

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 							_lowerCAmelCase		(     UpperCamelCase__	):




				def __init__( self		,		snake_case_		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = False		,		snake_case_ = False		,		snake_case_ = None		,		**snake_case_		,		)    ->      List[Any]:
											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_		,		)
											SCREAMING_SNAKE_CASE			: Any	     =path_or_paths if isinstance(snake_case_		,		snake_case_							) else {self.split: path_or_paths}
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =Csv(
											    cache_dir=snake_case_		,		data_files=snake_case_		,		features=snake_case_		,		**snake_case_		,		)







				def     __a		( self							)    ->      str:






											if self.streaming:
																		SCREAMING_SNAKE_CASE			: str	     =self.builder.as_streaming_dataset(split=self.split							)
											# Build regular (map-style) dataset
											else:
																		SCREAMING_SNAKE_CASE			: int	     =None
																		SCREAMING_SNAKE_CASE			: Dict	     =None
																		SCREAMING_SNAKE_CASE			: Dict	     =None
																		SCREAMING_SNAKE_CASE			: Union[str, 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		,		)
																		SCREAMING_SNAKE_CASE			: Dict	     =self.builder.as_dataset(
																		    split=self.split		,		verification_mode=snake_case_		,		in_memory=self.keep_in_memory							)
											return dataset







class 							_lowerCAmelCase		:




				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_ = None		,		snake_case_ = None		,		**snake_case_		,		)    ->      List[str]:






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

											SCREAMING_SNAKE_CASE			: Dict	     =dataset
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =path_or_buf
											SCREAMING_SNAKE_CASE			: List[str]	     =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =num_proc
											SCREAMING_SNAKE_CASE			: int	     ="utf-8"
											SCREAMING_SNAKE_CASE			: List[Any]	     =to_csv_kwargs




				def     __a		( self							)    ->      int:
											SCREAMING_SNAKE_CASE			: int	     =self.to_csv_kwargs.pop('''path_or_buf'''		,		snake_case_							)
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.to_csv_kwargs.pop('''header'''		,		snake_case_							)
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.to_csv_kwargs.pop('''index'''		,		snake_case_							)

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




				def     __a		( self		,		snake_case_							)    ->      Union[str, Any]:
											SCREAMING_SNAKE_CASE			: Optional[int]	     =args

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







				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		**snake_case_							)    ->      int:
											SCREAMING_SNAKE_CASE			: List[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 CSV from Arrow format'''		,		):
																									SCREAMING_SNAKE_CASE			: int	     =self._batch_csv((offset, header, index, to_csv_kwargs)							)
																									written += file_obj.write(snake_case_							)

											else:
																		SCREAMING_SNAKE_CASE			: Dict	     =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		,		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 CSV from Arrow format'''		,		):
																																written += file_obj.write(snake_case_							)

											return written












from __future__ import annotations
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			None:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =m - 1, 0
							while i >= 0 and j < n:
														if key == mat[i][j]:
																					print(f'Key {key} found at row- {i + 1} column- {j + 1}' )
																					return
														if key < mat[i][j]:
																					i -= 1
														else:
																					j += 1
							print(f'Key {key} not found' )
def 						lowerCAmelCase_       (	)       ->			None:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: int	     =[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
							SCREAMING_SNAKE_CASE			: List[str]	     =int(input('''Enter the element to be searched:''' ) )
							print(__a )
							search_in_a_sorted_matrix(__a	,	len(__a )	,	len(mat[0] )	,	__a )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()






