
import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
			from transformers import SpeechaTextFeatureExtractor

_lowerCamelCase	:    List[Any]          =      random.Random()






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     int=1.0	, __lowercase       :     Any=None	, __lowercase       :     List[str]=None			)			->  Dict:
			if rng is None:
						_snake_case          =      global_rng

			_snake_case          =      []
			for batch_idx in range(shape[0]			):
						values.append([]			)
						for _ in range(shape[1]			):
									values[-1].append(rng.random() * scale			)

			return values




@require_torch
@require_torchaudio
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def __init__(  self     :   Dict      ,  lowercase     :   str      ,  lowercase     :   str=7      ,  lowercase     :   Dict=400      ,  lowercase     :   Optional[Any]=2_000      ,  lowercase     :   str=24      ,  lowercase     :   Any=24      ,  lowercase     :   List[Any]=0.0      ,  lowercase     :   Tuple=16_000      ,  lowercase     :   Optional[int]=True      ,  lowercase     :   Dict=True      ,  ):


				'''simple docstring'''


				_snake_case          =      parent
				_snake_case          =      batch_size
				_snake_case          =      min_seq_length
				_snake_case          =      max_seq_length
				_snake_case          =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				_snake_case          =      feature_size
				_snake_case          =      num_mel_bins
				_snake_case          =      padding_value
				_snake_case          =      sampling_rate
				_snake_case          =      return_attention_mask
				_snake_case          =      do_normalize



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				return {
				    "feature_size": self.feature_size,
				    "num_mel_bins": self.num_mel_bins,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				    "do_normalize": self.do_normalize,
				}





	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   Optional[Any]=False      ,  lowercase     :   Dict=False     ):


				'''simple docstring'''


				def _flatten(lowercase     :   Tuple     ):
							return list(itertools.chain(*lowercase     )     )

				if equal_length:
							_snake_case          =      [floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
				else:
							# make sure that inputs increase in size
							_snake_case          =      [
							    floats_list((x, self.feature_size)     )
							    for x in range(self.min_seq_length      ,  self.max_seq_length      ,  self.seq_length_diff     )
							]
				if numpify:
							_snake_case          =      [np.asarray(lowercase     ) for x in speech_inputs]
				return speech_inputs




@require_torch
@require_torchaudio
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = SpeechaTextFeatureExtractor if is_speech_available() else None



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      SpeechaTextFeatureExtractionTester(self     )



	def 			A							(  self     :   Any      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				self.assertTrue(np.all(np.mean(lowercase      ,  axis=0     ) < 1E-3     )     )
				self.assertTrue(np.all(np.abs(np.var(lowercase      ,  axis=0     ) - 1     ) < 1E-3     )     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				# create three inputs of length 800, 1000, and 1200
				_snake_case          =      [floats_list((1, x)     )[0] for x in range(800      ,  1_400      ,  200     )]
				_snake_case          =      [np.asarray(lowercase     ) for speech_input in speech_inputs]

				# Test feature size
				_snake_case          =      feature_extractor(lowercase      ,  padding=lowercase      ,  return_tensors='np'     ).input_features
				self.assertTrue(input_features.ndim == 3     )
				self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size     )

				# Test not batched input
				_snake_case          =      feature_extractor(speech_inputs[0]      ,  return_tensors='np'     ).input_features
				_snake_case          =      feature_extractor(np_speech_inputs[0]      ,  return_tensors='np'     ).input_features
				self.assertTrue(np.allclose(lowercase      ,  lowercase      ,  atol=1E-3     )     )

				# Test batched
				_snake_case          =      feature_extractor(lowercase      ,  return_tensors='np'     ).input_features
				_snake_case          =      feature_extractor(lowercase      ,  return_tensors='np'     ).input_features
				for enc_seq_a, enc_seq_a in zip(lowercase      ,  lowercase     ):
							self.assertTrue(np.allclose(lowercase      ,  lowercase      ,  atol=1E-3     )     )

				# Test 2-D numpy arrays are batched.
				_snake_case          =      [floats_list((1, x)     )[0] for x in (800, 800, 800)]
				_snake_case          =      np.asarray(lowercase     )
				_snake_case          =      feature_extractor(lowercase      ,  return_tensors='np'     ).input_features
				_snake_case          =      feature_extractor(lowercase      ,  return_tensors='np'     ).input_features
				for enc_seq_a, enc_seq_a in zip(lowercase      ,  lowercase     ):
							self.assertTrue(np.allclose(lowercase      ,  lowercase      ,  atol=1E-3     )     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				_snake_case          =      [floats_list((1, x)     )[0] for x in range(800      ,  1_400      ,  200     )]

				_snake_case          =      ['longest', 'max_length', 'do_not_pad']
				_snake_case          =      [None, 16, None]
				for max_length, padding in zip(lowercase      ,  lowercase     ):
							_snake_case          =      feature_extractor(
							    lowercase      ,  padding=lowercase      ,  max_length=lowercase      ,  return_attention_mask=lowercase     )
							_snake_case          =      inputs.input_features
							_snake_case          =      inputs.attention_mask
							_snake_case          =      [np.sum(lowercase     ) for x in attention_mask]

							self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
							self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
							self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				_snake_case          =      [floats_list((1, x)     )[0] for x in range(800      ,  1_400      ,  200     )]

				_snake_case          =      ['longest', 'max_length', 'do_not_pad']
				_snake_case          =      [None, 16, None]
				for max_length, padding in zip(lowercase      ,  lowercase     ):
							_snake_case          =      feature_extractor(
							    lowercase      ,  max_length=lowercase      ,  padding=lowercase      ,  return_tensors='np'      ,  return_attention_mask=lowercase     )
							_snake_case          =      inputs.input_features
							_snake_case          =      inputs.attention_mask
							_snake_case          =      [np.sum(lowercase     ) for x in attention_mask]

							self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
							self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6     )
							self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
							self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6     )
							self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				_snake_case          =      [floats_list((1, x)     )[0] for x in range(800      ,  1_400      ,  200     )]
				_snake_case          =      feature_extractor(
				    lowercase      ,  padding='max_length'      ,  max_length=4      ,  truncation=lowercase      ,  return_tensors='np'      ,  return_attention_mask=lowercase      ,  )
				_snake_case          =      inputs.input_features
				_snake_case          =      inputs.attention_mask
				_snake_case          =      np.sum(attention_mask == 1      ,  axis=1     )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
				self._check_zero_mean_unit_variance(input_features[1]     )
				self._check_zero_mean_unit_variance(input_features[2]     )



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				_snake_case          =      [floats_list((1, x)     )[0] for x in range(800      ,  1_400      ,  200     )]
				_snake_case          =      feature_extractor(
				    lowercase      ,  padding='longest'      ,  max_length=4      ,  truncation=lowercase      ,  return_tensors='np'      ,  return_attention_mask=lowercase      ,  )
				_snake_case          =      inputs.input_features
				_snake_case          =      inputs.attention_mask
				_snake_case          =      np.sum(attention_mask == 1      ,  axis=1     )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
				self._check_zero_mean_unit_variance(input_features[2]     )

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape      ,  (3, 4, 24)     )

				_snake_case          =      [floats_list((1, x)     )[0] for x in range(800      ,  1_400      ,  200     )]
				_snake_case          =      feature_extractor(
				    lowercase      ,  padding='longest'      ,  max_length=16      ,  truncation=lowercase      ,  return_tensors='np'      ,  return_attention_mask=lowercase      ,  )
				_snake_case          =      inputs.input_features
				_snake_case          =      inputs.attention_mask
				_snake_case          =      np.sum(attention_mask == 1      ,  axis=1     )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
				self._check_zero_mean_unit_variance(input_features[2]     )

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape      ,  (3, 6, 24)     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				import torch

				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				_snake_case          =      np.random.rand(100      ,  32     ).astype(np.floataa     )
				_snake_case          =      np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
							_snake_case          =      feature_extractor.pad([{'input_features': inputs}]      ,  return_tensors='np'     )
							self.assertTrue(np_processed.input_features.dtype == np.floataa     )
							_snake_case          =      feature_extractor.pad([{'input_features': inputs}]      ,  return_tensors='pt'     )
							self.assertTrue(pt_processed.input_features.dtype == torch.floataa     )



	def 			A							(  self     :   int      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				from datasets import load_dataset

				_snake_case          =      load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,  'clean'      ,  split='validation'     )
				# automatic decoding with librispeech
				_snake_case          =      ds.sort('id'     ).select(range(lowercase     )     )[:num_samples]['audio']

				return [x["array"] for x in speech_samples]





	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      np.array([
				    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
				    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
				    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
				]     )
				# fmt: on

				_snake_case          =      self._load_datasamples(1     )
				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				_snake_case          =      feature_extractor(lowercase      ,  return_tensors='pt'     ).input_features
				self.assertEquals(input_features.shape      ,  (1, 584, 24)     )
				self.assertTrue(np.allclose(input_features[0, 0, :30]      ,  lowercase      ,  atol=1E-4     )     )

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        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def __init__(  self     :   int      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   str=7      ,  lowercase     :   Union[str, Any]=3      ,  lowercase     :   Tuple=30      ,  lowercase     :   Optional[Any]=400      ,  lowercase     :   List[Any]=True      ,  lowercase     :   Any=None      ,  lowercase     :   str=True      ,  lowercase     :   Tuple=[0.5, 0.5, 0.5]      ,  lowercase     :   List[Any]=[0.5, 0.5, 0.5]      ,  lowercase     :   Union[str, Any]=True      ,  lowercase     :   List[Any]=1 / 255      ,  lowercase     :   int=True      ,  ):


				'''simple docstring'''


				_snake_case          =      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
				_snake_case          =      parent
				_snake_case          =      batch_size
				_snake_case          =      num_channels
				_snake_case          =      min_resolution
				_snake_case          =      max_resolution
				_snake_case          =      do_resize
				_snake_case          =      size
				_snake_case          =      do_normalize
				_snake_case          =      image_mean
				_snake_case          =      image_std
				_snake_case          =      do_rescale
				_snake_case          =      rescale_factor
				_snake_case          =      do_pad



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				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 			A							(  self     :   Optional[int]      ,  lowercase     :   List[Any]      ,  lowercase     :   Tuple=False     ):


				'''simple docstring'''


				if not batched:
							_snake_case          =      image_inputs[0]
							if isinstance(lowercase      ,  Image.Image     ):
										_snake_case     , _snake_case          =      image.size
							else:
										_snake_case     , _snake_case          =      image.shape[1], image.shape[2]
							if w < h:
										_snake_case          =      int(self.size['shortest_edge'] * h / w     )
										_snake_case          =      self.size['shortest_edge']
							elif w > h:
										_snake_case          =      self.size['shortest_edge']
										_snake_case          =      int(self.size['shortest_edge'] * w / h     )
							else:
										_snake_case          =      self.size['shortest_edge']
										_snake_case          =      self.size['shortest_edge']

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

				return expected_height, expected_width




@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = DeformableDetrImageProcessor if is_vision_available() else None



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      DeformableDetrImageProcessingTester(self     )



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return self.image_processor_tester.prepare_image_processor_dict()



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				self.assertTrue(hasattr(lowercase      ,  'image_mean'     )     )
				self.assertTrue(hasattr(lowercase      ,  'image_std'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_normalize'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_resize'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_rescale'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_pad'     )     )
				self.assertTrue(hasattr(lowercase      ,  'size'     )     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class.from_dict(self.image_processor_dict     )
				self.assertEqual(image_processor.size      ,  {'shortest_edge': 18, 'longest_edge': 1_333}     )
				self.assertEqual(image_processor.do_pad      ,  lowercase     )

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



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				# create random PIL images
				_snake_case          =      prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=lowercase     )
				for image in image_inputs:
							self.assertIsInstance(lowercase      ,  Image.Image     )

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

				_snake_case          =      image_processing(lowercase      ,  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 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				# create random numpy tensors
				_snake_case          =      prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=lowercase      ,  numpify=lowercase     )
				for image in image_inputs:
							self.assertIsInstance(lowercase      ,  np.ndarray     )

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

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



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				# create random PyTorch tensors
				_snake_case          =      prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=lowercase      ,  torchify=lowercase     )
				for image in image_inputs:
							self.assertIsInstance(lowercase      ,  torch.Tensor     )

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

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



	@slow
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,  'r'     ) as f:
							_snake_case          =      json.loads(f.read()     )

				_snake_case          =      {'image_id': 39_769, 'annotations': target}

				# encode them
				_snake_case          =      DeformableDetrImageProcessor()
				_snake_case          =      image_processing(images=lowercase      ,  annotations=lowercase      ,  return_tensors='pt'     )

				# verify pixel values
				_snake_case          =      torch.Size([1, 3, 800, 1_066]     )
				self.assertEqual(encoding['pixel_values'].shape      ,  lowercase     )

				_snake_case          =      torch.tensor([0.2796, 0.3138, 0.3481]     )
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,  lowercase      ,  atol=1E-4     )     )

				# verify area
				_snake_case          =      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,  lowercase     )     )
				# verify boxes
				_snake_case          =      torch.Size([6, 4]     )
				self.assertEqual(encoding['labels'][0]['boxes'].shape      ,  lowercase     )
				_snake_case          =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,  lowercase      ,  atol=1E-3     )     )
				# verify image_id
				_snake_case          =      torch.tensor([39_769]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,  lowercase     )     )
				# verify is_crowd
				_snake_case          =      torch.tensor([0, 0, 0, 0, 0, 0]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,  lowercase     )     )
				# verify class_labels
				_snake_case          =      torch.tensor([75, 75, 63, 65, 17, 17]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,  lowercase     )     )
				# verify orig_size
				_snake_case          =      torch.tensor([480, 640]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,  lowercase     )     )
				# verify size
				_snake_case          =      torch.tensor([800, 1_066]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,  lowercase     )     )





	@slow
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
				with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,  'r'     ) as f:
							_snake_case          =      json.loads(f.read()     )

				_snake_case          =      {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

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

				# encode them
				_snake_case          =      DeformableDetrImageProcessor(format='coco_panoptic'     )
				_snake_case          =      image_processing(images=lowercase      ,  annotations=lowercase      ,  masks_path=lowercase      ,  return_tensors='pt'     )

				# verify pixel values
				_snake_case          =      torch.Size([1, 3, 800, 1_066]     )
				self.assertEqual(encoding['pixel_values'].shape      ,  lowercase     )

				_snake_case          =      torch.tensor([0.2796, 0.3138, 0.3481]     )
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,  lowercase      ,  atol=1E-4     )     )

				# verify area
				_snake_case          =      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,  lowercase     )     )
				# verify boxes
				_snake_case          =      torch.Size([6, 4]     )
				self.assertEqual(encoding['labels'][0]['boxes'].shape      ,  lowercase     )
				_snake_case          =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,  lowercase      ,  atol=1E-3     )     )
				# verify image_id
				_snake_case          =      torch.tensor([39_769]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,  lowercase     )     )
				# verify is_crowd
				_snake_case          =      torch.tensor([0, 0, 0, 0, 0, 0]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,  lowercase     )     )
				# verify class_labels
				_snake_case          =      torch.tensor([17, 17, 63, 75, 75, 93]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,  lowercase     )     )
				# verify masks
				_snake_case          =      822_873
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,  lowercase     )
				# verify orig_size
				_snake_case          =      torch.tensor([480, 640]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,  lowercase     )     )
				# verify size
				_snake_case          =      torch.tensor([800, 1_066]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,  lowercase     )     )