

import heapq
import sys

import numpy as np

a__												=						tuple[int, int]

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			)       ->       Dict:
												_a      :       str   =						[]
												_a      :       int   =						set()


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												if not self.empty():
																	return self.elements[0][0]
												else:
																	return float('''inf'''			)


							def 							__lowercase							(     self			)       ->       Tuple:
												return len(self.elements			) == 0


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       Optional[int]:
												if item not in self.set:
																	heapq.heappush(self.elements			,				(priority, item)			)
																	self.set.add(_a			)
												else:
																	# update
																	# print("update", item)
																	_a      :       Optional[Any]   =						[]
																	((_a) ,   (_a))      :       Optional[int]   =						heapq.heappop(self.elements			)
																	while x != item:
																						temp.append((pri, x)			)
																						((_a) ,   (_a))      :       Union[str, Any]   =						heapq.heappop(self.elements			)
																	temp.append((priority, item)			)
																	for pro, xxx in temp:
																						heapq.heappush(self.elements			,				(pro, xxx)			)


							def 							__lowercase							(     self			,				_a			)       ->       List[Any]:
												if item in self.set:
																	self.set.remove(_a			)
																	_a      :       Optional[Any]   =						[]
																	((_a) ,   (_a))      :       Union[str, Any]   =						heapq.heappop(self.elements			)
																	while x != item:
																						temp.append((pro, x)			)
																						((_a) ,   (_a))      :       Optional[int]   =						heapq.heappop(self.elements			)
																	for prito, yyy in temp:
																						heapq.heappush(self.elements			,				(prito, yyy)			)


							def 							__lowercase							(     self			)       ->       Tuple:
												return self.elements[0][1]




							def 							__lowercase							(     self			)       ->       int:
												((_a) ,   (_a))      :       List[Any]   =						heapq.heappop(self.elements			)
												self.set.remove(_a			)
												return (priority, item)



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       int   =						np.array(__a      )
					_a      :       int   =						np.array(__a      )
					return np.linalg.norm(a - b      )



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	List[Any]:

					"""simple docstring"""

					return consistent_heuristic(__a						,__a      ) // t



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	str:

					"""simple docstring"""

					return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )



def   __UpperCAmelCase					(      __a :     TPos						,__a :     int						,__a :     TPos						,__a :     dict[TPos, float]      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Optional[int]   =						g_function[start] + Wa * heuristics[i](__a						,__a      )
					return ans



def   __UpperCAmelCase					(      __a :     Any						,__a :     Any						,__a :     Dict      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						np.chararray((n, n)      )
					for i in range(__a      ):
										for j in range(__a      ):
															_a      :       Any   =						'''*'''

					for i in range(__a      ):
										for j in range(__a      ):
															if (j, (n - 1) - i) in blocks:
																				_a      :       Dict   =						'''#'''

					_a      :       Optional[int]   =						'''-'''
					_a      :       List[str]   =						back_pointer[goal]
					while x != start:
										((_a) ,   (_a))      :       int   =						x
										# print(x)
										_a      :       Dict   =						'''-'''
										_a      :       Tuple   =						back_pointer[x]
					_a      :       Optional[int]   =						'''-'''

					for i in range(__a      ):
										for j in range(__a      ):
															if (i, j) == (0, n - 1):
																				print(grid[i][j]						,end=''' '''      )
																				print('''<-- End position'''						,end=''' '''      )
															else:
																				print(grid[i][j]						,end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )
					print('''PATH TAKEN BY THE ALGORITHM IS:-'''      )
					_a      :       str   =						back_pointer[goal]
					while x != start:
										print(__a						,end=''' '''      )
										_a      :       str   =						back_pointer[x]
					print(__a      )
					sys.exit()



def   __UpperCAmelCase					(      __a :     TPos      )     ->	List[str]:

					"""simple docstring"""

					if p[0] < 0 or p[0] > n - 1:
										return False
					if p[1] < 0 or p[1] > n - 1:
										return False
					return True



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     List[str]						,__a :     List[str]						,__a :     Any						,__a :     Dict						,__a :     Optional[Any]						,__a :     List[Any]						,__a :     Any						,)     ->	Optional[Any]:

					"""simple docstring"""

					for itera in range(__a      ):
										open_list[itera].remove_element(__a      )
					# print("s", s)
					# print("j", j)
					((_a) ,   (_a))      :       Optional[Any]   =						s
					_a      :       Any   =						(x - 1, y)
					_a      :       Dict   =						(x + 1, y)
					_a      :       str   =						(x, y + 1)
					_a      :       str   =						(x, y - 1)

					for neighbours in [left, right, up, down]:
										if neighbours not in blocks:
															if valid(__a      ) and neighbours not in visited:
																				# print("neighbour", neighbours)
																				visited.add(__a      )
																				_a      :       Optional[int]   =						-1
																				_a      :       Union[str, Any]   =						float('''inf'''      )

															if valid(__a      ) and g_function[neighbours] > g_function[s] + 1:
																				_a      :       Dict   =						g_function[s] + 1
																				_a      :       Optional[Any]   =						s
																				if neighbours not in close_list_anchor:
																									open_list[0].put(__a						,key(__a						,0						,__a						,__a      )      )
																									if neighbours not in close_list_inad:
																														for var in range(1						,__a      ):
																																			if key(__a						,__a						,__a						,__a      ) <= Wa * key(
																																			    __a						,0						,__a						,__a      ):
																																								open_list[j].put(
																																								    __a						,key(__a						,__a						,__a						,__a      )      )



def   __UpperCAmelCase					(      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						[]
					for x in range(1						,5      ):
										for y in range(1						,6      ):
															some_list.append((x, y)      )

					for x in range(15						,20      ):
										some_list.append((x, 17)      )

					for x in range(10						,19      ):
										for y in range(1						,15      ):
															some_list.append((x, y)      )

    # L block
					for x in range(1						,4      ):
										for y in range(12						,19      ):
															some_list.append((x, y)      )
					for x in range(3						,13      ):
										for y in range(16						,19      ):
															some_list.append((x, y)      )
					return some_list


a__												=						{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a__												=						[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a__												=						make_common_ground()


a__												=						blocks_blk
# hyper parameters
a__												=						1
a__												=						1
a__												=						20
a__												=						3  # one consistent and two other inconsistent

# start and end destination
a__												=						(0, 0)
a__												=						(n - 1, n - 1)

a__												=						1



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos						,__a :     int      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						{start: 0, goal: float('''inf'''      )}
					_a      :       int   =						{start: -1, goal: -1}
					_a      :       Union[str, Any]   =						[]
					_a      :       Optional[int]   =						set()

					for i in range(__a      ):
										open_list.append(PriorityQueue()      )
										open_list[i].put(__a						,key(__a						,__a						,__a						,__a      )      )

					_a      :       list[int]   =						[]
					_a      :       list[int]   =						[]
					while open_list[0].minkey() < float('''inf'''      ):
										for i in range(1						,__a      ):
															# print(open_list[0].minkey(), open_list[i].minkey())
															if open_list[i].minkey() <= Wa * open_list[0].minkey():
																				global t
																				t += 1
																				if g_function[goal] <= open_list[i].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__a						,__a						,__a      )
																				else:
																									_a ,   _a      :       Any   =						open_list[i].top_show()
																									visited.add(__a      )
																									expand_state(
																									    __a						,__a						,__a						,__a						,__a						,__a						,__a						,__a						,)
																									close_list_inad.append(__a      )
															else:
																				if g_function[goal] <= open_list[0].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__a						,__a						,__a      )
																				else:
																									_a      :       Tuple   =						open_list[0].top_show()
																									visited.add(__a      )
																									expand_state(
																									    __a						,0						,__a						,__a						,__a						,__a						,__a						,__a						,)
																									close_list_anchor.append(__a      )
					print('''No path found to goal'''      )
					print()
					for i in range(n - 1						,-1						,-1      ):
										for j in range(__a      ):
															if (j, i) in blocks:
																				print('''#'''						,end=''' '''      )
															elif (j, i) in back_pointer:
																				if (j, i) == (n - 1, n - 1):
																									print('''*'''						,end=''' '''      )
																				else:
																									print('''-'''						,end=''' '''      )
															else:
																				print('''*'''						,end=''' '''      )
															if (j, i) == (n - 1, n - 1):
																				print('''<-- End position'''						,end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )


if __name__ == "__main__":
			multi_a_star(start, goal, n_heuristic)










"""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 ) )
