



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):



 """simple docstring"""

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

 def 						snake_case_			(						self				:				Union[str, Any]			)      ->  List[Any]:
    # Build iterable dataset
    if self.streaming:
       _A						     =					self.builder.as_streaming_dataset(split=self.split			)
    # Build regular (map-style) dataset
    else:
       _A						     =					None
       _A						     =					None
       _A						     =					None
       _A						     =					None

       self.builder.download_and_prepare(
           download_config=__lowerCAmelCase ,	download_mode=__lowerCAmelCase ,	verification_mode=__lowerCAmelCase ,	base_path=__lowerCAmelCase ,	num_proc=self.num_proc ,	)
       _A						     =					self.builder.as_dataset(
           split=self.split ,	verification_mode=__lowerCAmelCase ,	in_memory=self.keep_in_memory			)
    return dataset




class   lowerCamelCase__  :



 """simple docstring"""

 def __init__(						self				:				Dict ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				int ,	__lowerCAmelCase				:				Dict = None ,	__lowerCAmelCase				:				Union[str, Any] = None ,	**__lowerCAmelCase				:				Optional[int] ,	)      ->  List[Any]:
    if num_proc is not None and num_proc <= 0:
       raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''			)

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

 def 						snake_case_			(						self				:				Any			)      ->  int:
    _A						     =					self.to_csv_kwargs.pop('''path_or_buf''' ,	__lowerCAmelCase			)
    _A						     =					self.to_csv_kwargs.pop('''header''' ,	__lowerCAmelCase			)
    _A						     =					self.to_csv_kwargs.pop('''index''' ,	__lowerCAmelCase			)

    if isinstance(self.path_or_buf ,	(str, bytes, os.PathLike)			):
       with open(self.path_or_buf ,	'''wb+'''			) as buffer:
          _A						     =					self._write(file_obj=__lowerCAmelCase ,	header=__lowerCAmelCase ,	index=__lowerCAmelCase ,	**self.to_csv_kwargs			)
    else:
       _A						     =					self._write(file_obj=self.path_or_buf ,	header=__lowerCAmelCase ,	index=__lowerCAmelCase ,	**self.to_csv_kwargs			)
    return written

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

    _A						     =					query_table(
        table=self.dataset.data ,	key=slice(__lowerCAmelCase ,	offset + self.batch_size			) ,	indices=self.dataset._indices ,	)
    _A						     =					batch.to_pandas().to_csv(
        path_or_buf=__lowerCAmelCase ,	header=header if (offset == 0) else False ,	index=__lowerCAmelCase ,	**__lowerCAmelCase			)
    return csv_str.encode(self.encoding			)

 def 						snake_case_			(						self				:				Optional[Any] ,	__lowerCAmelCase				:				str ,	__lowerCAmelCase				:				Dict ,	__lowerCAmelCase				:				str ,	**__lowerCAmelCase				:				Dict			)      ->  int:
    _A						     =					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						     =					self._batch_csv((offset, header, index, to_csv_kwargs)			)
          written += file_obj.write(__lowerCAmelCase			)

    else:
       _A						     =					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 ,	__lowerCAmelCase ,	__lowerCAmelCase			)] ,	) ,	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(__lowerCAmelCase			)

    return written








def   SCREAMING_SNAKE_CASE_       (  _snake_case  :list							)     ->    list:
   if len(_snake_case							) <= 1:
      return data
   else:
      return [
          *quick_sort([e for e in data[1:] if e <= data[0]]							),
          data[0],
          *quick_sort([e for e in data[1:] if e > data[0]]							),
      ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



