


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						(     A__  ):






      def __init__(      self		:  List[Any]    ,     lowercase_		:  NestedDataStructureLike[PathLike]    ,     lowercase_		:  Optional[NamedSplit] = None    ,     lowercase_		:  Optional[Features] = None    ,     lowercase_		:  str = None    ,     lowercase_		:  bool = False    ,     lowercase_		:  bool = False    ,     lowercase_		:  Optional[int] = None    ,     **lowercase_		:  Union[str, Any]    ,     )      ->		int:
             """simple docstring"""




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






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




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

                    self.builder.download_and_prepare(
                        download_config=lowercase_    ,     download_mode=lowercase_    ,     verification_mode=lowercase_    ,     base_path=lowercase_    ,     num_proc=self.num_proc    ,     )
                    _lowerCamelCase :	Optional[Any]						=self.builder.as_dataset(
                        split=self.split    ,     verification_mode=lowercase_    ,     in_memory=self.keep_in_memory				)
             return dataset




class 			A						:






      def __init__(      self		:  List[str]    ,     lowercase_		:  Dataset    ,     lowercase_		:  Union[PathLike, BinaryIO]    ,     lowercase_		:  Optional[int] = None    ,     lowercase_		:  Optional[int] = None    ,     **lowercase_		:  Optional[Any]    ,     )      ->		Union[str, Any]:
             """simple docstring"""




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

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






      def  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	Dict						=self.to_csv_kwargs.pop('path_or_buf'    ,     lowercase_				)
             _lowerCamelCase :	Dict						=self.to_csv_kwargs.pop('header'    ,     lowercase_				)
             _lowerCamelCase :	Dict						=self.to_csv_kwargs.pop('index'    ,     lowercase_				)

             if isinstance(self.path_or_buf    ,     (str, bytes, os.PathLike)				):
                    with open(self.path_or_buf    ,     'wb+'				) as buffer:
                           _lowerCamelCase :	List[Any]						=self._write(file_obj=lowercase_    ,     header=lowercase_    ,     index=lowercase_    ,     **self.to_csv_kwargs				)
             else:
                    _lowerCamelCase :	int						=self._write(file_obj=self.path_or_buf    ,     header=lowercase_    ,     index=lowercase_    ,     **self.to_csv_kwargs				)
             return written






      def  lowerCamelCase (      self		:  Optional[Any]    ,     lowercase_		:  str				)      ->		List[str]:
             """simple docstring"""




             _lowerCamelCase :	str						=args

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






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  BinaryIO    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Union[str, Any]    ,     **lowercase_		:  List[str]				)      ->		Union[str, Any]:
             """simple docstring"""




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

             else:
                    _lowerCamelCase :	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    ,     lowercase_    ,     lowercase_				)]    ,     )    ,     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(lowercase_				)

             return written






from __future__ import annotations

from collections.abc import Generator




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) or n < 1:
              raise Exception('Sequence only defined for positive integers'  )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _lowerCamelCase :	Optional[int]						=3 * n + 1
              yield n




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=43
       _lowerCamelCase :	Dict						=tuple(collatz_sequence(SCREAMING_SNAKE_CASE__  )  )
       print(SCREAMING_SNAKE_CASE__  )
       print(F'''Collatz sequence from {n} took {len(SCREAMING_SNAKE_CASE__  )} steps.'''  )


if __name__ == "__main__":
 main()


