



'''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      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
      lowercase        =							np.inf

      def set_batch_size(lowercase_    :      Optional[int]						) -> None:
            nonlocal batch_size
            if isinstance(UpperCamelCase__ ,  UpperCamelCase__						):
                  lowercase        =							min(UpperCamelCase__ ,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS						)
            elif isinstance(UpperCamelCase__ ,  UpperCamelCase__						):
                  lowercase        =							min(UpperCamelCase__ ,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS						)
            elif isinstance(UpperCamelCase__ ,  UpperCamelCase__						) and feature.dtype == "binary":
                  lowercase        =							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      __UpperCamelCase       (lowercase_			):







 def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = False ,				_lowerCAmelCase = False ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    Dict:
       '''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   _a   (    self						)    ->    Tuple:
       '''simple docstring'''






       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       :







 def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    Optional[int]:
       '''simple docstring'''






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







 def   _a   (    self						)    ->    Union[str, 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   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Optional[int]:
       '''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







'''simple docstring'''


import os







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							os.path.join(os.path.dirname(lowercase_						) ,  """num.txt"""						)
						with open(lowercase_						) as file_hand:
												return str(sum(int(lowercase_						) for line in file_hand						)						)[:10]


if __name__ == "__main__":
						print(solution())


