






"""simple docstring"""



import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
						from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor





class 					_UpperCAmelCase       :




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




					def   A  (				self     :						Dict   )  ->  Any:
							lowercase_ : List[str]							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

							lowercase_ : Optional[Any]							       =   self.get_config()

							return config, pixel_values, labels




					def   A  (				self     :						int   )  ->  int:
							return RegNetConfig(
							    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    ,						)




					def   A  (				self     :						Any    ,						A     :						Dict    ,						A     :						List[Any]    ,						A     :						List[Any]   )  ->  Any:
							lowercase_ : str							       =   RegNetModel(config=A   )
							model.to(A   )
							model.eval()
							lowercase_ : List[Any]							       =   model(A   )
							# 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   A  (				self     :						List[str]    ,						A     :						Union[str, Any]    ,						A     :						int    ,						A     :						List[str]   )  ->  Optional[int]:
							lowercase_ : Tuple							       =   self.num_labels
							lowercase_ : Any							       =   RegNetForImageClassification(A   )
							model.to(A   )
							model.eval()
							lowercase_ : Dict							       =   model(A    ,						labels=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )






					def   A  (				self     :						Tuple   )  ->  str:
							lowercase_ : Tuple							       =   self.prepare_config_and_inputs()
							lowercase_       ,					lowercase_       ,					lowercase_ : Union[str, Any]							       =   config_and_inputs
							lowercase_ : str							       =   {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_torch
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   (
					    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE_		:				Tuple							    =   False
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   False
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   False
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   False




					def   A  (				self     :						Optional[Any]   )  ->  int:
							lowercase_ : Dict							       =   RegNetModelTester(self   )
							lowercase_ : Tuple							       =   ConfigTester(self    ,						config_class=A    ,						has_text_modality=A   )




					def   A  (				self     :						Dict   )  ->  str:
							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   A  (				self     :						Optional[int]   )  ->  int:
							return




					@unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
					def   A  (				self     :						List[str]   )  ->  int:
							pass




					@unittest.skip(reason='''RegNet does not support input and output embeddings'''   )
					def   A  (				self     :						Dict   )  ->  Dict:
							pass




					def   A  (				self     :						Any   )  ->  Tuple:
							lowercase_       ,					lowercase_ : int							       =   self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase_ : Union[str, Any]							       =   ['''pixel_values''']
									self.assertListEqual(arg_names[:1]    ,						A   )




					def   A  (				self     :						Any   )  ->  str:
							lowercase_ : int							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A   )




					def   A  (				self     :						List[Any]   )  ->  List[str]:
							lowercase_       ,					lowercase_ : str							       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase_ : List[Any]							       =   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   A  (				self     :						str   )  ->  Optional[int]:
							def check_hidden_states_output(A     :						Optional[int]    ,						A     :						Optional[int]    ,						A     :						Optional[int]   ):
									lowercase_ : Tuple							       =   model_class(A   )
									model.to(A   )
									model.eval()

									with torch.no_grad():
											lowercase_ : Optional[Any]							       =   model(**self._prepare_for_class(A    ,						A   )   )

									lowercase_ : Any							       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									lowercase_ : str							       =   self.model_tester.num_stages
									self.assertEqual(len(A   )    ,						expected_num_stages + 1   )

									# RegNet'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 // 2, self.model_tester.image_size // 2]    ,						)

							lowercase_       ,					lowercase_ : int							       =   self.model_tester.prepare_config_and_inputs_for_common()
							lowercase_ : List[Any]							       =   ['''basic''', '''bottleneck''']
							for model_class in self.all_model_classes:
									for layer_type in layers_type:
											lowercase_ : str							       =   layer_type
											lowercase_ : int							       =   True
											check_hidden_states_output(A    ,						A    ,						A   )

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

											check_hidden_states_output(A    ,						A    ,						A   )




					def   A  (				self     :						List[str]   )  ->  str:
							lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A   )






					@slow
					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Union[str, Any]							       =   RegNetModel.from_pretrained(A   )
									self.assertIsNotNone(A   )



def 						lowercase    (				):
		lowercase_ : Dict							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image






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




					@cached_property
					def   A  (				self     :						Any   )  ->  Optional[int]:
							return (
							    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
							    if is_vision_available()
							    else None
							)






					@slow
					def   A  (				self     :						str   )  ->  Any:
							lowercase_ : Dict							       =   RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(A   )

							lowercase_ : str							       =   self.default_image_processor
							lowercase_ : Optional[Any]							       =   prepare_img()
							lowercase_ : Any							       =   image_processor(images=A    ,						return_tensors='''pt'''   ).to(A   )

							# forward pass
							with torch.no_grad():
									lowercase_ : Any							       =   model(**A   )

							# verify the logits
							lowercase_ : Dict							       =   torch.Size((1, 10_00)   )
							self.assertEqual(outputs.logits.shape    ,						A   )

							lowercase_ : Any							       =   torch.tensor([-0.4180, -1.5051, -3.4836]   ).to(A   )

							self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,						A    ,						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 YolosImageProcessor





class 					_UpperCAmelCase       ( unittest.TestCase							):




					def __init__(				self     :						List[Any]    ,						A     :						Any    ,						A     :						Tuple=7    ,						A     :						Tuple=3    ,						A     :						Optional[Any]=30    ,						A     :						List[Any]=4_00    ,						A     :						Tuple=True    ,						A     :						Dict=None    ,						A     :						List[str]=True    ,						A     :						Optional[int]=[0.5, 0.5, 0.5]    ,						A     :						Tuple=[0.5, 0.5, 0.5]    ,						A     :						List[str]=True    ,						A     :						List[Any]=1 / 2_55    ,						A     :						Union[str, Any]=True    ,						)  ->  Tuple:
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							lowercase_ : Optional[int]							       =   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
							lowercase_ : Optional[int]							       =   parent
							lowercase_ : str							       =   batch_size
							lowercase_ : Tuple							       =   num_channels
							lowercase_ : str							       =   min_resolution
							lowercase_ : Any							       =   max_resolution
							lowercase_ : str							       =   do_resize
							lowercase_ : Any							       =   size
							lowercase_ : Optional[int]							       =   do_normalize
							lowercase_ : List[str]							       =   image_mean
							lowercase_ : Optional[Any]							       =   image_std
							lowercase_ : int							       =   do_rescale
							lowercase_ : List[str]							       =   rescale_factor
							lowercase_ : int							       =   do_pad




					def   A  (				self     :						Any   )  ->  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   A  (				self     :						Optional[Any]    ,						A     :						int    ,						A     :						int=False   )  ->  Tuple:
							if not batched:
									lowercase_ : Optional[int]							       =   image_inputs[0]
									if isinstance(A    ,						Image.Image   ):
											lowercase_       ,					lowercase_ : int							       =   image.size
									else:
											lowercase_       ,					lowercase_ : Tuple							       =   image.shape[1], image.shape[2]
									if w < h:
											lowercase_ : int							       =   int(self.size['''shortest_edge'''] * h / w   )
											lowercase_ : Optional[Any]							       =   self.size['''shortest_edge''']
									elif w > h:
											lowercase_ : Optional[Any]							       =   self.size['''shortest_edge''']
											lowercase_ : Optional[int]							       =   int(self.size['''shortest_edge'''] * w / h   )
									else:
											lowercase_ : Any							       =   self.size['''shortest_edge''']
											lowercase_ : Any							       =   self.size['''shortest_edge''']

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

							return expected_height, expected_width





@require_torch
@require_vision
class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   YolosImageProcessor if is_vision_available() else None




					def   A  (				self     :						Optional[int]   )  ->  Optional[int]:
							lowercase_ : Optional[Any]							       =   YolosImageProcessingTester(self   )




					@property
					def   A  (				self     :						str   )  ->  Any:
							return self.image_processor_tester.prepare_image_processor_dict()




					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							lowercase_ : Tuple							       =   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    ,						'''size'''   )   )




					def   A  (				self     :						Dict   )  ->  Tuple:
							lowercase_ : Dict							       =   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    ,						A   )

							lowercase_ : Tuple							       =   self.image_processing_class.from_dict(
							    self.image_processor_dict    ,						size=42    ,						max_size=84    ,						pad_and_return_pixel_mask=A   )
							self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 42, '''longest_edge''': 84}   )
							self.assertEqual(image_processor.do_pad    ,						A   )




					def   A  (				self     :						Optional[int]   )  ->  Tuple:
							pass




					def   A  (				self     :						Tuple   )  ->  int:
							# Initialize image_processing
							lowercase_ : Dict							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							lowercase_ : str							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						Image.Image   )

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

							lowercase_       ,					lowercase_ : 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
							lowercase_       ,					lowercase_ : Dict							       =   self.image_processor_tester.get_expected_values(A    ,						batched=A   )

							lowercase_ : str							       =   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   A  (				self     :						str   )  ->  Any:
							# Initialize image_processing
							lowercase_ : Any							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							lowercase_ : 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
							lowercase_ : List[Any]							       =   image_processing(image_inputs[0]    ,						return_tensors='''pt'''   ).pixel_values

							lowercase_       ,					lowercase_ : int							       =   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
							lowercase_ : Optional[int]							       =   image_processing(A    ,						return_tensors='''pt'''   ).pixel_values

							lowercase_       ,					lowercase_ : List[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   A  (				self     :						Tuple   )  ->  Optional[int]:
							# Initialize image_processing
							lowercase_ : Dict							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							lowercase_ : Optional[int]							       =   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
							lowercase_ : List[str]							       =   image_processing(image_inputs[0]    ,						return_tensors='''pt'''   ).pixel_values

							lowercase_       ,					lowercase_ : 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
							lowercase_ : Any							       =   image_processing(A    ,						return_tensors='''pt'''   ).pixel_values

							lowercase_       ,					lowercase_ : List[str]							       =   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   A  (				self     :						Tuple   )  ->  Optional[Any]:
							# Initialize image_processings
							lowercase_ : Optional[Any]							       =   self.image_processing_class(**self.image_processor_dict   )
							lowercase_ : Tuple							       =   self.image_processing_class(do_resize=A    ,						do_normalize=A    ,						do_rescale=A   )
							# create random PyTorch tensors
							lowercase_ : Union[str, Any]							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						torchify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						torch.Tensor   )

							# Test whether the method "pad" and calling the image processor return the same tensors
							lowercase_ : Union[str, Any]							       =   image_processing_a.pad(A    ,						return_tensors='''pt'''   )
							lowercase_ : List[Any]							       =   image_processing_a(A    ,						return_tensors='''pt'''   )

							self.assertTrue(
							    torch.allclose(encoded_images_with_method['''pixel_values''']    ,						encoded_images['''pixel_values''']    ,						atol=1e-4   )   )




					@slow
					def   A  (				self     :						str   )  ->  List[Any]:
							# prepare image and target
							lowercase_ : Dict							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
							with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,						'''r'''   ) as f:
									lowercase_ : List[Any]							       =   json.loads(f.read()   )

							lowercase_ : Tuple							       =   {'''image_id''': 3_97_69, '''annotations''': target}

							# encode them
							lowercase_ : Union[str, Any]							       =   YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''   )
							lowercase_ : List[Any]							       =   image_processing(images=A    ,						annotations=A    ,						return_tensors='''pt'''   )

							# verify pixel values
							lowercase_ : Union[str, Any]							       =   torch.Size([1, 3, 8_00, 10_66]   )
							self.assertEqual(encoding['''pixel_values'''].shape    ,						A   )

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

							# verify area
							lowercase_ : Tuple							       =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,						A   )   )
							# verify boxes
							lowercase_ : List[str]							       =   torch.Size([6, 4]   )
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,						A   )
							lowercase_ : Any							       =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,						A    ,						atol=1e-3   )   )
							# verify image_id
							lowercase_ : List[Any]							       =   torch.tensor([3_97_69]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,						A   )   )
							# verify is_crowd
							lowercase_ : Any							       =   torch.tensor([0, 0, 0, 0, 0, 0]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,						A   )   )
							# verify class_labels
							lowercase_ : Optional[Any]							       =   torch.tensor([75, 75, 63, 65, 17, 17]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,						A   )   )
							# verify orig_size
							lowercase_ : List[str]							       =   torch.tensor([4_80, 6_40]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,						A   )   )
							# verify size
							lowercase_ : Optional[Any]							       =   torch.tensor([8_00, 10_66]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,						A   )   )






					@slow
					def   A  (				self     :						List[Any]   )  ->  Dict:
							# prepare image, target and masks_path
							lowercase_ : Tuple							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
							with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,						'''r'''   ) as f:
									lowercase_ : str							       =   json.loads(f.read()   )

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

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

							# encode them
							lowercase_ : int							       =   YolosImageProcessor(format='''coco_panoptic'''   )
							lowercase_ : Any							       =   image_processing(images=A    ,						annotations=A    ,						masks_path=A    ,						return_tensors='''pt'''   )

							# verify pixel values
							lowercase_ : Optional[Any]							       =   torch.Size([1, 3, 8_00, 10_66]   )
							self.assertEqual(encoding['''pixel_values'''].shape    ,						A   )

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

							# verify area
							lowercase_ : List[Any]							       =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,						A   )   )
							# verify boxes
							lowercase_ : str							       =   torch.Size([6, 4]   )
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,						A   )
							lowercase_ : List[str]							       =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,						A    ,						atol=1e-3   )   )
							# verify image_id
							lowercase_ : List[str]							       =   torch.tensor([3_97_69]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,						A   )   )
							# verify is_crowd
							lowercase_ : List[str]							       =   torch.tensor([0, 0, 0, 0, 0, 0]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,						A   )   )
							# verify class_labels
							lowercase_ : Any							       =   torch.tensor([17, 17, 63, 75, 75, 93]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,						A   )   )
							# verify masks
							lowercase_ : Dict							       =   82_28_73
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,						A   )
							# verify orig_size
							lowercase_ : Tuple							       =   torch.tensor([4_80, 6_40]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,						A   )   )
							# verify size
							lowercase_ : List[str]							       =   torch.tensor([8_00, 10_66]   )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,						A   )   )



