
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {"vocab_file": "sentencepiece.bpe.model"}

lowercase__						       =      {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

lowercase__						       =      {
    "moussaKam/mbarthez": 1_0_2_4,
    "moussaKam/barthez": 1_0_2_4,
    "moussaKam/barthez-orangesum-title": 1_0_2_4,
}

lowercase__						       =      "▁"






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						VOCAB_FILES_NAMES
		lowerCamelCase     							=						PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase     							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase     							=						["""input_ids""", """attention_mask"""]



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Optional[int]="<s>"  ,	UpperCamelCase__				:			Union[str, Any]="</s>"  ,	UpperCamelCase__				:			Union[str, Any]="</s>"  ,	UpperCamelCase__				:			List[Any]="<s>"  ,	UpperCamelCase__				:			Any="<unk>"  ,	UpperCamelCase__				:			Optional[Any]="<pad>"  ,	UpperCamelCase__				:			int="<mask>"  ,	UpperCamelCase__				:			List[Any] = None  ,	**UpperCamelCase__				:			Any  ,	)						->			Any:






				"""simple docstring"""







				snake_case  :       Any     			=						AddedToken(__UpperCAmelCase  ,	lstrip=__UpperCAmelCase  ,	rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase  ,	__UpperCAmelCase      ) else mask_token

				snake_case  :       Optional[Any]     			=						{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=__UpperCAmelCase  ,	eos_token=__UpperCAmelCase  ,	unk_token=__UpperCAmelCase  ,	sep_token=__UpperCAmelCase  ,	cls_token=__UpperCAmelCase  ,	pad_token=__UpperCAmelCase  ,	mask_token=__UpperCAmelCase  ,	sp_model_kwargs=self.sp_model_kwargs  ,	**__UpperCAmelCase  ,	)

				snake_case  :       Dict     			=						vocab_file
				snake_case  :       Tuple     			=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
				self.sp_model.Load(str(__UpperCAmelCase      )      )

				snake_case  :       Optional[int]     			=						{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

				snake_case  :       Tuple     			=						len(self.sp_model      ) - 1
				snake_case  :       Any     			=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			List[Any] = None      )						->			Optional[int]:






				"""simple docstring"""







				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				snake_case  :       int     			=						[self.cls_token_id]
				snake_case  :       Optional[Any]     			=						[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			List[Any] = None  ,	UpperCamelCase__				:			Any = False      )						->			Optional[int]:






				"""simple docstring"""







				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=__UpperCAmelCase  ,	token_ids_a=__UpperCAmelCase  ,	already_has_special_tokens=__UpperCAmelCase      )

				if token_ids_a is None:
						return [1] + ([0] * len(__UpperCAmelCase      )) + [1]
				return [1] + ([0] * len(__UpperCAmelCase      )) + [1, 1] + ([0] * len(__UpperCAmelCase      )) + [1]



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			List[Any] = None      )						->			str:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						[self.sep_token_id]
				snake_case  :       Union[str, Any]     			=						[self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep      ) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]



		@property
		def       lowerCAmelCase				(      self				:			Tuple      )						->			List[str]:






				"""simple docstring"""







				return len(self.sp_model      )



		def       lowerCAmelCase				(      self				:			Tuple      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						{self.convert_ids_to_tokens(__UpperCAmelCase      ): i for i in range(self.vocab_size      )}
				vocab.update(self.added_tokens_encoder      )
				return vocab



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Dict      )						->			Union[str, Any]:






				"""simple docstring"""







				return self.sp_model.encode(__UpperCAmelCase  ,	out_type=__UpperCAmelCase      )



		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Dict      )						->			Any:






				"""simple docstring"""







				if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
				snake_case  :       Any     			=						self.sp_model.PieceToId(__UpperCAmelCase      )

				return spm_id if spm_id else self.unk_token_id



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			List[Any]      )						->			Dict:






				"""simple docstring"""







				if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(__UpperCAmelCase      )



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			Union[str, Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       Any     			=						[]
				snake_case  :       str     			=						''
				snake_case  :       Dict     			=						False
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								if not prev_is_special:
										out_string += " "
								out_string += self.sp_model.decode(__UpperCAmelCase      ) + token
								snake_case  :       List[str]     			=						True
								snake_case  :       List[Any]     			=						[]
						else:
								current_sub_tokens.append(__UpperCAmelCase      )
								snake_case  :       Union[str, Any]     			=						False
				out_string += self.sp_model.decode(__UpperCAmelCase      )
				return out_string.strip()



		def __getstate__(      self				:			List[str]      )						->			Any:






				"""simple docstring"""







				snake_case  :       int     			=						self.__dict__.copy()
				snake_case  :       List[str]     			=						None
				return state



		def __setstate__(      self				:			Tuple  ,	UpperCamelCase__				:			List[Any]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       int     			=						d

				# for backward compatibility
				if not hasattr(self  ,	'''sp_model_kwargs'''      ):
						snake_case  :       List[Any]     			=						{}

				snake_case  :       Optional[Any]     			=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
				self.sp_model.Load(self.vocab_file      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Any = None      )						->			str:






				"""simple docstring"""







				if not os.path.isdir(__UpperCAmelCase      ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
						return
				snake_case  :       Tuple     			=						os.path.join(
				    __UpperCAmelCase  ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

				if os.path.abspath(self.vocab_file      ) != os.path.abspath(__UpperCAmelCase      ) and os.path.isfile(self.vocab_file      ):
						copyfile(self.vocab_file  ,	__UpperCAmelCase      )
				elif not os.path.isfile(self.vocab_file      ):
						with open(__UpperCAmelCase  ,	'''wb'''      ) as fi:
								snake_case  :       List[Any]     			=						self.sp_model.serialized_model_proto()
								fi.write(__UpperCAmelCase      )

				return (out_vocab_file,)









"""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 DetaImageProcessor






class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=7       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=3_0       ,					__UpperCAmelCase=4_0_0       ,					__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_5_5       ,					__UpperCAmelCase=True       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
         lowerCAmelCase__     :List[Any]											=				parent
         lowerCAmelCase__     :int											=				batch_size
         lowerCAmelCase__     :Union[str, Any]											=				num_channels
         lowerCAmelCase__     :Any											=				min_resolution
         lowerCAmelCase__     :Dict											=				max_resolution
         lowerCAmelCase__     :Dict											=				do_resize
         lowerCAmelCase__     :Optional[Any]											=				size
         lowerCAmelCase__     :List[str]											=				do_normalize
         lowerCAmelCase__     :str											=				image_mean
         lowerCAmelCase__     :Tuple											=				image_std
         lowerCAmelCase__     :Dict											=				do_rescale
         lowerCAmelCase__     :Tuple											=				rescale_factor
         lowerCAmelCase__     :Optional[int]											=				do_pad






       def 	snake_case		(       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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False ):



         '''simple docstring'''

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

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

         return expected_height, expected_width






@require_torch
@require_vision
class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   DetaImageProcessor if is_vision_available() else None






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				DetaImageProcessingTester(self )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'image_mean' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'image_std' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'do_normalize' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'do_resize' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'do_rescale' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'do_pad' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'size' ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				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       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         lowerCAmelCase__     :Any											=				prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__UpperCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(__UpperCAmelCase       ,					Image.Image )

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

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				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
         lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,					batched=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[Any]											=				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 	snake_case		(       self ):



         '''simple docstring'''

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

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase )

         self.assertEqual(
             encoded_images.shape       ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,					)

         # Test batched
         lowerCAmelCase__     :Tuple											=				image_processing(__UpperCAmelCase       ,					return_tensors='pt' ).pixel_values

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,					batched=__UpperCAmelCase )

         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,					)






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         lowerCAmelCase__     :List[Any]											=				prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__UpperCAmelCase       ,					torchify=__UpperCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(__UpperCAmelCase       ,					torch.Tensor )

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

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				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
         lowerCAmelCase__     :str											=				image_processing(__UpperCAmelCase       ,					return_tensors='pt' ).pixel_values

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				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 	snake_case		(       self ):



         '''simple docstring'''

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

         lowerCAmelCase__     :int											=				{'image_id': 3_9_7_6_9, 'annotations': target}

         # encode them
         lowerCAmelCase__     :int											=				DetaImageProcessor()
         lowerCAmelCase__     :List[Any]											=				image_processing(images=__UpperCAmelCase       ,					annotations=__UpperCAmelCase       ,					return_tensors='pt' )

         # verify pixel values
         lowerCAmelCase__     :str											=				torch.Size([1, 3, 8_0_0, 1_0_6_6] )
         self.assertEqual(encoding['pixel_values'].shape       ,					__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				torch.tensor([0.27_96, 0.31_38, 0.34_81] )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,					__UpperCAmelCase       ,					atol=1E-4 ) )

         # verify area
         lowerCAmelCase__     :Dict											=				torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,					__UpperCAmelCase ) )
         # verify boxes
         lowerCAmelCase__     :Tuple											=				torch.Size([6, 4] )
         self.assertEqual(encoding['labels'][0]['boxes'].shape       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,					__UpperCAmelCase       ,					atol=1E-3 ) )
         # verify image_id
         lowerCAmelCase__     :Any											=				torch.tensor([3_9_7_6_9] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,					__UpperCAmelCase ) )
         # verify is_crowd
         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,					__UpperCAmelCase ) )
         # verify class_labels
         lowerCAmelCase__     :Optional[Any]											=				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,					__UpperCAmelCase ) )
         # verify orig_size
         lowerCAmelCase__     :str											=				torch.tensor([4_8_0, 6_4_0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,					__UpperCAmelCase ) )
         # verify size
         lowerCAmelCase__     :Any											=				torch.tensor([8_0_0, 1_0_6_6] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,					__UpperCAmelCase ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

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

         lowerCAmelCase__     :Dict											=				{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

         lowerCAmelCase__     :Union[str, Any]											=				pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

         # encode them
         lowerCAmelCase__     :Dict											=				DetaImageProcessor(format='coco_panoptic' )
         lowerCAmelCase__     :Optional[int]											=				image_processing(images=__UpperCAmelCase       ,					annotations=__UpperCAmelCase       ,					masks_path=__UpperCAmelCase       ,					return_tensors='pt' )

         # verify pixel values
         lowerCAmelCase__     :str											=				torch.Size([1, 3, 8_0_0, 1_0_6_6] )
         self.assertEqual(encoding['pixel_values'].shape       ,					__UpperCAmelCase )

         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([0.27_96, 0.31_38, 0.34_81] )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,					__UpperCAmelCase       ,					atol=1E-4 ) )

         # verify area
         lowerCAmelCase__     :Tuple											=				torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,					__UpperCAmelCase ) )
         # verify boxes
         lowerCAmelCase__     :int											=				torch.Size([6, 4] )
         self.assertEqual(encoding['labels'][0]['boxes'].shape       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,					__UpperCAmelCase       ,					atol=1E-3 ) )
         # verify image_id
         lowerCAmelCase__     :Optional[int]											=				torch.tensor([3_9_7_6_9] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,					__UpperCAmelCase ) )
         # verify is_crowd
         lowerCAmelCase__     :Tuple											=				torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,					__UpperCAmelCase ) )
         # verify class_labels
         lowerCAmelCase__     :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']       ,					__UpperCAmelCase ) )
         # verify masks
         lowerCAmelCase__     :Optional[int]											=				8_2_2_8_7_3
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,					__UpperCAmelCase )
         # verify orig_size
         lowerCAmelCase__     :Optional[int]											=				torch.tensor([4_8_0, 6_4_0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,					__UpperCAmelCase ) )
         # verify size
         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([8_0_0, 1_0_6_6] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,					__UpperCAmelCase ) )
