






'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFResNetForImageClassification, TFResNetModel
			from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class _lowercase	:




							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				int=3			,							UpperCamelCase__:				Union[str, Any]=32			,							UpperCamelCase__:				Any=3			,							UpperCamelCase__:				Optional[int]=10			,							UpperCamelCase__:				List[str]=[10, 20, 30, 40]			,							UpperCamelCase__:				Tuple=[1, 1, 2, 1]			,							UpperCamelCase__:				Union[str, Any]=True			,							UpperCamelCase__:				List[str]=True			,							UpperCamelCase__:				str="relu"			,							UpperCamelCase__:				Any=3			,							UpperCamelCase__:				Optional[int]=None			,							):
														lowerCamelCase__					:	List[str]         =       parent
														lowerCamelCase__					:	str         =       batch_size
														lowerCamelCase__					:	str         =       image_size
														lowerCamelCase__					:	List[str]         =       num_channels
														lowerCamelCase__					:	List[str]         =       embeddings_size
														lowerCamelCase__					:	Dict         =       hidden_sizes
														lowerCamelCase__					:	Optional[Any]         =       depths
														lowerCamelCase__					:	Dict         =       is_training
														lowerCamelCase__					:	str         =       use_labels
														lowerCamelCase__					:	Any         =       hidden_act
														lowerCamelCase__					:	str         =       num_labels
														lowerCamelCase__					:	List[str]         =       scope
														lowerCamelCase__					:	Optional[int]         =       len(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Dict         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	int         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Dict         =       ids_tensor([self.batch_size]			,							self.num_labels     )

														lowerCamelCase__					:	str         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				Any     ):
														return ResNetConfig(
														    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			,							image_size=self.image_size			,							)




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Union[str, Any]         =       TFResNetModel(config=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__     )
														# expected last hidden states: B, C, H // 32, W // 32
														self.parent.assertEqual(
														    result.last_hidden_state.shape			,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,							)




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.num_labels
														lowerCamelCase__					:	Dict         =       TFResNetForImageClassification(UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							labels=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels)     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       config_and_inputs
														lowerCamelCase__					:	Union[str, Any]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_tf
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
							a				      =       (
							    {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
							    if is_tf_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Union[str, Any]         =       TFResNetModelTester(self     )
														lowerCamelCase__					:	Any         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[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 		lowerCamelCase_				(							self:				int     ):
														return




							@unittest.skip(reason="""ResNet does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							@unittest.skip(reason="""ResNet does not support input and output embeddings"""     )
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														pass




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[Any]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	List[str]         =       inspect.signature(model.call     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	Union[str, Any]         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Dict         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														def check_hidden_states_output(UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				int     ):
																					lowerCamelCase__					:	List[str]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Tuple         =       model(**self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )     )

																					lowerCamelCase__					:	Tuple         =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					lowerCamelCase__					:	Optional[int]         =       self.model_tester.num_stages
																					self.assertEqual(len(UpperCamelCase__     )			,							expected_num_stages + 1     )

																					# ResNet'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]			,							)

														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()
														lowerCamelCase__					:	Tuple         =       ["""basic""", """bottleneck"""]
														for model_class in self.all_model_classes:
																					for layer_type in layers_type:
																												lowerCamelCase__					:	Tuple         =       layer_type
																												lowerCamelCase__					:	Optional[Any]         =       True
																												check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )

																												# check that output_hidden_states also work using config
																												del inputs_dict["output_hidden_states"]
																												lowerCamelCase__					:	Union[str, Any]         =       True

																												check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	str         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	List[str]         =       TFResNetModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Union[str, Any]:
							lowerCamelCase__					:	List[str]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_tf
@require_vision
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				int     ):
														return (
														    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
														    if is_vision_available()
														    else None
														)




							@slow
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	List[str]         =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )

														lowerCamelCase__					:	Union[str, Any]         =       self.default_image_processor
														lowerCamelCase__					:	Optional[Any]         =       prepare_img()
														lowerCamelCase__					:	Optional[Any]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""tf"""     )

														# forward pass
														lowerCamelCase__					:	Optional[int]         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	List[Any]         =       tf.TensorShape((1, 1_000)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	Tuple         =       tf.constant([-11.1_069, -9.7_877, -8.3_777]     )

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





'''simple docstring'''
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 ConditionalDetrImageProcessor
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=7    ,   UpperCamelCase_=3    ,   UpperCamelCase_=30    ,   UpperCamelCase_=400    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   UpperCamelCase_=True    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=True    ,   UpperCamelCase_=1 / 255    ,   UpperCamelCase_=True    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
							UpperCamelCase__   :str												= parent
							UpperCamelCase__   :List[Any]												= batch_size
							UpperCamelCase__   :Dict												= num_channels
							UpperCamelCase__   :str												= min_resolution
							UpperCamelCase__   :Optional[Any]												= max_resolution
							UpperCamelCase__   :int												= do_resize
							UpperCamelCase__   :Optional[Any]												= size
							UpperCamelCase__   :Tuple												= do_normalize
							UpperCamelCase__   :List[Any]												= image_mean
							UpperCamelCase__   :Dict												= image_std
							UpperCamelCase__   :Union[str, Any]												= do_rescale
							UpperCamelCase__   :Union[str, Any]												= rescale_factor
							UpperCamelCase__   :Union[str, Any]												= do_pad




		def     lowerCAmelCase__				(		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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=False							):

							'''simple docstring'''


							if not batched:
												UpperCamelCase__   :List[str]												= image_inputs[0]
												if isinstance(UpperCamelCase_    ,   Image.Image							):
																	UpperCamelCase__					,				UpperCamelCase__   :List[str]												= image.size
												else:
																	UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= image.shape[1], image.shape[2]
												if w < h:
																	UpperCamelCase__   :int												= int(self.size['''shortest_edge'''] * h / w							)
																	UpperCamelCase__   :Dict												= self.size['''shortest_edge''']
												elif w > h:
																	UpperCamelCase__   :int												= self.size['''shortest_edge''']
																	UpperCamelCase__   :Tuple												= int(self.size['''shortest_edge'''] * w / h							)
												else:
																	UpperCamelCase__   :str												= self.size['''shortest_edge''']
																	UpperCamelCase__   :str												= self.size['''shortest_edge''']

							else:
												UpperCamelCase__   :Any												= []
												for image in image_inputs:
																	UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.get_expected_values([image]							)
																	expected_values.append((expected_height, expected_width)							)
												UpperCamelCase__   :List[Any]												= max(UpperCamelCase_    ,   key=lambda UpperCamelCase_							: item[0]							)[0]
												UpperCamelCase__   :Optional[int]												= max(UpperCamelCase_    ,   key=lambda UpperCamelCase_							: item[1]							)[1]

							return expected_height, expected_width



@require_torch
@require_vision
class    lowercase	(							A__	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				ConditionalDetrImageProcessor if is_vision_available() else None




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessingTester(self							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= 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_    ,   '''size'''							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 18, '''longest_edge''': 1333}							)
							self.assertEqual(image_processor.do_pad    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__   :List[str]												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   Image.Image							)

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

							UpperCamelCase__					,				UpperCamelCase__   :str												= 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
							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							UpperCamelCase__   :Any												= 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
							UpperCamelCase__   :Union[str, Any]												= image_processing(image_inputs[0]    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= 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
							UpperCamelCase__   :Union[str, Any]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :str												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							UpperCamelCase__   :Any												= 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
							UpperCamelCase__   :str												= image_processing(image_inputs[0]    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :Dict												= 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
							UpperCamelCase__   :List[str]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,   '''r'''							) as f:
												UpperCamelCase__   :Optional[int]												= json.loads(f.read()							)

							UpperCamelCase__   :Any												= {'''image_id''': 39769, '''annotations''': target}

							# encode them
							UpperCamelCase__   :str												= ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''							)
							UpperCamelCase__   :List[Any]												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   return_tensors='''pt'''							)

							# verify pixel values
							UpperCamelCase__   :List[str]												= torch.Size([1, 3, 800, 1066]							)
							self.assertEqual(encoding['''pixel_values'''].shape    ,   UpperCamelCase_							)

							UpperCamelCase__   :str												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :str												= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Optional[Any]												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[Any]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :int												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :List[str]												= torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify orig_size
							UpperCamelCase__   :Tuple												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,   '''r'''							) as f:
												UpperCamelCase__   :Tuple												= json.loads(f.read()							)

							UpperCamelCase__   :List[str]												= {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

							UpperCamelCase__   :Any												= pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

							# encode them
							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessor(format='''coco_panoptic'''							)
							UpperCamelCase__   :Dict												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   masks_path=UpperCamelCase_    ,   return_tensors='''pt'''							)

							# verify pixel values
							UpperCamelCase__   :str												= torch.Size([1, 3, 800, 1066]							)
							self.assertEqual(encoding['''pixel_values'''].shape    ,   UpperCamelCase_							)

							UpperCamelCase__   :Optional[int]												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :Tuple												= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Any												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :List[Any]												= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[str]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :str												= torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify masks
							UpperCamelCase__   :Optional[Any]												= 822873
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,   UpperCamelCase_							)
							# verify orig_size
							UpperCamelCase__   :List[str]												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :List[Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)