"""simple docstring"""




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 DetrImageProcessor
class     UpperCamelCase__(			unittest.TestCase ):





      def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=7		,__UpperCAmelCase=3		,__UpperCAmelCase=30		,__UpperCAmelCase=4_00		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=1 / 2_55		,__UpperCAmelCase=True		,__UpperCAmelCase=[0.5, 0.5, 0.5]		,__UpperCAmelCase=[0.5, 0.5, 0.5]		,__UpperCAmelCase=True		,)					->				Optional[Any]:
             # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
             A__	     =							size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
             A__	     =							parent
             A__	     =							batch_size
             A__	     =							num_channels
             A__	     =							min_resolution
             A__	     =							max_resolution
             A__	     =							do_resize
             A__	     =							size
             A__	     =							do_rescale
             A__	     =							rescale_factor
             A__	     =							do_normalize
             A__	     =							image_mean
             A__	     =							image_std
             A__	     =							do_pad





      def 				snake_case__	(       self     )					->				Dict:
             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 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=False     )					->				Dict:
             if not batched:
                    A__	     =							image_inputs[0]
                    if isinstance(__UpperCAmelCase		,Image.Image     ):
                           A__     ,  A__	     =							image.size
                    else:
                           A__     ,  A__	     =							image.shape[1], image.shape[2]
                    if w < h:
                           A__	     =							int(self.size['shortest_edge'] * h / w     )
                           A__	     =							self.size['shortest_edge']
                    elif w > h:
                           A__	     =							self.size['shortest_edge']
                           A__	     =							int(self.size['shortest_edge'] * w / h     )
                    else:
                           A__	     =							self.size['shortest_edge']
                           A__	     =							self.size['shortest_edge']

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

             return expected_height, expected_width






@require_torch
@require_vision
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
      lowerCAmelCase__  :						Any													=		DetrImageProcessor if is_vision_available() else None





      def 				snake_case__	(       self     )					->				int:
             A__	     =							DetrImageProcessingTester(self     )





      @property
      def 				snake_case__	(       self     )					->				List[Any]:
             return self.image_processor_tester.prepare_image_processor_dict()





      def 				snake_case__	(       self     )					->				Tuple:
             A__	     =							self.image_processing_class(**self.image_processor_dict     )
             self.assertTrue(hasattr(__UpperCAmelCase		,'image_mean'     )     )
             self.assertTrue(hasattr(__UpperCAmelCase		,'image_std'     )     )
             self.assertTrue(hasattr(__UpperCAmelCase		,'do_normalize'     )     )
             self.assertTrue(hasattr(__UpperCAmelCase		,'do_rescale'     )     )
             self.assertTrue(hasattr(__UpperCAmelCase		,'rescale_factor'     )     )
             self.assertTrue(hasattr(__UpperCAmelCase		,'do_resize'     )     )
             self.assertTrue(hasattr(__UpperCAmelCase		,'size'     )     )
             self.assertTrue(hasattr(__UpperCAmelCase		,'do_pad'     )     )





      def 				snake_case__	(       self     )					->				Optional[int]:
             A__	     =							self.image_processing_class.from_dict(self.image_processor_dict     )
             self.assertEqual(image_processor.size		,{'shortest_edge': 18, 'longest_edge': 13_33}     )
             self.assertEqual(image_processor.do_pad		,__UpperCAmelCase     )

             A__	     =							self.image_processing_class.from_dict(
                 self.image_processor_dict		,size=42		,max_size=84		,pad_and_return_pixel_mask=__UpperCAmelCase     )
             self.assertEqual(image_processor.size		,{'shortest_edge': 42, 'longest_edge': 84}     )
             self.assertEqual(image_processor.do_pad		,__UpperCAmelCase     )





      def 				snake_case__	(       self     )					->				Any:
             pass





      def 				snake_case__	(       self     )					->				Dict:
             # Initialize image_processing
             A__	     =							self.image_processing_class(**self.image_processor_dict     )
             # create random PIL images
             A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=__UpperCAmelCase     )
             for image in image_inputs:
                    self.assertIsInstance(__UpperCAmelCase		,Image.Image     )

             # Test not batched input
             A__	     =							image_processing(image_inputs[0]		,return_tensors='pt'     ).pixel_values

             A__     ,  A__	     =							self.image_processor_tester.get_expected_values(__UpperCAmelCase     )

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

             # Test batched
             A__     ,  A__	     =							self.image_processor_tester.get_expected_values(__UpperCAmelCase		,batched=__UpperCAmelCase     )

             A__	     =							image_processing(__UpperCAmelCase		,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 				snake_case__	(       self     )					->				List[str]:
             # Initialize image_processing
             A__	     =							self.image_processing_class(**self.image_processor_dict     )
             # create random numpy tensors
             A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=__UpperCAmelCase		,numpify=__UpperCAmelCase     )
             for image in image_inputs:
                    self.assertIsInstance(__UpperCAmelCase		,np.ndarray     )

             # Test not batched input
             A__	     =							image_processing(image_inputs[0]		,return_tensors='pt'     ).pixel_values

             A__     ,  A__	     =							self.image_processor_tester.get_expected_values(__UpperCAmelCase     )

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

             # Test batched
             A__	     =							image_processing(__UpperCAmelCase		,return_tensors='pt'     ).pixel_values

             A__     ,  A__	     =							self.image_processor_tester.get_expected_values(__UpperCAmelCase		,batched=__UpperCAmelCase     )

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





      def 				snake_case__	(       self     )					->				Dict:
             # Initialize image_processing
             A__	     =							self.image_processing_class(**self.image_processor_dict     )
             # create random PyTorch tensors
             A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=__UpperCAmelCase		,torchify=__UpperCAmelCase     )
             for image in image_inputs:
                    self.assertIsInstance(__UpperCAmelCase		,torch.Tensor     )

             # Test not batched input
             A__	     =							image_processing(image_inputs[0]		,return_tensors='pt'     ).pixel_values

             A__     ,  A__	     =							self.image_processor_tester.get_expected_values(__UpperCAmelCase     )

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

             # Test batched
             A__	     =							image_processing(__UpperCAmelCase		,return_tensors='pt'     ).pixel_values

             A__     ,  A__	     =							self.image_processor_tester.get_expected_values(__UpperCAmelCase		,batched=__UpperCAmelCase     )

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





      @slow
      def 				snake_case__	(       self     )					->				Tuple:
             # prepare image and target
             A__	     =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
             with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,'r'     ) as f:
                    A__	     =							json.loads(f.read()     )

             A__	     =							{'image_id': 3_97_69, 'annotations': target}

             # encode them
             A__	     =							DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'     )
             A__	     =							image_processing(images=__UpperCAmelCase		,annotations=__UpperCAmelCase		,return_tensors='pt'     )

             # verify pixel values
             A__	     =							torch.Size([1, 3, 8_00, 10_66]     )
             self.assertEqual(encoding['pixel_values'].shape		,__UpperCAmelCase     )

             A__	     =							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,__UpperCAmelCase		,atol=1e-4     )     )

             # verify area
             A__	     =							torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,__UpperCAmelCase     )     )
             # verify boxes
             A__	     =							torch.Size([6, 4]     )
             self.assertEqual(encoding['labels'][0]['boxes'].shape		,__UpperCAmelCase     )
             A__	     =							torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,__UpperCAmelCase		,atol=1e-3     )     )
             # verify image_id
             A__	     =							torch.tensor([3_97_69]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,__UpperCAmelCase     )     )
             # verify is_crowd
             A__	     =							torch.tensor([0, 0, 0, 0, 0, 0]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,__UpperCAmelCase     )     )
             # verify class_labels
             A__	     =							torch.tensor([75, 75, 63, 65, 17, 17]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,__UpperCAmelCase     )     )
             # verify orig_size
             A__	     =							torch.tensor([4_80, 6_40]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,__UpperCAmelCase     )     )
             # verify size
             A__	     =							torch.tensor([8_00, 10_66]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,__UpperCAmelCase     )     )





      @slow
      def 				snake_case__	(       self     )					->				Any:
             # prepare image, target and masks_path
             A__	     =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
             with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,'r'     ) as f:
                    A__	     =							json.loads(f.read()     )

             A__	     =							{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

             A__	     =							pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

             # encode them
             A__	     =							DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'     )
             A__	     =							image_processing(images=__UpperCAmelCase		,annotations=__UpperCAmelCase		,masks_path=__UpperCAmelCase		,return_tensors='pt'     )

             # verify pixel values
             A__	     =							torch.Size([1, 3, 8_00, 10_66]     )
             self.assertEqual(encoding['pixel_values'].shape		,__UpperCAmelCase     )

             A__	     =							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,__UpperCAmelCase		,atol=1e-4     )     )

             # verify area
             A__	     =							torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,__UpperCAmelCase     )     )
             # verify boxes
             A__	     =							torch.Size([6, 4]     )
             self.assertEqual(encoding['labels'][0]['boxes'].shape		,__UpperCAmelCase     )
             A__	     =							torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,__UpperCAmelCase		,atol=1e-3     )     )
             # verify image_id
             A__	     =							torch.tensor([3_97_69]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,__UpperCAmelCase     )     )
             # verify is_crowd
             A__	     =							torch.tensor([0, 0, 0, 0, 0, 0]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,__UpperCAmelCase     )     )
             # verify class_labels
             A__	     =							torch.tensor([17, 17, 63, 75, 75, 93]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,__UpperCAmelCase     )     )
             # verify masks
             A__	     =							82_28_73
             self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,__UpperCAmelCase     )
             # verify orig_size
             A__	     =							torch.tensor([4_80, 6_40]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,__UpperCAmelCase     )     )
             # verify size
             A__	     =							torch.tensor([8_00, 10_66]     )
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,__UpperCAmelCase     )     )






"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


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

__lowerCamelCase											=  "\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'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.        ])}\n"

__lowerCamelCase											=  "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							if label_map is not None:
														for old_id, new_id in label_map.items():
																					A__	     =							new_id

    # turn into Numpy arrays
							A__	     =							np.array(UpperCamelCase__				)
							A__	     =							np.array(UpperCamelCase__				)

							if reduce_labels:
														A__	     =							255
														A__	     =							label - 1
														A__	     =							255

							A__	     =							label != ignore_index
							A__	     =							np.not_equal(UpperCamelCase__       ,    UpperCamelCase__				)
							A__	     =							pred_label[mask]
							A__	     =							np.array(UpperCamelCase__				)[mask]

							A__	     =							pred_label[pred_label == label]

							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]

							A__	     =							area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							for result, gt_seg_map in zip(UpperCamelCase__       ,    UpperCamelCase__				):
														A__     ,  A__     ,  A__     ,  A__	     =							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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__     ,  A__     ,  A__     ,  A__	     =							total_intersect_and_union(
							    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)

							# compute metrics
							A__	     =							{}

							A__	     =							total_area_intersect.sum() / total_area_label.sum()
							A__	     =							total_area_intersect / total_area_union
							A__	     =							total_area_intersect / total_area_label

							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							all_acc
							A__	     =							iou
							A__	     =							acc

							if nan_to_num is not None:
														A__	     =							{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     UpperCamelCase__(			datasets.Metric ):





						def 				snake_case__	(       self     )					->				str:
													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 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,)					->				Tuple:
													A__	     =							mean_iou(
													    results=__UpperCAmelCase		,gt_seg_maps=__UpperCAmelCase		,num_labels=__UpperCAmelCase		,ignore_index=__UpperCAmelCase		,nan_to_num=__UpperCAmelCase		,label_map=__UpperCAmelCase		,reduce_labels=__UpperCAmelCase		,)
													return iou_result





