




'''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     lowercase  ( UpperCAmelCase_):






      def __init__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = None			,	**a__			,	)							-> Any:
         super().__init__(
             a__			,	split=a__			,	features=a__			,	cache_dir=a__			,	keep_in_memory=a__			,	streaming=a__			,	num_proc=a__			,	**a__			,	)
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			path_or_paths if isinstance(a__			,	a__       ) else {self.split: path_or_paths}
         SCREAMING_SNAKE_CASE__      :					Any							    =			Csv(
             cache_dir=a__			,	data_files=a__			,	features=a__			,	**a__			,	)






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

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






class     lowercase  :






      def __init__(self			,	a__			,	a__			,	a__ = None			,	a__ = None			,	**a__			,	)							-> List[Any]:
         if num_proc is not None and num_proc <= 0:
            raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			dataset
         SCREAMING_SNAKE_CASE__      :					str							    =			path_or_buf
         SCREAMING_SNAKE_CASE__      :					Any							    =			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         SCREAMING_SNAKE_CASE__      :					int							    =			num_proc
         SCREAMING_SNAKE_CASE__      :					int							    =			'''utf-8'''
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			to_csv_kwargs






      def  A__(self       )							-> int:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.to_csv_kwargs.pop('''path_or_buf'''			,	a__       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.to_csv_kwargs.pop('''header'''			,	a__       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			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:
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			self._write(file_obj=a__			,	header=a__			,	index=a__			,	**self.to_csv_kwargs       )
         else:
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self._write(file_obj=self.path_or_buf			,	header=a__			,	index=a__			,	**self.to_csv_kwargs       )
         return written






      def  A__(self			,	a__       )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Dict							    =			args

         SCREAMING_SNAKE_CASE__      :					int							    =			query_table(
             table=self.dataset.data			,	key=slice(a__			,	offset + self.batch_size       )			,	indices=self.dataset._indices			,	)
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			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  A__(self			,	a__			,	a__			,	a__			,	**a__       )							-> int:
         SCREAMING_SNAKE_CASE__      :					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'''			,	):
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			self._batch_csv((offset, header, index, to_csv_kwargs)       )
               written += file_obj.write(a__       )

         else:
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					int							    =			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'''





import math
from decimal import Decimal, getcontext


def 					UpperCamelCase						(	snake_case = 4_00_00_00 ):




   '''simple docstring'''

   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			int(snake_case )
   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.''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			1_00
   SCREAMING_SNAKE_CASE__      :					int							    =			(Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )

   SCREAMING_SNAKE_CASE__      :					int							    =			(math.floor(math.log(n * (phi + 2)						,  snake_case ) - 1 ) // 3) * 3 + 2
   SCREAMING_SNAKE_CASE__      :					int							    =			Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
   SCREAMING_SNAKE_CASE__      :					Any							    =			num // 2
   return int(snake_case )


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

