transformers/tests/models/deimv2/test_modeling_deimv2.py

# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch DEIMv2 model."""

import copy
import inspect
import math
import tempfile
import unittest
from functools import cached_property

from parameterized import parameterized

from transformers import (
    AutoImageProcessor,
    Deimv2Config,
    DINOv3ViTConfig,
    HGNetV2Config,
    is_torch_available,
)
from transformers.testing_utils import (
    require_torch,
    require_torch_accelerator,
    require_vision,
    slow,
    torch_device,
)


if is_torch_available():
    import torch

    from transformers import Deimv2ForObjectDetection, Deimv2Model

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
    ModelTesterMixin,
    _test_eager_matches_sdpa_inference,
    floats_tensor,
)
from ...test_pipeline_mixin import PipelineTesterMixin
from ...test_processing_common import url_to_local_path


# TODO: Replace with the official Transformers ckpt once uploaded.
CHECKPOINT = "harshaljanjani/DEIMv2_HGNetv2_N_COCO_Transformers"
CHECKPOINT_LITE = "harshaljanjani/DEIMv2_HGNetv2_PICO_COCO_Transformers"
CHECKPOINT_DINOV3 = "harshaljanjani/DEIMv2_DINOv3_S_COCO_Transformers"


class Deimv2ModelTester:
    def __init__(
        self,
        parent,
        batch_size=3,
        is_training=True,
        use_labels=True,
        n_targets=3,
        num_labels=10,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        batch_norm_eps=1e-5,
        backbone_config=None,
        encoder_hidden_dim=32,
        encoder_in_channels=[128, 256, 512],
        feat_strides=[8, 16, 32],
        encoder_layers=1,
        encoder_ffn_dim=64,
        encoder_attention_heads=2,
        dropout=0.0,
        activation_dropout=0.0,
        encode_proj_layers=[2],
        positional_encoding_temperature=10000,
        encoder_activation_function="gelu",
        activation_function="silu",
        eval_size=None,
        normalize_before=False,
        d_model=32,
        num_queries=30,
        decoder_in_channels=[32, 32, 32],
        decoder_ffn_dim=64,
        num_feature_levels=3,
        decoder_n_points=[3, 6, 3],
        decoder_n_levels=3,
        decoder_layers=2,
        decoder_attention_heads=2,
        decoder_activation_function="relu",
        attention_dropout=0.0,
        num_denoising=0,
        label_noise_ratio=0.5,
        box_noise_scale=1.0,
        learn_initial_query=False,
        anchor_image_size=None,
        image_size=64,
        disable_custom_kernels=True,
        with_box_refine=True,
        decoder_offset_scale=0.5,
        eval_idx=-1,
        layer_scale=1,
        reg_max=32,
        reg_scale=4.0,
        depth_mult=0.34,
        hidden_expansion=0.5,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = 3
        self.is_training = is_training
        self.use_labels = use_labels
        self.n_targets = n_targets
        self.num_labels = num_labels
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.batch_norm_eps = batch_norm_eps
        self.backbone_config = backbone_config
        self.encoder_hidden_dim = encoder_hidden_dim
        self.encoder_in_channels = encoder_in_channels
        self.feat_strides = feat_strides
        self.encoder_layers = encoder_layers
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_attention_heads = encoder_attention_heads
        self.dropout = dropout
        self.activation_dropout = activation_dropout
        self.encode_proj_layers = encode_proj_layers
        self.positional_encoding_temperature = positional_encoding_temperature
        self.encoder_activation_function = encoder_activation_function
        self.activation_function = activation_function
        self.eval_size = eval_size
        self.normalize_before = normalize_before
        self.d_model = d_model
        self.num_queries = num_queries
        self.decoder_in_channels = decoder_in_channels
        self.decoder_ffn_dim = decoder_ffn_dim
        self.num_feature_levels = num_feature_levels
        self.decoder_n_points = decoder_n_points
        self.decoder_n_levels = decoder_n_levels
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.decoder_activation_function = decoder_activation_function
        self.attention_dropout = attention_dropout
        self.decoder_offset_scale = decoder_offset_scale
        self.eval_idx = eval_idx
        self.layer_scale = layer_scale
        self.reg_max = reg_max
        self.reg_scale = reg_scale
        self.depth_mult = depth_mult
        self.num_denoising = num_denoising
        self.label_noise_ratio = label_noise_ratio
        self.box_noise_scale = box_noise_scale
        self.learn_initial_query = learn_initial_query
        self.anchor_image_size = anchor_image_size
        self.image_size = image_size
        self.disable_custom_kernels = disable_custom_kernels
        self.with_box_refine = with_box_refine
        self.hidden_expansion = hidden_expansion

        self.encoder_seq_length = math.ceil(self.image_size / 32) * math.ceil(self.image_size / 32)

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)

        labels = None
        if self.use_labels:
            labels = []
            for i in range(self.batch_size):
                target = {}
                target["class_labels"] = torch.randint(
                    high=self.num_labels, size=(self.n_targets,), device=torch_device
                )
                target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
                labels.append(target)

        config = self.get_config()
        config.num_labels = self.num_labels
        return config, pixel_values, pixel_mask, labels

    def get_config(self):
        hidden_sizes = [64, 128, 256, 512]
        backbone_config = HGNetV2Config(
            stage_in_channels=[16, 64, 128, 256],
            stage_mid_channels=[16, 32, 64, 128],
            stage_out_channels=[64, 128, 256, 512],
            stage_num_blocks=[1, 1, 2, 1],
            stage_downsample=[False, True, True, True],
            stage_light_block=[False, False, True, True],
            stage_kernel_size=[3, 3, 5, 5],
            stage_numb_of_layers=[3, 3, 3, 3],
            embeddings_size=10,
            hidden_sizes=hidden_sizes,
            depths=[1, 1, 2, 1],
            out_features=["stage2", "stage3", "stage4"],
            out_indices=[2, 3, 4],
            stem_channels=[3, 16, 16],
            use_lab=True,
        )
        return Deimv2Config(
            backbone_config=backbone_config,
            encoder_hidden_dim=self.encoder_hidden_dim,
            encoder_in_channels=self.encoder_in_channels,
            feat_strides=self.feat_strides,
            encoder_layers=self.encoder_layers,
            encoder_ffn_dim=self.encoder_ffn_dim,
            encoder_attention_heads=self.encoder_attention_heads,
            dropout=self.dropout,
            activation_dropout=self.activation_dropout,
            encode_proj_layers=self.encode_proj_layers,
            positional_encoding_temperature=self.positional_encoding_temperature,
            encoder_activation_function=self.encoder_activation_function,
            activation_function=self.activation_function,
            eval_size=self.eval_size,
            normalize_before=self.normalize_before,
            d_model=self.d_model,
            num_queries=self.num_queries,
            decoder_in_channels=self.decoder_in_channels,
            decoder_ffn_dim=self.decoder_ffn_dim,
            num_feature_levels=self.num_feature_levels,
            decoder_n_points=self.decoder_n_points,
            decoder_n_levels=self.decoder_n_levels,
            decoder_layers=self.decoder_layers,
            decoder_attention_heads=self.decoder_attention_heads,
            decoder_activation_function=self.decoder_activation_function,
            decoder_offset_scale=self.decoder_offset_scale,
            eval_idx=self.eval_idx,
            layer_scale=self.layer_scale,
            reg_max=self.reg_max,
            reg_scale=self.reg_scale,
            depth_mult=self.depth_mult,
            attention_dropout=self.attention_dropout,
            num_denoising=self.num_denoising,
            label_noise_ratio=self.label_noise_ratio,
            box_noise_scale=self.box_noise_scale,
            learn_initial_query=self.learn_initial_query,
            anchor_image_size=self.anchor_image_size,
            image_size=self.image_size,
            disable_custom_kernels=self.disable_custom_kernels,
            with_box_refine=self.with_box_refine,
        )

    def prepare_config_and_inputs_for_common(self):
        config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict

    def create_and_check_deimv2_model(self, config, pixel_values, pixel_mask, labels):
        model = Deimv2Model(config=config)
        model.to(torch_device)
        model.eval()

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
        result = model(pixel_values)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.d_model))

    def create_and_check_deimv2_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
        model = Deimv2ForObjectDetection(config=config)
        model.to(torch_device)
        model.eval()

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
        result = model(pixel_values)

        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))


@require_torch
class Deimv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (Deimv2Model, Deimv2ForObjectDetection) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"image-feature-extraction": Deimv2Model, "object-detection": Deimv2ForObjectDetection}
        if is_torch_available()
        else {}
    )
    is_encoder_decoder = True
    test_resize_embeddings = False

    test_missing_keys = False

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class.__name__ == "Deimv2ForObjectDetection":
                labels = []
                for i in range(self.model_tester.batch_size):
                    target = {}
                    target["class_labels"] = torch.ones(
                        size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
                    )
                    target["boxes"] = torch.ones(
                        self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
                    )
                    labels.append(target)
                inputs_dict["labels"] = labels

        return inputs_dict

    def setUp(self):
        self.model_tester = Deimv2ModelTester(self)
        self.config_tester = ConfigTester(
            self,
            config_class=Deimv2Config,
            has_text_modality=False,
            common_properties=["hidden_size", "num_attention_heads"],
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_deimv2_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deimv2_model(*config_and_inputs)

    def test_deimv2_object_detection_head_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deimv2_object_detection_head_model(*config_and_inputs)

    @unittest.skip(reason="Multi-scale deformable attention is incompatible with nn.DataParallel")
    def test_multi_gpu_data_parallel_forward(self):
        pass

    @unittest.skip(
        reason="Deimv2 is a vision model but inputs_embeds is in the forward signature (inherited from D-FINE)"
    )
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Forward signature has inputs_embeds but no input_ids")
    def test_inputs_embeds_matches_input_ids(self):
        pass

    @unittest.skip(reason="Base test asserts get_input_embeddings() returns nn.Embedding which vision models lack")
    def test_model_get_set_embeddings(self):
        pass

    @unittest.skip(reason="Decoder heads are shared via reference assignment so untied saving is not applicable")
    def test_load_save_without_tied_weights(self):
        pass

    # Override: Multi-scale deformable attention outputs have different shapes than standard self-attention
    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            config.return_dict = True
            model = model_class._from_config(config, attn_implementation="eager")
            config = model.config
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions
            self.assertEqual(len(attentions), self.model_tester.encoder_layers)

            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions
            self.assertEqual(len(attentions), self.model_tester.encoder_layers)

            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [
                    self.model_tester.encoder_attention_heads,
                    self.model_tester.encoder_seq_length,
                    self.model_tester.encoder_seq_length,
                ],
            )
            out_len = len(outputs)

            correct_outlen = 15

            if "labels" in inputs_dict:
                correct_outlen += 1
            if model_class.__name__ == "Deimv2ForObjectDetection":
                correct_outlen += 2

            self.assertEqual(out_len, correct_outlen)

            decoder_attentions = outputs.decoder_attentions
            self.assertIsInstance(decoder_attentions, (list, tuple))
            self.assertEqual(len(decoder_attentions), self.model_tester.decoder_layers)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]),
                [
                    self.model_tester.decoder_attention_heads,
                    self.model_tester.num_queries,
                    self.model_tester.num_queries,
                ],
            )

            cross_attentions = outputs.cross_attentions
            self.assertIsInstance(cross_attentions, (list, tuple))
            self.assertEqual(len(cross_attentions), self.model_tester.decoder_layers)
            self.assertListEqual(
                list(cross_attentions[0].shape[-3:]),
                [
                    self.model_tester.num_queries,
                    self.model_tester.decoder_attention_heads,
                    self.model_tester.decoder_n_levels * self.model_tester.decoder_n_points
                    if isinstance(self.model_tester.decoder_n_points, int)
                    else sum(self.model_tester.decoder_n_points),
                ],
            )

            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            if hasattr(self.model_tester, "num_hidden_states_types"):
                added_hidden_states = self.model_tester.num_hidden_states_types
            else:
                added_hidden_states = 2
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.encoder_attentions

            self.assertEqual(len(self_attentions), self.model_tester.encoder_layers)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [
                    self.model_tester.encoder_attention_heads,
                    self.model_tester.encoder_seq_length,
                    self.model_tester.encoder_seq_length,
                ],
            )

    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            expected_num_layers = getattr(
                self.model_tester, "expected_num_hidden_layers", len(self.model_tester.encoder_in_channels) - 1
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

            self.assertListEqual(
                list(hidden_states[1].shape[-2:]),
                [
                    self.model_tester.image_size // self.model_tester.feat_strides[-1],
                    self.model_tester.image_size // self.model_tester.feat_strides[-1],
                ],
            )

            if config.is_encoder_decoder:
                hidden_states = outputs.decoder_hidden_states

                expected_num_layers = getattr(
                    self.model_tester, "expected_num_hidden_layers", self.model_tester.decoder_layers + 1
                )

                self.assertIsInstance(hidden_states, (list, tuple))
                self.assertEqual(len(hidden_states), expected_num_layers)

                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]),
                    [self.model_tester.num_queries, self.model_tester.d_model],
                )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

    # Override: Custom gradient retention check for multi-scale deformable attention outputs
    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)

        outputs = model(**inputs)

        output = outputs[0]

        encoder_hidden_states = outputs.encoder_hidden_states[0]
        encoder_attentions = outputs.encoder_attentions[0]
        encoder_hidden_states.retain_grad()
        encoder_attentions.retain_grad()

        decoder_attentions = outputs.decoder_attentions[0]
        decoder_attentions.retain_grad()

        cross_attentions = outputs.cross_attentions[0]
        cross_attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(encoder_hidden_states.grad)
        self.assertIsNotNone(encoder_attentions.grad)
        self.assertIsNotNone(decoder_attentions.grad)
        self.assertIsNotNone(cross_attentions.grad)

    # Override: Deimv2 uses pixel_values as main input, not input_ids
    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            arg_names = [*signature.parameters.keys()]
            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_backbone_selection(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def _validate_backbone_init(config):
            for model_class in self.all_model_classes:
                model = model_class(copy.deepcopy(config))
                model.to(torch_device)
                model.eval()
                with torch.no_grad():
                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))

                if model_class.__name__ == "Deimv2ForObjectDetection":
                    expected_shape = (
                        self.model_tester.batch_size,
                        self.model_tester.num_queries,
                        self.model_tester.num_labels,
                    )
                    self.assertEqual(outputs.logits.shape, expected_shape)
                    self.assertEqual(len(model.model.conv_encoder.intermediate_channel_sizes), 3)
                else:
                    self.assertEqual(len(model.conv_encoder.intermediate_channel_sizes), 3)

                self.assertTrue(outputs)

        config_dict = config.to_dict()
        config_dict["encoder_in_channels"] = [24, 40, 432]
        config_dict["backbone"] = "tf_mobilenetv3_small_075"
        config_dict["backbone_config"] = None
        config_dict["use_timm_backbone"] = True
        config_dict["backbone_kwargs"] = {"out_indices": [2, 3, 4]}
        config = config.__class__(**config_dict)
        _validate_backbone_init(config)

        config_dict = config.to_dict()
        config_dict["backbone"] = "microsoft/resnet-18"
        config_dict["backbone_config"] = None
        config_dict["use_timm_backbone"] = False
        config_dict["use_pretrained_backbone"] = True
        config_dict["backbone_kwargs"] = {"out_indices": [2, 3, 4]}
        config = config.__class__(**config_dict)
        _validate_backbone_init(config)

    @parameterized.expand(["float32", "float16", "bfloat16"])
    @require_torch_accelerator
    @slow
    def test_inference_with_different_dtypes(self, dtype_str):
        dtype = {
            "float32": torch.float32,
            "float16": torch.float16,
            "bfloat16": torch.bfloat16,
        }[dtype_str]

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device).to(dtype)
            model.eval()
            for key, tensor in inputs_dict.items():
                if tensor.dtype == torch.float32:
                    inputs_dict[key] = tensor.to(dtype)
            with torch.no_grad():
                _ = model(**self._prepare_for_class(inputs_dict, model_class))

    @parameterized.expand(["float32", "float16", "bfloat16"])
    @require_torch_accelerator
    @slow
    def test_inference_equivalence_for_static_and_dynamic_anchors(self, dtype_str):
        dtype = {
            "float32": torch.float32,
            "float16": torch.float16,
            "bfloat16": torch.bfloat16,
        }[dtype_str]

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        h, w = inputs_dict["pixel_values"].shape[-2:]

        for key, tensor in inputs_dict.items():
            if tensor.dtype == torch.float32:
                inputs_dict[key] = tensor.to(dtype)

        for model_class in self.all_model_classes:
            with tempfile.TemporaryDirectory() as tmpdirname:
                model_class(config).save_pretrained(tmpdirname)
                model_static = model_class.from_pretrained(
                    tmpdirname, anchor_image_size=[h, w], device_map=torch_device, dtype=dtype
                ).eval()
                model_dynamic = model_class.from_pretrained(
                    tmpdirname, anchor_image_size=None, device_map=torch_device, dtype=dtype
                ).eval()

            self.assertIsNotNone(model_static.config.anchor_image_size)
            self.assertIsNone(model_dynamic.config.anchor_image_size)

            with torch.no_grad():
                outputs_static = model_static(**self._prepare_for_class(inputs_dict, model_class))
                outputs_dynamic = model_dynamic(**self._prepare_for_class(inputs_dict, model_class))

            torch.testing.assert_close(
                outputs_static.last_hidden_state, outputs_dynamic.last_hidden_state, rtol=1e-4, atol=1e-4
            )


class Deimv2LiteEncoderModelTester:
    def __init__(
        self,
        parent,
        batch_size=3,
        is_training=True,
        use_labels=True,
        n_targets=3,
        num_labels=10,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        batch_norm_eps=1e-5,
        encoder_hidden_dim=32,
        encoder_in_channels=[256],
        feat_strides=[16, 32],
        dropout=0.0,
        activation_dropout=0.0,
        positional_encoding_temperature=10000,
        encoder_activation_function="gelu",
        activation_function="silu",
        eval_size=None,
        normalize_before=False,
        d_model=32,
        num_queries=10,
        decoder_in_channels=[32, 32],
        decoder_ffn_dim=64,
        num_feature_levels=2,
        decoder_n_points=[4, 2],
        decoder_n_levels=2,
        decoder_layers=2,
        decoder_attention_heads=2,
        decoder_activation_function="relu",
        attention_dropout=0.0,
        num_denoising=0,
        label_noise_ratio=0.5,
        box_noise_scale=1.0,
        learn_initial_query=False,
        anchor_image_size=None,
        image_size=64,
        disable_custom_kernels=True,
        with_box_refine=True,
        decoder_offset_scale=0.5,
        eval_idx=-1,
        layer_scale=1,
        reg_max=32,
        reg_scale=4.0,
        depth_mult=0.34,
        hidden_expansion=0.5,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = 3
        self.is_training = is_training
        self.use_labels = use_labels
        self.n_targets = n_targets
        self.num_labels = num_labels
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.batch_norm_eps = batch_norm_eps
        self.encoder_hidden_dim = encoder_hidden_dim
        self.encoder_in_channels = encoder_in_channels
        self.feat_strides = feat_strides
        self.encoder_layers = 0
        self.encoder_ffn_dim = 64
        self.encoder_attention_heads = 2
        self.dropout = dropout
        self.activation_dropout = activation_dropout
        self.encode_proj_layers = []
        self.positional_encoding_temperature = positional_encoding_temperature
        self.encoder_activation_function = encoder_activation_function
        self.activation_function = activation_function
        self.eval_size = eval_size
        self.normalize_before = normalize_before
        self.d_model = d_model
        self.num_queries = num_queries
        self.decoder_in_channels = decoder_in_channels
        self.decoder_ffn_dim = decoder_ffn_dim
        self.num_feature_levels = num_feature_levels
        self.decoder_n_points = decoder_n_points
        self.decoder_n_levels = decoder_n_levels
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.decoder_activation_function = decoder_activation_function
        self.attention_dropout = attention_dropout
        self.decoder_offset_scale = decoder_offset_scale
        self.eval_idx = eval_idx
        self.layer_scale = layer_scale
        self.reg_max = reg_max
        self.reg_scale = reg_scale
        self.depth_mult = depth_mult
        self.num_denoising = num_denoising
        self.label_noise_ratio = label_noise_ratio
        self.box_noise_scale = box_noise_scale
        self.learn_initial_query = learn_initial_query
        self.anchor_image_size = anchor_image_size
        self.image_size = image_size
        self.disable_custom_kernels = disable_custom_kernels
        self.with_box_refine = with_box_refine
        self.hidden_expansion = hidden_expansion

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)

        labels = None
        if self.use_labels:
            labels = []
            for i in range(self.batch_size):
                target = {}
                target["class_labels"] = torch.randint(
                    high=self.num_labels, size=(self.n_targets,), device=torch_device
                )
                target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
                labels.append(target)

        config = self.get_config()
        config.num_labels = self.num_labels
        return config, pixel_values, pixel_mask, labels

    def get_config(self):
        backbone_config = HGNetV2Config(
            stage_in_channels=[16, 64, 128],
            stage_mid_channels=[16, 32, 64],
            stage_out_channels=[64, 128, 256],
            stage_num_blocks=[1, 1, 1],
            stage_downsample=[False, True, True],
            stage_light_block=[False, False, True],
            stage_kernel_size=[3, 3, 3],
            stage_numb_of_layers=[3, 3, 3],
            embeddings_size=10,
            hidden_sizes=[64, 128, 256],
            depths=[1, 1, 1],
            out_features=["stage3"],
            out_indices=[3],
            stem_channels=[3, 16, 16],
            use_lab=True,
        )
        return Deimv2Config(
            backbone_config=backbone_config,
            encoder_hidden_dim=self.encoder_hidden_dim,
            encoder_in_channels=self.encoder_in_channels,
            feat_strides=self.feat_strides,
            encoder_layers=self.encoder_layers,
            encoder_ffn_dim=self.encoder_ffn_dim,
            encoder_attention_heads=self.encoder_attention_heads,
            dropout=self.dropout,
            activation_dropout=self.activation_dropout,
            encode_proj_layers=self.encode_proj_layers,
            positional_encoding_temperature=self.positional_encoding_temperature,
            encoder_activation_function=self.encoder_activation_function,
            activation_function=self.activation_function,
            eval_size=self.eval_size,
            normalize_before=self.normalize_before,
            d_model=self.d_model,
            num_queries=self.num_queries,
            decoder_in_channels=self.decoder_in_channels,
            decoder_ffn_dim=self.decoder_ffn_dim,
            num_feature_levels=self.num_feature_levels,
            decoder_n_points=self.decoder_n_points,
            decoder_n_levels=self.decoder_n_levels,
            decoder_layers=self.decoder_layers,
            decoder_attention_heads=self.decoder_attention_heads,
            decoder_activation_function=self.decoder_activation_function,
            decoder_offset_scale=self.decoder_offset_scale,
            eval_idx=self.eval_idx,
            layer_scale=self.layer_scale,
            reg_max=self.reg_max,
            reg_scale=self.reg_scale,
            depth_mult=self.depth_mult,
            attention_dropout=self.attention_dropout,
            num_denoising=self.num_denoising,
            label_noise_ratio=self.label_noise_ratio,
            box_noise_scale=self.box_noise_scale,
            learn_initial_query=self.learn_initial_query,
            anchor_image_size=self.anchor_image_size,
            image_size=self.image_size,
            disable_custom_kernels=self.disable_custom_kernels,
            with_box_refine=self.with_box_refine,
            encoder_type="lite",
            use_gateway=False,
            share_bbox_head=False,
        )

    def prepare_config_and_inputs_for_common(self):
        config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict

    def create_and_check_deimv2_model(self, config, pixel_values, pixel_mask, labels):
        model = Deimv2Model(config=config)
        model.to(torch_device)
        model.eval()

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
        result = model(pixel_values)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.d_model))

    def create_and_check_deimv2_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
        model = Deimv2ForObjectDetection(config=config)
        model.to(torch_device)
        model.eval()

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
        result = model(pixel_values)

        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))


@require_torch
class Deimv2LiteEncoderModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (Deimv2Model, Deimv2ForObjectDetection) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"image-feature-extraction": Deimv2Model, "object-detection": Deimv2ForObjectDetection}
        if is_torch_available()
        else {}
    )
    is_encoder_decoder = True
    test_resize_embeddings = False
    has_attentions = False

    test_missing_keys = False

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class.__name__ == "Deimv2ForObjectDetection":
                labels = []
                for i in range(self.model_tester.batch_size):
                    target = {}
                    target["class_labels"] = torch.ones(
                        size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
                    )
                    target["boxes"] = torch.ones(
                        self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
                    )
                    labels.append(target)
                inputs_dict["labels"] = labels

        return inputs_dict

    def setUp(self):
        self.model_tester = Deimv2LiteEncoderModelTester(self)
        self.config_tester = ConfigTester(
            self,
            config_class=Deimv2Config,
            has_text_modality=False,
            common_properties=["hidden_size", "num_attention_heads"],
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_deimv2_lite_encoder_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deimv2_model(*config_and_inputs)

    def test_deimv2_lite_encoder_object_detection_head_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deimv2_object_detection_head_model(*config_and_inputs)

    @unittest.skip(reason="Multi-scale deformable attention is incompatible with nn.DataParallel")
    def test_multi_gpu_data_parallel_forward(self):
        pass

    @unittest.skip(
        reason="Deimv2 is a vision model but inputs_embeds is in the forward signature (inherited from D-FINE)"
    )
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Forward signature has inputs_embeds but no input_ids")
    def test_inputs_embeds_matches_input_ids(self):
        pass

    @unittest.skip(reason="Base test asserts get_input_embeddings() returns nn.Embedding which vision models lack")
    def test_model_get_set_embeddings(self):
        pass

    @unittest.skip(reason="Decoder heads are shared via reference assignment so untied saving is not applicable")
    def test_load_save_without_tied_weights(self):
        pass

    @unittest.skip(
        reason="LiteEncoder has no encoder_hidden_states so the base test fails accessing encoder_hidden_states[0]"
    )
    def test_retain_grad_hidden_states_attentions(self):
        pass

    # Override: LiteEncoder has no encoder hidden states, only decoder hidden states
    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            if config.is_encoder_decoder:
                hidden_states = outputs.decoder_hidden_states

                expected_num_layers = self.model_tester.decoder_layers + 1

                self.assertIsInstance(hidden_states, (list, tuple))
                self.assertEqual(len(hidden_states), expected_num_layers)

                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]),
                    [self.model_tester.num_queries, self.model_tester.d_model],
                )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

    # Override: Deimv2 uses pixel_values as main input, not input_ids
    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            arg_names = [*signature.parameters.keys()]
            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)


class Deimv2DINOv3ModelTester:
    def __init__(
        self,
        parent,
        batch_size=3,
        is_training=True,
        use_labels=True,
        n_targets=3,
        num_labels=10,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        batch_norm_eps=1e-5,
        encoder_hidden_dim=32,
        encoder_in_channels=[32, 32, 32],
        feat_strides=[8, 16, 32],
        encoder_layers=1,
        encoder_ffn_dim=64,
        encoder_attention_heads=2,
        dropout=0.0,
        activation_dropout=0.0,
        encode_proj_layers=[2],
        positional_encoding_temperature=10000,
        encoder_activation_function="gelu",
        activation_function="silu",
        eval_size=None,
        normalize_before=False,
        d_model=32,
        num_queries=30,
        decoder_in_channels=[32, 32, 32],
        decoder_ffn_dim=64,
        num_feature_levels=3,
        decoder_n_points=4,
        decoder_n_levels=3,
        decoder_layers=2,
        decoder_attention_heads=2,
        decoder_activation_function="relu",
        attention_dropout=0.0,
        num_denoising=0,
        label_noise_ratio=0.5,
        box_noise_scale=1.0,
        learn_initial_query=False,
        anchor_image_size=None,
        image_size=64,
        disable_custom_kernels=True,
        with_box_refine=True,
        decoder_offset_scale=0.5,
        eval_idx=-1,
        layer_scale=1,
        reg_max=32,
        reg_scale=4.0,
        depth_mult=0.34,
        hidden_expansion=0.5,
        sta_inplanes=8,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = 3
        self.is_training = is_training
        self.use_labels = use_labels
        self.n_targets = n_targets
        self.num_labels = num_labels
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.batch_norm_eps = batch_norm_eps
        self.encoder_hidden_dim = encoder_hidden_dim
        self.encoder_in_channels = encoder_in_channels
        self.feat_strides = feat_strides
        self.encoder_layers = encoder_layers
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_attention_heads = encoder_attention_heads
        self.dropout = dropout
        self.activation_dropout = activation_dropout
        self.encode_proj_layers = encode_proj_layers
        self.positional_encoding_temperature = positional_encoding_temperature
        self.encoder_activation_function = encoder_activation_function
        self.activation_function = activation_function
        self.eval_size = eval_size
        self.normalize_before = normalize_before
        self.d_model = d_model
        self.num_queries = num_queries
        self.decoder_in_channels = decoder_in_channels
        self.decoder_ffn_dim = decoder_ffn_dim
        self.num_feature_levels = num_feature_levels
        self.decoder_n_points = decoder_n_points
        self.decoder_n_levels = decoder_n_levels
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.decoder_activation_function = decoder_activation_function
        self.attention_dropout = attention_dropout
        self.decoder_offset_scale = decoder_offset_scale
        self.eval_idx = eval_idx
        self.layer_scale = layer_scale
        self.reg_max = reg_max
        self.reg_scale = reg_scale
        self.depth_mult = depth_mult
        self.num_denoising = num_denoising
        self.label_noise_ratio = label_noise_ratio
        self.box_noise_scale = box_noise_scale
        self.learn_initial_query = learn_initial_query
        self.anchor_image_size = anchor_image_size
        self.image_size = image_size
        self.disable_custom_kernels = disable_custom_kernels
        self.with_box_refine = with_box_refine
        self.hidden_expansion = hidden_expansion
        self.sta_inplanes = sta_inplanes

        self.encoder_seq_length = math.ceil(self.image_size / 32) * math.ceil(self.image_size / 32)

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)

        labels = None
        if self.use_labels:
            labels = []
            for i in range(self.batch_size):
                target = {}
                target["class_labels"] = torch.randint(
                    high=self.num_labels, size=(self.n_targets,), device=torch_device
                )
                target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
                labels.append(target)

        config = self.get_config()
        config.num_labels = self.num_labels
        return config, pixel_values, pixel_mask, labels

    def get_config(self):
        backbone_config = DINOv3ViTConfig(
            hidden_size=32,
            num_attention_heads=2,
            num_hidden_layers=4,
            intermediate_size=64,
            num_register_tokens=1,
            layerscale_value=1.0,
            use_gated_mlp=False,
            rope_theta=100.0,
            patch_size=16,
            image_size=self.image_size,
            out_indices=[2, 3, 4],
            apply_layernorm=False,
            reshape_hidden_states=True,
        )
        return Deimv2Config(
            backbone_config=backbone_config,
            encoder_hidden_dim=self.encoder_hidden_dim,
            encoder_in_channels=self.encoder_in_channels,
            feat_strides=self.feat_strides,
            encoder_layers=self.encoder_layers,
            encoder_ffn_dim=self.encoder_ffn_dim,
            encoder_attention_heads=self.encoder_attention_heads,
            dropout=self.dropout,
            activation_dropout=self.activation_dropout,
            encode_proj_layers=self.encode_proj_layers,
            positional_encoding_temperature=self.positional_encoding_temperature,
            encoder_activation_function=self.encoder_activation_function,
            activation_function=self.activation_function,
            eval_size=self.eval_size,
            normalize_before=self.normalize_before,
            d_model=self.d_model,
            num_queries=self.num_queries,
            decoder_in_channels=self.decoder_in_channels,
            decoder_ffn_dim=self.decoder_ffn_dim,
            num_feature_levels=self.num_feature_levels,
            decoder_n_points=self.decoder_n_points,
            decoder_n_levels=self.decoder_n_levels,
            decoder_layers=self.decoder_layers,
            decoder_attention_heads=self.decoder_attention_heads,
            decoder_activation_function=self.decoder_activation_function,
            decoder_offset_scale=self.decoder_offset_scale,
            eval_idx=self.eval_idx,
            layer_scale=self.layer_scale,
            reg_max=self.reg_max,
            reg_scale=self.reg_scale,
            depth_mult=self.depth_mult,
            attention_dropout=self.attention_dropout,
            num_denoising=self.num_denoising,
            label_noise_ratio=self.label_noise_ratio,
            box_noise_scale=self.box_noise_scale,
            learn_initial_query=self.learn_initial_query,
            anchor_image_size=self.anchor_image_size,
            image_size=self.image_size,
            disable_custom_kernels=self.disable_custom_kernels,
            with_box_refine=self.with_box_refine,
            sta_inplanes=self.sta_inplanes,
            encoder_has_trailing_conv=False,
        )

    def prepare_config_and_inputs_for_common(self):
        config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict

    def create_and_check_deimv2_model(self, config, pixel_values, pixel_mask, labels):
        model = Deimv2Model(config=config)
        model.to(torch_device)
        model.eval()

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
        result = model(pixel_values)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.d_model))

    def create_and_check_deimv2_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
        model = Deimv2ForObjectDetection(config=config)
        model.to(torch_device)
        model.eval()

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
        result = model(pixel_values)

        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))

        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))


@require_torch
class Deimv2DINOv3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (Deimv2Model, Deimv2ForObjectDetection) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"image-feature-extraction": Deimv2Model, "object-detection": Deimv2ForObjectDetection}
        if is_torch_available()
        else {}
    )
    is_encoder_decoder = True
    test_resize_embeddings = False

    test_missing_keys = False

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class.__name__ == "Deimv2ForObjectDetection":
                labels = []
                for i in range(self.model_tester.batch_size):
                    target = {}
                    target["class_labels"] = torch.ones(
                        size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
                    )
                    target["boxes"] = torch.ones(
                        self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
                    )
                    labels.append(target)
                inputs_dict["labels"] = labels

        return inputs_dict

    def setUp(self):
        self.model_tester = Deimv2DINOv3ModelTester(self)
        self.config_tester = ConfigTester(
            self,
            config_class=Deimv2Config,
            has_text_modality=False,
            common_properties=["hidden_size", "num_attention_heads"],
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_deimv2_dinov3_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deimv2_model(*config_and_inputs)

    def test_deimv2_dinov3_object_detection_head_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deimv2_object_detection_head_model(*config_and_inputs)

    @unittest.skip(reason="Multi-scale deformable attention is incompatible with nn.DataParallel")
    def test_multi_gpu_data_parallel_forward(self):
        pass

    @unittest.skip(
        reason="Deimv2 is a vision model but inputs_embeds is in the forward signature (inherited from D-FINE)"
    )
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Forward signature has inputs_embeds but no input_ids")
    def test_inputs_embeds_matches_input_ids(self):
        pass

    @unittest.skip(reason="Base test asserts get_input_embeddings() returns nn.Embedding which vision models lack")
    def test_model_get_set_embeddings(self):
        pass

    @unittest.skip(reason="Decoder heads are shared via reference assignment so untied saving is not applicable")
    def test_load_save_without_tied_weights(self):
        pass

    @unittest.skip(reason="DINOv3 RoPE with dynamic interpolation causes torch.compile inductor overflow")
    def test_sdpa_can_compile_dynamic(self):
        pass

    # Override: DINOv3 backbone requires wider tolerances for SDPA vs eager comparison
    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
    def test_eager_matches_sdpa_inference(
        self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
    ):
        atols = {
            ("cpu", False, torch.float32): 1e-4,
            ("cpu", False, torch.float16): 5e-3,
            ("cpu", False, torch.bfloat16): 1e-2,
            ("cpu", True, torch.float32): 1e-4,
            ("cpu", True, torch.float16): 5e-3,
            ("cpu", True, torch.bfloat16): 1e-2,
            ("cuda", False, torch.float32): 1e-4,
            ("cuda", False, torch.bfloat16): 1e-2,
            ("cuda", False, torch.float16): 5e-3,
            ("cuda", True, torch.float32): 1e-4,
            ("cuda", True, torch.bfloat16): 1e-2,
            ("cuda", True, torch.float16): 5e-3,
        }
        rtols = {
            ("cpu", False, torch.float32): 1e-3,
            ("cpu", False, torch.float16): 5e-3,
            ("cpu", False, torch.bfloat16): 1e-2,
            ("cpu", True, torch.float32): 1e-3,
            ("cpu", True, torch.float16): 5e-3,
            ("cpu", True, torch.bfloat16): 1e-2,
            ("cuda", False, torch.float32): 1e-3,
            ("cuda", False, torch.bfloat16): 1e-2,
            ("cuda", False, torch.float16): 5e-3,
            ("cuda", True, torch.float32): 1e-3,
            ("cuda", True, torch.bfloat16): 3e-2,
            ("cuda", True, torch.float16): 5e-3,
        }
        _test_eager_matches_sdpa_inference(
            self,
            name,
            torch_dtype,
            padding_side,
            use_attention_mask,
            output_attentions,
            enable_kernels,
            atols=atols,
            rtols=rtols,
        )

    # Override: DINOv3 backbone numerical precision requires wider tolerances
    def test_batching_equivalence(self):
        super().test_batching_equivalence(atol=1e-4, rtol=1e-4)

    @unittest.skip(reason="Flex attention test requires decoder_input_ids which detection models don't have")
    def test_flex_attention_with_grads(self):
        pass

    # Override: Multi-scale deformable attention outputs have different shapes than standard self-attention
    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            config.return_dict = True
            model = model_class._from_config(config, attn_implementation="eager")
            config = model.config
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions
            self.assertEqual(len(attentions), self.model_tester.encoder_layers)

            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions
            self.assertEqual(len(attentions), self.model_tester.encoder_layers)

            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [
                    self.model_tester.encoder_attention_heads,
                    self.model_tester.encoder_seq_length,
                    self.model_tester.encoder_seq_length,
                ],
            )
            out_len = len(outputs)

            correct_outlen = 15

            if "labels" in inputs_dict:
                correct_outlen += 1
            if model_class.__name__ == "Deimv2ForObjectDetection":
                correct_outlen += 2

            self.assertEqual(out_len, correct_outlen)

            decoder_attentions = outputs.decoder_attentions
            self.assertIsInstance(decoder_attentions, (list, tuple))
            self.assertEqual(len(decoder_attentions), self.model_tester.decoder_layers)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]),
                [
                    self.model_tester.decoder_attention_heads,
                    self.model_tester.num_queries,
                    self.model_tester.num_queries,
                ],
            )

            cross_attentions = outputs.cross_attentions
            self.assertIsInstance(cross_attentions, (list, tuple))
            self.assertEqual(len(cross_attentions), self.model_tester.decoder_layers)
            self.assertListEqual(
                list(cross_attentions[0].shape[-3:]),
                [
                    self.model_tester.num_queries,
                    self.model_tester.decoder_attention_heads,
                    self.model_tester.decoder_n_levels * self.model_tester.decoder_n_points
                    if isinstance(self.model_tester.decoder_n_points, int)
                    else sum(self.model_tester.decoder_n_points),
                ],
            )

            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            if hasattr(self.model_tester, "num_hidden_states_types"):
                added_hidden_states = self.model_tester.num_hidden_states_types
            else:
                added_hidden_states = 2
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.encoder_attentions

            self.assertEqual(len(self_attentions), self.model_tester.encoder_layers)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [
                    self.model_tester.encoder_attention_heads,
                    self.model_tester.encoder_seq_length,
                    self.model_tester.encoder_seq_length,
                ],
            )

    # Override: Encoder hidden states are multi-scale feature maps, not a standard sequence of layer outputs
    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            expected_num_layers = getattr(
                self.model_tester, "expected_num_hidden_layers", len(self.model_tester.encoder_in_channels) - 1
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

            self.assertListEqual(
                list(hidden_states[1].shape[-2:]),
                [
                    self.model_tester.image_size // self.model_tester.feat_strides[-1],
                    self.model_tester.image_size // self.model_tester.feat_strides[-1],
                ],
            )

            if config.is_encoder_decoder:
                hidden_states = outputs.decoder_hidden_states

                expected_num_layers = getattr(
                    self.model_tester, "expected_num_hidden_layers", self.model_tester.decoder_layers + 1
                )

                self.assertIsInstance(hidden_states, (list, tuple))
                self.assertEqual(len(hidden_states), expected_num_layers)

                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]),
                    [self.model_tester.num_queries, self.model_tester.d_model],
                )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

    # Override: Custom gradient retention check for multi-scale deformable attention outputs
    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)

        outputs = model(**inputs)

        output = outputs[0]

        encoder_hidden_states = outputs.encoder_hidden_states[0]
        encoder_attentions = outputs.encoder_attentions[0]
        encoder_hidden_states.retain_grad()
        encoder_attentions.retain_grad()

        decoder_attentions = outputs.decoder_attentions[0]
        decoder_attentions.retain_grad()

        cross_attentions = outputs.cross_attentions[0]
        cross_attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(encoder_hidden_states.grad)
        self.assertIsNotNone(encoder_attentions.grad)
        self.assertIsNotNone(decoder_attentions.grad)
        self.assertIsNotNone(cross_attentions.grad)

    # Override: Deimv2 uses pixel_values as main input, not input_ids
    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            arg_names = [*signature.parameters.keys()]
            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    @parameterized.expand(["float32", "float16", "bfloat16"])
    @require_torch_accelerator
    @slow
    def test_inference_with_different_dtypes(self, dtype_str):
        dtype = {
            "float32": torch.float32,
            "float16": torch.float16,
            "bfloat16": torch.bfloat16,
        }[dtype_str]

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device).to(dtype)
            model.eval()
            for key, tensor in inputs_dict.items():
                if tensor.dtype == torch.float32:
                    inputs_dict[key] = tensor.to(dtype)
            with torch.no_grad():
                _ = model(**self._prepare_for_class(inputs_dict, model_class))

    @parameterized.expand(["float32", "float16", "bfloat16"])
    @require_torch_accelerator
    @slow
    def test_inference_equivalence_for_static_and_dynamic_anchors(self, dtype_str):
        dtype = {
            "float32": torch.float32,
            "float16": torch.float16,
            "bfloat16": torch.bfloat16,
        }[dtype_str]

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        h, w = inputs_dict["pixel_values"].shape[-2:]

        for key, tensor in inputs_dict.items():
            if tensor.dtype == torch.float32:
                inputs_dict[key] = tensor.to(dtype)

        for model_class in self.all_model_classes:
            with tempfile.TemporaryDirectory() as tmpdirname:
                model_class(config).save_pretrained(tmpdirname)
                model_static = model_class.from_pretrained(
                    tmpdirname, anchor_image_size=[h, w], device_map=torch_device, dtype=dtype
                ).eval()
                model_dynamic = model_class.from_pretrained(
                    tmpdirname, anchor_image_size=None, device_map=torch_device, dtype=dtype
                ).eval()

            self.assertIsNotNone(model_static.config.anchor_image_size)
            self.assertIsNone(model_dynamic.config.anchor_image_size)

            with torch.no_grad():
                outputs_static = model_static(**self._prepare_for_class(inputs_dict, model_class))
                outputs_dynamic = model_dynamic(**self._prepare_for_class(inputs_dict, model_class))

            torch.testing.assert_close(
                outputs_static.last_hidden_state, outputs_dynamic.last_hidden_state, rtol=5e-3, atol=5e-3
            )


def prepare_img():
    from transformers.image_utils import load_image

    url = url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg")
    return load_image(url)


@require_torch
@require_vision
@slow
class Deimv2ModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained(CHECKPOINT, use_fast=False)

    def test_inference_object_detection_head(self):
        model = Deimv2ForObjectDetection.from_pretrained(CHECKPOINT).to(torch_device)
        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        with torch.no_grad():
            outputs = model(**inputs)

        expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
        self.assertEqual(outputs.logits.shape, expected_shape_logits)

        expected_logits = torch.tensor(
            [[-4.0859, -6.9373, -5.4723], [-5.5887, -6.0078, -6.4360], [-6.1448, -6.8509, -6.8703]]
        ).to(torch_device)
        expected_boxes = torch.tensor(
            [[0.1886, 0.1662, 0.2875], [0.0690, 0.1814, 0.9368], [0.2510, 0.2141, 0.9115]]
        ).to(torch_device)

        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, atol=2e-4, rtol=2e-4)

        expected_shape_boxes = torch.Size((1, 300, 4))
        self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
        torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=2e-4, rtol=2e-4)

        results = image_processor.post_process_object_detection(
            outputs, threshold=0.0, target_sizes=[image.size[::-1]]
        )[0]

        expected_scores = torch.tensor([0.7606, 0.3165, 0.2726, 0.2488], device=torch_device)
        expected_labels = [65, 65, 15, 59]
        expected_slice_boxes = torch.tensor(
            [
                [4.0781e01, 6.8216e01, 1.7560e02, 1.1085e02],
                [4.8195e01, 7.5405e01, 2.1123e02, 9.1451e01],
                [1.1296e01, 6.8089e01, 6.1285e02, 4.0393e02],
                [1.9821e01, -9.0347e01, 7.0787e02, 3.7968e02],
            ],
            device=torch_device,
        )

        torch.testing.assert_close(results["scores"][:4], expected_scores, atol=1e-3, rtol=1e-4)
        self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels)
        torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes[:4], atol=5e-3, rtol=5e-4)


@require_torch
@require_vision
@slow
class Deimv2LiteEncoderIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained(CHECKPOINT_LITE, use_fast=False)

    def test_inference_object_detection_head(self):
        model = Deimv2ForObjectDetection.from_pretrained(CHECKPOINT_LITE).to(torch_device)
        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        with torch.no_grad():
            outputs = model(**inputs)

        expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels))
        self.assertEqual(outputs.logits.shape, expected_shape_logits)

        expected_logits = torch.tensor(
            [[-2.6151, -6.4701, -6.3505], [-3.8592, -6.2610, -7.2720], [-2.3801, -4.3216, -3.5101]]
        ).to(torch_device)
        expected_boxes = torch.tensor(
            [[0.7994, 0.2984, 0.3822], [0.5536, 0.5362, 0.0392], [0.3501, 0.4577, 0.7440]]
        ).to(torch_device)

        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, atol=2e-4, rtol=2e-4)

        expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
        self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
        torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=2e-4, rtol=2e-4)


@require_torch
@require_vision
@slow
class Deimv2DINOv3IntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained(CHECKPOINT_DINOV3, use_fast=False)

    def test_inference_object_detection_head(self):
        model = Deimv2ForObjectDetection.from_pretrained(CHECKPOINT_DINOV3).to(torch_device)
        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        with torch.no_grad():
            outputs = model(**inputs)

        expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
        self.assertEqual(outputs.logits.shape, expected_shape_logits)

        expected_logits = torch.tensor(
            [[-2.1404, -2.8207, -3.2710], [-2.3058, -2.7178, -3.2924], [-3.2780, -4.0269, -4.6266]]
        ).to(torch_device)
        expected_boxes = torch.tensor(
            [[0.5258, 0.7694, 0.7997], [0.3734, 0.1949, 0.7989], [0.5082, 0.5847, 0.8590]]
        ).to(torch_device)

        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, atol=2e-4, rtol=2e-4)

        expected_shape_boxes = torch.Size((1, 300, 4))
        self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
        torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=2e-4, rtol=2e-4)