






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import ViTMAEForPreTraining, ViTMAEModel
	from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Dict							,		__A					:  Tuple							,		__A					:  str=1_3							,		__A					:  List[str]=3_0							,		__A					:  Any=2							,		__A					:  Tuple=3							,		__A					:  Dict=True							,		__A					:  str=True							,		__A					:  Dict=3_2							,		__A					:  str=5							,		__A					:  Tuple=4							,		__A					:  List[str]=3_7							,		__A					:  List[str]="gelu"							,		__A					:  List[str]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=1_0							,		__A					:  List[str]=0.0_2							,		__A					:  Union[str, Any]=3							,		__A					:  Optional[Any]=0.6							,		__A					:  List[str]=None							,		):
						snake_case__			:				int       =  parent
						snake_case__			:				Union[str, Any]       =  batch_size
						snake_case__			:				List[Any]       =  image_size
						snake_case__			:				Optional[Any]       =  patch_size
						snake_case__			:				Tuple       =  num_channels
						snake_case__			:				List[str]       =  is_training
						snake_case__			:				Optional[Any]       =  use_labels
						snake_case__			:				str       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				Any       =  num_attention_heads
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				Optional[int]       =  hidden_act
						snake_case__			:				Tuple       =  hidden_dropout_prob
						snake_case__			:				Union[str, Any]       =  attention_probs_dropout_prob
						snake_case__			:				Dict       =  type_sequence_label_size
						snake_case__			:				Optional[Any]       =  initializer_range
						snake_case__			:				Union[str, Any]       =  mask_ratio
						snake_case__			:				int       =  scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						snake_case__			:				Tuple       =  (image_size // patch_size) ** 2
						snake_case__			:				Tuple       =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Optional[Any]       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

						snake_case__			:				int       =  None
						if self.use_labels:
							snake_case__			:				Tuple       =  ids_tensor([self.batch_size]							,		self.type_sequence_label_size	)

						snake_case__			:				List[str]       =  self.get_config()

						return config, pixel_values, labels



					def 	_lowercase					( self					:  Any	):
						return ViTMAEConfig(
						    image_size=self.image_size							,		patch_size=self.patch_size							,		num_channels=self.num_channels							,		hidden_size=self.hidden_size							,		num_hidden_layers=self.num_hidden_layers							,		num_attention_heads=self.num_attention_heads							,		intermediate_size=self.intermediate_size							,		hidden_act=self.hidden_act							,		hidden_dropout_prob=self.hidden_dropout_prob							,		attention_probs_dropout_prob=self.attention_probs_dropout_prob							,		is_decoder=__A							,		initializer_range=self.initializer_range							,		mask_ratio=self.mask_ratio							,		)



					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Optional[int]							,		__A					:  Optional[Any]							,		__A					:  Dict	):
						snake_case__			:				Dict       =  ViTMAEModel(config=__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				List[str]       =  model(__A	)
						self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)	)



					def 	_lowercase					( self					:  Dict							,		__A					:  Any							,		__A					:  Tuple							,		__A					:  str	):
						snake_case__			:				int       =  ViTMAEForPreTraining(__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				Any       =  model(__A	)
						snake_case__			:				Union[str, Any]       =  (self.image_size // self.patch_size) ** 2
						snake_case__			:				Union[str, Any]       =  self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, num_patches, expected_num_channels)	)

						# test greyscale images
						snake_case__			:				Any       =  1
						snake_case__			:				str       =  ViTMAEForPreTraining(__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				str       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
						snake_case__			:				Dict       =  model(__A	)
						snake_case__			:				int       =  self.patch_size**2
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, num_patches, expected_num_channels)	)






					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Tuple       =  self.prepare_config_and_inputs()
						snake_case__,						snake_case__,						snake_case__			:				Union[str, Any]       =  config_and_inputs
						snake_case__			:				List[str]       =  {"pixel_values": pixel_values}
						return config, inputs_dict



@require_torch
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
					a_     =		{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

					a_     =		False
					a_     =		False
					a_     =		False
					a_     =		False



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				List[str]       =  ViTMAEModelTester(self	)
						snake_case__			:				List[Any]       =  ConfigTester(self							,		config_class=__A							,		has_text_modality=__A							,		hidden_size=3_7	)



					def 	_lowercase					( self					:  int	):
						self.config_tester.run_common_tests()



					@unittest.skip(reason="ViTMAE does not use inputs_embeds"	)
					def 	_lowercase					( self					:  Dict	):
						pass



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__,						snake_case__			:				List[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__			:				int       =  model_class(__A	)
							self.assertIsInstance(model.get_input_embeddings()							,		(nn.Module)	)
							snake_case__			:				str       =  model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__A							,		nn.Linear	)	)



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__,						snake_case__			:				Tuple       =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__			:				List[str]       =  model_class(__A	)
							snake_case__			:				List[str]       =  inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case__			:				Tuple       =  [*signature.parameters.keys()]

							snake_case__			:				Union[str, Any]       =  ["pixel_values"]
							self.assertListEqual(arg_names[:1]							,		__A	)



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Optional[int]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A	)



					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				List[Any]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__A	)



					def 	_lowercase					( self					:  List[Any]							,		__A					:  Union[str, Any]							,		__A					:  List[str]							,		__A					:  Union[str, Any]	):
						# make masks reproducible
						np.random.seed(2	)

						snake_case__			:				Optional[int]       =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2	)
						snake_case__			:				Union[str, Any]       =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
						snake_case__			:				Optional[Any]       =  torch.from_numpy(__A	)

						# Add `noise` argument.
						# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
						snake_case__			:				Any       =  pt_noise

						super().check_pt_tf_models(__A							,		__A							,		__A	)



					def 	_lowercase					( self					:  int	):
						snake_case__,						snake_case__			:				str       =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__			:				List[Any]       =  model_class(__A	)
							model.to(__A	)
							model.eval()
							# make random mask reproducible
							torch.manual_seed(2	)
							with torch.no_grad():
								snake_case__			:				str       =  model(**self._prepare_for_class(__A							,		__A	)	)

							snake_case__			:				List[Any]       =  outputs[0].cpu().numpy()
							snake_case__			:				Tuple       =  0

							with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(__A	)
								snake_case__			:				List[str]       =  model_class.from_pretrained(__A	)
								model.to(__A	)
								# make random mask reproducible
								torch.manual_seed(2	)
								with torch.no_grad():
									snake_case__			:				List[Any]       =  model(**self._prepare_for_class(__A							,		__A	)	)

								# Make sure we don't have nans
								snake_case__			:				Tuple       =  after_outputs[0].cpu().numpy()
								snake_case__			:				int       =  0
								snake_case__			:				Dict       =  np.amax(np.abs(out_a - out_a	)	)
								self.assertLessEqual(__A							,		1e-5	)



					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."	)
					def 	_lowercase					( self					:  Optional[int]	):
						pass



					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."	)
					def 	_lowercase					( self					:  int	):
						pass



					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."	)
					def 	_lowercase					( self					:  Tuple	):
						pass



					@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"	)
					def 	_lowercase					( self					:  Dict	):
						pass



					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
					def 	_lowercase					( self					:  Union[str, Any]	):
						pass






					@slow
					def 	_lowercase					( self					:  List[str]	):
						for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__			:				Dict       =  ViTMAEModel.from_pretrained(__A	)
							self.assertIsNotNone(__A	)







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				List[Any]       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
	return image





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





					"""simple docstring"""






					@cached_property
					def 	_lowercase					( self					:  List[str]	):
						return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"	) if is_vision_available() else None






					@slow
					def 	_lowercase					( self					:  List[Any]	):
						# make random mask reproducible across the PT and TF model
						np.random.seed(2	)

						snake_case__			:				Union[str, Any]       =  ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"	).to(__A	)

						snake_case__			:				Dict       =  self.default_image_processor
						snake_case__			:				Optional[Any]       =  prepare_img()
						snake_case__			:				List[str]       =  image_processor(images=__A							,		return_tensors="pt"	).to(__A	)

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						snake_case__			:				List[str]       =  ViTMAEConfig()
						snake_case__			:				List[str]       =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
						snake_case__			:				Tuple       =  np.random.uniform(size=(1, num_patches)	)

						# forward pass
						with torch.no_grad():
							snake_case__			:				Optional[Any]       =  model(**__A							,		noise=torch.from_numpy(__A	).to(device=__A	)	)

						# verify the logits
						snake_case__			:				Optional[int]       =  torch.Size((1, 1_9_6, 7_6_8)	)
						self.assertEqual(outputs.logits.shape							,		__A	)

						snake_case__			:				Any       =  torch.tensor(
						    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]	)

						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]							,		expected_slice.to(__A	)							,		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					:  List[str]							,		__A					:  int							,		__A					:  str=7							,		__A					:  Union[str, Any]=3							,		__A					:  Union[str, Any]=3_0							,		__A					:  Optional[int]=4_0_0							,		__A					:  Optional[Any]=True							,		__A					:  Optional[int]=None							,		__A					:  Union[str, Any]=True							,		__A					:  Optional[int]=[0.5, 0.5, 0.5]							,		__A					:  Any=[0.5, 0.5, 0.5]							,		__A					:  Optional[int]=True							,		__A					:  Optional[Any]=1 / 2_5_5							,		__A					:  Union[str, Any]=True							,		):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__			:				Optional[Any]       =  size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__			:				List[Any]       =  parent
						snake_case__			:				Union[str, Any]       =  batch_size
						snake_case__			:				Tuple       =  num_channels
						snake_case__			:				List[Any]       =  min_resolution
						snake_case__			:				Optional[Any]       =  max_resolution
						snake_case__			:				str       =  do_resize
						snake_case__			:				List[str]       =  size
						snake_case__			:				List[Any]       =  do_normalize
						snake_case__			:				Dict       =  image_mean
						snake_case__			:				List[Any]       =  image_std
						snake_case__			:				int       =  do_rescale
						snake_case__			:				Tuple       =  rescale_factor
						snake_case__			:				str       =  do_pad



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

						else:
							snake_case__			:				Union[str, Any]       =  []
							for image in image_inputs:
								snake_case__,						snake_case__			:				int       =  self.get_expected_values([image]	)
								expected_values.append((expected_height, expected_width)	)
							snake_case__			:				Any       =  max(__A							,		key=lambda __A	: item[0]	)[0]
							snake_case__			:				Optional[int]       =  max(__A							,		key=lambda __A	: item[1]	)[1]

						return expected_height, expected_width



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





					"""simple docstring"""






					a_     =		DeformableDetrImageProcessor if is_vision_available() else None



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				str       =  DeformableDetrImageProcessingTester(self	)



					@property
					def 	_lowercase					( self					:  List[Any]	):
						return self.image_processor_tester.prepare_image_processor_dict()



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				int       =  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_resize"	)	)
						self.assertTrue(hasattr(__A							,		"do_rescale"	)	)
						self.assertTrue(hasattr(__A							,		"do_pad"	)	)
						self.assertTrue(hasattr(__A							,		"size"	)	)



					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				int       =  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	)

						snake_case__			:				List[Any]       =  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 	_lowercase					( self					:  Any	):
						pass



					def 	_lowercase					( self					:  Optional[int]	):
						# Initialize image_processing
						snake_case__			:				List[str]       =  self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						snake_case__			:				Tuple       =  prepare_image_inputs(self.image_processor_tester							,		equal_resolution=__A	)
						for image in image_inputs:
							self.assertIsInstance(__A							,		Image.Image	)

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

						snake_case__,						snake_case__			:				Dict       =  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
						snake_case__,						snake_case__			:				Optional[Any]       =  self.image_processor_tester.get_expected_values(__A							,		batched=__A	)

						snake_case__			:				Union[str, 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 	_lowercase					( self					:  Any	):
						# Initialize image_processing
						snake_case__			:				Optional[Any]       =  self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						snake_case__			:				List[Any]       =  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
						snake_case__			:				Union[str, Any]       =  image_processing(image_inputs[0]							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				Union[str, Any]       =  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
						snake_case__			:				Union[str, Any]       =  image_processing(__A							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				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 	_lowercase					( self					:  Union[str, Any]	):
						# Initialize image_processing
						snake_case__			:				str       =  self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						snake_case__			:				List[Any]       =  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
						snake_case__			:				Tuple       =  image_processing(image_inputs[0]							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				List[Any]       =  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
						snake_case__			:				Optional[Any]       =  image_processing(__A							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				int       =  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 	_lowercase					( self					:  Optional[int]	):
						# prepare image and target
						snake_case__			:				List[Any]       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,		"r"	) as f:
							snake_case__			:				Any       =  json.loads(f.read()	)

						snake_case__			:				List[str]       =  {"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__			:				Optional[Any]       =  DeformableDetrImageProcessor()
						snake_case__			:				Tuple       =  image_processing(images=__A							,		annotations=__A							,		return_tensors="pt"	)

						# verify pixel values
						snake_case__			:				str       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
						self.assertEqual(encoding["pixel_values"].shape							,		__A	)

						snake_case__			:				int       =  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]							,		__A							,		atol=1e-4	)	)

						# verify area
						snake_case__			:				int       =  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"]							,		__A	)	)
						# verify boxes
						snake_case__			:				str       =  torch.Size([6, 4]	)
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,		__A	)
						snake_case__			:				List[Any]       =  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]							,		__A							,		atol=1e-3	)	)
						# verify image_id
						snake_case__			:				int       =  torch.tensor([3_9_7_6_9]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,		__A	)	)
						# verify is_crowd
						snake_case__			:				Dict       =  torch.tensor([0, 0, 0, 0, 0, 0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,		__A	)	)
						# verify class_labels
						snake_case__			:				Optional[int]       =  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
						snake_case__			:				Union[str, Any]       =  torch.tensor([4_8_0, 6_4_0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,		__A	)	)
						# verify size
						snake_case__			:				List[str]       =  torch.tensor([8_0_0, 1_0_6_6]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,		__A	)	)






					@slow
					def 	_lowercase					( self					:  Union[str, Any]	):
						# prepare image, target and masks_path
						snake_case__			:				str       =  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__			:				Optional[int]       =  json.loads(f.read()	)

						snake_case__			:				Any       =  {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

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

						# encode them
						snake_case__			:				Dict       =  DeformableDetrImageProcessor(format="coco_panoptic"	)
						snake_case__			:				List[Any]       =  image_processing(images=__A							,		annotations=__A							,		masks_path=__A							,		return_tensors="pt"	)

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

						snake_case__			:				Tuple       =  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]							,		__A							,		atol=1e-4	)	)

						# verify area
						snake_case__			:				Any       =  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"]							,		__A	)	)
						# verify boxes
						snake_case__			:				Union[str, Any]       =  torch.Size([6, 4]	)
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,		__A	)
						snake_case__			:				Tuple       =  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]							,		__A							,		atol=1e-3	)	)
						# verify image_id
						snake_case__			:				Union[str, Any]       =  torch.tensor([3_9_7_6_9]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,		__A	)	)
						# verify is_crowd
						snake_case__			:				List[Any]       =  torch.tensor([0, 0, 0, 0, 0, 0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,		__A	)	)
						# verify class_labels
						snake_case__			:				List[str]       =  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
						snake_case__			:				Optional[Any]       =  8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,		__A	)
						# verify orig_size
						snake_case__			:				Dict       =  torch.tensor([4_8_0, 6_4_0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,		__A	)	)
						# verify size
						snake_case__			:				Dict       =  torch.tensor([8_0_0, 1_0_6_6]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,		__A	)	)



