






import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

__snake_case      :      Optional[Any]             =       collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__snake_case      :      List[str]             =       """https://storage.googleapis.com/cvdf-datasets/mnist/"""







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  numpy.dtype(numpy.uintaa						).newbyteorder('>'						)
						return numpy.frombuffer(bytestream.read(4						)      ,    dtype=UpperCamelCase_						)[0]







@deprecated(UpperCamelCase_      ,    'Please use tf.data to implement this functionality.'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Optional[int]:

						"""simple docstring"""



						print('Extracting'      ,    f.name						)
						with gzip.GzipFile(fileobj=UpperCamelCase_						) as bytestream:
												lowerCAmelCase__        =  _readaa(UpperCamelCase_						)
												if magic != 2051:
																		raise ValueError(
																		    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)						)
												lowerCAmelCase__        =  _readaa(UpperCamelCase_						)
												lowerCAmelCase__        =  _readaa(UpperCamelCase_						)
												lowerCAmelCase__        =  _readaa(UpperCamelCase_						)
												lowerCAmelCase__        =  bytestream.read(rows * cols * num_images						)
												lowerCAmelCase__        =  numpy.frombuffer(UpperCamelCase_      ,    dtype=numpy.uinta						)
												lowerCAmelCase__        =  data.reshape(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    1						)
												return data







@deprecated(UpperCamelCase_      ,    'Please use tf.one_hot on tensors.'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   str						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  labels_dense.shape[0]
						lowerCAmelCase__        =  numpy.arange(UpperCamelCase_						) * num_classes
						lowerCAmelCase__        =  numpy.zeros((num_labels, num_classes)						)
						lowerCAmelCase__        =  1
						return labels_one_hot







@deprecated(UpperCamelCase_      ,    'Please use tf.data to implement this functionality.'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Dict=False      ,    UpperCamelCase_    :   Any=10						)					->				Any:

						"""simple docstring"""



						print('Extracting'      ,    f.name						)
						with gzip.GzipFile(fileobj=UpperCamelCase_						) as bytestream:
												lowerCAmelCase__        =  _readaa(UpperCamelCase_						)
												if magic != 2049:
																		raise ValueError(
																		    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)						)
												lowerCAmelCase__        =  _readaa(UpperCamelCase_						)
												lowerCAmelCase__        =  bytestream.read(UpperCamelCase_						)
												lowerCAmelCase__        =  numpy.frombuffer(UpperCamelCase_      ,    dtype=numpy.uinta						)
												if one_hot:
																		return _dense_to_one_hot(UpperCamelCase_      ,    UpperCamelCase_						)
												return labels


class       __SCREAMING_SNAKE_CASE   :






						@deprecated(
						    _UpperCamelCase       ,  'Please use alternatives such as official/mnist/_DataSet.py'
						    ' from tensorflow/models.'       ,  )
						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=False       ,  _UpperCamelCase=False       ,  _UpperCamelCase=dtypes.floataa       ,  _UpperCamelCase=True       ,  _UpperCamelCase=None       ,  ):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  random_seed.get_seed(_UpperCamelCase				)
												# If op level seed is not set, use whatever graph level seed is returned
												numpy.random.seed(seeda if seed is None else seeda				)
												lowerCAmelCase__        =  dtypes.as_dtype(_UpperCamelCase				).base_dtype
												if dtype not in (dtypes.uinta, dtypes.floataa):
																		raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype				)
												if fake_data:
																		lowerCAmelCase__        =  1_00_00
																		lowerCAmelCase__        =  one_hot
												else:
																		assert (
																		    images.shape[0] == labels.shape[0]
																		), F"images.shape: {images.shape} labels.shape: {labels.shape}"
																		lowerCAmelCase__        =  images.shape[0]

																		# Convert shape from [num examples, rows, columns, depth]
																		# to [num examples, rows*columns] (assuming depth == 1)
																		if reshape:
																								assert images.shape[3] == 1
																								lowerCAmelCase__        =  images.reshape(
																								    images.shape[0]       ,  images.shape[1] * images.shape[2]				)
																		if dtype == dtypes.floataa:
																								# Convert from [0, 255] -> [0.0, 1.0].
																								lowerCAmelCase__        =  images.astype(numpy.floataa				)
																								lowerCAmelCase__        =  numpy.multiply(_UpperCamelCase       ,  1.0 / 2_55.0				)
												lowerCAmelCase__        =  images
												lowerCAmelCase__        =  labels
												lowerCAmelCase__        =  0
												lowerCAmelCase__        =  0






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self._images






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self._labels






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self._num_examples






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self._epochs_completed







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=False       ,  _UpperCamelCase=True				):






												"""simple docstring"""
												if fake_data:
																		lowerCAmelCase__        =  [1] * 7_84
																		lowerCAmelCase__        =  [1] + [0] * 9 if self.one_hot else 0
																		return (
																		    [fake_image for _ in range(_UpperCamelCase				)],
																		    [fake_label for _ in range(_UpperCamelCase				)],
																		)
												lowerCAmelCase__        =  self._index_in_epoch
												# Shuffle for the first epoch
												if self._epochs_completed == 0 and start == 0 and shuffle:
																		lowerCAmelCase__        =  numpy.arange(self._num_examples				)
																		numpy.random.shuffle(_UpperCamelCase				)
																		lowerCAmelCase__        =  self.images[perma]
																		lowerCAmelCase__        =  self.labels[perma]
												# Go to the next epoch
												if start + batch_size > self._num_examples:
																		# Finished epoch
																		self._epochs_completed += 1
																		# Get the rest examples in this epoch
																		lowerCAmelCase__        =  self._num_examples - start
																		lowerCAmelCase__        =  self._images[start : self._num_examples]
																		lowerCAmelCase__        =  self._labels[start : self._num_examples]
																		# Shuffle the data
																		if shuffle:
																								lowerCAmelCase__        =  numpy.arange(self._num_examples				)
																								numpy.random.shuffle(_UpperCamelCase				)
																								lowerCAmelCase__        =  self.images[perm]
																								lowerCAmelCase__        =  self.labels[perm]
																		# Start next epoch
																		lowerCAmelCase__        =  0
																		lowerCAmelCase__        =  batch_size - rest_num_examples
																		lowerCAmelCase__        =  self._index_in_epoch
																		lowerCAmelCase__        =  self._images[start:end]
																		lowerCAmelCase__        =  self._labels[start:end]
																		return (
																		    numpy.concatenate((images_rest_part, images_new_part)       ,  axis=0				),
																		    numpy.concatenate((labels_rest_part, labels_new_part)       ,  axis=0				),
																		)
												else:
																		self._index_in_epoch += batch_size
																		lowerCAmelCase__        =  self._index_in_epoch
																		return self._images[start:end], self._labels[start:end]







@deprecated(UpperCamelCase_      ,    'Please write your own downloading logic.'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   List[str]						)					->				Union[str, Any]:

						"""simple docstring"""



						if not gfile.Exists(UpperCamelCase_						):
												gfile.MakeDirs(UpperCamelCase_						)
						lowerCAmelCase__        =  os.path.join(UpperCamelCase_      ,    UpperCamelCase_						)
						if not gfile.Exists(UpperCamelCase_						):
												urllib.request.urlretrieve(UpperCamelCase_      ,    UpperCamelCase_						)  # noqa: S310
												with gfile.GFile(UpperCamelCase_						) as f:
																		lowerCAmelCase__        =  f.size()
												print('Successfully downloaded'      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    'bytes.'						)
						return filepath







@deprecated(
    UpperCamelCase_      ,    'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   List[str]=False      ,    UpperCamelCase_    :   Tuple=False      ,    UpperCamelCase_    :   Tuple=dtypes.floataa      ,    UpperCamelCase_    :   List[str]=True      ,    UpperCamelCase_    :   Dict=5000      ,    UpperCamelCase_    :   Optional[int]=None      ,    UpperCamelCase_    :   Dict=DEFAULT_SOURCE_URL      ,    )					->				Dict:

						"""simple docstring"""



						if fake_data:

												def fake():
																		return _DataSet(
																		    []      ,    []      ,    fake_data=UpperCamelCase_      ,    one_hot=UpperCamelCase_      ,    dtype=UpperCamelCase_      ,    seed=UpperCamelCase_						)

												lowerCAmelCase__        =  fake()
												lowerCAmelCase__        =  fake()
												lowerCAmelCase__        =  fake()
												return _Datasets(train=UpperCamelCase_      ,    validation=UpperCamelCase_      ,    test=UpperCamelCase_						)

						if not source_url:  # empty string check
												lowerCAmelCase__        =  DEFAULT_SOURCE_URL

						lowerCAmelCase__        =  'train-images-idx3-ubyte.gz'
						lowerCAmelCase__        =  'train-labels-idx1-ubyte.gz'
						lowerCAmelCase__        =  't10k-images-idx3-ubyte.gz'
						lowerCAmelCase__        =  't10k-labels-idx1-ubyte.gz'

						lowerCAmelCase__        =  _maybe_download(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    source_url + train_images_file						)
						with gfile.Open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  _extract_images(UpperCamelCase_						)

						lowerCAmelCase__        =  _maybe_download(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    source_url + train_labels_file						)
						with gfile.Open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  _extract_labels(UpperCamelCase_      ,    one_hot=UpperCamelCase_						)

						lowerCAmelCase__        =  _maybe_download(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    source_url + test_images_file						)
						with gfile.Open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  _extract_images(UpperCamelCase_						)

						lowerCAmelCase__        =  _maybe_download(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    source_url + test_labels_file						)
						with gfile.Open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  _extract_labels(UpperCamelCase_      ,    one_hot=UpperCamelCase_						)

						if not 0 <= validation_size <= len(UpperCamelCase_						):
												lowerCAmelCase__        =  (
												    'Validation size should be between 0 and '
												    F"{len(UpperCamelCase_						)}. Received: {validation_size}."
												)
												raise ValueError(UpperCamelCase_						)

						lowerCAmelCase__        =  train_images[:validation_size]
						lowerCAmelCase__        =  train_labels[:validation_size]
						lowerCAmelCase__        =  train_images[validation_size:]
						lowerCAmelCase__        =  train_labels[validation_size:]

						lowerCAmelCase__        =  {'dtype': dtype, 'reshape': reshape, 'seed': seed}

						lowerCAmelCase__        =  _DataSet(UpperCamelCase_      ,    UpperCamelCase_      ,    **UpperCamelCase_						)
						lowerCAmelCase__        =  _DataSet(UpperCamelCase_      ,    UpperCamelCase_      ,    **UpperCamelCase_						)
						lowerCAmelCase__        =  _DataSet(UpperCamelCase_      ,    UpperCamelCase_      ,    **UpperCamelCase_						)

						return _Datasets(train=UpperCamelCase_      ,    validation=UpperCamelCase_      ,    test=UpperCamelCase_						)













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):






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=7       ,  _UpperCamelCase=3       ,  _UpperCamelCase=30       ,  _UpperCamelCase=4_00       ,  _UpperCamelCase=True       ,  _UpperCamelCase=None       ,  _UpperCamelCase=True       ,  _UpperCamelCase=[0.5, 0.5, 0.5]       ,  _UpperCamelCase=[0.5, 0.5, 0.5]       ,  _UpperCamelCase=True       ,  _UpperCamelCase=1 / 2_55       ,  _UpperCamelCase=True       ,  ):






												"""simple docstring"""
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  num_channels
												lowerCAmelCase__        =  min_resolution
												lowerCAmelCase__        =  max_resolution
												lowerCAmelCase__        =  do_resize
												lowerCAmelCase__        =  size
												lowerCAmelCase__        =  do_normalize
												lowerCAmelCase__        =  image_mean
												lowerCAmelCase__        =  image_std
												lowerCAmelCase__        =  do_rescale
												lowerCAmelCase__        =  rescale_factor
												lowerCAmelCase__        =  do_pad






						def  UpperCamelCase__ (		self				):






												"""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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=False				):






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

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

												return expected_height, expected_width




@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Dict						=   DeformableDetrImageProcessor if is_vision_available() else None






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  DeformableDetrImageProcessingTester(self				)






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.image_processor_tester.prepare_image_processor_dict()






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  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				)

												lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random PIL images
												lowerCAmelCase__        =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_UpperCamelCase				)
												for image in image_inputs:
																		self.assertIsInstance(_UpperCamelCase       ,  Image.Image				)

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

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

												lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random numpy tensors
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  image_processing(image_inputs[0]       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  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
												lowerCAmelCase__        =  image_processing(_UpperCamelCase       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  image_processing(image_inputs[0]       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  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
												lowerCAmelCase__        =  image_processing(_UpperCamelCase       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# prepare image and target
												lowerCAmelCase__        =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
												with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,  'r'				) as f:
																		lowerCAmelCase__        =  json.loads(f.read()				)

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

												# encode them
												lowerCAmelCase__        =  DeformableDetrImageProcessor()
												lowerCAmelCase__        =  image_processing(images=_UpperCamelCase       ,  annotations=_UpperCamelCase       ,  return_tensors='pt'				)

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

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

												# verify area
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  torch.Size([6, 4]				)
												self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  _UpperCamelCase       ,  atol=1E-3				)				)
												# verify image_id
												lowerCAmelCase__        =  torch.tensor([3_97_69]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  _UpperCamelCase				)				)
												# verify is_crowd
												lowerCAmelCase__        =  torch.tensor([0, 0, 0, 0, 0, 0]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  _UpperCamelCase				)				)
												# verify class_labels
												lowerCAmelCase__        =  torch.tensor([75, 75, 63, 65, 17, 17]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  _UpperCamelCase				)				)
												# verify orig_size
												lowerCAmelCase__        =  torch.tensor([4_80, 6_40]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  _UpperCamelCase				)				)
												# verify size
												lowerCAmelCase__        =  torch.tensor([8_00, 10_66]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  _UpperCamelCase				)				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# prepare image, target and masks_path
												lowerCAmelCase__        =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
												with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,  'r'				) as f:
																		lowerCAmelCase__        =  json.loads(f.read()				)

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

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

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

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

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

												# verify area
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  torch.Size([6, 4]				)
												self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  _UpperCamelCase       ,  atol=1E-3				)				)
												# verify image_id
												lowerCAmelCase__        =  torch.tensor([3_97_69]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  _UpperCamelCase				)				)
												# verify is_crowd
												lowerCAmelCase__        =  torch.tensor([0, 0, 0, 0, 0, 0]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  _UpperCamelCase				)				)
												# verify class_labels
												lowerCAmelCase__        =  torch.tensor([17, 17, 63, 75, 75, 93]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  _UpperCamelCase				)				)
												# verify masks
												lowerCAmelCase__        =  82_28_73
												self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,  _UpperCamelCase				)
												# verify orig_size
												lowerCAmelCase__        =  torch.tensor([4_80, 6_40]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  _UpperCamelCase				)				)
												# verify size
												lowerCAmelCase__        =  torch.tensor([8_00, 10_66]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  _UpperCamelCase				)				)





