
"""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 lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     np.inf

							def set_batch_size(_snake_case		) -> None:
														nonlocal batch_size
														if isinstance(_snake_case     ,_snake_case		):
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     min(_snake_case     ,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
														elif isinstance(_snake_case     ,_snake_case		):
																					SCREAMING_SNAKE_CASE__		: Tuple     =     min(_snake_case     ,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
														elif isinstance(_snake_case     ,_snake_case		) and feature.dtype == "binary":
																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	str:



											"""simple docstring"""




											super().__init__(
											    SCREAMING_SNAKE_CASE__     , split=SCREAMING_SNAKE_CASE__     , features=SCREAMING_SNAKE_CASE__     , cache_dir=SCREAMING_SNAKE_CASE__     , keep_in_memory=SCREAMING_SNAKE_CASE__     , streaming=SCREAMING_SNAKE_CASE__     , num_proc=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__     , )
											SCREAMING_SNAKE_CASE__		: Tuple     =     path_or_paths if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) else {self.split: path_or_paths}
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     _PACKAGED_DATASETS_MODULES["""parquet"""][1]
											SCREAMING_SNAKE_CASE__		: Any     =     Parquet(
											    cache_dir=SCREAMING_SNAKE_CASE__     , data_files=SCREAMING_SNAKE_CASE__     , features=SCREAMING_SNAKE_CASE__     , hash=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__     , )



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											if self.streaming:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.builder.as_streaming_dataset(split=self.split					)
											# Build regular (map-style) dataset
											else:
																		SCREAMING_SNAKE_CASE__		: List[str]     =     None
																		SCREAMING_SNAKE_CASE__		: Any     =     None
																		SCREAMING_SNAKE_CASE__		: Any     =     None
																		SCREAMING_SNAKE_CASE__		: str     =     None

																		self.builder.download_and_prepare(
																		    download_config=SCREAMING_SNAKE_CASE__     , download_mode=SCREAMING_SNAKE_CASE__     , verification_mode=SCREAMING_SNAKE_CASE__     , base_path=SCREAMING_SNAKE_CASE__     , num_proc=self.num_proc     , )
																		SCREAMING_SNAKE_CASE__		: Any     =     self.builder.as_dataset(
																		    split=self.split     , verification_mode=SCREAMING_SNAKE_CASE__     , in_memory=self.keep_in_memory					)
											return dataset






class 			lowerCAmelCase_		:


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     dataset
											SCREAMING_SNAKE_CASE__		: Any     =     path_or_buf
											SCREAMING_SNAKE_CASE__		: Tuple     =     batch_size or get_writer_batch_size(dataset.features					)
											SCREAMING_SNAKE_CASE__		: int     =     parquet_writer_kwargs



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     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:
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     self._write(file_obj=SCREAMING_SNAKE_CASE__     , batch_size=SCREAMING_SNAKE_CASE__     , **self.parquet_writer_kwargs					)
											else:
																		SCREAMING_SNAKE_CASE__		: str     =     self._write(file_obj=self.path_or_buf     , batch_size=SCREAMING_SNAKE_CASE__     , **self.parquet_writer_kwargs					)
											return written



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     0
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     parquet_writer_kwargs.pop("""path_or_buf"""     , SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Any     =     self.dataset.features.arrow_schema

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     pq.ParquetWriter(SCREAMING_SNAKE_CASE__     , schema=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)

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






"""simple docstring"""




import baseaa

def lowercase_						(     _snake_case		):
							return baseaa.baaencode(string.encode("""utf-8"""		)		)

def lowercase_						(     _snake_case		):
							return baseaa.baadecode(_snake_case		).decode("""utf-8"""		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Dict												=				'Hello World!'
					UpperCAmelCase__   :					Tuple												=				baseaa_encode(test)
					print(encoded)

					UpperCAmelCase__   :					Tuple												=				baseaa_decode(encoded)
					print(decoded)




