transformers/examples/modular-transformers/modeling_test_detr.py

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import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import Tensor, nn

from ...activations import ACT2FN
from ...integrations import use_kernel_forward_from_hub
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_outputs import BaseModelOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import meshgrid
from ...utils import (
    ModelOutput,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_timm_available,
    replace_return_docstrings,
    requires_backends,
)
from ...utils.backbone_utils import load_backbone
from .configuration_test_detr import TestDetrConfig


if is_timm_available():
    from timm import create_model

_CONFIG_FOR_DOC = "TestDetrConfig"


@use_kernel_forward_from_hub("MultiScaleDeformableAttention")
class MultiScaleDeformableAttention(nn.Module):
    def forward(
        self,
        value: Tensor,
        value_spatial_shapes: Tensor,
        value_spatial_shapes_list: List[Tuple],
        level_start_index: Tensor,
        sampling_locations: Tensor,
        attention_weights: Tensor,
        im2col_step: int,
    ):
        batch_size, _, num_heads, hidden_dim = value.shape
        _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
        value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1)
        sampling_grids = 2 * sampling_locations - 1
        sampling_value_list = []
        for level_id, (height, width) in enumerate(value_spatial_shapes_list):
            # batch_size, height*width, num_heads, hidden_dim
            # -> batch_size, height*width, num_heads*hidden_dim
            # -> batch_size, num_heads*hidden_dim, height*width
            # -> batch_size*num_heads, hidden_dim, height, width
            value_l_ = (
                value_list[level_id]
                .flatten(2)
                .transpose(1, 2)
                .reshape(batch_size * num_heads, hidden_dim, height, width)
            )
            # batch_size, num_queries, num_heads, num_points, 2
            # -> batch_size, num_heads, num_queries, num_points, 2
            # -> batch_size*num_heads, num_queries, num_points, 2
            sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
            # batch_size*num_heads, hidden_dim, num_queries, num_points
            sampling_value_l_ = nn.functional.grid_sample(
                value_l_,
                sampling_grid_l_,
                mode="bilinear",
                padding_mode="zeros",
                align_corners=False,
            )
            sampling_value_list.append(sampling_value_l_)
        # (batch_size, num_queries, num_heads, num_levels, num_points)
        # -> (batch_size, num_heads, num_queries, num_levels, num_points)
        # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
        attention_weights = attention_weights.transpose(1, 2).reshape(
            batch_size * num_heads, 1, num_queries, num_levels * num_points
        )
        output = (
            (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
            .sum(-1)
            .view(batch_size, num_heads * hidden_dim, num_queries)
        )
        return output.transpose(1, 2).contiguous()


@dataclass
class TestDetrDecoderOutput(ModelOutput):
    """
    Base class for outputs of the TestDetrDecoder. This class adds two attributes to
    BaseModelOutputWithCrossAttentions, namely:
    - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
    - a stacked tensor of intermediate reference points.

    Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
            Stacked intermediate hidden states (output of each layer of the decoder).
        intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`):
            Stacked intermediate reference points (reference points of each layer of the decoder).
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
            plus the initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
            the self-attention heads.
        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
            used to compute the weighted average in the cross-attention heads.
    """

    last_hidden_state: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None


@dataclass
class TestDetrModelOutput(ModelOutput):
    """
    Base class for outputs of the Deformable DETR encoder-decoder model.

    Args:
        init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
            Initial reference points sent through the Transformer decoder.
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the decoder of the model.
        intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
            Stacked intermediate hidden states (output of each layer of the decoder).
        intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
            Stacked intermediate reference points (reference points of each layer of the decoder).
        decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, num_queries, hidden_size)`. Hidden-states of the decoder at the output of each layer
            plus the initial embedding outputs.
        decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, num_queries,
            num_queries)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted
            average in the self-attention heads.
        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_queries, num_heads, 4, 4)`.
            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
            weighted average in the cross-attention heads.
        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder of the model.
        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
            layer plus the initial embedding outputs.
        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_queries, num_heads, 4, 4)`.
            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
        enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
            Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are
            picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
            foreground and background).
        enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
            Logits of predicted bounding boxes coordinates in the first stage.
    """

    init_reference_points: Optional[torch.FloatTensor] = None
    last_hidden_state: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
    enc_outputs_class: Optional[torch.FloatTensor] = None
    enc_outputs_coord_logits: Optional[torch.FloatTensor] = None


class TestDetrFrozenBatchNorm2d(nn.Module):
    """
    BatchNorm2d where the batch statistics and the affine parameters are fixed.

    Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
    torchvision.models.resnet[18,34,50,101] produce nans.
    """

    def __init__(self, n):
        super().__init__()
        self.register_buffer("weight", torch.ones(n))
        self.register_buffer("bias", torch.zeros(n))
        self.register_buffer("running_mean", torch.zeros(n))
        self.register_buffer("running_var", torch.ones(n))

    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        num_batches_tracked_key = prefix + "num_batches_tracked"
        if num_batches_tracked_key in state_dict:
            del state_dict[num_batches_tracked_key]

        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

    def forward(self, x):
        # move reshapes to the beginning
        # to make it user-friendly
        weight = self.weight.reshape(1, -1, 1, 1)
        bias = self.bias.reshape(1, -1, 1, 1)
        running_var = self.running_var.reshape(1, -1, 1, 1)
        running_mean = self.running_mean.reshape(1, -1, 1, 1)
        epsilon = 1e-5
        scale = weight * (running_var + epsilon).rsqrt()
        bias = bias - running_mean * scale
        return x * scale + bias


def replace_batch_norm(model):
    r"""
    Recursively replace all `torch.nn.BatchNorm2d` with `TestDetrFrozenBatchNorm2d`.

    Args:
        model (torch.nn.Module):
            input model
    """
    for name, module in model.named_children():
        if isinstance(module, nn.BatchNorm2d):
            new_module = TestDetrFrozenBatchNorm2d(module.num_features)

            if not module.weight.device == torch.device("meta"):
                new_module.weight.data.copy_(module.weight)
                new_module.bias.data.copy_(module.bias)
                new_module.running_mean.data.copy_(module.running_mean)
                new_module.running_var.data.copy_(module.running_var)

            model._modules[name] = new_module

        if len(list(module.children())) > 0:
            replace_batch_norm(module)


class TestDetrConvEncoder(nn.Module):
    """
    Convolutional backbone, using either the AutoBackbone API or one from the timm library.

    nn.BatchNorm2d layers are replaced by TestDetrFrozenBatchNorm2d as defined above.

    """

    def __init__(self, config):
        super().__init__()

        self.config = config

        # For backwards compatibility we have to use the timm library directly instead of the AutoBackbone API
        if config.use_timm_backbone:
            # We default to values which were previously hard-coded. This enables configurability from the config
            # using backbone arguments, while keeping the default behavior the same.
            requires_backends(self, ["timm"])
            kwargs = getattr(config, "backbone_kwargs", {})
            kwargs = {} if kwargs is None else kwargs.copy()
            out_indices = kwargs.pop("out_indices", (2, 3, 4) if config.num_feature_levels > 1 else (4,))
            num_channels = kwargs.pop("in_chans", config.num_channels)
            if config.dilation:
                kwargs["output_stride"] = kwargs.get("output_stride", 16)
            backbone = create_model(
                config.backbone,
                pretrained=config.use_pretrained_backbone,
                features_only=True,
                out_indices=out_indices,
                in_chans=num_channels,
                **kwargs,
            )
        else:
            backbone = load_backbone(config)

        # replace batch norm by frozen batch norm
        with torch.no_grad():
            replace_batch_norm(backbone)
        self.model = backbone
        self.intermediate_channel_sizes = (
            self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels
        )

        backbone_model_type = None
        if config.backbone is not None:
            backbone_model_type = config.backbone
        elif config.backbone_config is not None:
            backbone_model_type = config.backbone_config.model_type
        else:
            raise ValueError("Either `backbone` or `backbone_config` should be provided in the config")

        if "resnet" in backbone_model_type:
            for name, parameter in self.model.named_parameters():
                if config.use_timm_backbone:
                    if "layer2" not in name and "layer3" not in name and "layer4" not in name:
                        parameter.requires_grad_(False)
                else:
                    if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name:
                        parameter.requires_grad_(False)

    def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
        # send pixel_values through the model to get list of feature maps
        features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps

        out = []
        for feature_map in features:
            # downsample pixel_mask to match shape of corresponding feature_map
            mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
            out.append((feature_map, mask))
        return out


class TestDetrConvModel(nn.Module):
    """
    This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
    """

    def __init__(self, conv_encoder, position_embedding):
        super().__init__()
        self.conv_encoder = conv_encoder
        self.position_embedding = position_embedding

    def forward(self, pixel_values, pixel_mask):
        # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples
        out = self.conv_encoder(pixel_values, pixel_mask)
        pos = []
        for feature_map, mask in out:
            # position encoding
            pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype))

        return out, pos


class TestDetrSinePositionEmbedding(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, embedding_dim=64, temperature=10000, normalize=False, scale=None):
        super().__init__()
        self.embedding_dim = embedding_dim
        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, pixel_values, pixel_mask):
        if pixel_mask is None:
            raise ValueError("No pixel mask provided")
        y_embed = pixel_mask.cumsum(1, dtype=pixel_values.dtype)
        x_embed = pixel_mask.cumsum(2, dtype=pixel_values.dtype)
        if self.normalize:
            eps = 1e-6
            y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(self.embedding_dim, dtype=pixel_values.dtype, device=pixel_values.device)
        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)

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


class TestDetrLearnedPositionEmbedding(nn.Module):
    """
    This module learns positional embeddings up to a fixed maximum size.
    """

    def __init__(self, embedding_dim=256):
        super().__init__()
        self.row_embeddings = nn.Embedding(50, embedding_dim)
        self.column_embeddings = nn.Embedding(50, embedding_dim)

    def forward(self, pixel_values, pixel_mask=None):
        height, width = pixel_values.shape[-2:]
        width_values = torch.arange(width, device=pixel_values.device)
        height_values = torch.arange(height, device=pixel_values.device)
        x_emb = self.column_embeddings(width_values)
        y_emb = self.row_embeddings(height_values)
        pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1)
        pos = pos.permute(2, 0, 1)
        pos = pos.unsqueeze(0)
        pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
        return pos


class TestDetrMultiscaleDeformableAttention(nn.Module):
    """
    Multiscale deformable attention as proposed in Deformable DETR.
    """

    def __init__(self, config: TestDetrConfig, num_heads: int, n_points: int):
        super().__init__()

        self.attn = MultiScaleDeformableAttention()

        if config.d_model % num_heads != 0:
            raise ValueError(
                f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}"
            )
        dim_per_head = config.d_model // num_heads
        # check if dim_per_head is power of 2
        if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
            warnings.warn(
                "You'd better set embed_dim (d_model) in TestDetrMultiscaleDeformableAttention to make the"
                " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
                " implementation."
            )

        self.im2col_step = 64

        self.d_model = config.d_model
        self.n_levels = config.num_feature_levels
        self.n_heads = num_heads
        self.n_points = n_points

        self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2)
        self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points)
        self.value_proj = nn.Linear(config.d_model, config.d_model)
        self.output_proj = nn.Linear(config.d_model, config.d_model)

        self.disable_custom_kernels = config.disable_custom_kernels

    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
        return tensor if position_embeddings is None else tensor + position_embeddings

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        output_attentions: bool = False,
    ):
        # add position embeddings to the hidden states before projecting to queries and keys
        if position_embeddings is not None:
            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)

        batch_size, num_queries, _ = hidden_states.shape
        batch_size, sequence_length, _ = encoder_hidden_states.shape
        total_elements = sum(height * width for height, width in spatial_shapes_list)
        if total_elements != sequence_length:
            raise ValueError(
                "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
            )

        value = self.value_proj(encoder_hidden_states)
        if attention_mask is not None:
            # we invert the attention_mask
            value = value.masked_fill(~attention_mask[..., None], float(0))
        value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
        sampling_offsets = self.sampling_offsets(hidden_states).view(
            batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
        )
        attention_weights = self.attention_weights(hidden_states).view(
            batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
        )
        attention_weights = F.softmax(attention_weights, -1).view(
            batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
        )
        # batch_size, num_queries, n_heads, n_levels, n_points, 2
        num_coordinates = reference_points.shape[-1]
        if num_coordinates == 2:
            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
            sampling_locations = (
                reference_points[:, :, None, :, None, :]
                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
            )
        elif num_coordinates == 4:
            sampling_locations = (
                reference_points[:, :, None, :, None, :2]
                + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
            )
        else:
            raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")

        output = self.attn(
            value,
            spatial_shapes,
            spatial_shapes_list,
            level_start_index,
            sampling_locations,
            attention_weights,
            self.im2col_step,
        )

        output = self.output_proj(output)

        return output, attention_weights


class TestDetrMultiheadAttention(nn.Module):
    """
    Multi-headed attention from 'Attention Is All You Need' paper.

    Here, we add position embeddings to the queries and keys (as explained in the Deformable DETR paper).
    """

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        if self.head_dim * num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {num_heads})."
            )
        self.scaling = self.head_dim**-0.5

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
        return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
        return tensor if position_embeddings is None else tensor + position_embeddings

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_embeddings: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        batch_size, target_len, embed_dim = hidden_states.size()
        # add position embeddings to the hidden states before projecting to queries and keys
        if position_embeddings is not None:
            hidden_states_original = hidden_states
            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)

        # get queries, keys and values
        query_states = self.q_proj(hidden_states) * self.scaling
        key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
        value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)

        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)

        source_len = key_states.size(1)

        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

        if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
            raise ValueError(
                f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
                f" {attn_weights.size()}"
            )

        # expand attention_mask
        if attention_mask is not None:
            # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
            attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)

        if attention_mask is not None:
            if attention_mask.size() != (batch_size, 1, target_len, source_len):
                raise ValueError(
                    f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
                    f" {attention_mask.size()}"
                )
            attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask
            attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len)

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
            attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
        else:
            attn_weights_reshaped = None

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.bmm(attn_probs, value_states)

        if attn_output.size() != (
            batch_size * self.num_heads,
            target_len,
            self.head_dim,
        ):
            raise ValueError(
                f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(batch_size, target_len, embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped


class TestDetrEncoderLayer(nn.Module):
    def __init__(self, config: TestDetrConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = TestDetrMultiscaleDeformableAttention(
            config,
            num_heads=config.encoder_attention_heads,
            n_points=config.encoder_n_points,
        )
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        output_attentions: bool = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Input to the layer.
            attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
                Attention mask.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings, to be added to `hidden_states`.
            reference_points (`torch.FloatTensor`, *optional*):
                Reference points.
            spatial_shapes (`torch.LongTensor`, *optional*):
                Spatial shapes of the backbone feature maps.
            level_start_index (`torch.LongTensor`, *optional*):
                Level start index.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)

        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        if self.training:
            if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class TestDetrDecoderLayer(nn.Module):
    def __init__(self, config: TestDetrConfig):
        super().__init__()
        self.embed_dim = config.d_model

        # self-attention
        self.self_attn = TestDetrMultiheadAttention(
            embed_dim=self.embed_dim,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        # cross-attention
        self.encoder_attn = TestDetrMultiscaleDeformableAttention(
            config,
            num_heads=config.decoder_attention_heads,
            n_points=config.decoder_n_points,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        # feedforward neural networks
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                Input to the layer of shape `(seq_len, batch, embed_dim)`.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings that are added to the queries and keys in the self-attention layer.
            reference_points (`torch.FloatTensor`, *optional*):
                Reference points.
            spatial_shapes (`torch.LongTensor`, *optional*):
                Spatial shapes.
            level_start_index (`torch.LongTensor`, *optional*):
                Level start index.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        second_residual = hidden_states

        # Cross-Attention
        cross_attn_weights = None
        hidden_states, cross_attn_weights = self.encoder_attn(
            hidden_states=hidden_states,
            attention_mask=encoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            position_embeddings=position_embeddings,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = second_residual + hidden_states

        hidden_states = self.encoder_attn_layer_norm(hidden_states)

        # Fully Connected
        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        return outputs


class TestDetrPreTrainedModel(PreTrainedModel):
    config_class = TestDetrConfig
    base_model_prefix = "model"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = [
        r"TestDetrConvEncoder",
        r"TestDetrEncoderLayer",
        r"TestDetrDecoderLayer",
    ]

    def _init_weights(self, module):
        std = self.config.init_std

        if isinstance(module, TestDetrLearnedPositionEmbedding):
            nn.init.uniform_(module.row_embeddings.weight)
            nn.init.uniform_(module.column_embeddings.weight)
        elif isinstance(module, TestDetrMultiscaleDeformableAttention):
            nn.init.constant_(module.sampling_offsets.weight.data, 0.0)
            default_dtype = torch.get_default_dtype()
            thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * (
                2.0 * math.pi / module.n_heads
            )
            grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
            grid_init = (
                (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
                .view(module.n_heads, 1, 1, 2)
                .repeat(1, module.n_levels, module.n_points, 1)
            )
            for i in range(module.n_points):
                grid_init[:, :, i, :] *= i + 1
            with torch.no_grad():
                module.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
            nn.init.constant_(module.attention_weights.weight.data, 0.0)
            nn.init.constant_(module.attention_weights.bias.data, 0.0)
            nn.init.xavier_uniform_(module.value_proj.weight.data)
            nn.init.constant_(module.value_proj.bias.data, 0.0)
            nn.init.xavier_uniform_(module.output_proj.weight.data)
            nn.init.constant_(module.output_proj.bias.data, 0.0)
        elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        if hasattr(module, "reference_points") and not self.config.two_stage:
            nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0)
            nn.init.constant_(module.reference_points.bias.data, 0.0)
        if hasattr(module, "level_embed"):
            nn.init.normal_(module.level_embed)


class TestDetrEncoder(TestDetrPreTrainedModel):
    """
    Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
    [`TestDetrEncoderLayer`].

    The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.

    Args:
        config: TestDetrConfig
    """

    def __init__(self, config: TestDetrConfig):
        super().__init__(config)
        self.gradient_checkpointing = False

        self.dropout = config.dropout
        self.layers = nn.ModuleList([TestDetrEncoderLayer(config) for _ in range(config.encoder_layers)])

        # Initialize weights and apply final processing
        self.post_init()

    @staticmethod
    def get_reference_points(spatial_shapes, valid_ratios, device):
        """
        Get reference points for each feature map. Used in decoder.

        Args:
            spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of each feature map.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
                Valid ratios of each feature map.
            device (`torch.device`):
                Device on which to create the tensors.
        Returns:
            `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
        """
        reference_points_list = []
        for level, (height, width) in enumerate(spatial_shapes):
            ref_y, ref_x = meshgrid(
                torch.linspace(0.5, height - 0.5, height, dtype=valid_ratios.dtype, device=device),
                torch.linspace(0.5, width - 0.5, width, dtype=valid_ratios.dtype, device=device),
                indexing="ij",
            )
            # TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36
            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height)
            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width)
            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,
        inputs_embeds=None,
        attention_mask=None,
        position_embeddings=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        valid_ratios=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
                - 1 for pixel features that are real (i.e. **not masked**),
                - 0 for pixel features that are padding (i.e. **masked**).
                [What are attention masks?](../glossary#attention-mask)
            position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Position embeddings that are added to the queries and keys in each self-attention layer.
            spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of each feature map.
            level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
                Starting index of each feature map.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
                Ratio of valid area in each feature level.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        hidden_states = inputs_embeds
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        spatial_shapes_tuple = tuple(spatial_shapes_list)
        reference_points = self.get_reference_points(spatial_shapes_tuple, valid_ratios, device=inputs_embeds.device)

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    encoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    position_embeddings,
                    reference_points,
                    spatial_shapes,
                    spatial_shapes_list,
                    level_start_index,
                    output_attentions,
                )
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    attention_mask,
                    position_embeddings=position_embeddings,
                    reference_points=reference_points,
                    spatial_shapes=spatial_shapes,
                    spatial_shapes_list=spatial_shapes_list,
                    level_start_index=level_start_index,
                    output_attentions=output_attentions,
                )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=encoder_states,
            attentions=all_attentions,
        )


def inverse_sigmoid(x, eps=1e-5):
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1 / x2)


class TestDetrDecoder(TestDetrPreTrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TestDetrDecoderLayer`].

    The decoder updates the query embeddings through multiple self-attention and cross-attention layers.

    Some tweaks for Deformable DETR:

    - `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass.
    - it also returns a stack of intermediate outputs and reference points from all decoding layers.

    Args:
        config: TestDetrConfig
    """

    def __init__(self, config: TestDetrConfig):
        super().__init__(config)

        self.dropout = config.dropout
        self.layers = nn.ModuleList([TestDetrDecoderLayer(config) for _ in range(config.decoder_layers)])
        self.gradient_checkpointing = False

        # hack implementation for iterative bounding box refinement and two-stage Deformable DETR
        self.bbox_embed = None
        self.class_embed = None

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        position_embeddings=None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        valid_ratios=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
                The query embeddings that are passed into the decoder.
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                of the decoder.
            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected
                in `[0, 1]`:
                - 1 for pixels that are real (i.e. **not masked**),
                - 0 for pixels that are padding (i.e. **masked**).
            position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
                Position embeddings that are added to the queries and keys in each self-attention layer.
            reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*):
                Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area.
            spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of the feature maps.
            level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*):
                Indexes for the start of each feature level. In range `[0, sequence_length]`.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*):
                Ratio of valid area in each feature level.

            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if inputs_embeds is not None:
            hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        intermediate = ()
        intermediate_reference_points = ()

        for idx, decoder_layer in enumerate(self.layers):
            num_coordinates = reference_points.shape[-1]
            if num_coordinates == 4:
                reference_points_input = (
                    reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
                )
            elif reference_points.shape[-1] == 2:
                reference_points_input = reference_points[:, :, None] * valid_ratios[:, None]
            else:
                raise ValueError("Reference points' last dimension must be of size 2")

            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    position_embeddings,
                    reference_points_input,
                    spatial_shapes,
                    spatial_shapes_list,
                    level_start_index,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    output_attentions,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    position_embeddings=position_embeddings,
                    encoder_hidden_states=encoder_hidden_states,
                    reference_points=reference_points_input,
                    spatial_shapes=spatial_shapes,
                    spatial_shapes_list=spatial_shapes_list,
                    level_start_index=level_start_index,
                    encoder_attention_mask=encoder_attention_mask,
                    output_attentions=output_attentions,
                )

            hidden_states = layer_outputs[0]

            # hack implementation for iterative bounding box refinement
            if self.bbox_embed is not None:
                tmp = self.bbox_embed[idx](hidden_states)
                num_coordinates = reference_points.shape[-1]
                if num_coordinates == 4:
                    new_reference_points = tmp + inverse_sigmoid(reference_points)
                    new_reference_points = new_reference_points.sigmoid()
                elif num_coordinates == 2:
                    new_reference_points = tmp
                    new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)
                    new_reference_points = new_reference_points.sigmoid()
                else:
                    raise ValueError(
                        f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}"
                    )
                reference_points = new_reference_points.detach()

            intermediate += (hidden_states,)
            intermediate_reference_points += (reference_points,)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        # Keep batch_size as first dimension
        intermediate = torch.stack(intermediate, dim=1)
        intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    intermediate,
                    intermediate_reference_points,
                    all_hidden_states,
                    all_self_attns,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return TestDetrDecoderOutput(
            last_hidden_state=hidden_states,
            intermediate_hidden_states=intermediate,
            intermediate_reference_points=intermediate_reference_points,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )


def build_position_encoding(config):
    n_steps = config.d_model // 2
    if config.position_embedding_type == "sine":
        # TODO find a better way of exposing other arguments
        position_embedding = TestDetrSinePositionEmbedding(n_steps, normalize=True)
    elif config.position_embedding_type == "learned":
        position_embedding = TestDetrLearnedPositionEmbedding(n_steps)
    else:
        raise ValueError(f"Not supported {config.position_embedding_type}")

    return position_embedding


TEST_DETR_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`TestDetrConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

TEST_DETR_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it.

            Pixel values can be obtained using [`AutoImageProcessor`]. See [`TestDetrImageProcessor.__call__`]
            for details.

        pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:

            - 1 for pixels that are real (i.e. **not masked**),
            - 0 for pixels that are padding (i.e. **masked**).

            [What are attention masks?](../glossary#attention-mask)

        decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*):
            Not used by default. Can be used to mask object queries.
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
            can choose to directly pass a flattened representation of an image.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
            Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
            embedded representation.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    """
    The bare Deformable DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
    hidden-states without any specific head on top.
    """,
    TEST_DETR_START_DOCSTRING,
)
class TestDetrModel(TestDetrPreTrainedModel):
    def __init__(self, config: TestDetrConfig):
        super().__init__(config)

        # Create backbone + positional encoding
        backbone = TestDetrConvEncoder(config)
        position_embeddings = build_position_encoding(config)
        self.backbone = TestDetrConvModel(backbone, position_embeddings)

        # Create input projection layers
        if config.num_feature_levels > 1:
            num_backbone_outs = len(backbone.intermediate_channel_sizes)
            input_proj_list = []
            for _ in range(num_backbone_outs):
                in_channels = backbone.intermediate_channel_sizes[_]
                input_proj_list.append(
                    nn.Sequential(
                        nn.Conv2d(in_channels, config.d_model, kernel_size=1),
                        nn.GroupNorm(32, config.d_model),
                    )
                )
            for _ in range(config.num_feature_levels - num_backbone_outs):
                input_proj_list.append(
                    nn.Sequential(
                        nn.Conv2d(
                            in_channels,
                            config.d_model,
                            kernel_size=3,
                            stride=2,
                            padding=1,
                        ),
                        nn.GroupNorm(32, config.d_model),
                    )
                )
                in_channels = config.d_model
            self.input_proj = nn.ModuleList(input_proj_list)
        else:
            self.input_proj = nn.ModuleList(
                [
                    nn.Sequential(
                        nn.Conv2d(
                            backbone.intermediate_channel_sizes[-1],
                            config.d_model,
                            kernel_size=1,
                        ),
                        nn.GroupNorm(32, config.d_model),
                    )
                ]
            )

        if not config.two_stage:
            self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model * 2)

        self.encoder = TestDetrEncoder(config)
        self.decoder = TestDetrDecoder(config)

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

        if config.two_stage:
            self.enc_output = nn.Linear(config.d_model, config.d_model)
            self.enc_output_norm = nn.LayerNorm(config.d_model)
            self.pos_trans = nn.Linear(config.d_model * 2, config.d_model * 2)
            self.pos_trans_norm = nn.LayerNorm(config.d_model * 2)
        else:
            self.reference_points = nn.Linear(config.d_model, 2)

        self.post_init()

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def freeze_backbone(self):
        for name, param in self.backbone.conv_encoder.model.named_parameters():
            param.requires_grad_(False)

    def unfreeze_backbone(self):
        for name, param in self.backbone.conv_encoder.model.named_parameters():
            param.requires_grad_(True)

    def get_valid_ratio(self, mask, dtype=torch.float32):
        """Get the valid ratio of all feature maps."""

        _, height, width = mask.shape
        valid_height = torch.sum(mask[:, :, 0], 1)
        valid_width = torch.sum(mask[:, 0, :], 1)
        valid_ratio_height = valid_height.to(dtype) / height
        valid_ratio_width = valid_width.to(dtype) / width
        valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
        return valid_ratio

    def get_proposal_pos_embed(self, proposals):
        """Get the position embedding of the proposals."""

        num_pos_feats = self.config.d_model // 2
        temperature = 10000
        scale = 2 * math.pi

        dim_t = torch.arange(num_pos_feats, dtype=proposals.dtype, device=proposals.device)
        dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
        # batch_size, num_queries, 4
        proposals = proposals.sigmoid() * scale
        # batch_size, num_queries, 4, 128
        pos = proposals[:, :, :, None] / dim_t
        # batch_size, num_queries, 4, 64, 2 -> batch_size, num_queries, 512
        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
        return pos

    def gen_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes):
        """Generate the encoder output proposals from encoded enc_output.

        Args:
            enc_output (Tensor[batch_size, sequence_length, hidden_size]): Output of the encoder.
            padding_mask (Tensor[batch_size, sequence_length]): Padding mask for `enc_output`.
            spatial_shapes (List[Tuple[int, int]]): Spatial shapes of the feature maps.

        Returns:
            `tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction.
                - object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to
                  directly predict a bounding box. (without the need of a decoder)
                - output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse
                  sigmoid.
        """
        batch_size = enc_output.shape[0]
        proposals = []
        _cur = 0
        for level, (height, width) in enumerate(spatial_shapes):
            mask_flatten_ = padding_mask[:, _cur : (_cur + height * width)].view(batch_size, height, width, 1)
            valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
            valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1)

            grid_y, grid_x = meshgrid(
                torch.linspace(
                    0,
                    height - 1,
                    height,
                    dtype=enc_output.dtype,
                    device=enc_output.device,
                ),
                torch.linspace(
                    0,
                    width - 1,
                    width,
                    dtype=enc_output.dtype,
                    device=enc_output.device,
                ),
                indexing="ij",
            )
            grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)

            scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2)
            grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale
            width_height = torch.ones_like(grid) * 0.05 * (2.0**level)
            proposal = torch.cat((grid, width_height), -1).view(batch_size, -1, 4)
            proposals.append(proposal)
            _cur += height * width
        output_proposals = torch.cat(proposals, 1)
        output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
        output_proposals = torch.log(output_proposals / (1 - output_proposals))  # inverse sigmoid
        output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf"))
        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))

        # assign each pixel as an object query
        object_query = enc_output
        object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0))
        object_query = object_query.masked_fill(~output_proposals_valid, float(0))
        object_query = self.enc_output_norm(self.enc_output(object_query))
        return object_query, output_proposals

    @add_start_docstrings_to_model_forward(TEST_DETR_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TestDetrModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        pixel_mask: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.FloatTensor] = None,
        encoder_outputs: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], TestDetrModelOutput]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, TestDetrModel
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr")
        >>> model = TestDetrModel.from_pretrained("SenseTime/deformable-detr")

        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
        >>> list(last_hidden_states.shape)
        [1, 300, 256]
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        batch_size, num_channels, height, width = pixel_values.shape
        device = pixel_values.device

        if pixel_mask is None:
            pixel_mask = torch.ones(((batch_size, height, width)), dtype=torch.long, device=device)

        # Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper)
        # First, sent pixel_values + pixel_mask through Backbone to obtain the features
        # which is a list of tuples
        features, position_embeddings_list = self.backbone(pixel_values, pixel_mask)

        # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
        sources = []
        masks = []
        for level, (source, mask) in enumerate(features):
            sources.append(self.input_proj[level](source))
            masks.append(mask)
            if mask is None:
                raise ValueError("No attention mask was provided")

        # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage
        if self.config.num_feature_levels > len(sources):
            _len_sources = len(sources)
            for level in range(_len_sources, self.config.num_feature_levels):
                if level == _len_sources:
                    source = self.input_proj[level](features[-1][0])
                else:
                    source = self.input_proj[level](sources[-1])
                mask = nn.functional.interpolate(pixel_mask[None].to(pixel_values.dtype), size=source.shape[-2:]).to(
                    torch.bool
                )[0]
                pos_l = self.backbone.position_embedding(source, mask).to(source.dtype)
                sources.append(source)
                masks.append(mask)
                position_embeddings_list.append(pos_l)

        # Create queries
        query_embeds = None
        if not self.config.two_stage:
            query_embeds = self.query_position_embeddings.weight

        # Prepare encoder inputs (by flattening)
        source_flatten = []
        mask_flatten = []
        lvl_pos_embed_flatten = []
        spatial_shapes_list = []
        for level, (source, mask, pos_embed) in enumerate(zip(sources, masks, position_embeddings_list)):
            batch_size, num_channels, height, width = source.shape
            spatial_shape = (height, width)
            spatial_shapes_list.append(spatial_shape)
            source = source.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_embed[level].view(1, 1, -1)
            lvl_pos_embed_flatten.append(lvl_pos_embed)
            source_flatten.append(source)
            mask_flatten.append(mask)
        source_flatten = torch.cat(source_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_list, dtype=torch.long, device=source_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, dtype=source_flatten.dtype) for m in masks], 1)

        # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder
        # Also provide spatial_shapes, level_start_index and valid_ratios
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                inputs_embeds=source_flatten,
                attention_mask=mask_flatten,
                position_embeddings=lvl_pos_embed_flatten,
                spatial_shapes=spatial_shapes,
                spatial_shapes_list=spatial_shapes_list,
                level_start_index=level_start_index,
                valid_ratios=valid_ratios,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
            )

        # Fifth, prepare decoder inputs
        batch_size, _, num_channels = encoder_outputs[0].shape
        enc_outputs_class = None
        enc_outputs_coord_logits = None
        if self.config.two_stage:
            object_query_embedding, output_proposals = self.gen_encoder_output_proposals(
                encoder_outputs[0], ~mask_flatten, spatial_shapes_list
            )

            # hack implementation for two-stage Deformable DETR
            # apply a detection head to each pixel (A.4 in paper)
            # linear projection for bounding box binary classification (i.e. foreground and background)
            enc_outputs_class = self.decoder.class_embed[-1](object_query_embedding)
            # 3-layer FFN to predict bounding boxes coordinates (bbox regression branch)
            delta_bbox = self.decoder.bbox_embed[-1](object_query_embedding)
            enc_outputs_coord_logits = delta_bbox + output_proposals

            # only keep top scoring `config.two_stage_num_proposals` proposals
            topk = self.config.two_stage_num_proposals
            topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]
            topk_coords_logits = torch.gather(
                enc_outputs_coord_logits,
                1,
                topk_proposals.unsqueeze(-1).repeat(1, 1, 4),
            )

            topk_coords_logits = topk_coords_logits.detach()
            reference_points = topk_coords_logits.sigmoid()
            init_reference_points = reference_points
            pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_logits)))
            query_embed, target = torch.split(pos_trans_out, num_channels, dim=2)
        else:
            query_embed, target = torch.split(query_embeds, num_channels, dim=1)
            query_embed = query_embed.unsqueeze(0).expand(batch_size, -1, -1)
            target = target.unsqueeze(0).expand(batch_size, -1, -1)
            reference_points = self.reference_points(query_embed).sigmoid()
            init_reference_points = reference_points

        decoder_outputs = self.decoder(
            inputs_embeds=target,
            position_embeddings=query_embed,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=mask_flatten,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            valid_ratios=valid_ratios,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            enc_outputs = tuple(value for value in [enc_outputs_class, enc_outputs_coord_logits] if value is not None)
            tuple_outputs = (init_reference_points,) + decoder_outputs + encoder_outputs + enc_outputs

            return tuple_outputs

        return TestDetrModelOutput(
            init_reference_points=init_reference_points,
            last_hidden_state=decoder_outputs.last_hidden_state,
            intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
            intermediate_reference_points=decoder_outputs.intermediate_reference_points,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            enc_outputs_class=enc_outputs_class,
            enc_outputs_coord_logits=enc_outputs_coord_logits,
        )