


"""simple docstring"""
from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
 import PIL

# soft dependency
if is_pytesseract_available():
 import pytesseract

UpperCAmelCase				=						logging.get_logger(__name__)

def 				_snake_case      (   __snake_case							:      Dict						, __snake_case							:      Dict						, __snake_case							:      Optional[int]   ):




   """simple docstring"""







   return [
       int(1000 * (box[0] / width)   ),
       int(1000 * (box[1] / height)   ),
       int(1000 * (box[2] / width)   ),
       int(1000 * (box[3] / height)   ),
   ]

def 				_snake_case      (   __snake_case							:      Optional[Any]						, __snake_case							:      int						, __snake_case							:      List[str]   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		to_pil_image(lowercase__   )
   _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		pil_image.size
   _lowerCamelCase	:					Any   =		pytesseract.image_to_data(lowercase__						, lang=lowercase__						, output_type="""dict"""						, config=lowercase__   )
   _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

   # filter empty words and corresponding coordinates
   _lowerCamelCase	:					Dict   =		[idx for idx, word in enumerate(lowercase__   ) if not word.strip()]
   _lowerCamelCase	:					List[Any]   =		[word for idx, word in enumerate(lowercase__   ) if idx not in irrelevant_indices]
   _lowerCamelCase	:					Union[str, Any]   =		[coord for idx, coord in enumerate(lowercase__   ) if idx not in irrelevant_indices]
   _lowerCamelCase	:					Tuple   =		[coord for idx, coord in enumerate(lowercase__   ) if idx not in irrelevant_indices]
   _lowerCamelCase	:					Tuple   =		[coord for idx, coord in enumerate(lowercase__   ) if idx not in irrelevant_indices]
   _lowerCamelCase	:					Optional[int]   =		[coord for idx, coord in enumerate(lowercase__   ) if idx not in irrelevant_indices]

   # turn coordinates into (left, top, left+width, top+height) format
   _lowerCamelCase	:					Tuple   =		[]
   for x, y, w, h in zip(lowercase__						, lowercase__						, lowercase__						, lowercase__   ):
      _lowerCamelCase	:					Union[str, Any]   =		[x, y, x + w, y + h]
      actual_boxes.append(lowercase__   )

   # finally, normalize the bounding boxes
   _lowerCamelCase	:					Union[str, Any]   =		[]
   for box in actual_boxes:
      normalized_boxes.append(normalize_box(lowercase__						, lowercase__						, lowercase__   )   )

   assert len(lowercase__   ) == len(lowercase__   ), "Not as many words as there are bounding boxes"

   return words, normalized_boxes





class 	lowercase__				(					__snake_case				):
   __UpperCAmelCase							       =					['''pixel_values''']





   def __init__(							self      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = 1 / 255      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = ""      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  List[Any]:
      super().__init__(**__UpperCamelCase)
      _lowerCamelCase	:					Dict   =		size if size is not None else {"""height""": 224, """width""": 224}
      _lowerCamelCase	:					List[Any]   =		get_size_dict(__UpperCamelCase)

      _lowerCamelCase	:					Dict   =		do_resize
      _lowerCamelCase	:					Any   =		size
      _lowerCamelCase	:					Union[str, Any]   =		resample
      _lowerCamelCase	:					Any   =		do_rescale
      _lowerCamelCase	:					List[Any]   =		rescale_value
      _lowerCamelCase	:					List[Any]   =		do_normalize
      _lowerCamelCase	:					int   =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCamelCase	:					Optional[int]   =		image_std if image_std is not None else IMAGENET_STANDARD_STD
      _lowerCamelCase	:					Optional[int]   =		apply_ocr
      _lowerCamelCase	:					List[Any]   =		ocr_lang
      _lowerCamelCase	:					str   =		tesseract_config





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Optional[Any]:
      _lowerCamelCase	:					List[str]   =		get_size_dict(__UpperCamelCase)
      if "height" not in size or "width" not in size:
         raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}')
      _lowerCamelCase	:					Dict   =		(size["""height"""], size["""width"""])
      return resize(__UpperCamelCase      ,   size=__UpperCamelCase      ,   resample=__UpperCamelCase      ,   data_format=__UpperCamelCase      ,   **__UpperCamelCase)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  List[str]:
      return rescale(__UpperCamelCase      ,   scale=__UpperCamelCase      ,   data_format=__UpperCamelCase      ,   **__UpperCamelCase)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Dict:
      return normalize(__UpperCamelCase      ,   mean=__UpperCamelCase      ,   std=__UpperCamelCase      ,   data_format=__UpperCamelCase      ,   **__UpperCamelCase)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Dict:
      _lowerCamelCase	:					Tuple   =		do_resize if do_resize is not None else self.do_resize
      _lowerCamelCase	:					int   =		size if size is not None else self.size
      _lowerCamelCase	:					List[str]   =		get_size_dict(__UpperCamelCase)
      _lowerCamelCase	:					Any   =		resample if resample is not None else self.resample
      _lowerCamelCase	:					Union[str, Any]   =		do_rescale if do_rescale is not None else self.do_rescale
      _lowerCamelCase	:					List[str]   =		rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCamelCase	:					int   =		do_normalize if do_normalize is not None else self.do_normalize
      _lowerCamelCase	:					List[Any]   =		image_mean if image_mean is not None else self.image_mean
      _lowerCamelCase	:					Optional[int]   =		image_std if image_std is not None else self.image_std
      _lowerCamelCase	:					Dict   =		apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCamelCase	:					Any   =		ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCamelCase	:					Union[str, Any]   =		tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCamelCase	:					int   =		make_list_of_images(__UpperCamelCase)

      if not valid_images(__UpperCamelCase):
         raise ValueError(
             """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
             """torch.Tensor, tf.Tensor or jax.ndarray.""")

      if do_resize and size is None:
         raise ValueError("""Size must be specified if do_resize is True.""")

      if do_rescale and rescale_factor is None:
         raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

      if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("""If do_normalize is True, image_mean and image_std must be specified.""")

      # All transformations expect numpy arrays.
      _lowerCamelCase	:					List[Any]   =		[to_numpy_array(__UpperCamelCase) for image in images]

      # Tesseract OCR to get words + normalized bounding boxes
      if apply_ocr:
         requires_backends(self      ,   """pytesseract""")
         _lowerCamelCase	:					Optional[int]   =		[]
         _lowerCamelCase	:					Optional[int]   =		[]
         for image in images:
            _lowerCamelCase   ,    _lowerCamelCase	:					str   =		apply_tesseract(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase)
            words_batch.append(__UpperCamelCase)
            boxes_batch.append(__UpperCamelCase)

      if do_resize:
         _lowerCamelCase	:					Optional[int]   =		[self.resize(image=__UpperCamelCase      ,   size=__UpperCamelCase      ,   resample=__UpperCamelCase) for image in images]

      if do_rescale:
         _lowerCamelCase	:					str   =		[self.rescale(image=__UpperCamelCase      ,   scale=__UpperCamelCase) for image in images]

      if do_normalize:
         _lowerCamelCase	:					List[Any]   =		[self.normalize(image=__UpperCamelCase      ,   mean=__UpperCamelCase      ,   std=__UpperCamelCase) for image in images]

      _lowerCamelCase	:					Dict   =		[to_channel_dimension_format(__UpperCamelCase      ,   __UpperCamelCase) for image in images]

      _lowerCamelCase	:					Tuple   =		BatchFeature(data={"""pixel_values""": images}      ,   tensor_type=__UpperCamelCase)

      if apply_ocr:
         _lowerCamelCase	:					List[str]   =		words_batch
         _lowerCamelCase	:					Dict   =		boxes_batch
      return data




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 DetaImageProcessor


class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=7			,					__UpperCamelCase=3			,					__UpperCamelCase=30			,					__UpperCamelCase=4_00			,					__UpperCamelCase=True			,					__UpperCamelCase=None			,					__UpperCamelCase=True			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=True			,					__UpperCamelCase=1 / 2_55			,					__UpperCamelCase=True			,					):

				"""simple docstring"""


				snake_case_   		=			size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
				snake_case_   		=			parent
				snake_case_   		=			batch_size
				snake_case_   		=			num_channels
				snake_case_   		=			min_resolution
				snake_case_   		=			max_resolution
				snake_case_   		=			do_resize
				snake_case_   		=			size
				snake_case_   		=			do_normalize
				snake_case_   		=			image_mean
				snake_case_   		=			image_std
				snake_case_   		=			do_rescale
				snake_case_   		=			rescale_factor
				snake_case_   		=			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:
					snake_case_   		=			image_inputs[0]
					if isinstance(__UpperCamelCase			,					Image.Image				):
						snake_case_		,							snake_case_   		=			image.size
					else:
						snake_case_		,							snake_case_   		=			image.shape[1], image.shape[2]
					if w < h:
						snake_case_   		=			int(self.size['shortest_edge'] * h / w				)
						snake_case_   		=			self.size['shortest_edge']
					elif w > h:
						snake_case_   		=			self.size['shortest_edge']
						snake_case_   		=			int(self.size['shortest_edge'] * w / h				)
					else:
						snake_case_   		=			self.size['shortest_edge']
						snake_case_   		=			self.size['shortest_edge']

				else:
					snake_case_   		=			[]
					for image in image_inputs:
						snake_case_		,							snake_case_   		=			self.get_expected_values([image]				)
						expected_values.append((expected_height, expected_width)				)
					snake_case_   		=			max(__UpperCamelCase			,					key=lambda __UpperCamelCase				: item[0]				)[0]
					snake_case_   		=			max(__UpperCamelCase			,					key=lambda __UpperCamelCase				: item[1]				)[1]

				return expected_height, expected_width







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	DetaImageProcessor if is_vision_available() else None


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			DetaImageProcessingTester(self				)


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			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			,					'do_rescale'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_pad'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'size'				)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			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			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random PIL images
				snake_case_   		=			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=__UpperCamelCase				)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase			,					Image.Image				)

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

				snake_case_		,							snake_case_   		=			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
				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				snake_case_   		=			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"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random numpy tensors
				snake_case_   		=			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
				snake_case_   		=			image_processing(image_inputs[0]			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			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
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			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"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random PyTorch tensors
				snake_case_   		=			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
				snake_case_   		=			image_processing(image_inputs[0]			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			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
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			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"""


				snake_case_   		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,					'r'				) as f:
					snake_case_   		=			json.loads(f.read()				)

				snake_case_   		=			{'image_id': 3_97_69, 'annotations': target}

				# encode them
				snake_case_   		=			DetaImageProcessor()
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					return_tensors='pt'				)

				# verify pixel values
				snake_case_   		=			torch.Size([1, 3, 8_00, 10_66]				)
				self.assertEqual(encoding['pixel_values'].shape			,					__UpperCamelCase				)

				snake_case_   		=			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
				snake_case_   		=			torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,					__UpperCamelCase				)				)
				# verify boxes
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			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
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([75, 75, 63, 65, 17, 17]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			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_   		=			json.loads(f.read()				)

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

				snake_case_   		=			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

				# encode them
				snake_case_   		=			DetaImageProcessor(format='coco_panoptic'				)
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					masks_path=__UpperCamelCase			,					return_tensors='pt'				)

				# verify pixel values
				snake_case_   		=			torch.Size([1, 3, 8_00, 10_66]				)
				self.assertEqual(encoding['pixel_values'].shape			,					__UpperCamelCase				)

				snake_case_   		=			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
				snake_case_   		=			torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,					__UpperCamelCase				)				)
				# verify boxes
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			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
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([17, 17, 63, 75, 75, 93]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify masks
				snake_case_   		=			82_28_73
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,					__UpperCamelCase				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)
