





'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def __init__(					self	:  Dict       ,     __snake_case	:  NestedDataStructureLike[PathLike]       ,     __snake_case	:  Optional[NamedSplit] = None       ,     __snake_case	:  Optional[Features] = None       ,     __snake_case	:  str = None       ,     __snake_case	:  bool = False       ,     __snake_case	:  bool = False       ,     __snake_case	:  Optional[str] = None       ,     __snake_case	:  Optional[int] = None       ,     **__snake_case	:  List[str]       ,     ):





											'''simple docstring'''





											super().__init__(
											    __snake_case       ,     split=__snake_case       ,     features=__snake_case       ,     cache_dir=__snake_case       ,     keep_in_memory=__snake_case       ,     streaming=__snake_case       ,     num_proc=__snake_case       ,     **__snake_case       ,     )
											_snake_case:   List[Any]  					=		field
											_snake_case:   int  					=		path_or_paths if isinstance(__snake_case       ,     __snake_case       ) else {self.split: path_or_paths}
											_snake_case:   int  					=		Json(
											    cache_dir=__snake_case       ,     data_files=__snake_case       ,     features=__snake_case       ,     field=__snake_case       ,     **__snake_case       ,     )




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											if self.streaming:
																	_snake_case:   int  					=		self.builder.as_streaming_dataset(split=self.split       )
											# Build regular (map-style) dataset
											else:
																	_snake_case:   Tuple  					=		None
																	_snake_case:   Any  					=		None
																	_snake_case:   Tuple  					=		None
																	_snake_case:   Any  					=		None

																	self.builder.download_and_prepare(
																	    download_config=__snake_case       ,     download_mode=__snake_case       ,     verification_mode=__snake_case       ,     base_path=__snake_case       ,     num_proc=self.num_proc       ,     )
																	_snake_case:   Any  					=		self.builder.as_dataset(
																	    split=self.split       ,     verification_mode=__snake_case       ,     in_memory=self.keep_in_memory       )
											return dataset







class 			lowerCamelCase					:


					def __init__(					self	:  Optional[int]       ,     __snake_case	:  Dataset       ,     __snake_case	:  Union[PathLike, BinaryIO]       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  Optional[int] = None       ,     **__snake_case	:  List[str]       ,     ):





											'''simple docstring'''





											if num_proc is not None and num_proc <= 0:
																	raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''       )

											_snake_case:   int  					=		dataset
											_snake_case:   Tuple  					=		path_or_buf
											_snake_case:   Tuple  					=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											_snake_case:   Tuple  					=		num_proc
											_snake_case:   str  					=		'utf-8'
											_snake_case:   Union[str, Any]  					=		to_json_kwargs


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.to_json_kwargs.pop('path_or_buf'       ,     __snake_case       )
											_snake_case:   Any  					=		self.to_json_kwargs.pop('orient'       ,     'records'       )
											_snake_case:   str  					=		self.to_json_kwargs.pop('lines'       ,     True if orient == 'records' else False       )
											_snake_case:   List[str]  					=		self.to_json_kwargs.pop('index'       ,     False if orient in ['split', 'table'] else True       )
											_snake_case:   Optional[Any]  					=		self.to_json_kwargs.pop('compression'       ,     __snake_case       )

											if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																	raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''       )

											if isinstance(self.path_or_buf       ,     (str, bytes, os.PathLike)       ):
																	with fsspec.open(self.path_or_buf       ,     'wb'       ,     compression=__snake_case       ) as buffer:
																							_snake_case:   Optional[Any]  					=		self._write(file_obj=__snake_case       ,     orient=__snake_case       ,     lines=__snake_case       ,     index=__snake_case       ,     **self.to_json_kwargs       )
											else:
																	if compression:
																							raise NotImplementedError(
																							    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
																							    ' was passed. Please provide a local path instead.'       )
																	_snake_case:   Optional[Any]  					=		self._write(
																	    file_obj=self.path_or_buf       ,     orient=__snake_case       ,     lines=__snake_case       ,     index=__snake_case       ,     **self.to_json_kwargs       )
											return written


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





											'''simple docstring'''





											_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case:   Any  					=		args

											_snake_case:   Union[str, Any]  					=		query_table(
											    table=self.dataset.data       ,     key=slice(__snake_case       ,     offset + self.batch_size       )       ,     indices=self.dataset._indices       ,     )
											_snake_case:   Any  					=		batch.to_pandas().to_json(
											    path_or_buf=__snake_case       ,     orient=__snake_case       ,     lines=__snake_case       ,     index=__snake_case       ,     **__snake_case       )
											if not json_str.endswith('\n'       ):
																	json_str += "\n"
											return json_str.encode(self.encoding       )




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





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		0

											if self.num_proc is None or self.num_proc == 1:
																	for offset in logging.tqdm(
																	    range(0       ,     len(self.dataset       )       ,     self.batch_size       )       ,     unit='ba'       ,     disable=not logging.is_progress_bar_enabled()       ,     desc='Creating json from Arrow format'       ,     ):
																							_snake_case:   Optional[int]  					=		self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
																							written += file_obj.write(__snake_case       )
											else:
																	_snake_case					,						_snake_case:   List[Any]  					=		len(self.dataset       ), self.batch_size
																	with multiprocessing.Pool(self.num_proc       ) as pool:
																							for json_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_json       ,     [(offset, orient, lines, index, to_json_kwargs) for offset in range(0       ,     __snake_case       ,     __snake_case       )]       ,     )       ,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size       ,     unit='ba'       ,     disable=not logging.is_progress_bar_enabled()       ,     desc='Creating json from Arrow format'       ,     ):
																													written += file_obj.write(__snake_case       )

											return written












'''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() = }')





