





'''simple docstring'''






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							   =    "Pix2StructImageProcessor"
					_SCREAMING_SNAKE_CASE							   =    ("T5Tokenizer", "T5TokenizerFast")


					def __init__(					self	:  int       ,     __snake_case	:  Tuple       ,     __snake_case	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		False
											super().__init__(__snake_case       ,     __snake_case       )


					def __call__(					self	:  Union[str, Any]       ,     __snake_case	:  Any=None       ,     __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	:  Optional[int] = 20_48       ,     __snake_case	:  int = 0       ,     __snake_case	:  Optional[int] = 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 = False       ,     __snake_case	:  bool = True       ,     __snake_case	:  Optional[Union[str, TensorType]] = None       ,     **__snake_case	:  Tuple       ,     ):





											'''simple docstring'''





											if images is None and text is None:
																	raise ValueError('You have to specify either images or text.'       )

											# Get only text
											if images is None and not self.image_processor.is_vqa:
																	_snake_case:   str  					=		self.tokenizer
																	_snake_case:   List[str]  					=		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_attention_mask=__snake_case       ,     return_overflowing_tokens=__snake_case       ,     return_special_tokens_mask=__snake_case       ,     return_offsets_mapping=__snake_case       ,     return_token_type_ids=__snake_case       ,     return_length=__snake_case       ,     verbose=__snake_case       ,     return_tensors=__snake_case       ,     **__snake_case       ,     )
																	return text_encoding

											if not self.image_processor.is_vqa:
																	# add pixel_values
																	_snake_case:   List[Any]  					=		self.image_processor(
																	    __snake_case       ,     return_tensors=__snake_case       ,     max_patches=__snake_case       ,     **__snake_case       )
											else:
																	# add pixel_values and bbox
																	_snake_case:   Tuple  					=		self.image_processor(
																	    __snake_case       ,     return_tensors=__snake_case       ,     max_patches=__snake_case       ,     header_text=__snake_case       ,     **__snake_case       )

											if text is not None and not self.image_processor.is_vqa:
																	_snake_case:   List[str]  					=		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_attention_mask=__snake_case       ,     return_overflowing_tokens=__snake_case       ,     return_special_tokens_mask=__snake_case       ,     return_offsets_mapping=__snake_case       ,     return_token_type_ids=__snake_case       ,     return_length=__snake_case       ,     verbose=__snake_case       ,     return_tensors=__snake_case       ,     **__snake_case       ,     )

																	if "attention_mask" in text_encoding:
																							_snake_case:   Optional[Any]  					=		text_encoding.pop('attention_mask'       )
																	if "input_ids" in text_encoding:
																							_snake_case:   Optional[Any]  					=		text_encoding.pop('input_ids'       )
											else:
																	_snake_case:   Union[str, Any]  					=		None

											if text_encoding is not None:
																	encoding_image_processor.update(__snake_case       )

											return encoding_image_processor


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





											'''simple docstring'''





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


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





											'''simple docstring'''





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




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





											'''simple docstring'''





											_snake_case:   str  					=		self.tokenizer.model_input_names
											_snake_case:   Tuple  					=		self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )












'''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() = }')





