





'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ChineseCLIPImageProcessor







class 			lowerCamelCase					(						unittest.TestCase  ):


					def __init__(					self	:  Any       ,     __snake_case	:  Tuple       ,     __snake_case	:  Optional[int]=7       ,     __snake_case	:  Tuple=3       ,     __snake_case	:  Dict=18       ,     __snake_case	:  List[Any]=30       ,     __snake_case	:  List[str]=4_00       ,     __snake_case	:  int=True       ,     __snake_case	:  str=None       ,     __snake_case	:  int=True       ,     __snake_case	:  Any=None       ,     __snake_case	:  Optional[int]=True       ,     __snake_case	:  Optional[Any]=[0.48_145_466, 0.4_578_275, 0.40_821_073]       ,     __snake_case	:  int=[0.26_862_954, 0.26_130_258, 0.27_577_711]       ,     __snake_case	:  List[Any]=True       ,     ):





											'''simple docstring'''





											_snake_case:   str  					=		size if size is not None else {'height': 2_24, 'width': 2_24}
											_snake_case:   str  					=		crop_size if crop_size is not None else {'height': 18, 'width': 18}
											_snake_case:   Dict  					=		parent
											_snake_case:   List[str]  					=		batch_size
											_snake_case:   Optional[int]  					=		num_channels
											_snake_case:   Tuple  					=		image_size
											_snake_case:   Optional[Any]  					=		min_resolution
											_snake_case:   str  					=		max_resolution
											_snake_case:   Dict  					=		do_resize
											_snake_case:   str  					=		size
											_snake_case:   int  					=		do_center_crop
											_snake_case:   int  					=		crop_size
											_snake_case:   str  					=		do_normalize
											_snake_case:   str  					=		image_mean
											_snake_case:   Optional[Any]  					=		image_std
											_snake_case:   Optional[int]  					=		do_convert_rgb


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_convert_rgb": self.do_convert_rgb,
											}




					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Tuple=False       ,     __snake_case	:  List[Any]=False       ,     __snake_case	:  List[Any]=False       ):





											'''simple docstring'''





											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
																	_snake_case:   str  					=		[]
																	for i in range(self.batch_size       ):
																							image_inputs.append(
																							    np.random.randint(
																							        2_55       ,     size=(self.num_channels, self.max_resolution, self.max_resolution)       ,     dtype=np.uinta       )       )
											else:
																	_snake_case:   Optional[Any]  					=		[]
																	for i in range(self.batch_size       ):
																							_snake_case					,						_snake_case:   Union[str, Any]  					=		np.random.choice(np.arange(self.min_resolution       ,     self.max_resolution       )       ,     2       )
																							image_inputs.append(np.random.randint(2_55       ,     size=(self.num_channels, width, height)       ,     dtype=np.uinta       )       )

											if not numpify and not torchify:
																	# PIL expects the channel dimension as last dimension
																	_snake_case:   List[Any]  					=		[Image.fromarray(np.moveaxis(__snake_case       ,     0       ,     -1       )       ) for x in image_inputs]

											if torchify:
																	_snake_case:   List[Any]  					=		[torch.from_numpy(__snake_case       ) for x in image_inputs]

											return image_inputs







@require_torch
@require_vision
class 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    ChineseCLIPImageProcessor if is_vision_available() else None


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		ChineseCLIPImageProcessingTester(self       ,     do_center_crop=__snake_case       )


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											return self.image_processor_tester.prepare_image_processor_dict()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.image_processing_class(**self.image_processor_dict       )
											self.assertTrue(hasattr(__snake_case       ,     'do_resize'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'size'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'do_center_crop'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'center_crop'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'do_normalize'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'image_mean'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'image_std'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'do_convert_rgb'       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   int  					=		self.image_processing_class.from_dict(self.image_processor_dict       )
											self.assertEqual(image_processor.size       ,     {'height': 2_24, 'width': 2_24}       )
											self.assertEqual(image_processor.crop_size       ,     {'height': 18, 'width': 18}       )

											_snake_case:   Union[str, Any]  					=		self.image_processing_class.from_dict(self.image_processor_dict       ,     size=42       ,     crop_size=84       )
											self.assertEqual(image_processor.size       ,     {'shortest_edge': 42}       )
											self.assertEqual(image_processor.crop_size       ,     {'height': 84, 'width': 84}       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											pass


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											_snake_case:   Any  					=		self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case       )
											for image in image_inputs:
																	self.assertIsInstance(__snake_case       ,     Image.Image       )

											# 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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,     )

											# Test batched
											_snake_case:   Union[str, Any]  					=		image_processing(__snake_case       ,     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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.image_processing_class(**self.image_processor_dict       )
											# create random numpy tensors
											_snake_case:   Optional[int]  					=		self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case       ,     numpify=__snake_case       )
											for image in image_inputs:
																	self.assertIsInstance(__snake_case       ,     np.ndarray       )

											# Test not batched input
											_snake_case:   Optional[Any]  					=		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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,     )

											# Test batched
											_snake_case:   Union[str, Any]  					=		image_processing(__snake_case       ,     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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,     )




					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		self.image_processing_class(**self.image_processor_dict       )
											# create random PyTorch tensors
											_snake_case:   Union[str, Any]  					=		self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case       ,     torchify=__snake_case       )
											for image in image_inputs:
																	self.assertIsInstance(__snake_case       ,     torch.Tensor       )

											# Test not batched input
											_snake_case:   Dict  					=		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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,     )

											# Test batched
											_snake_case:   str  					=		image_processing(__snake_case       ,     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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,     )







@require_torch
@require_vision
class 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    ChineseCLIPImageProcessor if is_vision_available() else None


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   str  					=		ChineseCLIPImageProcessingTester(self       ,     num_channels=4       ,     do_center_crop=__snake_case       )
											_snake_case:   Optional[int]  					=		3


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											return self.image_processor_tester.prepare_image_processor_dict()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.image_processing_class(**self.image_processor_dict       )
											self.assertTrue(hasattr(__snake_case       ,     'do_resize'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'size'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'do_center_crop'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'center_crop'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'do_normalize'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'image_mean'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'image_std'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'do_convert_rgb'       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											pass




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											_snake_case:   str  					=		self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case       )
											for image in image_inputs:
																	self.assertIsInstance(__snake_case       ,     Image.Image       )

											# Test not batched input
											_snake_case:   Union[str, Any]  					=		image_processing(image_inputs[0]       ,     return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,     (
											        1,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,     )

											# Test batched
											_snake_case:   Union[str, Any]  					=		image_processing(__snake_case       ,     return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,     (
											        self.image_processor_tester.batch_size,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )       ,     )












'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

A   : Optional[Any]             =			tuple[int, int]







class 			lowerCamelCase					:


					def __init__(					self	:  Tuple       ,     __snake_case	:  set[int]       ,     __snake_case	:  Mapping[EdgeT, int]       ):





											'''simple docstring'''





											_snake_case:   set[int]  					=		vertices
											_snake_case:   dict[EdgeT, int]  					=		{
											    (min(__snake_case       ), max(__snake_case       )): weight for edge, weight in edges.items()
											}


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  EdgeT       ,     __snake_case	:  int       ):





											'''simple docstring'''





											self.vertices.add(edge[0]       )
											self.vertices.add(edge[1]       )
											_snake_case:   Dict  					=		weight




					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_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:   List[str]  					=		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:   Optional[Any]  					=		edge
																																			_snake_case:   Optional[int]  					=		weight

																	subgraph.add_edge(__snake_case       ,     __snake_case       )

											return subgraph





def       lowercase_      (		lowercase__ = "p107_network.txt"						)   ->int:
						_snake_case:   str  					=		os.path.abspath(os.path.dirname(lowercase__						)						)
						_snake_case:   str  					=		os.path.join(lowercase__				,  lowercase__						)
						_snake_case:   dict[EdgeT, int]  					=		{}
						_snake_case:   list[str]
						_snake_case:   int
						_snake_case:   int

						with open(lowercase__						) as f:
												_snake_case:   Tuple  					=		f.read().strip().split('\n'						)

						_snake_case:   Tuple  					=		[line.split(','						) for line in data]

						for edgea in range(1				,  len(lowercase__						)						):
												for edgea in range(lowercase__						):
																		if adjaceny_matrix[edgea][edgea] != "-":
																								_snake_case:   int  					=		int(adjaceny_matrix[edgea][edgea]						)

						_snake_case:   Graph  					=		Graph(set(range(len(lowercase__						)						)						)				,  lowercase__						)

						_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() = }')





