


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 					snake_case__  ( __A       ):





	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = None				,    **UpperCamelCase_				,    )		->			str:
								"""simple docstring"""






								super().__init__(
								    UpperCamelCase_				,    split=UpperCamelCase_				,    features=UpperCamelCase_				,    cache_dir=UpperCamelCase_				,    keep_in_memory=UpperCamelCase_				,    streaming=UpperCamelCase_				,    num_proc=UpperCamelCase_				,    **UpperCamelCase_				,    )
								a_       :   int			=	path_or_paths if isinstance(UpperCamelCase_				,    UpperCamelCase_							) else {self.split: path_or_paths}
								a_       :   Dict			=	Csv(
								    cache_dir=UpperCamelCase_				,    data_files=UpperCamelCase_				,    features=UpperCamelCase_				,    **UpperCamelCase_				,    )





	def     A				(  self							)		->			Optional[Any]:
								"""simple docstring"""






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

															self.builder.download_and_prepare(
															    download_config=UpperCamelCase_				,    download_mode=UpperCamelCase_				,    verification_mode=UpperCamelCase_				,    base_path=UpperCamelCase_				,    num_proc=self.num_proc				,    )
															a_       :   Optional[int]			=	self.builder.as_dataset(
															    split=self.split				,    verification_mode=UpperCamelCase_				,    in_memory=self.keep_in_memory							)
								return dataset





class 					snake_case__  :





	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    **UpperCamelCase_				,    )		->			Optional[int]:
								"""simple docstring"""






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

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





	def     A				(  self							)		->			int:
								"""simple docstring"""






								a_       :   Dict			=	self.to_csv_kwargs.pop("""path_or_buf"""				,    UpperCamelCase_							)
								a_       :   Dict			=	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_       :   Tuple			=	self._write(file_obj=UpperCamelCase_				,    header=UpperCamelCase_				,    index=UpperCamelCase_				,    **self.to_csv_kwargs							)
								else:
															a_       :   Optional[int]			=	self._write(file_obj=self.path_or_buf				,    header=UpperCamelCase_				,    index=UpperCamelCase_				,    **self.to_csv_kwargs							)
								return written





	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
								"""simple docstring"""






								a_       :   int			=	args

								a_       :   Union[str, Any]			=	query_table(
								    table=self.dataset.data				,    key=slice(UpperCamelCase_				,    offset + self.batch_size							)				,    indices=self.dataset._indices				,    )
								a_       :   str			=	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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							)		->			int:
								"""simple docstring"""






								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_       :   List[Any]			=	self._batch_csv((offset, header, index, to_csv_kwargs)							)
																						written += file_obj.write(UpperCamelCase_							)

								else:
															a_       :   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				,    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 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list[int]      ):






							"""simple docstring"""



							a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      )
							if n in (0, 1):
														return 0
							return sum(query * (n - i - 1) for i, query in enumerate(sorted(SCREAMING_SNAKE_CASE_      )      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


