



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 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






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







							def _a   (				self		):
											# 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=a_	,	download_mode=a_	,	verification_mode=a_	,	base_path=a_	,	num_proc=self.num_proc	,	)
															a__					   =		self.builder.as_dataset(
															    split=self.split	,	verification_mode=a_	,	in_memory=self.keep_in_memory		)
											return dataset




class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_ = None	,	a_ = None	,	**a_	,	):
											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 _a   (				self		):
											a__					   =		self.to_csv_kwargs.pop("""path_or_buf"""	,	a_		)
											a__					   =		self.to_csv_kwargs.pop("""header"""	,	a_		)
											a__					   =		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:
																			a__					   =		self._write(file_obj=a_	,	header=a_	,	index=a_	,	**self.to_csv_kwargs		)
											else:
															a__					   =		self._write(file_obj=self.path_or_buf	,	header=a_	,	index=a_	,	**self.to_csv_kwargs		)
											return written


							def _a   (				self	,	a_		):
											a__   ,      a__   ,      a__   ,      a__					   =		args

											a__					   =		query_table(
											    table=self.dataset.data	,	key=slice(a_	,	offset + self.batch_size		)	,	indices=self.dataset._indices	,	)
											a__					   =		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_		):
											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(a_		)

											else:
															a__   ,      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	,	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






import math
from decimal import Decimal, getcontext

def       A_   (						__a  :					int = 4_000_000		):

				"""simple docstring"""

				try:
								a__					   =		int(__a		)
				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__					   =		100
				a__					   =		(Decimal(5		) ** Decimal(0.5		) + 1) / Decimal(2		)

				a__					   =		(math.floor(math.log(n * (phi + 2)			,		__a		) - 1		) // 3) * 3 + 2
				a__					   =		Decimal(round(phi ** Decimal(index + 1		)		)		) / (phi + 2)
				a__					   =		num // 2
				return int(__a		)


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

