
"""simple docstring"""





import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def       lowerCamelCase__		(			__snake_case	)							->		Optional[int]:


					"""simple docstring"""





					_UpperCamelCase							 =  np.inf

					def set_batch_size(__snake_case	) -> None:
										nonlocal batch_size
										if isinstance(__snake_case,     __snake_case	):
															_UpperCamelCase							 =  min(__snake_case,     config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS	)
										elif isinstance(__snake_case,     __snake_case	):
															_UpperCamelCase							 =  min(__snake_case,     config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS	)
										elif isinstance(__snake_case,     __snake_case	) and feature.dtype == "binary":
															_UpperCamelCase							 =  min(__snake_case,     config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS	)

					_visit(__snake_case,     __snake_case	)

					return None if batch_size is np.inf else batch_size






class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a = None				,		__a = None				,		__a = None				,		__a = False				,		__a = False				,		__a = None				,		**__a				,		)       ->       Optional[Any]:






							'''simple docstring'''
							super().__init__(
							    __a				,		split=__a				,		features=__a				,		cache_dir=__a				,		keep_in_memory=__a				,		streaming=__a				,		num_proc=__a				,		**__a				,		)
							_UpperCamelCase							 =  path_or_paths if isinstance(__a				,		__a) else {self.split: path_or_paths}
							_UpperCamelCase							 =  _PACKAGED_DATASETS_MODULES['''parquet'''][1]
							_UpperCamelCase							 =  Parquet(
							    cache_dir=__a				,		data_files=__a				,		features=__a				,		hash=__a				,		**__a				,		)




		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							# Build iterable dataset
							if self.streaming:
												_UpperCamelCase							 =  self.builder.as_streaming_dataset(split=self.split)
							# Build regular (map-style) dataset
							else:
												_UpperCamelCase							 =  None
												_UpperCamelCase							 =  None
												_UpperCamelCase							 =  None
												_UpperCamelCase							 =  None

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






class     _UpperCAmelCase:



		def __init__(					self				,		__a				,		__a				,		__a = None				,		**__a				,		)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  dataset
							_UpperCamelCase							 =  path_or_buf
							_UpperCamelCase							 =  batch_size or get_writer_batch_size(dataset.features)
							_UpperCamelCase							 =  parquet_writer_kwargs



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

							if isinstance(self.path_or_buf				,		(str, bytes, os.PathLike)):
												with open(self.path_or_buf				,		'''wb+''') as buffer:
																	_UpperCamelCase							 =  self._write(file_obj=__a				,		batch_size=__a				,		**self.parquet_writer_kwargs)
							else:
												_UpperCamelCase							 =  self._write(file_obj=self.path_or_buf				,		batch_size=__a				,		**self.parquet_writer_kwargs)
							return written




		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		**__a)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  0
							_UpperCamelCase							 =  parquet_writer_kwargs.pop('''path_or_buf'''				,		__a)
							_UpperCamelCase							 =  self.dataset.features.arrow_schema

							_UpperCamelCase							 =  pq.ParquetWriter(__a				,		schema=__a				,		**__a)

							for offset in logging.tqdm(
							    range(0				,		len(self.dataset)				,		__a)				,		unit='''ba'''				,		disable=not logging.is_progress_bar_enabled()				,		desc='''Creating parquet from Arrow format'''				,		):
												_UpperCamelCase							 =  query_table(
												    table=self.dataset._data				,		key=slice(__a				,		offset + batch_size)				,		indices=self.dataset._indices if self.dataset._indices is not None else None				,		)
												writer.write_table(__a)
												written += batch.nbytes
							writer.close()
							return written








"""simple docstring"""





def       lowerCamelCase__		(			__snake_case = 10_00	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  2**power
					_UpperCamelCase							 =  str(__snake_case	)
					_UpperCamelCase							 =  list(__snake_case	)
					_UpperCamelCase							 =  0

					for i in list_num:
										sum_of_num += int(__snake_case	)

					return sum_of_num


if __name__ == "__main__":
				_a														=		int(input("""Enter the power of 2: """).strip())
				print("""2 ^ """, power, """ = """, 2**power)
				_a														=		solution(power)
				print("""Sum of the digits is: """, result)






