





'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["image_processor", "tokenizer"]
					_SCREAMING_SNAKE_CASE							   =    "ViltImageProcessor"
					_SCREAMING_SNAKE_CASE							   =    ("BertTokenizer", "BertTokenizerFast")


					def __init__(					self	:  Any       ,     __snake_case	:  Any=None       ,     __snake_case	:  Optional[int]=None       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'       ,     __snake_case       ,     )
																	_snake_case:   Dict  					=		kwargs.pop('feature_extractor'       )

											_snake_case:   Dict  					=		image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You need to specify an `image_processor`.'       )
											if tokenizer is None:
																	raise ValueError('You need to specify a `tokenizer`.'       )

											super().__init__(__snake_case       ,     __snake_case       )
											_snake_case:   str  					=		self.image_processor


					def __call__(					self	:  Dict       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,     __snake_case	:  bool = True       ,     __snake_case	:  Union[bool, str, PaddingStrategy] = False       ,     __snake_case	:  Union[bool, str, TruncationStrategy] = None       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  int = 0       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  Optional[bool] = None       ,     __snake_case	:  Optional[bool] = None       ,     __snake_case	:  bool = False       ,     __snake_case	:  bool = False       ,     __snake_case	:  bool = False       ,     __snake_case	:  bool = False       ,     __snake_case	:  bool = True       ,     __snake_case	:  Optional[Union[str, TensorType]] = None       ,     **__snake_case	:  Optional[int]       ,     ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.tokenizer(
											    text=__snake_case       ,     add_special_tokens=__snake_case       ,     padding=__snake_case       ,     truncation=__snake_case       ,     max_length=__snake_case       ,     stride=__snake_case       ,     pad_to_multiple_of=__snake_case       ,     return_token_type_ids=__snake_case       ,     return_attention_mask=__snake_case       ,     return_overflowing_tokens=__snake_case       ,     return_special_tokens_mask=__snake_case       ,     return_offsets_mapping=__snake_case       ,     return_length=__snake_case       ,     verbose=__snake_case       ,     return_tensors=__snake_case       ,     **__snake_case       ,     )
											# add pixel_values + pixel_mask
											_snake_case:   Tuple  					=		self.image_processor(__snake_case       ,     return_tensors=__snake_case       )
											encoding.update(__snake_case       )

											return encoding


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





											'''simple docstring'''





											return self.tokenizer.batch_decode(*__snake_case       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Optional[int]       ):





											'''simple docstring'''





											return self.tokenizer.decode(*__snake_case       ,     **__snake_case       )


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.tokenizer.model_input_names
											_snake_case:   List[Any]  					=		self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,     __snake_case       ,     )
											return self.image_processor_class




					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,     __snake_case       ,     )
											return self.image_processor












'''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() = }')





