# coding=utf-8
# Copyright 2022 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 Deformable DETR model. """


import inspect
import math
import unittest
from typing import Dict, List, Tuple

from transformers import DeformableDetrConfig, ResNetConfig, is_torch_available, is_vision_available
from transformers.file_utils import cached_property
from transformers.testing_utils import (
    require_timm,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import DeformableDetrForObjectDetection, DeformableDetrModel


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class DeformableDetrModelTester:
    def __init__(
        self,
        parent,
        batch_size=8,
        is_training=True,
        use_labels=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=8,
        intermediate_size=4,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        num_queries=12,
        num_channels=3,
        image_size=196,
        n_targets=8,
        num_labels=91,
        num_feature_levels=4,
        encoder_n_points=2,
        decoder_n_points=6,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.num_queries = num_queries
        self.num_channels = num_channels
        self.image_size = image_size
        self.n_targets = n_targets
        self.num_labels = num_labels
        self.num_feature_levels = num_feature_levels
        self.encoder_n_points = encoder_n_points
        self.decoder_n_points = decoder_n_points

        # we also set the expected seq length for both encoder and decoder
        self.encoder_seq_length = (
            math.ceil(self.image_size / 8) ** 2
            + math.ceil(self.image_size / 16) ** 2
            + math.ceil(self.image_size / 32) ** 2
            + math.ceil(self.image_size / 64) ** 2
        )
        self.decoder_seq_length = self.num_queries

    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 is a list of Dict (each Dict being the labels for a given example in the batch)
            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)
                target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device)
                labels.append(target)

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

    def get_config(self):
        resnet_config = ResNetConfig(
            num_channels=3,
            embeddings_size=10,
            hidden_sizes=[10, 20, 30, 40],
            depths=[1, 1, 2, 1],
            hidden_act="relu",
            num_labels=3,
            out_features=["stage2", "stage3", "stage4"],
            out_indices=[2, 3, 4],
        )
        return DeformableDetrConfig(
            d_model=self.hidden_size,
            encoder_layers=self.num_hidden_layers,
            decoder_layers=self.num_hidden_layers,
            encoder_attention_heads=self.num_attention_heads,
            decoder_attention_heads=self.num_attention_heads,
            encoder_ffn_dim=self.intermediate_size,
            decoder_ffn_dim=self.intermediate_size,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            num_queries=self.num_queries,
            num_labels=self.num_labels,
            num_feature_levels=self.num_feature_levels,
            encoder_n_points=self.encoder_n_points,
            decoder_n_points=self.decoder_n_points,
            use_timm_backbone=False,
            backbone_config=resnet_config,
        )

    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, "pixel_mask": pixel_mask}
        return config, inputs_dict

    def create_and_check_deformable_detr_model(self, config, pixel_values, pixel_mask, labels):
        model = DeformableDetrModel(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.hidden_size))

    def create_and_check_deformable_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
        model = DeformableDetrForObjectDetection(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 DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection}
        if is_torch_available()
        else {}
    )
    is_encoder_decoder = True
    test_torchscript = False
    test_pruning = False
    test_head_masking = False
    test_missing_keys = False

    # special case for head models
    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__ == "DeformableDetrForObjectDetection":
                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
                    )
                    target["masks"] = torch.ones(
                        self.model_tester.n_targets,
                        self.model_tester.image_size,
                        self.model_tester.image_size,
                        device=torch_device,
                        dtype=torch.float,
                    )
                    labels.append(target)
                inputs_dict["labels"] = labels

        return inputs_dict

    def setUp(self):
        self.model_tester = DeformableDetrModelTester(self)
        self.config_tester = ConfigTester(self, config_class=DeformableDetrConfig, has_text_modality=False)

    def test_config(self):
        # we don't test common_properties and arguments_init as these don't apply for Deformable DETR
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()

    def test_deformable_detr_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deformable_detr_model(*config_and_inputs)

    def test_deformable_detr_object_detection_head_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deformable_detr_object_detection_head_model(*config_and_inputs)

    @unittest.skip(reason="Deformable DETR does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Deformable DETR does not have a get_input_embeddings method")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="Deformable DETR is not a generative model")
    def test_generate_without_input_ids(self):
        pass

    @unittest.skip(reason="Deformable DETR does not use token embeddings")
    def test_resize_tokens_embeddings(self):
        pass

    @unittest.skip(reason="Feed forward chunking is not implemented")
    def test_feed_forward_chunking(self):
        pass

    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(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.num_hidden_layers)

            # check that output_attentions also work using config
            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.num_hidden_layers)

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

            correct_outlen = 8

            # loss is at first position
            if "labels" in inputs_dict:
                correct_outlen += 1  # loss is added to beginning
            # Object Detection model returns pred_logits and pred_boxes
            if model_class.__name__ == "DeformableDetrForObjectDetection":
                correct_outlen += 2

            self.assertEqual(out_len, correct_outlen)

            # decoder attentions
            decoder_attentions = outputs.decoder_attentions
            self.assertIsInstance(decoder_attentions, (list, tuple))
            self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries],
            )

            # cross attentions
            cross_attentions = outputs.cross_attentions
            self.assertIsInstance(cross_attentions, (list, tuple))
            self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(cross_attentions[0].shape[-3:]),
                [
                    self.model_tester.num_attention_heads,
                    self.model_tester.num_feature_levels,
                    self.model_tester.decoder_n_points,
                ],
            )

            # Check attention is always last and order is fine
            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
            elif self.is_encoder_decoder:
                added_hidden_states = 2
            else:
                added_hidden_states = 1
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.encoder_attentions

            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [
                    self.model_tester.num_attention_heads,
                    self.model_tester.num_feature_levels,
                    self.model_tester.encoder_n_points,
                ],
            )

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

        def set_nan_tensor_to_zero(t):
            t[t != t] = 0
            return t

        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            with torch.no_grad():
                tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
                dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()

                def recursive_check(tuple_object, dict_object):
                    if isinstance(tuple_object, (List, Tuple)):
                        for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif isinstance(tuple_object, Dict):
                        for tuple_iterable_value, dict_iterable_value in zip(
                            tuple_object.values(), dict_object.values()
                        ):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif tuple_object is None:
                        return
                    else:
                        self.assertTrue(
                            torch.allclose(
                                set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
                            ),
                            msg=(
                                "Tuple and dict output are not equal. Difference:"
                                f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
                                f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
                                f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
                            ),
                        )

                recursive_check(tuple_output, dict_output)

        for model_class in self.all_model_classes:
            print("Model class:", model_class)
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(
                model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
            )

    def test_retain_grad_hidden_states_attentions(self):
        # removed retain_grad and grad on decoder_hidden_states, as queries don't require grad

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        # no need to test all models as different heads yield the same functionality
        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)

        # we take the second output since last_hidden_state is the second item
        output = outputs[1]

        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)

    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)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            if model.config.is_encoder_decoder:
                expected_arg_names = ["pixel_values", "pixel_mask"]
                expected_arg_names.extend(
                    ["head_mask", "decoder_head_mask", "encoder_outputs"]
                    if "head_mask" and "decoder_head_mask" in arg_names
                    else []
                )
                self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
            else:
                expected_arg_names = ["pixel_values", "pixel_mask"]
                self.assertListEqual(arg_names[:1], expected_arg_names)

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

        # let's pick a random timm backbone
        config.backbone = "tf_mobilenetv3_small_075"

        for model_class in self.all_model_classes:
            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 model_class.__name__ == "DeformableDetrForObjectDetection":
                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.assertTrue(outputs)

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

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            print("Model class:", model_class)
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if param.requires_grad:
                    if param.requires_grad:
                        if (
                            "level_embed" in name
                            or "sampling_offsets.bias" in name
                            or "value_proj" in name
                            or "output_proj" in name
                            or "reference_points" in name
                        ):
                            continue
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )


TOLERANCE = 1e-4


# We will verify our results on an image of cute cats
def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_timm
@require_vision
@slow
class DeformableDetrModelIntegrationTests(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") if is_vision_available() else None

    def test_inference_object_detection_head(self):
        model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr").to(torch_device)

        image_processor = self.default_image_processor
        image = prepare_img()
        encoding = image_processor(images=image, return_tensors="pt").to(torch_device)
        pixel_values = encoding["pixel_values"].to(torch_device)
        pixel_mask = encoding["pixel_mask"].to(torch_device)

        with torch.no_grad():
            outputs = model(pixel_values, pixel_mask)

        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(
            [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]
        ).to(torch_device)
        expected_boxes = torch.tensor(
            [[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]
        ).to(torch_device)

        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))

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

        # verify postprocessing
        results = image_processor.post_process_object_detection(
            outputs, threshold=0.3, target_sizes=[image.size[::-1]]
        )[0]
        expected_scores = torch.tensor([0.7999, 0.7894, 0.6331, 0.4720, 0.4382]).to(torch_device)
        expected_labels = [17, 17, 75, 75, 63]
        expected_slice_boxes = torch.tensor([16.5028, 52.8390, 318.2544, 470.7841]).to(torch_device)

        self.assertEqual(len(results["scores"]), 5)
        self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))
        self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
        self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))

    def test_inference_object_detection_head_with_box_refine_two_stage(self):
        model = DeformableDetrForObjectDetection.from_pretrained(
            "SenseTime/deformable-detr-with-box-refine-two-stage"
        ).to(torch_device)

        image_processor = self.default_image_processor
        image = prepare_img()
        encoding = image_processor(images=image, return_tensors="pt").to(torch_device)
        pixel_values = encoding["pixel_values"].to(torch_device)
        pixel_mask = encoding["pixel_mask"].to(torch_device)

        with torch.no_grad():
            outputs = model(pixel_values, pixel_mask)

        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(
            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]
        ).to(torch_device)
        expected_boxes = torch.tensor(
            [[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]
        ).to(torch_device)

        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))

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

    @require_torch_gpu
    def test_inference_object_detection_head_equivalence_cpu_gpu(self):
        image_processor = self.default_image_processor
        image = prepare_img()
        encoding = image_processor(images=image, return_tensors="pt")
        pixel_values = encoding["pixel_values"]
        pixel_mask = encoding["pixel_mask"]

        # 1. run model on CPU
        model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr-single-scale")

        with torch.no_grad():
            cpu_outputs = model(pixel_values, pixel_mask)

        # 2. run model on GPU
        model.to("cuda")

        with torch.no_grad():
            gpu_outputs = model(pixel_values.to("cuda"), pixel_mask.to("cuda"))

        # 3. assert equivalence
        for key in cpu_outputs.keys():
            assert torch.allclose(cpu_outputs[key], gpu_outputs[key].cpu(), atol=1e-4)

        expected_logits = torch.tensor(
            [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]
        )
        assert torch.allclose(cpu_outputs.logits[0, :3, :3], expected_logits, atol=1e-4)
