import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


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

if is_torch_available():
							import torch





def 	__lowerCamelCase							(UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							str=1.0      , UpperCAmelCase__     :							Optional[int]=None      , UpperCAmelCase__     :							Tuple=None  ):
					if rng is None:
										SCREAMING_SNAKE_CASE    			=       global_rng

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

					return values


class 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					int	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					int=7	,							_UpperCamelCase   :					Optional[Any]=400	,							_UpperCamelCase   :					List[str]=2_000	,							_UpperCamelCase   :					List[str]=1	,							_UpperCamelCase   :					List[str]=0.0	,							_UpperCamelCase   :					Optional[Any]=16_000	,							_UpperCamelCase   :					str=True	,							_UpperCamelCase   :					int=True	,							)					->				Dict:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Dict      )					->				str:



												'''simple docstring'''

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





							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Union[str, Any]=False	,							_UpperCamelCase   :					Dict=False      )					->				Tuple:



												'''simple docstring'''

												def _flatten(_UpperCamelCase   :					int      ):
																	return list(itertools.chain(*_UpperCamelCase      )      )

												if equal_length:
																	SCREAMING_SNAKE_CASE    			=       floats_list((self.batch_size, self.max_seq_length)      )
												else:
																	# make sure that inputs increase in size
																	SCREAMING_SNAKE_CASE    			=       [
																	    _flatten(floats_list((x, self.feature_size)      )      )
																	    for x in range(self.min_seq_length	,							self.max_seq_length	,							self.seq_length_diff      )
																	]

												if numpify:
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase      ) for x in speech_inputs]

												return speech_inputs



@require_torch
@require_torchaudio
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       List[Any]								=							ASTFeatureExtractor




							def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ASTFeatureExtractionTester(self      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

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

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       feat_extract(speech_inputs[0]	,							return_tensors="np"      ).input_values
												SCREAMING_SNAKE_CASE    			=       feat_extract(np_speech_inputs[0]	,							return_tensors="np"      ).input_values
												self.assertTrue(np.allclose(_UpperCamelCase	,							_UpperCamelCase	,							atol=1e-3      )      )

												# Test batched
												SCREAMING_SNAKE_CASE    			=       feat_extract(_UpperCamelCase	,							padding=_UpperCamelCase	,							return_tensors="np"      ).input_values
												SCREAMING_SNAKE_CASE    			=       feat_extract(_UpperCamelCase	,							padding=_UpperCamelCase	,							return_tensors="np"      ).input_values
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase	,							_UpperCamelCase      ):
																	self.assertTrue(np.allclose(_UpperCamelCase	,							_UpperCamelCase	,							atol=1e-3      )      )

												# Test 2-D numpy arrays are batched.
												SCREAMING_SNAKE_CASE    			=       [floats_list((1, x)      )[0] for x in (800, 800, 800)]
												SCREAMING_SNAKE_CASE    			=       np.asarray(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       feat_extract(_UpperCamelCase	,							return_tensors="np"      ).input_values
												SCREAMING_SNAKE_CASE    			=       feat_extract(_UpperCamelCase	,							return_tensors="np"      ).input_values
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase	,							_UpperCamelCase      ):
																	self.assertTrue(np.allclose(_UpperCamelCase	,							_UpperCamelCase	,							atol=1e-3      )      )




							@require_torch
							def 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

												import torch

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

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




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					Union[str, Any]      )					->				List[Any]:



												'''simple docstring'''

												from datasets import load_dataset

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

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





							@require_torch
							def 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       torch.tensor(
												    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
												     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
												     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
												     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]      )
												# fmt: on

												SCREAMING_SNAKE_CASE    			=       self._load_datasamples(1      )
												SCREAMING_SNAKE_CASE    			=       ASTFeatureExtractor()
												SCREAMING_SNAKE_CASE    			=       feature_extractor(_UpperCamelCase	,							return_tensors="pt"      ).input_values
												self.assertEquals(input_values.shape	,							(1, 1_024, 128)      )
												self.assertTrue(torch.allclose(input_values[0, 0, :30]	,							_UpperCamelCase	,							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 DetaImageProcessor


class 						lowercase    (     unittest.TestCase     ):




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



												'''simple docstring'''

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




							def 	__snake_case(						self   :					List[str]      )					->				Union[str, Any]:



												'''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 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[Any]=False      )					->				List[Any]:



												'''simple docstring'''

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

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

												return expected_height, expected_width



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Optional[int]								=							DetaImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DetaImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					int      )					->				Tuple:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_rescale"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_pad"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_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	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					str      )					->				List[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       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]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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   :					str      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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   :					Dict      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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    			=       json.loads(f.read()      )

												SCREAMING_SNAKE_CASE    			=       {"image_id": 39_769, "annotations": target}

												# encode them
												SCREAMING_SNAKE_CASE    			=       DetaImageProcessor()
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							return_tensors="pt"      )

												# verify pixel values
												SCREAMING_SNAKE_CASE    			=       torch.Size([1, 3, 800, 1_066]      )
												self.assertEqual(encoding["pixel_values"].shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,							_UpperCamelCase      )      )
												# verify boxes
												SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
												self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
												# verify is_crowd
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
												# verify class_labels
												SCREAMING_SNAKE_CASE    			=       torch.tensor([75, 75, 63, 65, 17, 17]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
												# verify orig_size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
												# verify size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )





							@slow
							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_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:
																	SCREAMING_SNAKE_CASE    			=       json.loads(f.read()      )

												SCREAMING_SNAKE_CASE    			=       {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

												# encode them
												SCREAMING_SNAKE_CASE    			=       DetaImageProcessor(format="coco_panoptic"      )
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							masks_path=_UpperCamelCase	,							return_tensors="pt"      )

												# verify pixel values
												SCREAMING_SNAKE_CASE    			=       torch.Size([1, 3, 800, 1_066]      )
												self.assertEqual(encoding["pixel_values"].shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,							_UpperCamelCase      )      )
												# verify boxes
												SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
												self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
												# verify is_crowd
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
												# verify class_labels
												SCREAMING_SNAKE_CASE    			=       torch.tensor([17, 17, 63, 75, 75, 93]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
												# verify masks
												SCREAMING_SNAKE_CASE    			=       822_873
												self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,							_UpperCamelCase      )
												# verify orig_size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
												# verify size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )




