
'''simple docstring'''
class 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Tuple     			=						None
				snake_case  :       Any     			=						None
				snake_case  :       Any     			=						graph

				self._normalize_graph(__UpperCAmelCase  ,	__UpperCAmelCase      )
				snake_case  :       int     			=						len(__UpperCAmelCase      )
				snake_case  :       Optional[int]     			=						None



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






				"""simple docstring"""







				if sources is int:
						snake_case  :       List[Any]     			=						[sources]
				if sinks is int:
						snake_case  :       int     			=						[sinks]

				if len(__UpperCAmelCase      ) == 0 or len(__UpperCAmelCase      ) == 0:
						return

				snake_case  :       List[str]     			=						sources[0]
				snake_case  :       str     			=						sinks[0]

				# make fake vertex if there are more
				# than one source or sink
				if len(__UpperCAmelCase      ) > 1 or len(__UpperCAmelCase      ) > 1:
						snake_case  :       Any     			=						0
						for i in sources:
								max_input_flow += sum(self.graph[i]      )

						snake_case  :       Optional[int]     			=						len(self.graph      ) + 1
						for room in self.graph:
								room.insert(0  ,	0      )
						self.graph.insert(0  ,	[0] * size      )
						for i in sources:
								snake_case  :       Optional[Any]     			=						max_input_flow
						snake_case  :       int     			=						0

						snake_case  :       Optional[Any]     			=						len(self.graph      ) + 1
						for room in self.graph:
								room.append(0      )
						self.graph.append([0] * size      )
						for i in sinks:
								snake_case  :       Any     			=						max_input_flow
						snake_case  :       int     			=						size - 1



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






				"""simple docstring"""







				if self.maximum_flow_algorithm is None:
						raise Exception('''You need to set maximum flow algorithm before.'''      )
				if self.source_index is None or self.sink_index is None:
						return 0

				self.maximum_flow_algorithm.execute()
				return self.maximum_flow_algorithm.getMaximumFlow()



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






				"""simple docstring"""







				snake_case  :       List[str]     			=						algorithm(self      )







class 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Dict      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       str     			=						flow_network
				snake_case  :       Optional[int]     			=						flow_network.verticesCount
				snake_case  :       Optional[Any]     			=						flow_network.sourceIndex
				snake_case  :       Dict     			=						flow_network.sinkIndex
				# it's just a reference, so you shouldn't change
				# it in your algorithms, use deep copy before doing that
				snake_case  :       Optional[int]     			=						flow_network.graph
				snake_case  :       Optional[Any]     			=						False



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






				"""simple docstring"""







				if not self.executed:
						self._algorithm()
						snake_case  :       List[Any]     			=						True



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






				"""simple docstring"""







				pass







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			int  ,	UpperCamelCase__				:			int      )						->			List[str]:






				"""simple docstring"""







				super().__init__(__UpperCAmelCase      )
				# use this to save your result
				snake_case  :       Optional[int]     			=						-1



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






				"""simple docstring"""







				if not self.executed:
						raise Exception('''You should execute algorithm before using its result!'''      )

				return self.maximum_flow







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Dict  ,	UpperCamelCase__				:			Dict      )						->			Optional[int]:






				"""simple docstring"""







				super().__init__(__UpperCAmelCase      )

				snake_case  :       int     			=						[[0] * self.verticies_count for i in range(self.verticies_count      )]

				snake_case  :       Union[str, Any]     			=						[0] * self.verticies_count
				snake_case  :       Optional[int]     			=						[0] * self.verticies_count



		def       lowerCAmelCase				(      self				:			Tuple      )						->			str:






				"""simple docstring"""







				snake_case  :       Dict     			=						self.verticies_count

				# push some substance to graph
				for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]      ):
						self.preflow[self.source_index][nextvertex_index] += bandwidth
						self.preflow[nextvertex_index][self.source_index] -= bandwidth
						self.excesses[nextvertex_index] += bandwidth

				# Relabel-to-front selection rule
				snake_case  :       List[str]     			=						[
				    i
				    for i in range(self.verticies_count      )
				    if i != self.source_index and i != self.sink_index
				]

				# move through list
				snake_case  :       str     			=						0
				while i < len(__UpperCAmelCase      ):
						snake_case  :       Any     			=						vertices_list[i]
						snake_case  :       List[Any]     			=						self.heights[vertex_index]
						self.process_vertex(__UpperCAmelCase      )
						if self.heights[vertex_index] > previous_height:
								# if it was relabeled, swap elements
								# and start from 0 index
								vertices_list.insert(0  ,	vertices_list.pop(__UpperCAmelCase      )      )
								snake_case  :       int     			=						0
						else:
								i += 1

				snake_case  :       Any     			=						sum(self.preflow[self.source_index]      )



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






				"""simple docstring"""







				while self.excesses[vertex_index] > 0:
						for neighbour_index in range(self.verticies_count      ):
								# if it's neighbour and current vertex is higher
								if (
								    self.graph[vertex_index][neighbour_index]
								    - self.preflow[vertex_index][neighbour_index]
								    > 0
								    and self.heights[vertex_index] > self.heights[neighbour_index]
								):
										self.push(__UpperCAmelCase  ,	__UpperCAmelCase      )

						self.relabel(__UpperCAmelCase      )



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






				"""simple docstring"""







				snake_case  :       List[str]     			=						min(
				    self.excesses[from_index]  ,	self.graph[from_index][to_index] - self.preflow[from_index][to_index]  ,	)
				self.preflow[from_index][to_index] += preflow_delta
				self.preflow[to_index][from_index] -= preflow_delta
				self.excesses[from_index] -= preflow_delta
				self.excesses[to_index] += preflow_delta



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






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						None
				for to_index in range(self.verticies_count      ):
						if (
						    self.graph[vertex_index][to_index]
						    - self.preflow[vertex_index][to_index]
						    > 0
						) and (min_height is None or self.heights[to_index] < min_height):
								snake_case  :       Union[str, Any]     			=						self.heights[to_index]

				if min_height is not None:
						snake_case  :       Optional[Any]     			=						min_height + 1


if __name__ == "__main__":
						lowercase__						       =      [0]
						lowercase__						       =      [3]
						# graph = [
						#     [0, 0, 4, 6, 0, 0],
						#     [0, 0, 5, 2, 0, 0],
						#     [0, 0, 0, 0, 4, 4],
						#     [0, 0, 0, 0, 6, 6],
						#     [0, 0, 0, 0, 0, 0],
						#     [0, 0, 0, 0, 0, 0],
						# ]
						lowercase__						       =      [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

						# prepare our network
						lowercase__						       =      FlowNetwork(graph, entrances, exits)
						# set algorithm
						flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
						# and calculate
						lowercase__						       =      flow_network.find_maximum_flow()

						print(f"maximum flow is {maximum_flow}")









"""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 ) )
