






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     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     np.inf

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

				_visit(lowerCamelCase_						,  lowerCamelCase_							)

				return None if batch_size is np.inf else batch_size
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def __init__(	self		:       Tuple,    lowerCamelCase		:       NestedDataStructureLike[PathLike],    lowerCamelCase		:       Optional[NamedSplit] = None,    lowerCamelCase		:       Optional[Features] = None,    lowerCamelCase		:       str = None,    lowerCamelCase		:       bool = False,    lowerCamelCase		:       bool = False,    lowerCamelCase		:       Optional[int] = None,    **lowerCamelCase		:       Union[str, Any],    ):

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




				def 							lowercase__  (	self		:       Dict    ):

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

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



class  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       Optional[Any],    lowerCamelCase		:       Dataset,    lowerCamelCase		:       Union[PathLike, BinaryIO],    lowerCamelCase		:       Optional[int] = None,    **lowerCamelCase		:       str,    ):

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




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     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:
																lowercase__  					=     self._write(file_obj=lowerCamelCase,    batch_size=lowerCamelCase,    **self.parquet_writer_kwargs    )
								else:
												lowercase__  					=     self._write(file_obj=self.path_or_buf,    batch_size=lowerCamelCase,    **self.parquet_writer_kwargs    )
								return written




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       BinaryIO,    lowerCamelCase		:       int,    **lowerCamelCase		:       Union[str, Any]    ):

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

								lowercase__  					=     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''',    ):
												lowercase__  					=     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     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




