


'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_pytesseract_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor
class 						__UpperCamelCase	(						unittest.TestCase							):





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[Any]=7  ,_UpperCamelCase		:str=3  ,_UpperCamelCase		:Optional[int]=1_8  ,_UpperCamelCase		:Any=3_0  ,_UpperCamelCase		:Optional[Any]=4_0_0  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:List[str]=None  ,_UpperCamelCase		:Tuple=True  ,):
									snake_case_		:							Dict			       =						size if size is not None else {"""height""": 1_8, """width""": 1_8}
									snake_case_		:							int			       =						parent
									snake_case_		:							str			       =						batch_size
									snake_case_		:							Any			       =						num_channels
									snake_case_		:							List[str]			       =						image_size
									snake_case_		:							str			       =						min_resolution
									snake_case_		:							Dict			       =						max_resolution
									snake_case_		:							Optional[int]			       =						do_resize
									snake_case_		:							int			       =						size
									snake_case_		:							List[Any]			       =						apply_ocr





							def 		a__   (  self		:List[Any]			):
									return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 						__UpperCamelCase	(						lowercase__						,  unittest.TestCase							):
							lowercase			:		Dict   		=  LayoutLMvaImageProcessor if is_pytesseract_available() else None





							def 		a__   (  self		:Dict			):
									snake_case_		:							Union[str, Any]			       =						LayoutLMvaImageProcessingTester(self			)





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





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Union[str, Any]			       =						self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(_UpperCamelCase  ,"""do_resize"""			)			)
									self.assertTrue(hasattr(_UpperCamelCase  ,"""size"""			)			)
									self.assertTrue(hasattr(_UpperCamelCase  ,"""apply_ocr"""			)			)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Tuple			       =						self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size  ,{"""height""": 1_8, """width""": 1_8}			)

									snake_case_		:							Optional[int]			       =						self.image_processing_class.from_dict(self.image_processor_dict  ,size=4_2			)
									self.assertEqual(image_processor.size  ,{"""height""": 4_2, """width""": 4_2}			)





							def 		a__   (  self		:Dict			):
									pass





							def 		a__   (  self		:Any			):
									# Initialize image_processing
									snake_case_		:							Tuple			       =						self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									snake_case_		:							Dict			       =						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_		:							Optional[Any]			       =						image_processing(image_inputs[0]  ,return_tensors="""pt"""			)
									self.assertEqual(
									    encoding.pixel_values.shape  ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)

									self.assertIsInstance(encoding.words  ,_UpperCamelCase			)
									self.assertIsInstance(encoding.boxes  ,_UpperCamelCase			)

									# Test batched
									snake_case_		:							Tuple			       =						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,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)





							def 		a__   (  self		:Optional[Any]			):
									# Initialize image_processing
									snake_case_		:							Dict			       =						self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									snake_case_		:							Tuple			       =						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_		:							str			       =						image_processing(image_inputs[0]  ,return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)

									# Test batched
									snake_case_		:							List[str]			       =						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,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)





							def 		a__   (  self		:List[str]			):
									# Initialize image_processing
									snake_case_		:							Dict			       =						self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									snake_case_		:							Optional[int]			       =						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_		:							Tuple			       =						image_processing(image_inputs[0]  ,return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)

									# Test batched
									snake_case_		:							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,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)





							def 		a__   (  self		:List[str]			):
									# with apply_OCR = True
									snake_case_		:							List[Any]			       =						LayoutLMvaImageProcessor()

									from datasets import load_dataset

									snake_case_		:							Optional[int]			       =						load_dataset("""hf-internal-testing/fixtures_docvqa"""  ,split="""test"""			)

									snake_case_		:							Optional[Any]			       =						Image.open(ds[0]["""file"""]			).convert("""RGB"""			)

									snake_case_		:							Optional[int]			       =						image_processing(_UpperCamelCase  ,return_tensors="""pt"""			)

									self.assertEqual(encoding.pixel_values.shape  ,(1, 3, 2_2_4, 2_2_4)			)
									self.assertEqual(len(encoding.words			)  ,len(encoding.boxes			)			)

									# fmt: off
									# the words and boxes were obtained with Tesseract 4.1.1
									snake_case_		:							Tuple			       =						[["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
									snake_case_		:							Dict			       =						[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
									# fmt: on

									self.assertListEqual(encoding.words  ,_UpperCamelCase			)
									self.assertListEqual(encoding.boxes  ,_UpperCamelCase			)

									# with apply_OCR = False
									snake_case_		:							str			       =						LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase			)

									snake_case_		:							str			       =						image_processing(_UpperCamelCase  ,return_tensors="""pt"""			)

									self.assertEqual(encoding.pixel_values.shape  ,(1, 3, 2_2_4, 2_2_4)			)



'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

__A	:	str     =     tuple[int, int]
class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:set[int]  ,_UpperCamelCase		:Mapping[EdgeT, int]			):
									snake_case_		:							set[int]			       =						vertices
									snake_case_		:							dict[EdgeT, int]			       =						{
									    (min(_UpperCamelCase			), max(_UpperCamelCase			)): weight for edge, weight in edges.items()
									}





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:EdgeT  ,_UpperCamelCase		:int			):
									self.vertices.add(edge[0]			)
									self.vertices.add(edge[1]			)
									snake_case_		:							str			       =						weight





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Graph			       =						Graph({min(self.vertices			)}  ,{}			)
									snake_case_		:							EdgeT
									snake_case_		:							int
									snake_case_		:							EdgeT
									snake_case_		:							int

									while len(subgraph.vertices			) < len(self.vertices			):
											snake_case_		:							int			       =						max(self.edges.values()			) + 1
											for edge, weight in self.edges.items():
													if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
															if weight < min_weight:
																	snake_case_		:							Dict			       =						edge
																	snake_case_		:							Dict			       =						weight

											subgraph.add_edge(_UpperCamelCase  ,_UpperCamelCase			)

									return subgraph





def      UpperCAmelCase      (		lowerCamelCase_  :str = "p107_network.txt" ):







		'''simple docstring'''
		snake_case_		:							str			       =						os.path.abspath(os.path.dirname(lowerCamelCase_ ) )
		snake_case_		:							str			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							dict[EdgeT, int]			       =						{}
		snake_case_		:							list[str]
		snake_case_		:							int
		snake_case_		:							int

		with open(lowerCamelCase_ ) as f:
				snake_case_		:							Optional[int]			       =						f.read().strip().split("""\n""" )

		snake_case_		:							Any			       =						[line.split(""",""" ) for line in data]

		for edgea in range(1				,      len(lowerCamelCase_ ) ):
				for edgea in range(lowerCamelCase_ ):
						if adjaceny_matrix[edgea][edgea] != "-":
								snake_case_		:							str			       =						int(adjaceny_matrix[edgea][edgea] )

		snake_case_		:							Graph			       =						Graph(set(range(len(lowerCamelCase_ ) ) )				,      lowerCamelCase_ )

		snake_case_		:							Graph			       =						graph.prims_algorithm()

		snake_case_		:							int			       =						sum(graph.edges.values() )
		snake_case_		:							int			       =						sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
							print(F'{solution() = }')