

from typing import Dict, Optional

import numpy as np

import datasets


lowerCAmelCase							:  Dict            =						"""
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""

lowerCAmelCase							:  int            =						"""
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
"""

lowerCAmelCase							:  List[str]            =						"""\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = False    ,     ):
						if label_map is not None:
												for old_id, new_id in label_map.items():
																		SCREAMING_SNAKE_CASE_:			Dict  		=	new_id

    # turn into Numpy arrays
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.array(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	np.array(_UpperCAmelCase   )

						if reduce_labels:
												SCREAMING_SNAKE_CASE_:			Any  		=	2_55
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	label - 1
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	2_55

						SCREAMING_SNAKE_CASE_:			Tuple  		=	label != ignore_index
						SCREAMING_SNAKE_CASE_:			List[str]  		=	np.not_equal(_UpperCAmelCase    ,     _UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	pred_label[mask]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.array(_UpperCAmelCase   )[mask]

						SCREAMING_SNAKE_CASE_:			str  		=	pred_label[pred_label == label]

						SCREAMING_SNAKE_CASE_:			Tuple  		=	np.histogram(_UpperCAmelCase    ,     bins=_UpperCAmelCase    ,     range=(0, num_labels - 1)   )[0]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	np.histogram(_UpperCAmelCase    ,     bins=_UpperCAmelCase    ,     range=(0, num_labels - 1)   )[0]
						SCREAMING_SNAKE_CASE_:			Any  		=	np.histogram(_UpperCAmelCase    ,     bins=_UpperCAmelCase    ,     range=(0, num_labels - 1)   )[0]

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = False    ,     ):
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.zeros((num_labels,)    ,     dtype=np.floataa   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	np.zeros((num_labels,)    ,     dtype=np.floataa   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.zeros((num_labels,)    ,     dtype=np.floataa   )
						SCREAMING_SNAKE_CASE_:			str  		=	np.zeros((num_labels,)    ,     dtype=np.floataa   )
						for result, gt_seg_map in zip(_UpperCAmelCase    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Tuple  		=	intersect_and_union(
												    _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
												total_area_intersect += area_intersect
												total_area_union += area_union
												total_area_pred_label += area_pred_label
												total_area_label += area_label
						return total_area_intersect, total_area_union, total_area_pred_label, total_area_label







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = False    ,     ):
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[Any]  		=	total_intersect_and_union(
						    _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )

						# compute metrics
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{}

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	total_area_intersect.sum() / total_area_label.sum()
						SCREAMING_SNAKE_CASE_:			int  		=	total_area_intersect / total_area_union
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	total_area_intersect / total_area_label

						SCREAMING_SNAKE_CASE_:			Any  		=	np.nanmean(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.nanmean(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	all_acc
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	iou
						SCREAMING_SNAKE_CASE_:			str  		=	acc

						if nan_to_num is not None:
												SCREAMING_SNAKE_CASE_:			Dict  		=	{metric: np.nan_to_num(_UpperCAmelCase    ,     nan=_UpperCAmelCase   ) for metric, metric_value in metrics.items()}

						return metrics



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION     )
class   __lowercase    (			datasets.Metric     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return datasets.MetricInfo(
									    description=_DESCRIPTION       ,						citation=_CITATION       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(
									        # 1st Seq - height dim, 2nd - width dim
									        {
									            "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"))),
									            "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"))),
									        })       ,						reference_urls=[
									        "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
									    ]       ,						)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					bool       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					Optional[Dict[int, int]] = None       ,						lowerCAmelCase__  :					bool = False       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	mean_iou(
									    results=lowerCAmelCase__       ,						gt_seg_maps=lowerCAmelCase__       ,						num_labels=lowerCAmelCase__       ,						ignore_index=lowerCAmelCase__       ,						nan_to_num=lowerCAmelCase__       ,						label_map=lowerCAmelCase__       ,						reduce_labels=lowerCAmelCase__       ,						)
									return iou_result









import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DeformableDetrImageProcessor



class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					int=False):
									if not batched:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	image_inputs[0]
															if isinstance(lowerCAmelCase__       ,						Image.Image):
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image.size
															else:
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image.shape[1], image.shape[2]
															if w < h:
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	int(self.size["shortest_edge"] * h / w)
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.size["shortest_edge"]
															elif w > h:
																					SCREAMING_SNAKE_CASE_:			Any  		=	self.size["shortest_edge"]
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	int(self.size["shortest_edge"] * w / h)
															else:
																					SCREAMING_SNAKE_CASE_:			int  		=	self.size["shortest_edge"]
																					SCREAMING_SNAKE_CASE_:			Dict  		=	self.size["shortest_edge"]

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

									return expected_height, expected_width







@require_torch
@require_vision
class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					DeformableDetrImageProcessor if is_vision_available() else None



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	DeformableDetrImageProcessingTester(self)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self.image_processor_tester.prepare_image_processor_dict()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"image_mean"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"image_std"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_normalize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_resize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_rescale"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_pad"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"size"))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.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       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_class.from_dict(
									    self.image_processor_dict       ,						size=42       ,						max_size=84       ,						pad_and_return_pixel_mask=lowerCAmelCase__)
									self.assertEqual(image_processor.size       ,						{"shortest_edge": 42, "longest_edge": 84})
									self.assertEqual(image_processor.do_pad       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						Image.Image)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_processing(image_inputs[0]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

									# Test batched
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values
									self.assertEqual(
									    encoded_images.shape       ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__       ,						numpify=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						np.ndarray)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:			str  		=	image_processing(image_inputs[0]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

									# Test batched
									SCREAMING_SNAKE_CASE_:			Any  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE_:			int  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__       ,						torchify=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						torch.Tensor)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_processing(image_inputs[0]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

									# Test batched
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									self.assertEqual(
									    encoded_images.shape       ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,						)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# prepare image and target
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
									with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,						"r") as f:
															SCREAMING_SNAKE_CASE_:			str  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{"image_id": 3_9769, "annotations": target}

									# encode them
									SCREAMING_SNAKE_CASE_:			str  		=	DeformableDetrImageProcessor()
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						return_tensors="pt")

									# verify pixel values
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.Size([1, 3, 800, 1066])
									self.assertEqual(encoding["pixel_values"].shape       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([0.2796, 0.3138, 0.3481])
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCAmelCase__       ,						atol=1E-4))

									# verify area
									SCREAMING_SNAKE_CASE_:			int  		=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			str  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			int  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.tensor([75, 75, 63, 65, 17, 17])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# prepare image, target and masks_path
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
									with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,						"r") as f:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

									SCREAMING_SNAKE_CASE_:			int  		=	pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

									# encode them
									SCREAMING_SNAKE_CASE_:			Any  		=	DeformableDetrImageProcessor(format="coco_panoptic")
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						masks_path=lowerCAmelCase__       ,						return_tensors="pt")

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

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor([0.2796, 0.3138, 0.3481])
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCAmelCase__       ,						atol=1E-4))

									# verify area
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			Any  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor([17, 17, 63, 75, 75, 93])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify masks
									SCREAMING_SNAKE_CASE_:			Tuple  		=	82_2873
									self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,						lowerCAmelCase__)
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))






