





"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

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






class  _UpperCAmelCase       (     lowercase_			):


					def __init__( self      :Union[str, Any]					,	__UpperCamelCase      :NestedDataStructureLike[PathLike]					,	__UpperCamelCase      :Optional[NamedSplit] = None					,	__UpperCamelCase      :Optional[Features] = None					,	__UpperCamelCase      :str = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :Optional[int] = None					,	**__UpperCamelCase      :Tuple					,	):
												super().__init__(
												    __UpperCamelCase					,	split=__UpperCamelCase					,	features=__UpperCamelCase					,	cache_dir=__UpperCamelCase					,	keep_in_memory=__UpperCamelCase					,	streaming=__UpperCamelCase					,	num_proc=__UpperCamelCase					,	**__UpperCamelCase					,	)
												A		=  path_or_paths if isinstance(__UpperCamelCase					,	__UpperCamelCase		) else {self.split: path_or_paths}
												A		=  Csv(
												    cache_dir=__UpperCamelCase					,	data_files=__UpperCamelCase					,	features=__UpperCamelCase					,	**__UpperCamelCase					,	)


					def      lowerCamelCase	( self      :Optional[int]		):
												# Build iterable dataset
												if self.streaming:
																			A		=  self.builder.as_streaming_dataset(split=self.split		)
												# Build regular (map-style) dataset
												else:
																			A		=  None
																			A		=  None
																			A		=  None
																			A		=  None

																			self.builder.download_and_prepare(
																			    download_config=__UpperCamelCase					,	download_mode=__UpperCamelCase					,	verification_mode=__UpperCamelCase					,	base_path=__UpperCamelCase					,	num_proc=self.num_proc					,	)
																			A		=  self.builder.as_dataset(
																			    split=self.split					,	verification_mode=__UpperCamelCase					,	in_memory=self.keep_in_memory		)
												return dataset







class  _UpperCAmelCase       :


					def __init__( self      :Optional[int]					,	__UpperCamelCase      :Dataset					,	__UpperCamelCase      :Union[PathLike, BinaryIO]					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[int] = None					,	**__UpperCamelCase      :str					,	):
												if num_proc is not None and num_proc <= 0:
																			raise ValueError(f"num_proc {num_proc} must be an integer > 0."		)

												A		=  dataset
												A		=  path_or_buf
												A		=  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
												A		=  num_proc
												A		=  "utf-8"
												A		=  to_csv_kwargs


					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  self.to_csv_kwargs.pop("path_or_buf"					,	__UpperCamelCase		)
												A		=  self.to_csv_kwargs.pop("header"					,	__UpperCamelCase		)
												A		=  self.to_csv_kwargs.pop("index"					,	__UpperCamelCase		)

												if isinstance(self.path_or_buf					,	(str, bytes, os.PathLike)		):
																			with open(self.path_or_buf					,	"wb+"		) as buffer:
																										A		=  self._write(file_obj=__UpperCamelCase					,	header=__UpperCamelCase					,	index=__UpperCamelCase					,	**self.to_csv_kwargs		)
												else:
																			A		=  self._write(file_obj=self.path_or_buf					,	header=__UpperCamelCase					,	index=__UpperCamelCase					,	**self.to_csv_kwargs		)
												return written


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str		):
												A,     A,     A,     A		=  args

												A		=  query_table(
												    table=self.dataset.data					,	key=slice(__UpperCamelCase					,	offset + self.batch_size		)					,	indices=self.dataset._indices					,	)
												A		=  batch.to_pandas().to_csv(
												    path_or_buf=__UpperCamelCase					,	header=header if (offset == 0) else False					,	index=__UpperCamelCase					,	**__UpperCamelCase		)
												return csv_str.encode(self.encoding		)


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :BinaryIO					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :Dict					,	**__UpperCamelCase      :Optional[int]		):
												A		=  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 CSV from Arrow format"					,	):
																										A		=  self._batch_csv((offset, header, index, to_csv_kwargs)		)
																										written += file_obj.write(__UpperCamelCase		)

												else:
																			A,     A		=  len(self.dataset		), self.batch_size
																			with multiprocessing.Pool(self.num_proc		) as pool:
																										for csv_str in logging.tqdm(
																										    pool.imap(
																										        self._batch_csv					,	[(offset, header, index, to_csv_kwargs) for offset in range(0					,	__UpperCamelCase					,	__UpperCamelCase		)]					,	)					,	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 CSV from Arrow format"					,	):
																																	written += file_obj.write(__UpperCamelCase		)

												return written









"""simple docstring"""
from __future__ import annotations






class  _UpperCAmelCase       :


					def __init__( self      :Optional[Any]					,	__UpperCamelCase      :int		):
												A		=  vertices
												A		=  [[0] * vertices for _ in range(__UpperCamelCase		)]


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :list[int]		):
												print("Vertex \t Distance from Source"		)
												for vertex in range(self.vertices		):
																			print(__UpperCamelCase					,	"\t\t"					,	distances_from_source[vertex]		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :list[int]					,	__UpperCamelCase      :list[bool]		):
												A		=  1e7
												A		=  0

												# Search not nearest vertex not in the shortest path tree
												for vertex in range(self.vertices		):
																			if distances_from_source[vertex] < minimum and visited[vertex] is False:
																										A		=  distances_from_source[vertex]
																										A		=  vertex
												return min_index


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :int		):
												A		=  [int(1e7		)] * self.vertices  # distances from the source
												A		=  0
												A		=  [False] * self.vertices

												for _ in range(self.vertices		):
																			A		=  self.minimum_distance(__UpperCamelCase					,	__UpperCamelCase		)
																			A		=  True

																			# Update dist value of the adjacent vertices
																			# of the picked vertex only if the current
																			# distance is greater than new distance and
																			# the vertex in not in the shortest path tree
																			for v in range(self.vertices		):
																										if (
																										    self.graph[u][v] > 0
																										    and visited[v] is False
																										    and distances[v] > distances[u] + self.graph[u][v]
																										):
																																	A		=  distances[u] + self.graph[u][v]

												self.print_solution(__UpperCamelCase		)


if __name__ == "__main__":
		_snake_case					:		List[Any]							=			Graph(9)
		_snake_case					:		List[str]							=			[
		    [0, 4, 0, 0, 0, 0, 0, 8, 0],
		    [4, 0, 8, 0, 0, 0, 0, 11, 0],
		    [0, 8, 0, 7, 0, 4, 0, 0, 2],
		    [0, 0, 7, 0, 9, 14, 0, 0, 0],
		    [0, 0, 0, 9, 0, 10, 0, 0, 0],
		    [0, 0, 4, 14, 10, 0, 2, 0, 0],
		    [0, 0, 0, 0, 0, 2, 0, 1, 6],
		    [8, 11, 0, 0, 0, 0, 1, 0, 7],
		    [0, 0, 2, 0, 0, 0, 6, 7, 0],
		]
		graph.dijkstra(0)


