"""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__				(    __lowerCamelCase			):


	"""simple docstring"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = False       ,     _SCREAMING_SNAKE_CASE = False       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	Dict:
							super().__init__(
							    _SCREAMING_SNAKE_CASE       ,     split=_SCREAMING_SNAKE_CASE       ,     features=_SCREAMING_SNAKE_CASE       ,     cache_dir=_SCREAMING_SNAKE_CASE       ,     keep_in_memory=_SCREAMING_SNAKE_CASE       ,     streaming=_SCREAMING_SNAKE_CASE       ,     num_proc=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE       ,     )
							a_							:			List[str]     		=					path_or_paths if isinstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ) else {self.split: path_or_paths}
							a_							:			List[Any]     		=					Csv(
							    cache_dir=_SCREAMING_SNAKE_CASE       ,     data_files=_SCREAMING_SNAKE_CASE       ,     features=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE       ,     )







	def       A    (			self )    ->	List[str]:
							# Build iterable dataset
							if self.streaming:
													a_							:			Tuple     		=					self.builder.as_streaming_dataset(split=self.split )
							# Build regular (map-style) dataset
							else:
													a_							:			Optional[Any]     		=					None
													a_							:			str     		=					None
													a_							:			Optional[int]     		=					None
													a_							:			List[str]     		=					None

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




class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	str:
							if num_proc is not None and num_proc <= 0:
													raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )

							a_							:			Any     		=					dataset
							a_							:			Any     		=					path_or_buf
							a_							:			List[str]     		=					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							a_							:			str     		=					num_proc
							a_							:			List[Any]     		=					"utf-8"
							a_							:			Union[str, Any]     		=					to_csv_kwargs







	def       A    (			self )    ->	int:
							a_							:			int     		=					self.to_csv_kwargs.pop("path_or_buf"       ,     _SCREAMING_SNAKE_CASE )
							a_							:			Optional[Any]     		=					self.to_csv_kwargs.pop("header"       ,     _SCREAMING_SNAKE_CASE )
							a_							:			List[str]     		=					self.to_csv_kwargs.pop("index"       ,     _SCREAMING_SNAKE_CASE )

							if isinstance(self.path_or_buf       ,     (str, bytes, os.PathLike) ):
													with open(self.path_or_buf       ,     "wb+" ) as buffer:
																			a_							:			List[Any]     		=					self._write(file_obj=_SCREAMING_SNAKE_CASE       ,     header=_SCREAMING_SNAKE_CASE       ,     index=_SCREAMING_SNAKE_CASE       ,     **self.to_csv_kwargs )
							else:
													a_							:			List[str]     		=					self._write(file_obj=self.path_or_buf       ,     header=_SCREAMING_SNAKE_CASE       ,     index=_SCREAMING_SNAKE_CASE       ,     **self.to_csv_kwargs )
							return written







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	Tuple:
							a_		,					a_		,					a_		,					a_							:			Any     		=					args

							a_							:			Union[str, Any]     		=					query_table(
							    table=self.dataset.data       ,     key=slice(_SCREAMING_SNAKE_CASE       ,     offset + self.batch_size )       ,     indices=self.dataset._indices       ,     )
							a_							:			str     		=					batch.to_pandas().to_csv(
							    path_or_buf=_SCREAMING_SNAKE_CASE       ,     header=header if (offset == 0) else False       ,     index=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )
							return csv_str.encode(self.encoding )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )    ->	int:
							a_							:			List[str]     		=					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_							:			Dict     		=					self._batch_csv((offset, header, index, to_csv_kwargs) )
																			written += file_obj.write(_SCREAMING_SNAKE_CASE )

							else:
													a_		,					a_							:			Any     		=					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       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_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 CSV from Arrow format"       ,     ):
																									written += file_obj.write(_SCREAMING_SNAKE_CASE )

							return written





"""simple docstring"""






from __future__ import annotations

from queue import Queue

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:int  )				-> tuple[int, int]:
						a ^= b
						b ^= a
						a ^= b
						return a, b

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:list[list[int]]  )				-> list[list[int]]:
						a_							:			List[str]     		=					1
						while (1 << j) < max_node:
												for i in range(1		,			max_node + 1  ):
																		a_							:			Union[str, Any]     		=					parent[j - 1][parent[j - 1][i]]
												j += 1
						return parent

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:list[int]		,			_SCREAMING_SNAKE_CASE	:list[list[int]]  )				-> int:
						# u must be deeper in the tree than v
						if level[u] < level[v]:
												a_		,					a_							:			Tuple     		=					swap(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
						# making depth of u same as depth of v
						for i in range(18		,			-1		,			-1  ):
												if level[u] - (1 << i) >= level[v]:
																		a_							:			Union[str, Any]     		=					parent[i][u]
    # at the same depth if u==v that mean lca is found
						if u == v:
												return u
						# moving both nodes upwards till lca in found
						for i in range(18		,			-1		,			-1  ):
												if parent[i][u] not in [0, parent[i][v]]:
																		a_		,					a_							:			Optional[Any]     		=					parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
						return parent[0][u]

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:list[int]		,			_SCREAMING_SNAKE_CASE	:list[list[int]]		,			_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:dict[int, list[int]]		,			_SCREAMING_SNAKE_CASE	:int = 1		,			)				-> tuple[list[int], list[list[int]]]:
						a_							:			List[Any]     		=					0
						a_							:			Queue[int]     		=					Queue(maxsize=_SCREAMING_SNAKE_CASE  )
						q.put(_SCREAMING_SNAKE_CASE  )
						while q.qsize() != 0:
												a_							:			List[str]     		=					q.get()
												for v in graph[u]:
																		if level[v] == -1:
																								a_							:			Tuple     		=					level[u] + 1
																								q.put(_SCREAMING_SNAKE_CASE  )
																								a_							:			Optional[int]     		=					u
						return level, parent

def  lowerCAmelCase_      ()				-> None:
						a_							:			List[Any]     		=					13
						# initializing with 0
						a_							:			Dict     		=					[[0 for _ in range(max_node + 10  )] for _ in range(20  )]
						# initializing with -1 which means every node is unvisited
						a_							:			Optional[int]     		=					[-1 for _ in range(max_node + 10  )]
						a_							:			dict[int, list[int]]     		=					{
						    1: [2, 3, 4],
						    2: [5],
						    3: [6, 7],
						    4: [8],
						    5: [9, 10],
						    6: [11],
						    7: [],
						    8: [12, 13],
						    9: [],
						    10: [],
						    11: [],
						    12: [],
						    13: [],
						}
						a_		,					a_							:			Any     		=					breadth_first_search(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			1  )
						a_							:			Any     		=					create_sparse(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
						print("LCA of node 1 and 3 is: "		,			lowest_common_ancestor(1		,			3		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 5 and 6 is: "		,			lowest_common_ancestor(5		,			6		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 7 and 11 is: "		,			lowest_common_ancestor(7		,			11		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 6 and 7 is: "		,			lowest_common_ancestor(6		,			7		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 4 and 12 is: "		,			lowest_common_ancestor(4		,			12		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 8 and 8 is: "		,			lowest_common_ancestor(8		,			8		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )


if __name__ == "__main__":
				main()




