
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 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""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_  ,						)-> Optional[int]:





													'''simple docstring'''






													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_  ,						)
													__UpperCamelCase  	=      path_or_paths if isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) else {self.split: path_or_paths}
													__UpperCamelCase  	=      Csv(
													    cache_dir=SCREAMING_SNAKE_CASE_  ,						data_files=SCREAMING_SNAKE_CASE_  ,						features=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_  ,						)



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													if self.streaming:
																			__UpperCamelCase  	=      self.builder.as_streaming_dataset(split=self.split      )
													# Build regular (map-style) dataset
													else:
																			__UpperCamelCase  	=      None
																			__UpperCamelCase  	=      None
																			__UpperCamelCase  	=      None
																			__UpperCamelCase  	=      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  ,						)
																			__UpperCamelCase  	=      self.builder.as_dataset(
																			    split=self.split  ,						verification_mode=SCREAMING_SNAKE_CASE_  ,						in_memory=self.keep_in_memory      )
													return dataset






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> str:





													'''simple docstring'''






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

													__UpperCamelCase  	=      dataset
													__UpperCamelCase  	=      path_or_buf
													__UpperCamelCase  	=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													__UpperCamelCase  	=      num_proc
													__UpperCamelCase  	=      '''utf-8'''
													__UpperCamelCase  	=      to_csv_kwargs



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.to_csv_kwargs.pop('''path_or_buf'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.to_csv_kwargs.pop('''header'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      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:
																									__UpperCamelCase  	=      self._write(file_obj=SCREAMING_SNAKE_CASE_  ,						header=SCREAMING_SNAKE_CASE_  ,						index=SCREAMING_SNAKE_CASE_  ,						**self.to_csv_kwargs      )
													else:
																			__UpperCamelCase  	=      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_      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      args

													__UpperCamelCase  	=      query_table(
													    table=self.dataset.data  ,						key=slice(SCREAMING_SNAKE_CASE_  ,						offset + self.batch_size      )  ,						indices=self.dataset._indices  ,						)
													__UpperCamelCase  	=      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:





													'''simple docstring'''






													__UpperCamelCase  	=      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'''  ,						):
																									__UpperCamelCase  	=      self._batch_csv((offset, header, index, to_csv_kwargs)      )
																									written += file_obj.write(SCREAMING_SNAKE_CASE_      )

													else:
																			__UpperCamelCase	,				__UpperCamelCase  	=      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"""






def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				if len(lowerCamelCase_	) == 1:
								return True
				try:
								SCREAMING_SNAKE_CASE__  				=					series[1] / series[0]
								for index in range(len(lowerCamelCase_	) - 1	):
												if series[index + 1] / series[index] != common_ratio:
																return False
				except ZeroDivisionError:
								return False
				return True


def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				SCREAMING_SNAKE_CASE__  				=					1
				for value in series:
								answer *= value
				return pow(lowerCamelCase_    ,       1 / len(lowerCamelCase_	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
