# Copyright 2021 HuggingFace Inc.
#
# 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.

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import (
    require_torch,
    require_torch_accelerator,
    require_torchvision,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image


class DetrImageProcessingTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        do_rescale=True,
        rescale_factor=1 / 255,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        do_pad=True,
    ):
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = size
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_pad = do_pad

    def prepare_image_processor_dict(self):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }

    def get_expected_values(self, image_inputs, batched=False):
        """
        This function computes the expected height and width when providing images to DetrImageProcessor,
        assuming do_resize is set to True with a scalar size.
        """
        if not batched:
            image = image_inputs[0]
            if isinstance(image, Image.Image):
                w, h = image.size
            elif isinstance(image, np.ndarray):
                h, w = image.shape[0], image.shape[1]
            else:
                h, w = image.shape[1], image.shape[2]
            if w < h:
                expected_height = int(self.size["shortest_edge"] * h / w)
                expected_width = self.size["shortest_edge"]
            elif w > h:
                expected_height = self.size["shortest_edge"]
                expected_width = int(self.size["shortest_edge"] * w / h)
            else:
                expected_height = self.size["shortest_edge"]
                expected_width = self.size["shortest_edge"]

        else:
            expected_values = []
            for image in image_inputs:
                expected_height, expected_width = self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
            expected_height = max(expected_values, key=lambda item: item[0])[0]
            expected_width = max(expected_values, key=lambda item: item[1])[1]

        return expected_height, expected_width

    def expected_output_image_shape(self, images):
        height, width = self.get_expected_values(images, batched=True)
        return self.num_channels, height, width

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_image_inputs(
            batch_size=self.batch_size,
            num_channels=self.num_channels,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=equal_resolution,
            numpify=numpify,
            torchify=torchify,
        )


@require_torch
@require_vision
class DetrImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase):
    def setUp(self):
        super().setUp()
        self.image_processor_tester = DetrImageProcessingTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_image_processor_properties(self):
        for image_processing_class in self.image_processing_classes.values():
            image_processing = image_processing_class(**self.image_processor_dict)
            self.assertTrue(hasattr(image_processing, "image_mean"))
            self.assertTrue(hasattr(image_processing, "image_std"))
            self.assertTrue(hasattr(image_processing, "do_normalize"))
            self.assertTrue(hasattr(image_processing, "do_rescale"))
            self.assertTrue(hasattr(image_processing, "rescale_factor"))
            self.assertTrue(hasattr(image_processing, "do_resize"))
            self.assertTrue(hasattr(image_processing, "size"))
            self.assertTrue(hasattr(image_processing, "do_pad"))

    def test_image_processor_from_dict_with_kwargs(self):
        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class.from_dict(self.image_processor_dict)
            self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333})
            self.assertEqual(image_processor.do_pad, True)

            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
            self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 1333})

    def test_should_raise_if_annotation_format_invalid(self):
        image_processor_dict = self.image_processor_tester.prepare_image_processor_dict()

        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
            detection_target = json.loads(f.read())

        annotations = {"image_id": 39769, "annotations": detection_target}

        params = {
            "images": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
            "annotations": annotations,
            "return_tensors": "pt",
        }

        image_processor_params = {**image_processor_dict, **{"format": "_INVALID_FORMAT_"}}
        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class(**image_processor_params)

            with self.assertRaises(ValueError) as e:
                image_processor(**params)

                self.assertTrue(str(e.exception).startswith("_INVALID_FORMAT_ is not a valid AnnotationFormat"))

    def test_valid_coco_detection_annotations(self):
        # prepare image and target
        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
            target = json.loads(f.read())

        params = {"image_id": 39769, "annotations": target}

        for image_processing_class in self.image_processing_classes.values():
            # encode them
            image_processing = image_processing_class.from_pretrained("facebook/detr-resnet-50")

            # legal encodings (single image)
            _ = image_processing(images=image, annotations=params, return_tensors="pt")
            _ = image_processing(images=image, annotations=[params], return_tensors="pt")

            # legal encodings (batch of one image)
            _ = image_processing(images=[image], annotations=params, return_tensors="pt")
            _ = image_processing(images=[image], annotations=[params], return_tensors="pt")

            # legal encoding (batch of more than one image)
            n = 5
            _ = image_processing(images=[image] * n, annotations=[params] * n, return_tensors="pt")

            # example of an illegal encoding (missing the 'image_id' key)
            with self.assertRaises(ValueError) as e:
                image_processing(images=image, annotations={"annotations": target}, return_tensors="pt")

            self.assertTrue(str(e.exception).startswith("Invalid COCO detection annotations"))

            # example of an illegal encoding (unequal lengths of images and annotations)
            with self.assertRaises(ValueError) as e:
                image_processing(images=[image] * n, annotations=[params] * (n - 1), return_tensors="pt")

            self.assertTrue(str(e.exception) == "The number of images (5) and annotations (4) do not match.")

    @slow
    def test_call_pytorch_with_coco_detection_annotations(self):
        # prepare image and target
        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
            target = json.loads(f.read())

        target = {"image_id": 39769, "annotations": target}

        for image_processing_class in self.image_processing_classes.values():
            # encode them
            image_processing = image_processing_class.from_pretrained("facebook/detr-resnet-50")
            encoding = image_processing(images=image, annotations=target, return_tensors="pt")

            # verify pixel values
            expected_shape = torch.Size([1, 3, 800, 1066])
            self.assertEqual(encoding["pixel_values"].shape, expected_shape)

            expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
            torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)

            # verify area
            expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
            torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
            # verify boxes
            expected_boxes_shape = torch.Size([6, 4])
            self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
            expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
            torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
            # verify image_id
            expected_image_id = torch.tensor([39769])
            torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
            # verify is_crowd
            expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
            torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
            # verify class_labels
            expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
            torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
            # verify orig_size
            expected_orig_size = torch.tensor([480, 640])
            torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
            # verify size
            expected_size = torch.tensor([800, 1066])
            torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)

    @slow
    def test_call_pytorch_with_coco_panoptic_annotations(self):
        # prepare image, target and masks_path
        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt") as f:
            target = json.loads(f.read())

        target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

        masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

        for image_processing_class in self.image_processing_classes.values():
            # encode them
            image_processing = image_processing_class.from_pretrained("facebook/detr-resnet-50-panoptic")
            encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")

            # verify pixel values
            expected_shape = torch.Size([1, 3, 800, 1066])
            self.assertEqual(encoding["pixel_values"].shape, expected_shape)

            expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
            torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)

            # verify area
            expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
            torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
            # verify boxes
            expected_boxes_shape = torch.Size([6, 4])
            self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
            expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
            torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
            # verify image_id
            expected_image_id = torch.tensor([39769])
            torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
            # verify is_crowd
            expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
            torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
            # verify class_labels
            expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
            torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
            # verify masks
            expected_masks_sum = 822873
            relative_error = torch.abs(encoding["labels"][0]["masks"].sum() - expected_masks_sum) / expected_masks_sum
            self.assertTrue(relative_error < 1e-3)
            # verify orig_size
            expected_orig_size = torch.tensor([480, 640])
            torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
            # verify size
            expected_size = torch.tensor([800, 1066])
            torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)

    @slow
    def test_batched_coco_detection_annotations(self):
        image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))

        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
            target = json.loads(f.read())

        annotations_0 = {"image_id": 39769, "annotations": target}
        annotations_1 = {"image_id": 39769, "annotations": target}

        # Adjust the bounding boxes for the resized image
        w_0, h_0 = image_0.size
        w_1, h_1 = image_1.size
        for i in range(len(annotations_1["annotations"])):
            coords = annotations_1["annotations"][i]["bbox"]
            new_bbox = [
                coords[0] * w_1 / w_0,
                coords[1] * h_1 / h_0,
                coords[2] * w_1 / w_0,
                coords[3] * h_1 / h_0,
            ]
            annotations_1["annotations"][i]["bbox"] = new_bbox

        images = [image_0, image_1]
        annotations = [annotations_0, annotations_1]

        for image_processing_class in self.image_processing_classes.values():
            image_processing = image_processing_class()
            encoding = image_processing(
                images=images,
                annotations=annotations,
                return_segmentation_masks=True,
                return_tensors="pt",  # do_convert_annotations=True
            )

            # Check the pixel values have been padded
            postprocessed_height, postprocessed_width = 800, 1066
            expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
            self.assertEqual(encoding["pixel_values"].shape, expected_shape)

            # Check the bounding boxes have been adjusted for padded images
            self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
            self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
            expected_boxes_0 = torch.tensor(
                [
                    [0.6879, 0.4609, 0.0755, 0.3691],
                    [0.2118, 0.3359, 0.2601, 0.1566],
                    [0.5011, 0.5000, 0.9979, 1.0000],
                    [0.5010, 0.5020, 0.9979, 0.9959],
                    [0.3284, 0.5944, 0.5884, 0.8112],
                    [0.8394, 0.5445, 0.3213, 0.9110],
                ]
            )
            expected_boxes_1 = torch.tensor(
                [
                    [0.4130, 0.2765, 0.0453, 0.2215],
                    [0.1272, 0.2016, 0.1561, 0.0940],
                    [0.3757, 0.4933, 0.7488, 0.9865],
                    [0.3759, 0.5002, 0.7492, 0.9955],
                    [0.1971, 0.5456, 0.3532, 0.8646],
                    [0.5790, 0.4115, 0.3430, 0.7161],
                ]
            )
            torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3)
            torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3)

            # Check the masks have also been padded
            self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
            self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))

            # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
            # format and not in the range [0, 1]
            encoding = image_processing(
                images=images,
                annotations=annotations,
                return_segmentation_masks=True,
                do_convert_annotations=False,
                return_tensors="pt",
            )
            self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
            self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
            # Convert to absolute coordinates
            unnormalized_boxes_0 = torch.vstack(
                [
                    expected_boxes_0[:, 0] * postprocessed_width,
                    expected_boxes_0[:, 1] * postprocessed_height,
                    expected_boxes_0[:, 2] * postprocessed_width,
                    expected_boxes_0[:, 3] * postprocessed_height,
                ]
            ).T
            unnormalized_boxes_1 = torch.vstack(
                [
                    expected_boxes_1[:, 0] * postprocessed_width,
                    expected_boxes_1[:, 1] * postprocessed_height,
                    expected_boxes_1[:, 2] * postprocessed_width,
                    expected_boxes_1[:, 3] * postprocessed_height,
                ]
            ).T
            # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
            expected_boxes_0 = torch.vstack(
                [
                    unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
                    unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
                    unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
                    unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
                ]
            ).T
            expected_boxes_1 = torch.vstack(
                [
                    unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
                    unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
                    unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
                    unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
                ]
            ).T
            torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1)
            torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1)

    def test_batched_coco_panoptic_annotations(self):
        # prepare image, target and masks_path
        image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))

        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt") as f:
            target = json.loads(f.read())

        annotation_0 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
        annotation_1 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

        w_0, h_0 = image_0.size
        w_1, h_1 = image_1.size
        for i in range(len(annotation_1["segments_info"])):
            coords = annotation_1["segments_info"][i]["bbox"]
            new_bbox = [
                coords[0] * w_1 / w_0,
                coords[1] * h_1 / h_0,
                coords[2] * w_1 / w_0,
                coords[3] * h_1 / h_0,
            ]
            annotation_1["segments_info"][i]["bbox"] = new_bbox

        masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

        images = [image_0, image_1]
        annotations = [annotation_0, annotation_1]

        for image_processing_class in self.image_processing_classes.values():
            # encode them
            image_processing = image_processing_class(format="coco_panoptic")
            encoding = image_processing(
                images=images,
                annotations=annotations,
                masks_path=masks_path,
                return_tensors="pt",
                return_segmentation_masks=True,
            )

            # Check the pixel values have been padded
            postprocessed_height, postprocessed_width = 800, 1066
            expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
            self.assertEqual(encoding["pixel_values"].shape, expected_shape)

            # Check the bounding boxes have been adjusted for padded images
            self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
            self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
            expected_boxes_0 = torch.tensor(
                [
                    [0.2625, 0.5437, 0.4688, 0.8625],
                    [0.7719, 0.4104, 0.4531, 0.7125],
                    [0.5000, 0.4927, 0.9969, 0.9854],
                    [0.1688, 0.2000, 0.2063, 0.0917],
                    [0.5492, 0.2760, 0.0578, 0.2187],
                    [0.4992, 0.4990, 0.9984, 0.9979],
                ]
            )
            expected_boxes_1 = torch.tensor(
                [
                    [0.1576, 0.3262, 0.2814, 0.5175],
                    [0.4634, 0.2463, 0.2720, 0.4275],
                    [0.3002, 0.2956, 0.5985, 0.5913],
                    [0.1013, 0.1200, 0.1238, 0.0550],
                    [0.3297, 0.1656, 0.0347, 0.1312],
                    [0.2997, 0.2994, 0.5994, 0.5987],
                ]
            )
            torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3)
            torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3)

            # Check the masks have also been padded
            self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
            self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))

            # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
            # format and not in the range [0, 1]
            encoding = image_processing(
                images=images,
                annotations=annotations,
                masks_path=masks_path,
                return_segmentation_masks=True,
                do_convert_annotations=False,
                return_tensors="pt",
            )
            self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
            self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
            # Convert to absolute coordinates
            unnormalized_boxes_0 = torch.vstack(
                [
                    expected_boxes_0[:, 0] * postprocessed_width,
                    expected_boxes_0[:, 1] * postprocessed_height,
                    expected_boxes_0[:, 2] * postprocessed_width,
                    expected_boxes_0[:, 3] * postprocessed_height,
                ]
            ).T
            unnormalized_boxes_1 = torch.vstack(
                [
                    expected_boxes_1[:, 0] * postprocessed_width,
                    expected_boxes_1[:, 1] * postprocessed_height,
                    expected_boxes_1[:, 2] * postprocessed_width,
                    expected_boxes_1[:, 3] * postprocessed_height,
                ]
            ).T
            # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
            expected_boxes_0 = torch.vstack(
                [
                    unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
                    unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
                    unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
                    unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
                ]
            ).T
            expected_boxes_1 = torch.vstack(
                [
                    unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
                    unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
                    unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
                    unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
                ]
            ).T
            torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1)
            torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1)

    def test_max_width_max_height_resizing_and_pad_strategy(self):
        for image_processing_class in self.image_processing_classes.values():
            image_1 = torch.ones([200, 100, 3], dtype=torch.uint8)

            # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50
            image_processor = image_processing_class(
                size={"max_height": 100, "max_width": 100},
                do_pad=False,
            )
            inputs = image_processor(images=[image_1], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50]))

            # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100
            image_processor = image_processing_class(
                size={"max_height": 300, "max_width": 100},
                do_pad=False,
            )
            inputs = image_processor(images=[image_1], return_tensors="pt")

            # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
            image_processor = image_processing_class(
                size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
            )
            inputs = image_processor(images=[image_1], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))

            # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100
            image_processor = image_processing_class(
                size={"max_height": 300, "max_width": 100},
                do_pad=True,
                pad_size={"height": 301, "width": 101},
            )
            inputs = image_processor(images=[image_1], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101]))

            ### Check for batch
            image_2 = torch.ones([100, 150, 3], dtype=torch.uint8)

            # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100
            image_processor = image_processing_class(
                size={"max_height": 150, "max_width": 100},
                do_pad=True,
                pad_size={"height": 150, "width": 100},
            )
            inputs = image_processor(images=[image_1, image_2], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100]))

    def test_longest_edge_shortest_edge_resizing_strategy(self):
        for image_processing_class in self.image_processing_classes.values():
            image_1 = torch.ones([958, 653, 3], dtype=torch.uint8)

            # max size is set; width < height;
            # do_pad=False, longest_edge=640, shortest_edge=640, image=958x653 -> 640x436
            image_processor = image_processing_class(
                size={"longest_edge": 640, "shortest_edge": 640},
                do_pad=False,
            )
            inputs = image_processor(images=[image_1], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 640, 436]))

            image_2 = torch.ones([653, 958, 3], dtype=torch.uint8)
            # max size is set; height < width;
            # do_pad=False, longest_edge=640, shortest_edge=640, image=653x958 -> 436x640
            image_processor = image_processing_class(
                size={"longest_edge": 640, "shortest_edge": 640},
                do_pad=False,
            )
            inputs = image_processor(images=[image_2], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 436, 640]))

            image_3 = torch.ones([100, 120, 3], dtype=torch.uint8)
            # max size is set; width == size; height > max_size;
            # do_pad=False, longest_edge=118, shortest_edge=100, image=120x100 -> 118x98
            image_processor = image_processing_class(
                size={"longest_edge": 118, "shortest_edge": 100},
                do_pad=False,
            )
            inputs = image_processor(images=[image_3], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 98, 118]))

            image_4 = torch.ones([128, 50, 3], dtype=torch.uint8)
            # max size is set; height == size; width < max_size;
            # do_pad=False, longest_edge=256, shortest_edge=50, image=50x128 -> 50x128
            image_processor = image_processing_class(
                size={"longest_edge": 256, "shortest_edge": 50},
                do_pad=False,
            )
            inputs = image_processor(images=[image_4], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 128, 50]))

            image_5 = torch.ones([50, 50, 3], dtype=torch.uint8)
            # max size is set; height == width; width < max_size;
            # do_pad=False, longest_edge=117, shortest_edge=50, image=50x50 -> 50x50
            image_processor = image_processing_class(
                size={"longest_edge": 117, "shortest_edge": 50},
                do_pad=False,
            )
            inputs = image_processor(images=[image_5], return_tensors="pt")
            self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 50, 50]))

    @slow
    @require_torch_accelerator
    @require_torchvision
    def test_torchvision_processor_equivalence_cpu_accelerator_coco_detection_annotations(self):
        # prepare image and target
        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
            target = json.loads(f.read())

        target = {"image_id": 39769, "annotations": target}

        if "torchvision" not in self.image_processing_classes:
            self.skipTest("torchvision backend not available")
        processor = self.image_processing_classes["torchvision"]()
        # 1. run processor on CPU
        encoding_cpu = processor(images=image, annotations=target, return_tensors="pt", device="cpu")
        # 2. run processor on accelerator
        encoding_gpu = processor(images=image, annotations=target, return_tensors="pt", device=torch_device)

        # verify pixel values
        self.assertEqual(encoding_cpu["pixel_values"].shape, encoding_gpu["pixel_values"].shape)
        self.assertTrue(
            torch.allclose(
                encoding_cpu["pixel_values"][0, 0, 0, :3],
                encoding_gpu["pixel_values"][0, 0, 0, :3].to("cpu"),
                atol=1e-4,
            )
        )
        # verify area
        torch.testing.assert_close(encoding_cpu["labels"][0]["area"], encoding_gpu["labels"][0]["area"].to("cpu"))
        # verify boxes
        self.assertEqual(encoding_cpu["labels"][0]["boxes"].shape, encoding_gpu["labels"][0]["boxes"].shape)
        self.assertTrue(
            torch.allclose(
                encoding_cpu["labels"][0]["boxes"][0], encoding_gpu["labels"][0]["boxes"][0].to("cpu"), atol=1e-3
            )
        )
        # verify image_id
        torch.testing.assert_close(
            encoding_cpu["labels"][0]["image_id"], encoding_gpu["labels"][0]["image_id"].to("cpu")
        )
        # verify is_crowd
        torch.testing.assert_close(
            encoding_cpu["labels"][0]["iscrowd"], encoding_gpu["labels"][0]["iscrowd"].to("cpu")
        )
        # verify class_labels
        self.assertTrue(
            torch.allclose(
                encoding_cpu["labels"][0]["class_labels"], encoding_gpu["labels"][0]["class_labels"].to("cpu")
            )
        )
        # verify orig_size
        torch.testing.assert_close(
            encoding_cpu["labels"][0]["orig_size"], encoding_gpu["labels"][0]["orig_size"].to("cpu")
        )
        # verify size
        torch.testing.assert_close(encoding_cpu["labels"][0]["size"], encoding_gpu["labels"][0]["size"].to("cpu"))

    @slow
    @require_torch_accelerator
    @require_torchvision
    def test_torchvision_processor_equivalence_cpu_accelerator_coco_panoptic_annotations(self):
        # prepare image, target and masks_path
        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt") as f:
            target = json.loads(f.read())

        target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

        masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

        if "torchvision" not in self.image_processing_classes:
            self.skipTest("torchvision backend not available")
        processor = self.image_processing_classes["torchvision"](format="coco_panoptic")
        # 1. run processor on CPU
        encoding_cpu = processor(
            images=image, annotations=target, masks_path=masks_path, return_tensors="pt", device="cpu"
        )
        # 2. run processor on accelerator
        encoding_gpu = processor(
            images=image, annotations=target, masks_path=masks_path, return_tensors="pt", device=torch_device
        )

        # verify pixel values
        self.assertEqual(encoding_cpu["pixel_values"].shape, encoding_gpu["pixel_values"].shape)
        self.assertTrue(
            torch.allclose(
                encoding_cpu["pixel_values"][0, 0, 0, :3],
                encoding_gpu["pixel_values"][0, 0, 0, :3].to("cpu"),
                atol=1e-4,
            )
        )
        # verify area
        torch.testing.assert_close(encoding_cpu["labels"][0]["area"], encoding_gpu["labels"][0]["area"].to("cpu"))
        # verify boxes
        self.assertEqual(encoding_cpu["labels"][0]["boxes"].shape, encoding_gpu["labels"][0]["boxes"].shape)
        self.assertTrue(
            torch.allclose(
                encoding_cpu["labels"][0]["boxes"][0], encoding_gpu["labels"][0]["boxes"][0].to("cpu"), atol=1e-3
            )
        )
        # verify image_id
        torch.testing.assert_close(
            encoding_cpu["labels"][0]["image_id"], encoding_gpu["labels"][0]["image_id"].to("cpu")
        )
        # verify is_crowd
        torch.testing.assert_close(
            encoding_cpu["labels"][0]["iscrowd"], encoding_gpu["labels"][0]["iscrowd"].to("cpu")
        )
        # verify class_labels
        self.assertTrue(
            torch.allclose(
                encoding_cpu["labels"][0]["class_labels"], encoding_gpu["labels"][0]["class_labels"].to("cpu")
            )
        )
        # verify masks
        masks_sum_cpu = encoding_cpu["labels"][0]["masks"].sum()
        masks_sum_gpu = encoding_gpu["labels"][0]["masks"].sum()
        relative_error = torch.abs(masks_sum_cpu - masks_sum_gpu) / masks_sum_cpu
        self.assertTrue(relative_error < 1e-3)
        # verify orig_size
        torch.testing.assert_close(
            encoding_cpu["labels"][0]["orig_size"], encoding_gpu["labels"][0]["orig_size"].to("cpu")
        )
        # verify size
        torch.testing.assert_close(encoding_cpu["labels"][0]["size"], encoding_gpu["labels"][0]["size"].to("cpu"))
