






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 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     def __init__(							self       :Union[str, Any]			,      snake_case       :NestedDataStructureLike[PathLike]			,      snake_case       :Optional[NamedSplit] = None			,      snake_case       :Optional[Features] = None			,      snake_case       :str = None			,      snake_case       :bool = False			,      snake_case       :bool = False			,      snake_case       :Optional[int] = None			,      **snake_case       :Dict			,      ):





        '''simple docstring'''




        super().__init__(
            snake_case			,      split=snake_case			,      features=snake_case			,      cache_dir=snake_case			,      keep_in_memory=snake_case			,      streaming=snake_case			,      num_proc=snake_case			,      **snake_case			,      )
        A_					:				Union[str, Any]          =			path_or_paths if isinstance(snake_case			,      snake_case  ) else {self.split: path_or_paths}
        A_					:				Optional[int]          =			Csv(
            cache_dir=snake_case			,      data_files=snake_case			,      features=snake_case			,      **snake_case			,      )





     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




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

           self.builder.download_and_prepare(
               download_config=snake_case			,      download_mode=snake_case			,      verification_mode=snake_case			,      base_path=snake_case			,      num_proc=self.num_proc			,      )
           A_					:				Dict          =			self.builder.as_dataset(
               split=self.split			,      verification_mode=snake_case			,      in_memory=self.keep_in_memory  )
        return dataset




class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :List[Any]			,      snake_case       :Dataset			,      snake_case       :Union[PathLike, BinaryIO]			,      snake_case       :Optional[int] = None			,      snake_case       :Optional[int] = None			,      **snake_case       :Tuple			,      ):





        '''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_					:				List[Any]          =			dataset
        A_					:				List[Any]          =			path_or_buf
        A_					:				Optional[Any]          =			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        A_					:				Dict          =			num_proc
        A_					:				Tuple          =			"utf-8"
        A_					:				Optional[int]          =			to_csv_kwargs



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				int          =			self.to_csv_kwargs.pop("path_or_buf"			,      snake_case  )
        A_					:				int          =			self.to_csv_kwargs.pop("header"			,      snake_case  )
        A_					:				str          =			self.to_csv_kwargs.pop("index"			,      snake_case  )

        if isinstance(self.path_or_buf			,      (str, bytes, os.PathLike)  ):
           with open(self.path_or_buf			,      "wb+"  ) as buffer:
              A_					:				Optional[Any]          =			self._write(file_obj=snake_case			,      header=snake_case			,      index=snake_case			,      **self.to_csv_kwargs  )
        else:
           A_					:				Any          =			self._write(file_obj=self.path_or_buf			,      header=snake_case			,      index=snake_case			,      **self.to_csv_kwargs  )
        return written



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]			,      snake_case       :Optional[int]  ):





        '''simple docstring'''




        A_					:				List[Any]          =			args

        A_					:				List[Any]          =			query_table(
            table=self.dataset.data			,      key=slice(snake_case			,      offset + self.batch_size  )			,      indices=self.dataset._indices			,      )
        A_					:				Optional[Any]          =			batch.to_pandas().to_csv(
            path_or_buf=snake_case			,      header=header if (offset == 0) else False			,      index=snake_case			,      **snake_case  )
        return csv_str.encode(self.encoding  )





     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :BinaryIO			,      snake_case       :Tuple			,      snake_case       :Optional[int]			,      **snake_case       :List[Any]  ):





        '''simple docstring'''




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

        else:
           A_					:				List[str]          =			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			,      snake_case			,      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(snake_case  )

        return written







def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						float:
   A_					:				List[str]          =			[1 / first_term]
   A_					:				Tuple          =			1 / first_term
   for _ in range(number_of_terms - 1	):
      first_term += common_difference
      arithmetic_progression.append(_lowerCAmelCase	)
   A_					:				List[str]          =			[1 / step for step in arithmetic_progression]
   return sum(_lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))
