






"""simple docstring"""



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__A		:       str  				=		logging.get_logger(__name__)

__A		:       Union[str, Any]  				=		OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

__A		:       List[Any]  				=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def 						lowercase    (				__snake_case				: str	):
		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						lowercase_ : Dict							       =   model_type_to_module_name(__snake_case	)

						lowercase_ : Optional[Any]							       =   importlib.import_module(F'''.{module_name}'''		,					'''transformers.models'''	)
						try:
								return getattr(__snake_case		,					__snake_case	)
						except AttributeError:
								continue

		for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
				if getattr(__snake_case		,					'''__name__'''		,					__snake_case	) == class_name:
						return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
		lowercase_ : str							       =   importlib.import_module('''transformers'''	)
		if hasattr(__snake_case		,					__snake_case	):
				return getattr(__snake_case		,					__snake_case	)

		return None



def 						lowercase    (				__snake_case				: Union[str, os.PathLike]		,					__snake_case				: Optional[Union[str, os.PathLike]] = None		,					__snake_case				: bool = False		,					__snake_case				: bool = False		,					__snake_case				: Optional[Dict[str, str]] = None		,					__snake_case				: Optional[Union[bool, str]] = None		,					__snake_case				: Optional[str] = None		,					__snake_case				: bool = False		,					**__snake_case				: List[str]		,					):
		lowercase_ : Optional[Any]							       =   get_file_from_repo(
		    __snake_case		,					__snake_case		,					cache_dir=__snake_case		,					force_download=__snake_case		,					resume_download=__snake_case		,					proxies=__snake_case		,					use_auth_token=__snake_case		,					revision=__snake_case		,					local_files_only=__snake_case		,					)
		if resolved_config_file is None:
				logger.info(
				    '''Could not locate the image processor configuration file, will try to use the model config instead.'''	)
				return {}

		with open(__snake_case		,					encoding='''utf-8'''	) as reader:
				return json.load(__snake_case	)






class 					_UpperCAmelCase       :




					def __init__(				self     :						Tuple   )  ->  Union[str, Any]:
							raise EnvironmentError(
							    '''AutoImageProcessor is designed to be instantiated '''
							    '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''   )




					@classmethod
					@replace_list_option_in_docstrings(A   )
					def   A  (				cls     :						Union[str, Any]    ,						A     :						List[Any]    ,						**A     :						Tuple   )  ->  Dict:
							lowercase_ : Tuple							       =   kwargs.pop('''config'''    ,						A   )
							lowercase_ : Tuple							       =   kwargs.pop('''trust_remote_code'''    ,						A   )
							lowercase_ : Optional[Any]							       =   True

							lowercase_       ,					lowercase_ : Optional[Any]							       =   ImageProcessingMixin.get_image_processor_dict(A    ,						**A   )
							lowercase_ : Union[str, Any]							       =   config_dict.get('''image_processor_type'''    ,						A   )
							lowercase_ : Tuple							       =   None
							if "AutoImageProcessor" in config_dict.get('''auto_map'''    ,						{}   ):
									lowercase_ : Optional[int]							       =   config_dict['''auto_map''']['''AutoImageProcessor''']

							# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
							# and if so, infer the image processor class from there.
							if image_processor_class is None and image_processor_auto_map is None:
									lowercase_ : Union[str, Any]							       =   config_dict.pop('''feature_extractor_type'''    ,						A   )
									if feature_extractor_class is not None:
											logger.warning(
											    '''Could not find image processor class in the image processor config or the model config. Loading'''
											    ''' based on pattern matching with the model\'s feature extractor configuration.'''   )
											lowercase_ : List[Any]							       =   feature_extractor_class.replace('''FeatureExtractor'''    ,						'''ImageProcessor'''   )
									if "AutoFeatureExtractor" in config_dict.get('''auto_map'''    ,						{}   ):
											lowercase_ : str							       =   config_dict['''auto_map''']['''AutoFeatureExtractor''']
											lowercase_ : int							       =   feature_extractor_auto_map.replace('''FeatureExtractor'''    ,						'''ImageProcessor'''   )
											logger.warning(
											    '''Could not find image processor auto map in the image processor config or the model config.'''
											    ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''   )

        # If we don't find the image processor class in the image processor config, let's try the model config.
							if image_processor_class is None and image_processor_auto_map is None:
									if not isinstance(A    ,						A   ):
											lowercase_ : List[Any]							       =   AutoConfig.from_pretrained(A    ,						**A   )
									# It could be in `config.image_processor_type``
									lowercase_ : Tuple							       =   getattr(A    ,						'''image_processor_type'''    ,						A   )
									if hasattr(A    ,						'''auto_map'''   ) and "AutoImageProcessor" in config.auto_map:
											lowercase_ : Tuple							       =   config.auto_map['''AutoImageProcessor''']

							if image_processor_class is not None:
									lowercase_ : Tuple							       =   image_processor_class_from_name(A   )

							lowercase_ : Tuple							       =   image_processor_auto_map is not None
							lowercase_ : Any							       =   image_processor_class is not None or type(A   ) in IMAGE_PROCESSOR_MAPPING
							lowercase_ : Tuple							       =   resolve_trust_remote_code(
							    A    ,						A    ,						A    ,						A   )

							if has_remote_code and trust_remote_code:
									lowercase_ : Dict							       =   get_class_from_dynamic_module(
									    A    ,						A    ,						**A   )
									lowercase_ : List[str]							       =   kwargs.pop('''code_revision'''    ,						A   )
									if os.path.isdir(A   ):
											image_processor_class.register_for_auto_class()
									return image_processor_class.from_dict(A    ,						**A   )
							elif image_processor_class is not None:
									return image_processor_class.from_dict(A    ,						**A   )
							# Last try: we use the IMAGE_PROCESSOR_MAPPING.
							elif type(A   ) in IMAGE_PROCESSOR_MAPPING:
									lowercase_ : int							       =   IMAGE_PROCESSOR_MAPPING[type(A   )]
									return image_processor_class.from_dict(A    ,						**A   )

							raise ValueError(
							    F'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
							    F'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
							    F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()   )}'''   )






					@staticmethod
					def   A  (				A     :						int    ,						A     :						Dict   )  ->  Dict:
							IMAGE_PROCESSOR_MAPPING.register(A    ,						A   )











"""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   )   )



