






import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  str							,		__A					:  Optional[int]							,		__A					:  Union[str, Any]=3							,		__A					:  Dict=3_2							,		__A					:  List[str]=3							,		__A					:  Optional[int]=1_0							,		__A					:  Any=[8, 1_6, 3_2, 6_4]							,		__A					:  Optional[Any]=[1, 1, 2, 1]							,		__A					:  Dict=True							,		__A					:  Optional[Any]=True							,		__A					:  Any="relu"							,		__A					:  Dict=3							,		__A					:  Dict=None							,		__A					:  int=["stage2", "stage3", "stage4"]							,		__A					:  List[Any]=[2, 3, 4]							,		__A					:  Tuple=1							,		):
						snake_case__			:				List[Any]       =  parent
						snake_case__			:				Optional[Any]       =  batch_size
						snake_case__			:				List[str]       =  image_size
						snake_case__			:				Optional[int]       =  num_channels
						snake_case__			:				int       =  embeddings_size
						snake_case__			:				Union[str, Any]       =  hidden_sizes
						snake_case__			:				Optional[int]       =  depths
						snake_case__			:				Union[str, Any]       =  is_training
						snake_case__			:				List[str]       =  use_labels
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Dict       =  num_labels
						snake_case__			:				Any       =  scope
						snake_case__			:				Any       =  len(__A	)
						snake_case__			:				List[str]       =  out_features
						snake_case__			:				Optional[int]       =  out_indices
						snake_case__			:				str       =  num_groups



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

						snake_case__			:				Union[str, Any]       =  None
						if self.use_labels:
							snake_case__			:				List[Any]       =  ids_tensor([self.batch_size]							,		self.num_labels	)

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

						return config, pixel_values, labels



					def 	_lowercase					( self					:  str	):
						return BitConfig(
						    num_channels=self.num_channels							,		embeddings_size=self.embeddings_size							,		hidden_sizes=self.hidden_sizes							,		depths=self.depths							,		hidden_act=self.hidden_act							,		num_labels=self.num_labels							,		out_features=self.out_features							,		out_indices=self.out_indices							,		num_groups=self.num_groups							,		)



					def 	_lowercase					( self					:  Dict							,		__A					:  Tuple							,		__A					:  int							,		__A					:  Tuple	):
						snake_case__			:				List[Any]       =  BitModel(config=__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				List[Any]       =  model(__A	)
						self.parent.assertEqual(
						    result.last_hidden_state.shape							,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,		)



					def 	_lowercase					( self					:  Dict							,		__A					:  int							,		__A					:  Dict							,		__A					:  Tuple	):
						snake_case__			:				Optional[Any]       =  self.num_labels
						snake_case__			:				List[str]       =  BitForImageClassification(__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				Union[str, Any]       =  model(__A							,		labels=__A	)
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels)	)



					def 	_lowercase					( self					:  int							,		__A					:  int							,		__A					:  List[Any]							,		__A					:  Optional[Any]	):
						snake_case__			:				Optional[Any]       =  BitBackbone(config=__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				Optional[Any]       =  model(__A	)

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps	)							,		len(config.out_features	)	)
						self.parent.assertListEqual(list(result.feature_maps[0].shape	)							,		[self.batch_size, self.hidden_sizes[1], 4, 4]	)

						# verify channels
						self.parent.assertEqual(len(model.channels	)							,		len(config.out_features	)	)
						self.parent.assertListEqual(model.channels							,		config.hidden_sizes[1:]	)

						# verify backbone works with out_features=None
						snake_case__			:				Union[str, Any]       =  None
						snake_case__			:				List[Any]       =  BitBackbone(config=__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				Any       =  model(__A	)

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps	)							,		1	)
						self.parent.assertListEqual(list(result.feature_maps[0].shape	)							,		[self.batch_size, self.hidden_sizes[-1], 1, 1]	)

						# verify channels
						self.parent.assertEqual(len(model.channels	)							,		1	)
						self.parent.assertListEqual(model.channels							,		[config.hidden_sizes[-1]]	)






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



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





					"""simple docstring"""






					a_     =		(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
					a_     =		(
					    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
					    if is_torch_available()
					    else {}
					)

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



					def 	_lowercase					( self					:  int	):
						snake_case__			:				List[Any]       =  BitModelTester(self	)
						snake_case__			:				int       =  ConfigTester(self							,		config_class=__A							,		has_text_modality=__A	)



					def 	_lowercase					( self					:  int	):
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()



					def 	_lowercase					( self					:  int	):
						return



					@unittest.skip(reason="Bit does not output attentions"	)
					def 	_lowercase					( self					:  Optional[int]	):
						pass



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



					@unittest.skip(reason="Bit does not support input and output embeddings"	)
					def 	_lowercase					( self					:  str	):
						pass



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

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

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



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



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



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

						for model_class in self.all_model_classes:
							snake_case__			:				str       =  model_class(config=__A	)
							for name, module in model.named_modules():
								if isinstance(__A							,		(nn.BatchNormad, nn.GroupNorm)	):
									self.assertTrue(
									    torch.all(module.weight == 1	)							,		msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,		)
									self.assertTrue(
									    torch.all(module.bias == 0	)							,		msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,		)



					def 	_lowercase					( self					:  Any	):
						def check_hidden_states_output(__A					:  Dict							,		__A					:  List[Any]							,		__A					:  List[Any]	):
							snake_case__			:				Any       =  model_class(__A	)
							model.to(__A	)
							model.eval()

							with torch.no_grad():
								snake_case__			:				Dict       =  model(**self._prepare_for_class(__A							,		__A	)	)

							snake_case__			:				int       =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							snake_case__			:				int       =  self.model_tester.num_stages
							self.assertEqual(len(__A	)							,		expected_num_stages + 1	)

							# Bit's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]	)							,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,		)

						snake_case__,						snake_case__			:				Optional[int]       =  self.model_tester.prepare_config_and_inputs_for_common()
						snake_case__			:				List[str]       =  ["preactivation", "bottleneck"]
						for model_class in self.all_model_classes:
							for layer_type in layers_type:
								snake_case__			:				int       =  layer_type
								snake_case__			:				Tuple       =  True
								check_hidden_states_output(__A							,		__A							,		__A	)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								snake_case__			:				Optional[int]       =  True

								check_hidden_states_output(__A							,		__A							,		__A	)



					@unittest.skip(reason="Bit does not use feedforward chunking"	)
					def 	_lowercase					( self					:  Optional[int]	):
						pass



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Optional[Any]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__A	)






					@slow
					def 	_lowercase					( self					:  int	):
						for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__			:				str       =  BitModel.from_pretrained(__A	)
							self.assertIsNotNone(__A	)







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				int       =  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					:  Tuple	):
						return (
						    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	) if is_vision_available() else None
						)






					@slow
					def 	_lowercase					( self					:  List[str]	):
						snake_case__			:				Any       =  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(__A	)

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

						# forward pass
						with torch.no_grad():
							snake_case__			:				Dict       =  model(**__A	)

						# verify the logits
						snake_case__			:				Optional[int]       =  torch.Size((1, 1_0_0_0)	)
						self.assertEqual(outputs.logits.shape							,		__A	)

						snake_case__			:				Dict       =  torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]	).to(__A	)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]							,		__A							,		atol=1e-4	)	)



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





					"""simple docstring"""






					a_     =		(BitBackbone,) if is_torch_available() else ()
					a_     =		BitConfig

					a_     =		False



					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				int       =  BitModelTester(self	)











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	)	)



