






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      lowercase__      (     __snake_case    :							Features  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Dict            =						np.inf

					def set_batch_size(__snake_case    :							FeatureType  ) -> None:
										nonlocal batch_size
										if isinstance(__snake_case     ,       __snake_case  ):
															UpperCAmelCase_    :      Optional[Any]            =						min(__snake_case     ,       config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS  )
										elif isinstance(__snake_case     ,       __snake_case  ):
															UpperCAmelCase_    :      Optional[int]            =						min(__snake_case     ,       config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS  )
										elif isinstance(__snake_case     ,       __snake_case  ) and feature.dtype == "binary":
															UpperCAmelCase_    :      Dict            =						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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = False				,		_UpperCamelCase = False				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							str:
							super().__init__(
							    _UpperCamelCase				,		split=_UpperCamelCase				,		features=_UpperCamelCase				,		cache_dir=_UpperCamelCase				,		keep_in_memory=_UpperCamelCase				,		streaming=_UpperCamelCase				,		num_proc=_UpperCamelCase				,		**_UpperCamelCase				,		)
							UpperCAmelCase_    :      Tuple            =						path_or_paths if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) else {self.split: path_or_paths}
							UpperCAmelCase_    :      Optional[Any]            =						_PACKAGED_DATASETS_MODULES['parquet'][1]
							UpperCAmelCase_    :      str            =						Parquet(
							    cache_dir=_UpperCamelCase				,		data_files=_UpperCamelCase				,		features=_UpperCamelCase				,		hash=_UpperCamelCase				,		**_UpperCamelCase				,		)



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							# Build iterable dataset
							if self.streaming:
												UpperCAmelCase_    :      int            =						self.builder.as_streaming_dataset(split=self.split   )
							# Build regular (map-style) dataset
							else:
												UpperCAmelCase_    :      str            =						None
												UpperCAmelCase_    :      Optional[int]            =						None
												UpperCAmelCase_    :      Any            =						None
												UpperCAmelCase_    :      Optional[Any]            =						None

												self.builder.download_and_prepare(
												    download_config=_UpperCamelCase				,		download_mode=_UpperCamelCase				,		verification_mode=_UpperCamelCase				,		base_path=_UpperCamelCase				,		num_proc=self.num_proc				,		)
												UpperCAmelCase_    :      List[str]            =						self.builder.as_dataset(
												    split=self.split				,		verification_mode=_UpperCamelCase				,		in_memory=self.keep_in_memory   )
							return dataset





class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Union[str, Any]:
							UpperCAmelCase_    :      Dict            =						dataset
							UpperCAmelCase_    :      Dict            =						path_or_buf
							UpperCAmelCase_    :      Dict            =						batch_size or get_writer_batch_size(dataset.features   )
							UpperCAmelCase_    :      Any            =						parquet_writer_kwargs



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Optional[Any]            =						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_    :      Optional[int]            =						self._write(file_obj=_UpperCamelCase				,		batch_size=_UpperCamelCase				,		**self.parquet_writer_kwargs   )
							else:
												UpperCAmelCase_    :      Any            =						self._write(file_obj=self.path_or_buf				,		batch_size=_UpperCamelCase				,		**self.parquet_writer_kwargs   )
							return written



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							int:
							UpperCAmelCase_    :      Any            =						0
							UpperCAmelCase_    :      Dict            =						parquet_writer_kwargs.pop('path_or_buf'				,		_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.dataset.features.arrow_schema

							UpperCAmelCase_    :      List[str]            =						pq.ParquetWriter(_UpperCamelCase				,		schema=_UpperCamelCase				,		**_UpperCamelCase   )

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














def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      list[list[int]]            =						[[0 for _ in range(__snake_case  )] for _ in range(m + 1  )]
					for i in range(m + 1  ):
										UpperCAmelCase_    :      Optional[Any]            =						1

					for n in range(m + 1  ):
										for k in range(1     ,       __snake_case  ):
															memo[n][k] += memo[n][k - 1]
															if n - k > 0:
																				memo[n][k] += memo[n - k - 1][k]

					return memo[m][m - 1]


if __name__ == "__main__":
		import sys

		if len(sys.argv) == 1:
				try:
						__UpperCAmelCase								=						int(input('Enter a number: ').strip())
						print(partition(n))
				except ValueError:
						print('Please enter a number.')
		else:
				try:
						__UpperCAmelCase								=						int(sys.argv[1])
						print(partition(n))
				except ValueError:
						print('Please pass a number.')






