





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				(   a  )      ->      Optional[int]:
					__A	:						List[Any]       =				np.inf

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

					_visit(a					,						a  )

					return None if batch_size is np.inf else batch_size





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = False     ,					_A = False     ,					_A = None     ,					**_A     ,					):
									super().__init__(
									    _A     ,					split=_A     ,					features=_A     ,					cache_dir=_A     ,					keep_in_memory=_A     ,					streaming=_A     ,					num_proc=_A     ,					**_A     ,					)
									__A	:						Dict       =				path_or_paths if isinstance(_A     ,					_A     ) else {self.split: path_or_paths}
									__A	:						List[Any]       =				_PACKAGED_DATASETS_MODULES['parquet'][1]
									__A	:						Any       =				Parquet(
									    cache_dir=_A     ,					data_files=_A     ,					features=_A     ,					hash=_A     ,					**_A     ,					)





				def 					UpperCAmelCase_		(			self     ):
									# Build iterable dataset
									if self.streaming:
														__A	:						str       =				self.builder.as_streaming_dataset(split=self.split     )
									# Build regular (map-style) dataset
									else:
														__A	:						Tuple       =				None
														__A	:						int       =				None
														__A	:						List[str]       =				None
														__A	:						int       =				None

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





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A = None     ,					**_A     ,					):
									__A	:						Optional[int]       =				dataset
									__A	:						int       =				path_or_buf
									__A	:						Optional[int]       =				batch_size or get_writer_batch_size(dataset.features     )
									__A	:						List[str]       =				parquet_writer_kwargs



				def 					UpperCAmelCase_		(			self     ):
									__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	:						List[str]       =				self._write(file_obj=_A     ,					batch_size=_A     ,					**self.parquet_writer_kwargs     )
									else:
														__A	:						List[Any]       =				self._write(file_obj=self.path_or_buf     ,					batch_size=_A     ,					**self.parquet_writer_kwargs     )
									return written





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					**_A     ):
									__A	:						Any       =				0
									__A	:						Tuple       =				parquet_writer_kwargs.pop('path_or_buf'     ,					_A     )
									__A	:						Union[str, Any]       =				self.dataset.features.arrow_schema

									__A	:						Optional[Any]       =				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'     ,					):
														__A	:						Optional[Any]       =				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











def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




