






'''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__				(   __UpperCamelCase					)->				Optional[int]:
      UpperCamelCase       						=      np.inf

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

      _visit(__UpperCamelCase	,		__UpperCamelCase					)

      return None if batch_size is np.inf else batch_size






class        a_		(    lowerCamelCase       ):

  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    ,	)						->   Any:



        """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    ,	)
        UpperCamelCase       						=      path_or_paths if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) else {self.split: path_or_paths}
        UpperCamelCase       						=      _PACKAGED_DATASETS_MODULES["""parquet"""][1]
        UpperCamelCase       						=      Parquet(
            cache_dir=_SCREAMING_SNAKE_CASE    ,	data_files=_SCREAMING_SNAKE_CASE    ,	features=_SCREAMING_SNAKE_CASE    ,	hash=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)


  def 						A__		(   self   )						->   Tuple:



        """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=_SCREAMING_SNAKE_CASE    ,	download_mode=_SCREAMING_SNAKE_CASE    ,	verification_mode=_SCREAMING_SNAKE_CASE    ,	base_path=_SCREAMING_SNAKE_CASE    ,	num_proc=self.num_proc    ,	)
              UpperCamelCase       						=      self.builder.as_dataset(
                  split=self.split    ,	verification_mode=_SCREAMING_SNAKE_CASE    ,	in_memory=self.keep_in_memory   )
        return dataset






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Union[str, Any]:



        """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=_SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	**self.parquet_writer_kwargs   )
        else:
              UpperCamelCase       						=      self._write(file_obj=self.path_or_buf    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	**self.parquet_writer_kwargs   )
        return written


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      0
        UpperCamelCase       						=      parquet_writer_kwargs.pop("""path_or_buf"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.dataset.features.arrow_schema

        UpperCamelCase       						=      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"""    ,	):
              UpperCamelCase       						=      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'''

def 					lowercase__				(   __UpperCamelCase					)->				str:
      if not all(char in """01""" for char in bin_string					):
            raise ValueError("""Non-binary value was passed to the function"""					)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""					)
      UpperCamelCase       						=      """"""
      while len(__UpperCamelCase					) % 3 != 0:
            UpperCamelCase       						=      """0""" + bin_string
      UpperCamelCase       						=      [
          bin_string[index : index + 3]
          for index in range(len(__UpperCamelCase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            UpperCamelCase       						=      0
            for index, val in enumerate(__UpperCamelCase					):
                  oct_val += int(2 ** (2 - index) * int(__UpperCamelCase					)					)
            oct_string += str(__UpperCamelCase					)
      return oct_string


if __name__ == "__main__":
     from doctest import testmod

     testmod()
