






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


__lowerCamelCase      :    int   		=   get_logger(__name__)



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					a_			=	"dummy_data"
					a_			=	"datasets"
					a_			=	False



					def __init__(      self  :						Optional[int]							,	__A  :						str							,	__A  :						str							,	__A  :						Union[Version, str]							,	__A  :						Optional[str] = None							,	__A  :						bool = False							,	__A  :						bool = True							,	__A  :						Optional[List[Callable]] = None							,	):
						snake_case__				:   Tuple     =						0
						snake_case__				:   Union[str, Any]     =						dataset_name
						snake_case__				:   List[str]     =						cache_dir
						snake_case__				:   str     =						use_local_dummy_data
						snake_case__				:   Any     =						config
						# download_callbacks take a single url as input
						snake_case__				:   List[Callable]     =						download_callbacks or []
						# if False, it doesn't load existing files and it returns the paths of the dummy files relative
						# to the dummy_data zip file root
						snake_case__				:   Union[str, Any]     =						load_existing_dummy_data

						# TODO(PVP, QL) might need to make this more general
						snake_case__				:   Union[str, Any]     =						str(__A )
						# to be downloaded
						snake_case__				:   List[str]     =						None
						snake_case__				:   Union[str, Any]     =						None



					@property
					def  _lowercase  (      self  :						str ):
						if self._dummy_file is None:
							snake_case__				:   Dict     =						self.download_dummy_data()
						return self._dummy_file



					@property
					def  _lowercase  (      self  :						Any ):
						if self.config is not None:
							# structure is dummy / config_name / version_name
							return os.path.join("dummy"							,	self.config.name							,	self.version_name )
						# structure is dummy / version_name
						return os.path.join("dummy"							,	self.version_name )



					@property
					def  _lowercase  (      self  :						Optional[Any] ):
						return os.path.join(self.dummy_data_folder							,	"dummy_data.zip" )



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   List[Any]     =						(
						    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
						)

						snake_case__				:   Dict     =						cached_path(
						    __A							,	cache_dir=self.cache_dir							,	extract_compressed_file=__A							,	force_extract=__A )

						return os.path.join(__A							,	self.dummy_file_name )



					@property
					def  _lowercase  (      self  :						str ):
						return os.path.join(self.datasets_scripts_dir							,	self.dataset_name							,	self.dummy_zip_file )



					@property
					def  _lowercase  (      self  :						List[str] ):
						if self._bucket_url is None:
							snake_case__				:   List[Any]     =						hf_github_url(self.dataset_name							,	self.dummy_zip_file.replace(os.sep							,	"/" ) )
						return self._bucket_url



					@property
					def  _lowercase  (      self  :						List[str] ):
						# return full path if its a dir
						if os.path.isdir(self.dummy_file ):
							return self.dummy_file
						# else cut off path to file -> example `xsum`.
						return "/".join(self.dummy_file.replace(os.sep							,	"/" ).split("/" )[:-1] )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[Any]							,	*__A  :						Optional[int] ):
						if self.load_existing_dummy_data:
							# dummy data is downloaded and tested
							snake_case__				:   List[str]     =						self.dummy_file
						else:
							# dummy data cannot be downloaded and only the path to dummy file is returned
							snake_case__				:   Dict     =						self.dummy_file_name

						# special case when data_url is a dict
						if isinstance(__A							,	__A ):
							return self.create_dummy_data_dict(__A							,	__A )
						elif isinstance(__A							,	(list, tuple) ):
							return self.create_dummy_data_list(__A							,	__A )
						else:
							return self.create_dummy_data_single(__A							,	__A )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						List[str]							,	*__A  :						List[Any] ):
						return self.download_and_extract(__A )



					def  _lowercase  (      self  :						Any							,	__A  :						Union[str, Any]							,	__A  :						str ):
						return self.download_and_extract(__A )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						int							,	*__A  :						List[str]							,	**__A  :						str ):
						return path



					def  _lowercase  (      self  :						Optional[Any] ):
						return {}



					def  _lowercase  (      self  :						Dict							,	__A  :						int							,	__A  :						Tuple ):
						snake_case__				:   str     =						{}
						for key, single_urls in data_url.items():
							for download_callback in self.download_callbacks:
								if isinstance(__A							,	__A ):
									for single_url in single_urls:
										download_callback(__A )
								else:
									snake_case__				:   Optional[Any]     =						single_urls
									download_callback(__A )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
							if isinstance(__A							,	__A ):
								snake_case__				:   List[str]     =						[os.path.join(__A							,	urllib.parse.quote_plus(Path(__A ).name ) ) for x in single_urls]
							else:
								snake_case__				:   List[str]     =						single_urls
								snake_case__				:   Dict     =						os.path.join(__A							,	urllib.parse.quote_plus(Path(__A ).name ) )
							snake_case__				:   List[str]     =						value

						# make sure that values are unique
						if all(isinstance(__A							,	__A ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
						    dummy_data_dict.values() ):
							# append key to value to make its name unique
							snake_case__				:   str     =						{key: value + key for key, value in dummy_data_dict.items()}

						return dummy_data_dict



					def  _lowercase  (      self  :						List[Any]							,	__A  :						Optional[Any]							,	__A  :						Union[str, Any] ):
						snake_case__				:   Tuple     =						[]
						# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
						snake_case__				:   List[str]     =						all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"							,	__A ) ) for url in data_url )
						snake_case__				:   Optional[int]     =						all(
						    url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
						if data_url and (is_tf_records or is_pubmed_records):
							snake_case__				:   Optional[Any]     =						[data_url[0]] * len(__A )
						for single_url in data_url:
							for download_callback in self.download_callbacks:
								download_callback(__A )
							# we force the name of each key to be the last file / folder name of the url path
							# if the url has arguments, we need to encode them with urllib.parse.quote_plus
							snake_case__				:   List[str]     =						os.path.join(__A							,	urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
							dummy_data_list.append(__A )
						return dummy_data_list



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						List[Any]							,	__A  :						Any ):
						for download_callback in self.download_callbacks:
							download_callback(__A )
						# we force the name of each key to be the last file / folder name of the url path
						# if the url has arguments, we need to encode them with urllib.parse.quote_plus
						snake_case__				:   int     =						os.path.join(__A							,	urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
						if os.path.exists(__A ) or not self.load_existing_dummy_data:
							return value
						else:
							# Backward compatibility, maybe deprecate at one point.
							# For many datasets with single url calls to dl_manager.download_and_extract,
							# the dummy_data.zip file is actually the zipped downloaded file
							# while now we expected the dummy_data.zip file to be a directory containing
							# the downloaded file.
							return path_to_dummy_data



					def  _lowercase  (      self  :						List[str] ):
						pass



					def  _lowercase  (      self  :						Dict ):
						pass



					def  _lowercase  (      self  :						Any							,	__A  :						Any ):
						def _iter_archive_members(__A  :						Tuple ):
							# this preserves the order of the members inside the ZIP archive
							snake_case__				:   List[str]     =						Path(self.dummy_file ).parent
							snake_case__				:   Optional[Any]     =						path.relative_to(__A )
							with ZipFile(self.local_path_to_dummy_data ) as zip_file:
								snake_case__				:   List[Any]     =						zip_file.namelist()
							for member in members:
								if member.startswith(relative_path.as_posix() ):
									yield dummy_parent_path.joinpath(__A )

						snake_case__				:   int     =						Path(__A )
						snake_case__				:   Any     =						_iter_archive_members(__A ) if self.use_local_dummy_data else path.rglob("*" )
						for file_path in file_paths:
							if file_path.is_file() and not file_path.name.startswith((".", "__") ):
								yield file_path.relative_to(__A ).as_posix(), file_path.open("rb" )






					def  _lowercase  (      self  :						int							,	__A  :						Any ):
						if not isinstance(__A							,	__A ):
							snake_case__				:   Union[str, Any]     =						[paths]
						for path in paths:
							if os.path.isfile(__A ):
								if os.path.basename(__A ).startswith((".", "__") ):
									return
								yield path
							else:
								for dirpath, dirnames, filenames in os.walk(__A ):
									if os.path.basename(__A ).startswith((".", "__") ):
										continue
									dirnames.sort()
									for filename in sorted(__A ):
										if filename.startswith((".", "__") ):
											continue
										yield os.path.join(__A							,	__A )











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



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

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



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



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Any     =						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  :						Any ):
						snake_case__				:   Union[str, Any]     =						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__				:   Tuple     =						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  :						str ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						# Initialize image_processing
						snake_case__				:   Any     =						self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						snake_case__				:   Optional[Any]     =						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__				:   List[str]     =						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__				:   List[Any]     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

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

						snake_case__,		snake_case__				:   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
						snake_case__				:   Tuple     =						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,
						    )							,	)



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

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

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

						# encode them
						snake_case__				:   str     =						DeformableDetrImageProcessor()
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

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

						snake_case__				:   Union[str, Any]     =						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__				:   str     =						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__				:   Union[str, Any]     =						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__				:   Any     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   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__				:   List[str]     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   Tuple     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






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

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

						snake_case__				:   int     =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

						# encode them
						snake_case__				:   List[str]     =						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__				:   List[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

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



