




'''simple docstring'''





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    _a				(						_UpperCamelCase							):






     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					,)							->			int:
          super().__init__(
              a__					,split=a__					,features=a__					,cache_dir=a__					,keep_in_memory=a__					,streaming=a__					,num_proc=a__					,**a__					,)
          _snake_case							=      path_or_paths if isinstance(a__					,a__    ) else {self.split: path_or_paths}
          _snake_case							=      Csv(
              cache_dir=a__					,data_files=a__					,features=a__					,**a__					,)






     def 				_lowercase	( self    )							->			Dict:







          if self.streaming:
               _snake_case							=      self.builder.as_streaming_dataset(split=self.split    )
          # Build regular (map-style) dataset
          else:
               _snake_case							=      None
               _snake_case							=      None
               _snake_case							=      None
               _snake_case							=      None

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







class    _a				:






     def __init__( self					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE = None					,_SCREAMING_SNAKE_CASE = None					,**_SCREAMING_SNAKE_CASE					,)							->			Tuple:







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

          _snake_case							=      dataset
          _snake_case							=      path_or_buf
          _snake_case							=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          _snake_case							=      num_proc
          _snake_case							=      "utf-8"
          _snake_case							=      to_csv_kwargs






     def 				_lowercase	( self    )							->			int:
          _snake_case							=      self.to_csv_kwargs.pop("path_or_buf"					,a__    )
          _snake_case							=      self.to_csv_kwargs.pop("header"					,a__    )
          _snake_case							=      self.to_csv_kwargs.pop("index"					,a__    )

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






     def 				_lowercase	( self					,_SCREAMING_SNAKE_CASE    )							->			List[str]:
          _snake_case							,					_snake_case							,					_snake_case							,					_snake_case							=      args

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






     def 				_lowercase	( self					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,**_SCREAMING_SNAKE_CASE    )							->			int:
          _snake_case							=      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"					,):
                    _snake_case							=      self._batch_csv((offset, header, index, to_csv_kwargs)    )
                    written += file_obj.write(a__    )

          else:
               _snake_case							,					_snake_case							=      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					,a__					,a__    )]					,)					,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(a__    )

          return written






"""simple docstring"""


def    a__       ( __lowercase = 20      )		->		int:

				try:
								_A         =	int(__lowercase      )
				except (TypeError, ValueError):
								raise TypeError("Parameter n must be int or castable to int."      )
				if n <= 0:
								raise ValueError("Parameter n must be greater than or equal to one."      )
				_A         =	0
				while 1:
								i += n * (n - 1)
								_A         =	0
								for j in range(2   ,	__lowercase      ):
												if i % j != 0:
																_A         =	1
																break
								if nfound == 0:
												if i == 0:
																_A         =	1
												return i
				return None


if __name__ == "__main__":
		print(f'''{solution() = }''')