = src.shape
            spatial_shape = (h, w)
            spatial_shapes.append(spatial_shape)
            src = src.flatten(2).transpose(1, 2)
            mask = mask.flatten(1)
            pos_embed = pos_embed.flatten(2).transpose(1, 2)
            lvl_pos_embed = pos_embed + self.level_encoding[lvl].view(1, 1, -1)
            lvl_pos_embed_flatten.append(lvl_pos_embed)
            src_flatten.append(src)
            mask_flatten.append(mask)
        src_flatten = torch.cat(src_flatten, 1)
        mask_flatten = torch.cat(mask_flatten, 1)
        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
        level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)

        # encoder
        memory = self.encoder(
            src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten
        )

        return memory, spatial_shapes, level_start_index


class MSDeformAttnTransformerEncoderLayer(nn.Module):
    def __init__(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu", n_levels=4, n_heads=8, n_points=4):
        super().__init__()

        # self attention
        self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
        self.dropout1 = nn.Dropout(dropout)
        self.norm1 = nn.LayerNorm(d_model)

        # ffn
        self.linear1 = nn.Linear(d_model, d_ffn)
        self.activation = _get_activation_fn(activation)
        self.dropout2 = nn.Dropout(dropout)
        self.linear2 = nn.Linear(d_ffn, d_model)
        self.dropout3 = nn.Dropout(dropout)
        self.norm2 = nn.LayerNorm(d_model)

    @staticmethod
    def with_pos_embed(tensor, pos):
        return tensor if pos is None else tensor + pos

    def forward_ffn(self, src):
        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
        src = src + self.dropout3(src2)
        src = self.norm2(src)
        return src

    def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
        # self attention
        src2 = self.self_attn(
            self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask
        )
        src = src + self.dropout1(src2)
        src = self.norm1(src)

        # ffn
        src = self.forward_ffn(src)

        return src


class MSDeformAttnTransformerEncoder(nn.Module):
    def __init__(self, encoder_layer, num_layers):
        super().__init__()
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers

    @staticmethod
    def get_reference_points(spatial_shapes, valid_ratios, device):
        reference_points_list = []
        for lvl, (H_, W_) in enumerate(spatial_shapes):
            ref_y, ref_x = torch.meshgrid(
                torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
                torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
            )
            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
            ref = torch.stack((ref_x, ref_y), -1)
            reference_points_list.append(ref)
        reference_points = torch.cat(reference_points_list, 1)
        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
        return reference_points

    def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
        output = src
        reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
        for _, layer in enumerate(self.layers):
            output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)

        return output


# @SEM_SEG_HEADS_REGISTRY.register()
class MSDeformAttnPixelDecoder(nn.Module):
    # @configurable
    def __init__(
        self,
        input_shape: Dict[str, Tuple[int]],  # ShapeSpec: [channels, height, width, stride]
        *,
        transformer_dropout: float,
        transformer_nheads: int,
        transformer_dim_feedforward: int,
        transformer_enc_layers: int,
        conv_dim: int,
        mask_dim: int,
        norm: Optional[Union[str, Callable]] = None,
        # deformable transformer encoder args
        transformer_in_features: List[str],
        common_stride: int,
    ):
        """
        NOTE: this interface is experimental.
        Args:
            input_shape: shapes (channels and stride) of the input features
            transformer_dropout: dropout probability in transformer
            transformer_nheads: number of heads in transformer
            transformer_dim_feedforward: dimension of feedforward network
            transformer_enc_layers: number of transformer encoder layers
            conv_dims: number of output channels for the intermediate conv layers.
            mask_dim: number of output channels for the final conv layer.
            norm (str or callable): normalization for all conv layers
        """
        super().__init__()
        transformer_input_shape = {k: v for k, v in input_shape.items() if k in transformer_in_features}

        # this is the input shape of pixel decoder  # ShapeSpec: [channels, height, width, stride]
        input_shape = sorted(input_shape.items(), key=lambda x: x[1][-1])
        self.in_features = [k for k, v in input_shape]  # starting from "res2" to "res5"
        self.feature_strides = [v[-1] for k, v in input_shape]
        self.feature_channels = [v[0] for k, v in input_shape]

        # this is the input shape of transformer encoder (could use less features than pixel decoder
        transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1][-1])
        self.transformer_in_features = [k for k, v in transformer_input_shape]  # starting from "res2" to "res5"
        transformer_in_channels = [v[0] for k, v in transformer_input_shape]
        self.transformer_feature_strides = [v[-1] for k, v in transformer_input_shape]  # to decide extra FPN layers

        self.transformer_num_feature_levels = 3  # TODO switch with len(self.transformer_in_features)
        if self.transformer_num_feature_levels > 1:
            input_proj_list = []
            # from low resolution to high resolution (res5 -> res2)
            for in_channels in transformer_in_channels[::-1][:-1]:  # TODO remove [:-1]
                input_proj_list.append(
                    nn.Sequential(
                        nn.Conv2d(in_channels, conv_dim, kernel_size=1),
                        nn.GroupNorm(32, conv_dim),
                    )
                )
            self.input_convs = nn.ModuleList(input_proj_list)
        else:
            self.input_convs = nn.ModuleList(
                [
                    nn.Sequential(
                        nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),
                        nn.GroupNorm(32, conv_dim),
                    )
                ]
            )

        for proj in self.input_convs:
            nn.init.xavier_uniform_(proj[0].weight, gain=1)
            nn.init.constant_(proj[0].bias, 0)

        self.encoder = MSDeformAttnTransformerEncoderOnly(
            d_model=conv_dim,
            dropout=transformer_dropout,
            nhead=transformer_nheads,
            dim_feedforward=transformer_dim_feedforward,
            num_encoder_layers=transformer_enc_layers,
            num_feature_levels=self.transformer_num_feature_levels,
        )
        N_steps = conv_dim // 2
        self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)

        self.mask_dim = mask_dim
        # use 1x1 conv instead
        self.mask_feature = Conv2d(
            conv_dim,
            mask_dim,
            kernel_size=1,
            stride=1,
            padding=0,
        )
        c2_xavier_fill(self.mask_feature)

        self.maskformer_num_feature_levels = 3  # always use 3 scales
        self.common_stride = common_stride

        # extra fpn levels
        stride = min(self.transformer_feature_strides)
        self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))

        lateral_convs = []
        output_convs = []

        use_bias = norm == ""
        for idx, in_channels in enumerate(self.feature_channels[:1]):  # TODO self.num_fpn_levels]):
            lateral_norm = get_norm(norm, conv_dim)
            output_norm = get_norm(norm, conv_dim)

            lateral_conv = Conv2d(in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm)
            output_conv = Conv2d(
                conv_dim,
                conv_dim,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=use_bias,
                norm=output_norm,
                activation=F.relu,
            )
            c2_xavier_fill(lateral_conv)
            c2_xavier_fill(output_conv)
            # self.add_module("lateral_convs".format(idx + 1), lateral_conv)  # TODO replace "adapter_{}"
            # self.add_module("output_convs".format(idx + 1), output_conv)  # TODO replace layer_{}""

            lateral_convs.append(lateral_conv)
            output_convs.append(output_conv)
        # Place convs into top-down order (from low to high resolution)
        # to make the top-down computation in forward clearer.
        self.lateral_convs = nn.ModuleList(lateral_convs[::-1])
        self.output_convs = nn.ModuleList(output_convs[::-1])

    @autocast(device_type="cuda", enabled=False)
    def forward_features(self, features):
        srcs = []
        pos = []
        # Reverse feature maps into top-down order (from low to high resolution)
        for idx, f in enumerate(self.transformer_in_features[::-1][:-1]):  # TODO remove [:-1]
            x = features[f].float()  # deformable detr does not support half precision
            srcs.append(self.input_convs[idx](x))
            pos.append(self.pe_layer(x))

        y, spatial_shapes, level_start_index = self.encoder(srcs, pos)
        bs = y.shape[0]

        split_size_or_sections = [None] * self.transformer_num_feature_levels
        for i in range(self.transformer_num_feature_levels):
            if i < self.transformer_num_feature_levels - 1:
                split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
            else:
                split_size_or_sections[i] = y.shape[1] - level_start_index[i]
        y = torch.split(y, split_size_or_sections, dim=1)

        out = []
        multi_scale_features = []
        num_cur_levels = 0
        for i, z in enumerate(y):
            out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))

        # append `out` with extra FPN levels
        # Reverse feature maps into top-down order (from low to high resolution)
        for idx, f in enumerate(self.in_features[0]):  # TODO re put [:self.num_fpn_levels][::-1]):
            x = features[f].float()
            lateral_conv = self.lateral_convs[idx]
            output_conv = self.output_convs[idx]
            cur_fpn = lateral_conv(x)
            # Following FPN implementation, we use nearest upsampling here
            y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False)
            y = output_conv(y)
            out.append(y)

        for o in out:
            if num_cur_levels < self.maskformer_num_feature_levels:
                multi_scale_features.append(o)
                num_cur_levels += 1

        return self.mask_feature(out[-1]), out[0], multi_scale_features

	# Copyright (c) Meta Platforms, Inc. and affiliates.
	#
	# This software may be used and distributed in accordance with
	# the terms of the DINOv3 License Agreement.

	# Copyright (c) Facebook, Inc. and its affiliates.
	import numpy as np
	from typing import Callable, Dict, List, Optional, Tuple, Union

	import torch
	from torch import nn
	from torch.nn import functional as F
	from torch.nn.init import normal_
	from torch.amp import autocast

	from dinov3.eval.segmentation.models.utils.batch_norm import get_norm
	from dinov3.eval.segmentation.models.utils.position_encoding import PositionEmbeddingSine
	from dinov3.eval.segmentation.models.utils.transformer import _get_clones, _get_activation_fn
	from dinov3.eval.segmentation.models.utils.ms_deform_attn import MSDeformAttn


	def c2_xavier_fill(module: nn.Module) -> None:
	"""
	Initialize `module.weight` using the "XavierFill" implemented in Caffe2.
	Also initializes `module.bias` to 0.

	Args:
	module (torch.nn.Module): module to initialize.
	"""
	# Caffe2 implementation of XavierFill in fact
	# corresponds to kaiming_uniform_ in PyTorch
	# pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Module, Tensor]`.
	nn.init.kaiming_uniform_(module.weight, a=1)
	if module.bias is not None:
	# pyre-fixme[6]: Expected `Tensor` for 1st param but got `Union[nn.Module,
	# torch.Tensor]`.
	nn.init.constant_(module.bias, 0)


	class Conv2d(torch.nn.Conv2d):
	"""
	A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
	"""

	def __init__(self, args, *kwargs):
	"""
	Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:

	Args:
	norm (nn.Module, optional): a normalization layer
	activation (callable(Tensor) -> Tensor): a callable activation function

	It assumes that norm layer is used before activation.
	"""
	norm = kwargs.pop("norm", None)
	activation = kwargs.pop("activation", None)
	super().__init__(args, *kwargs)

	self.norm = norm
	self.activation = activation

	def forward(self, x):
	# torchscript does not support SyncBatchNorm yet
	# https://github.com/pytorch/pytorch/issues/40507
	# and we skip these codes in torchscript since:
	# 1. currently we only support torchscript in evaluation mode
	# 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or
	# later version, `Conv2d` in these PyTorch versions has already supported empty inputs.
	# if not torch.jit.is_scripting():
	# # Dynamo doesn't support context managers yet
	# is_dynamo_compiling = check_if_dynamo_compiling()
	# if not is_dynamo_compiling:
	# with warnings.catch_warnings(record=True):
	# if x.numel() == 0 and self.training:
	# # https://github.com/pytorch/pytorch/issues/12013
	# assert not isinstance(
	# self.norm, torch.nn.SyncBatchNorm
	# ), "SyncBatchNorm does not support empty inputs!"

	x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
	if self.norm is not None:
	x = self.norm(x)
	if self.activation is not None:
	x = self.activation(x)
	return x


	# MSDeformAttn Transformer encoder in deformable detr
	class MSDeformAttnTransformerEncoderOnly(nn.Module):
	def __init__(
	self,
	d_model=256,
	nhead=8,
	num_encoder_layers=6,
	dim_feedforward=1024,
	dropout=0.1,
	activation="relu",
	num_feature_levels=4,
	enc_n_points=4,
	):
	super().__init__()

	self.d_model = d_model
	self.nhead = nhead

	encoder_layer = MSDeformAttnTransformerEncoderLayer(
	d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
	)
	self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)

	self.level_encoding = nn.Parameter(torch.Tensor(num_feature_levels, d_model))

	self._reset_parameters()

	def _reset_parameters(self):
	for p in self.parameters():
	if p.dim() > 1:
	nn.init.xavier_uniform_(p)
	for m in self.modules():
	if isinstance(m, MSDeformAttn):
	m._reset_parameters()
	normal_(self.level_encoding)

	def get_valid_ratio(self, mask):
	_, H, W = mask.shape
	valid_H = torch.sum(~mask[:, :, 0], 1)
	valid_W = torch.sum(~mask[:, 0, :], 1)
	valid_ratio_h = valid_H.float() / H
	valid_ratio_w = valid_W.float() / W
	valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
	return valid_ratio

	def forward(self, srcs, pos_embeds):
	masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]
	# prepare input for encoder
	src_flatten = []
	mask_flatten = []
	lvl_pos_embed_flatten = []
	spatial_shapes = []
	for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
	bs, c, h, w = src.shape
	spatial_shape = (h, w)
	spatial_shapes.append(spatial_shape)
	src = src.flatten(2).transpose(1, 2)
	mask = mask.flatten(1)
	pos_embed = pos_embed.flatten(2).transpose(1, 2)
	lvl_pos_embed = pos_embed + self.level_encoding[lvl].view(1, 1, -1)
	lvl_pos_embed_flatten.append(lvl_pos_embed)
	src_flatten.append(src)
	mask_flatten.append(mask)
	src_flatten = torch.cat(src_flatten, 1)
	mask_flatten = torch.cat(mask_flatten, 1)
	lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
	spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
	level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
	valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)

	# encoder
	memory = self.encoder(
	src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten
	)

	return memory, spatial_shapes, level_start_index


	class MSDeformAttnTransformerEncoderLayer(nn.Module):
	def __init__(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu", n_levels=4, n_heads=8, n_points=4):
	super().__init__()

	# self attention
	self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
	self.dropout1 = nn.Dropout(dropout)
	self.norm1 = nn.LayerNorm(d_model)

	# ffn
	self.linear1 = nn.Linear(d_model, d_ffn)
	self.activation = _get_activation_fn(activation)
	self.dropout2 = nn.Dropout(dropout)
	self.linear2 = nn.Linear(d_ffn, d_model)
	self.dropout3 = nn.Dropout(dropout)
	self.norm2 = nn.LayerNorm(d_model)

	@staticmethod
	def with_pos_embed(tensor, pos):
	return tensor if pos is None else tensor + pos

	def forward_ffn(self, src):
	src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
	src = src + self.dropout3(src2)
	src = self.norm2(src)
	return src

	def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
	# self attention
	src2 = self.self_attn(
	self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask
	)
	src = src + self.dropout1(src2)
	src = self.norm1(src)

	# ffn
	src = self.forward_ffn(src)

	return src


	class MSDeformAttnTransformerEncoder(nn.Module):
	def __init__(self, encoder_layer, num_layers):
	super().__init__()
	self.layers = _get_clones(encoder_layer, num_layers)
	self.num_layers = num_layers

	@staticmethod
	def get_reference_points(spatial_shapes, valid_ratios, device):
	reference_points_list = []
	for lvl, (H_, W_) in enumerate(spatial_shapes):
	ref_y, ref_x = torch.meshgrid(
	torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
	torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
	)
	ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
	ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
	ref = torch.stack((ref_x, ref_y), -1)
	reference_points_list.append(ref)
	reference_points = torch.cat(reference_points_list, 1)
	reference_points = reference_points[:, :, None] * valid_ratios[:, None]
	return reference_points

	def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
	output = src
	reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
	for _, layer in enumerate(self.layers):
	output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)

	return output


	# @SEM_SEG_HEADS_REGISTRY.register()
	class MSDeformAttnPixelDecoder(nn.Module):
	# @configurable
	def __init__(
	self,
	input_shape: Dict[str, Tuple[int]], # ShapeSpec: [channels, height, width, stride]
	*,
	transformer_dropout: float,
	transformer_nheads: int,
	transformer_dim_feedforward: int,
	transformer_enc_layers: int,
	conv_dim: int,
	mask_dim: int,
	norm: Optional[Union[str, Callable]] = None,
	# deformable transformer encoder args
	transformer_in_features: List[str],
	common_stride: int,
	):
	"""
	NOTE: this interface is experimental.
	Args:
	input_shape: shapes (channels and stride) of the input features
	transformer_dropout: dropout probability in transformer
	transformer_nheads: number of heads in transformer
	transformer_dim_feedforward: dimension of feedforward network
	transformer_enc_layers: number of transformer encoder layers
	conv_dims: number of output channels for the intermediate conv layers.
	mask_dim: number of output channels for the final conv layer.
	norm (str or callable): normalization for all conv layers
	"""
	super().__init__()
	transformer_input_shape = {k: v for k, v in input_shape.items() if k in transformer_in_features}

	# this is the input shape of pixel decoder # ShapeSpec: [channels, height, width, stride]
	input_shape = sorted(input_shape.items(), key=lambda x: x[1][-1])
	self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
	self.feature_strides = [v[-1] for k, v in input_shape]
	self.feature_channels = [v[0] for k, v in input_shape]

	# this is the input shape of transformer encoder (could use less features than pixel decoder
	transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1][-1])
	self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5"
	transformer_in_channels = [v[0] for k, v in transformer_input_shape]
	self.transformer_feature_strides = [v[-1] for k, v in transformer_input_shape] # to decide extra FPN layers

	self.transformer_num_feature_levels = 3 # TODO switch with len(self.transformer_in_features)
	if self.transformer_num_feature_levels > 1:
	input_proj_list = []
	# from low resolution to high resolution (res5 -> res2)
	for in_channels in transformer_in_channels[::-1][:-1]: # TODO remove [:-1]
	input_proj_list.append(
	nn.Sequential(
	nn.Conv2d(in_channels, conv_dim, kernel_size=1),
	nn.GroupNorm(32, conv_dim),
	)
	)
	self.input_convs = nn.ModuleList(input_proj_list)
	else:
	self.input_convs = nn.ModuleList(
	[
	nn.Sequential(
	nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),
	nn.GroupNorm(32, conv_dim),
	)
	]
	)

	for proj in self.input_convs:
	nn.init.xavier_uniform_(proj[0].weight, gain=1)
	nn.init.constant_(proj[0].bias, 0)

	self.encoder = MSDeformAttnTransformerEncoderOnly(
	d_model=conv_dim,
	dropout=transformer_dropout,
	nhead=transformer_nheads,
	dim_feedforward=transformer_dim_feedforward,
	num_encoder_layers=transformer_enc_layers,
	num_feature_levels=self.transformer_num_feature_levels,
	)
	N_steps = conv_dim // 2
	self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)

	self.mask_dim = mask_dim
	# use 1x1 conv instead
	self.mask_feature = Conv2d(
	conv_dim,
	mask_dim,
	kernel_size=1,
	stride=1,
	padding=0,
	)
	c2_xavier_fill(self.mask_feature)

	self.maskformer_num_feature_levels = 3 # always use 3 scales
	self.common_stride = common_stride

	# extra fpn levels
	stride = min(self.transformer_feature_strides)
	self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))

	lateral_convs = []
	output_convs = []

	use_bias = norm == ""
	for idx, in_channels in enumerate(self.feature_channels[:1]): # TODO self.num_fpn_levels]):
	lateral_norm = get_norm(norm, conv_dim)
	output_norm = get_norm(norm, conv_dim)

	lateral_conv = Conv2d(in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm)
	output_conv = Conv2d(
	conv_dim,
	conv_dim,
	kernel_size=3,
	stride=1,
	padding=1,
	bias=use_bias,
	norm=output_norm,
	activation=F.relu,
	)
	c2_xavier_fill(lateral_conv)
	c2_xavier_fill(output_conv)
	# self.add_module("lateral_convs".format(idx + 1), lateral_conv) # TODO replace "adapter_{}"
	# self.add_module("output_convs".format(idx + 1), output_conv) # TODO replace layer_{}""

	lateral_convs.append(lateral_conv)
	output_convs.append(output_conv)
	# Place convs into top-down order (from low to high resolution)
	# to make the top-down computation in forward clearer.
	self.lateral_convs = nn.ModuleList(lateral_convs[::-1])
	self.output_convs = nn.ModuleList(output_convs[::-1])

	@autocast(device_type="cuda", enabled=False)
	def forward_features(self, features):
	srcs = []
	pos = []
	# Reverse feature maps into top-down order (from low to high resolution)
	for idx, f in enumerate(self.transformer_in_features[::-1][:-1]): # TODO remove [:-1]
	x = features[f].float() # deformable detr does not support half precision
	srcs.append(self.input_convs[idx](x))
	pos.append(self.pe_layer(x))

	y, spatial_shapes, level_start_index = self.encoder(srcs, pos)
	bs = y.shape[0]

	split_size_or_sections = [None] * self.transformer_num_feature_levels
	for i in range(self.transformer_num_feature_levels):
	if i < self.transformer_num_feature_levels - 1:
	split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
	else:
	split_size_or_sections[i] = y.shape[1] - level_start_index[i]
	y = torch.split(y, split_size_or_sections, dim=1)

	out = []
	multi_scale_features = []
	num_cur_levels = 0
	for i, z in enumerate(y):
	out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))

	# append `out` with extra FPN levels
	# Reverse feature maps into top-down order (from low to high resolution)
	for idx, f in enumerate(self.in_features[0]): # TODO re put [:self.num_fpn_levels][::-1]):
	x = features[f].float()
	lateral_conv = self.lateral_convs[idx]
	output_conv = self.output_convs[idx]
	cur_fpn = lateral_conv(x)
	# Following FPN implementation, we use nearest upsampling here
	y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False)
	y = output_conv(y)
	out.append(y)

	for o in out:
	if num_cur_levels < self.maskformer_num_feature_levels:
	multi_scale_features.append(o)
	num_cur_levels += 1

	return self.mask_feature(out[-1]), out[0], multi_scale_features