





"""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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Features					) ->Optional[int]:






	'''simple docstring'''






	a      :		str	=				np.inf

	def set_batch_size(_lowercase						:    FeatureType					) -> None:
		nonlocal batch_size
		if isinstance(_lowercase    ,       _lowercase					):
			a      :		Dict	=				min(_lowercase    ,       config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
		elif isinstance(_lowercase    ,       _lowercase					):
			a      :		Tuple	=				min(_lowercase    ,       config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
		elif isinstance(_lowercase    ,       _lowercase					) and feature.dtype == "binary":
			a      :		Optional[int]	=				min(_lowercase    ,       config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS					)

	_visit(_lowercase    ,       _lowercase					)

	return None if batch_size is np.inf else batch_size


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> List[str]:
								super().__init__(
								    lowerCAmelCase__      ,					split=lowerCAmelCase__      ,					features=lowerCAmelCase__      ,					cache_dir=lowerCAmelCase__      ,					keep_in_memory=lowerCAmelCase__      ,					streaming=lowerCAmelCase__      ,					num_proc=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)
								a      :		Union[str, Any]	=				path_or_paths if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ) else {self.split: path_or_paths}
								a      :		Dict	=				_PACKAGED_DATASETS_MODULES["parquet"][1]
								a      :		str	=				Parquet(
								    cache_dir=lowerCAmelCase__      ,					data_files=lowerCAmelCase__      ,					features=lowerCAmelCase__      ,					hash=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)






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

									self.builder.download_and_prepare(
									    download_config=lowerCAmelCase__      ,					download_mode=lowerCAmelCase__      ,					verification_mode=lowerCAmelCase__      ,					base_path=lowerCAmelCase__      ,					num_proc=self.num_proc      ,					)
									a      :		List[Any]	=				self.builder.as_dataset(
									    split=self.split      ,					verification_mode=lowerCAmelCase__      ,					in_memory=self.keep_in_memory      )
								return dataset


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> Any:
								a      :		Tuple	=				dataset
								a      :		int	=				path_or_buf
								a      :		List[str]	=				batch_size or get_writer_batch_size(dataset.features      )
								a      :		Any	=				parquet_writer_kwargs






							def     __a   (   self      )	-> int:
								a      :		Dict	=				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:
										a      :		Any	=				self._write(file_obj=lowerCAmelCase__      ,					batch_size=lowerCAmelCase__      ,					**self.parquet_writer_kwargs      )
								else:
									a      :		Optional[Any]	=				self._write(file_obj=self.path_or_buf      ,					batch_size=lowerCAmelCase__      ,					**self.parquet_writer_kwargs      )
								return written






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> int:
								a      :		Optional[int]	=				0
								a      :		Union[str, Any]	=				parquet_writer_kwargs.pop("path_or_buf"      ,					lowerCAmelCase__      )
								a      :		Any	=				self.dataset.features.arrow_schema

								a      :		Optional[Any]	=				pq.ParquetWriter(lowerCAmelCase__      ,					schema=lowerCAmelCase__      ,					**lowerCAmelCase__      )

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








def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       return x if y == 0 else greatest_common_divisor(_SCREAMING_SNAKE_CASE ,x % y    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       return (x * y) // greatest_common_divisor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

def      A      (      _SCREAMING_SNAKE_CASE = 20    )	-> int:
       lowerCamelCase   :      List[Any]			 =					1
       for i in range(1 ,n + 1    ):
              lowerCamelCase   :      List[str]			 =					lcm(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       return g


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






