



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__			(		_a ):


						def __init__(			self    :	Dict   ,							_a    :	Any   ,							_a    :	Tuple = None   ,							_a    :	str = None   ,							_a    :	List[Any] = None   ,							_a    :	Tuple = False   ,							_a    :	Union[str, Any] = False   ,							_a    :	Dict = None   ,							**_a    :	Any   ,							):
													super().__init__(
													    UpperCAmelCase_   ,							split=UpperCAmelCase_   ,							features=UpperCAmelCase_   ,							cache_dir=UpperCAmelCase_   ,							keep_in_memory=UpperCAmelCase_   ,							streaming=UpperCAmelCase_   ,							num_proc=UpperCAmelCase_   ,							**UpperCAmelCase_   ,							)
													a__:							Dict									=path_or_paths if isinstance(UpperCAmelCase_   ,							UpperCAmelCase_      ) else {self.split: path_or_paths}
													a__:							Tuple									=Csv(
													    cache_dir=UpperCAmelCase_   ,							data_files=UpperCAmelCase_   ,							features=UpperCAmelCase_   ,							**UpperCAmelCase_   ,							)







						def 	_lowerCamelCase      (			self    :	Any      ):
													# Build iterable dataset
													if self.streaming:
																				a__:							Optional[Any]									=self.builder.as_streaming_dataset(split=self.split      )
													# Build regular (map-style) dataset
													else:
																				a__:							Tuple									=None
																				a__:							str									=None
																				a__:							str									=None
																				a__:							Union[str, Any]									=None

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


class 						lowerCamelCase__			:


						def __init__(			self    :	str   ,							_a    :	List[Any]   ,							_a    :	List[Any]   ,							_a    :	str = None   ,							_a    :	List[str] = None   ,							**_a    :	Optional[Any]   ,							):
													if num_proc is not None and num_proc <= 0:
																				raise ValueError(F"num_proc {num_proc} must be an integer > 0."      )

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


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							Any									=self.to_csv_kwargs.pop("path_or_buf"   ,							UpperCAmelCase_      )
													a__:							List[Any]									=self.to_csv_kwargs.pop("header"   ,							UpperCAmelCase_      )
													a__:							int									=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__:							List[str]									=self._write(file_obj=UpperCAmelCase_   ,							header=UpperCAmelCase_   ,							index=UpperCAmelCase_   ,							**self.to_csv_kwargs      )
													else:
																				a__:							int									=self._write(file_obj=self.path_or_buf   ,							header=UpperCAmelCase_   ,							index=UpperCAmelCase_   ,							**self.to_csv_kwargs      )
													return written


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	Optional[Any]      ):
													a__				,      a__				,      a__				,      a__:							Union[str, Any]									=args

													a__:							int									=query_table(
													    table=self.dataset.data   ,							key=slice(UpperCAmelCase_   ,							offset + self.batch_size      )   ,							indices=self.dataset._indices   ,							)
													a__:							Optional[Any]									=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    :	Optional[int]   ,							_a    :	Optional[Any]   ,							_a    :	int   ,							_a    :	Dict   ,							**_a    :	List[Any]      ):
													a__:							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__:							Union[str, Any]									=self._batch_csv((offset, header, index, to_csv_kwargs)      )
																											written += file_obj.write(UpperCAmelCase_      )

													else:
																				a__				,      a__:							int									=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












def   _lowercase       (    _UpperCAmelCase					)    ->	bool:
				if not isinstance(_UpperCAmelCase      ,   _UpperCAmelCase					):
								lowerCamelCase									=F"""Input value of [number={number}] must be an integer"""
								raise TypeError(_UpperCAmelCase					)
				if number < 0 or number % 2 == 1:
								return False
				lowerCamelCase									=int(number**0.5					)
				return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





