
"""simple docstring"""


from copy import deepcopy







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   Optional[Any]	,A_  :   list[int] | None = None	,A_  :   int | None = None   )  -> None:

							if arr is None and size is not None:
								A     = size
								A     = [0] * size
							elif arr is not None:
								self.init(A_   )
							else:
								raise ValueError('Either arr or size must be specified'   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   list[int]   )  -> None:
							A     = len(A_   )
							A     = deepcopy(A_   )
							for i in range(1	,self.size   ):
								A     = self.next_(A_   )
								if j < self.size:
									self.tree[j] += self.tree[i]


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> list[int]:
							A     = self.tree[:]
							for i in range(self.size - 1	,0	,-1   ):
								A     = self.next_(A_   )
								if j < self.size:
									arr[j] -= arr[i]
							return arr


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   int   )  -> int:
							return index + (index & (-index))


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   int   )  -> int:
							return index - (index & (-index))


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   int	,A_  :   int   )  -> None:
							if index == 0:
								self.tree[0] += value
								return
							while index < self.size:
								self.tree[index] += value
								A     = self.next_(A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   int	,A_  :   int   )  -> None:
							self.add(A_	,value - self.get(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   int   )  -> int:
							if right == 0:
								return 0
							A     = self.tree[0]
							right -= 1  # make right inclusive
							while right > 0:
								result += self.tree[right]
								A     = self.prev(A_   )
							return result


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   int	,A_  :   int   )  -> int:
							return self.prefix(A_   ) - self.prefix(A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   int   )  -> int:
							return self.query(A_	,index + 1   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   int   )  -> int:
							value -= self.tree[0]
							if value < 0:
								return -1

							A     = 1  # Largest power of 2 <= size
							while j * 2 < self.size:
								j *= 2

							A     = 0

							while j > 0:
								if i + j < self.size and self.tree[i + j] <= value:
									value -= self.tree[i + j]
									i += j
								j //= 2
							return i


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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			)		->					Optional[int]:

		"""simple docstring"""






		_UpperCamelCase         =						np.inf

		def set_batch_size(lowerCAmelCase			) -> None:
				nonlocal batch_size
				if isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			) and feature.dtype == "binary":
						_UpperCamelCase         =						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 							lowerCAmelCase__   ( __lowercase						):

						def __init__(      self							,				a							,				a = None							,				a = None							,				a = None							,				a = False							,				a = False							,				a = None							,				**a							,				)       ->   Tuple:


								'''simple docstring'''

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







						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

								if self.streaming:
										_UpperCamelCase         =						self.builder.as_streaming_dataset(split=self.split					)
								# Build regular (map-style) dataset
								else:
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None

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







class 							lowerCAmelCase__   :

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


								'''simple docstring'''

								_UpperCamelCase         =						dataset
								_UpperCamelCase         =						path_or_buf
								_UpperCamelCase         =						batch_size or get_writer_batch_size(dataset.features					)
								_UpperCamelCase         =						parquet_writer_kwargs

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						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         =						self._write(file_obj=a							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								else:
										_UpperCamelCase         =						self._write(file_obj=self.path_or_buf							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								return written







						def 			A_					(      self							,				a							,				a							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						0
								_UpperCamelCase         =						parquet_writer_kwargs.pop("""path_or_buf"""							,				a					)
								_UpperCamelCase         =						self.dataset.features.arrow_schema

								_UpperCamelCase         =						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"""							,				):
										_UpperCamelCase         =						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



