





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_A :   Tuple  			=  '\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n'

_A :   Optional[int]  			=  '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'

_A :   Optional[int]  			=  '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for \'record\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'prediction_text\': the predicted answer text\n        - for \'multirc\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question-answer pair as specified by the dataset\n            - \'prediction\': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for \'record\': list of question-answers dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'answers\': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for \'record\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1\': F1 score\n    - for \'multirc\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1_m\': Per-question macro-F1 score\n        - \'f1_a\': Average F1 score over all answers\n    - for \'axb\':\n        \'matthews_correlation\': Matthew Correlation\n    - for \'cb\':\n        - \'accuracy\': Accuracy\n        - \'f1\': F1 score\n    - for all others:\n        - \'accuracy\': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    str:







   """simple docstring"""
   return float((preds == labels).mean()					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase="binary"					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							simple_accuracy(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Any        =							float(fa_score(y_true=UpperCAmelCase      ,  y_pred=UpperCAmelCase      ,  average=UpperCAmelCase					)					)
   return {
       "accuracy": acc,
       "f1": fa,
   }



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						Optional[Any]        =							{}
   for id_pred, label in zip(UpperCAmelCase      ,  UpperCAmelCase					):
      lowerCamelCase__       :						Optional[Any]        =							f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
      lowerCamelCase__       :						Dict        =							id_pred['''prediction''']
      if question_id in question_map:
         question_map[question_id].append((pred, label)					)
      else:
         lowerCamelCase__       :						int        =							[(pred, label)]
   lowerCamelCase__       :						int        =							[], []
   for question, preds_labels in question_map.items():
      lowerCamelCase__       :						List[Any]        =							zip(*UpperCAmelCase					)
      lowerCamelCase__       :						List[str]        =							fa_score(y_true=UpperCAmelCase      ,  y_pred=UpperCAmelCase      ,  average='''macro'''					)
      fas.append(UpperCAmelCase					)
      lowerCamelCase__       :						List[str]        =							int(sum(pred == label for pred, label in preds_labels					) == len(UpperCAmelCase					)					)
      ems.append(UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							float(sum(UpperCAmelCase					) / len(UpperCAmelCase					)					)
   lowerCamelCase__       :						List[Any]        =							sum(UpperCAmelCase					) / len(UpperCAmelCase					)
   lowerCamelCase__       :						Optional[int]        =							float(fa_score(y_true=UpperCAmelCase      ,  y_pred=[id_pred['''prediction'''] for id_pred in ids_preds]					)					)
   return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}







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







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->str:
          if self.config_name not in [
              "boolq",
              "cb",
              "copa",
              "multirc",
              "record",
              "rte",
              "wic",
              "wsc",
              "wsc.fixed",
              "axb",
              "axg",
          ]:
             raise KeyError(
                 '''You should supply a configuration name selected in '''
                 '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''      )
          return datasets.MetricInfo(
              description=_DESCRIPTION		, citation=_CITATION		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(self._get_feature_types()      )		, codebase_urls=[]		, reference_urls=[]		, format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None		, )







       def 						__lowerCamelCase   (				self					:     Any      )       ->str:
          if self.config_name == "record":
             return {
                 "predictions": {
                     "idx": {
                         "passage": datasets.Value('''int64'''      ),
                         "query": datasets.Value('''int64'''      ),
                     },
                     "prediction_text": datasets.Value('''string'''      ),
                 },
                 "references": {
                     "idx": {
                         "passage": datasets.Value('''int64'''      ),
                         "query": datasets.Value('''int64'''      ),
                     },
                     "answers": datasets.Sequence(datasets.Value('''string'''      )      ),
                 },
             }
          elif self.config_name == "multirc":
             return {
                 "predictions": {
                     "idx": {
                         "answer": datasets.Value('''int64'''      ),
                         "paragraph": datasets.Value('''int64'''      ),
                         "question": datasets.Value('''int64'''      ),
                     },
                     "prediction": datasets.Value('''int64'''      ),
                 },
                 "references": datasets.Value('''int64'''      ),
             }
          else:
             return {
                 "predictions": datasets.Value('''int64'''      ),
                 "references": datasets.Value('''int64'''      ),
             }







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     Optional[Any]		, A					:     List[Any]      )       ->int:
          if self.config_name == "axb":
             return {"matthews_correlation": matthews_corrcoef(__lowercase		, __lowercase      )}
          elif self.config_name == "cb":
             return acc_and_fa(__lowercase		, __lowercase		, fa_avg='''macro'''      )
          elif self.config_name == "record":
             lowerCamelCase__       :						Optional[Any]        =							[
                 {
                     '''qas''': [
                         {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
                         for ref in references
                     ]
                 }
             ]
             lowerCamelCase__       :						Optional[int]        =							{pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
             return evaluate_record(__lowercase		, __lowercase      )[0]
          elif self.config_name == "multirc":
             return evaluate_multirc(__lowercase		, __lowercase      )
          elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
             return {"accuracy": simple_accuracy(__lowercase		, __lowercase      )}
          else:
             raise KeyError(
                 '''You should supply a configuration name selected in '''
                 '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''      )










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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def __init__(				self					:     Optional[int]		, A					:     Union[str, Any]		, A					:     Any=7		, A					:     Optional[int]=3		, A					:     Tuple=3_0		, A					:     List[Any]=4_0_0		, A					:     str=True		, A					:     Optional[int]=None		, A					:     Tuple=True		, A					:     Union[str, Any]=1 / 2_5_5		, A					:     Any=True		, A					:     Optional[int]=[0.5, 0.5, 0.5]		, A					:     Optional[int]=[0.5, 0.5, 0.5]		, A					:     str=True		, )       ->List[Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          lowerCamelCase__       :						Union[str, Any]        =							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
          lowerCamelCase__       :						List[str]        =							parent
          lowerCamelCase__       :						Union[str, Any]        =							batch_size
          lowerCamelCase__       :						int        =							num_channels
          lowerCamelCase__       :						Optional[Any]        =							min_resolution
          lowerCamelCase__       :						List[Any]        =							max_resolution
          lowerCamelCase__       :						int        =							do_resize
          lowerCamelCase__       :						List[str]        =							size
          lowerCamelCase__       :						Union[str, Any]        =							do_rescale
          lowerCamelCase__       :						Optional[int]        =							rescale_factor
          lowerCamelCase__       :						List[str]        =							do_normalize
          lowerCamelCase__       :						Tuple        =							image_mean
          lowerCamelCase__       :						str        =							image_std
          lowerCamelCase__       :						List[Any]        =							do_pad







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->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 						__lowerCamelCase   (				self					:     Tuple		, A					:     int		, A					:     List[str]=False      )       ->int:
          if not batched:
             lowerCamelCase__       :						Union[str, Any]        =							image_inputs[0]
             if isinstance(A		, Image.Image      ):
                lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							image.size
             else:
                lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							image.shape[1], image.shape[2]
             if w < h:
                lowerCamelCase__       :						List[Any]        =							int(self.size['''shortest_edge'''] * h / w      )
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
             elif w > h:
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
                lowerCamelCase__       :						Dict        =							int(self.size['''shortest_edge'''] * w / h      )
             else:
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
                lowerCamelCase__       :						Any        =							self.size['''shortest_edge''']

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

          return expected_height, expected_width


@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   DetrImageProcessor if is_vision_available() else None







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessingTester(self      )







       @property
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          return self.image_processor_tester.prepare_image_processor_dict()







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(A		, '''image_mean'''      )      )
          self.assertTrue(hasattr(A		, '''image_std'''      )      )
          self.assertTrue(hasattr(A		, '''do_normalize'''      )      )
          self.assertTrue(hasattr(A		, '''do_rescale'''      )      )
          self.assertTrue(hasattr(A		, '''rescale_factor'''      )      )
          self.assertTrue(hasattr(A		, '''do_resize'''      )      )
          self.assertTrue(hasattr(A		, '''size'''      )      )
          self.assertTrue(hasattr(A		, '''do_pad'''      )      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						List[Any]        =							self.image_processing_class.from_dict(self.image_processor_dict      )
          self.assertEqual(image_processor.size		, {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}      )
          self.assertEqual(image_processor.do_pad		, A      )

          lowerCamelCase__       :						str        =							self.image_processing_class.from_dict(
              self.image_processor_dict		, size=4_2		, max_size=8_4		, pad_and_return_pixel_mask=A      )
          self.assertEqual(image_processor.size		, {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}      )
          self.assertEqual(image_processor.do_pad		, A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          pass







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[Any]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          lowerCamelCase__       :						str        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, Image.Image      )

          # Test not batched input
          lowerCamelCase__       :						Optional[Any]        =							image_processing(image_inputs[0]		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

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

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

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       :						List[Any]        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[Any]        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

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







       def 						__lowerCamelCase   (				self					:     Any      )       ->Any:
          # Initialize image_processing
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowerCamelCase__       :						List[str]        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A		, torchify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, torch.Tensor      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       :						str        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

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







       @slow
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
          # prepare image and target
          lowerCamelCase__       :						Union[str, Any]        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		, '''r'''      ) as f:
             lowerCamelCase__       :						Union[str, Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						List[str]        =							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

          # encode them
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''      )
          lowerCamelCase__       :						List[Any]        =							image_processing(images=A		, annotations=A		, return_tensors='''pt'''      )

          # verify pixel values
          lowerCamelCase__       :						Optional[int]        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
          self.assertEqual(encoding['''pixel_values'''].shape		, A      )

          lowerCamelCase__       :						Any        =							torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A		, atol=1e-4      )      )

          # verify area
          lowerCamelCase__       :						Dict        =							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''']		, A      )      )
          # verify boxes
          lowerCamelCase__       :						Any        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						str        =							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A		, atol=1e-3      )      )
          # verify image_id
          lowerCamelCase__       :						str        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						List[str]        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						List[Any]        =							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, A      )      )
          # verify orig_size
          lowerCamelCase__       :						str        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						str        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )







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

          lowerCamelCase__       :						Union[str, Any]        =							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

          lowerCamelCase__       :						List[Any]        =							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

          # encode them
          lowerCamelCase__       :						Any        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''      )
          lowerCamelCase__       :						Tuple        =							image_processing(images=A		, annotations=A		, masks_path=A		, return_tensors='''pt'''      )

          # verify pixel values
          lowerCamelCase__       :						Optional[Any]        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
          self.assertEqual(encoding['''pixel_values'''].shape		, A      )

          lowerCamelCase__       :						str        =							torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A		, atol=1e-4      )      )

          # verify area
          lowerCamelCase__       :						int        =							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''']		, A      )      )
          # verify boxes
          lowerCamelCase__       :						Optional[Any]        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						Optional[Any]        =							torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A		, atol=1e-3      )      )
          # verify image_id
          lowerCamelCase__       :						int        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						Dict        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						int        =							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, A      )      )
          # verify masks
          lowerCamelCase__       :						Union[str, Any]        =							8_2_2_8_7_3
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		, A      )
          # verify orig_size
          lowerCamelCase__       :						List[Any]        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						Tuple        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )



