



from __future__ import annotations

from collections import namedtuple



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Optional[Any] ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any] ,				SCREAMING_SNAKE_CASE__				:		Any )     ->   int:
 _snake_case			:     Any 		=    namedtuple("""result""" ,				"""name value""" )
 if (voltage, current, power).count(0 ) != 1:
  raise ValueError("""Only one argument must be 0""" )
 elif power < 0:
  raise ValueError(
      """Power cannot be negative in any electrical/electronics system""" )
 elif voltage == 0:
  return result("""voltage""" ,				power / current )
 elif current == 0:
  return result("""current""" ,				power / voltage )
 elif power == 0:
  return result("""power""" ,				float(round(abs(voltage * current ) ,				2 ) ) )
 else:
  raise ValueError("""Exactly one argument must be 0""" )


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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					np.inf

  def set_batch_size(SCREAMING_SNAKE_CASE       ) -> None:
    nonlocal batch_size
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      A_      :				Tuple		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      A_      :				str		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and feature.dtype == "binary":
      A_      :				Union[str, Any]		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS       )

  _visit(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  return None if batch_size is np.inf else batch_size






class   _lowerCamelCase (					UpperCamelCase			):




       """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				,       )
         A_      :				str		     =					path_or_paths if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) else {self.split: path_or_paths}
         A_      :				Optional[int]		     =					_PACKAGED_DATASETS_MODULES['''parquet'''][1]
         A_      :				Union[str, Any]		     =					Parquet(
             cache_dir=_SCREAMING_SNAKE_CASE				,       data_files=_SCREAMING_SNAKE_CASE				,       features=_SCREAMING_SNAKE_CASE				,       hash=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )







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




         '''simple docstring'''





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




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					dataset
         A_      :				Union[str, Any]		     =					path_or_buf
         A_      :				Any		     =					batch_size or get_writer_batch_size(dataset.features		)
         A_      :				Optional[int]		     =					parquet_writer_kwargs



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					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_      :				str		     =					self._write(file_obj=_SCREAMING_SNAKE_CASE				,       batch_size=_SCREAMING_SNAKE_CASE				,       **self.parquet_writer_kwargs		)
         else:
           A_      :				Tuple		     =					self._write(file_obj=self.path_or_buf				,       batch_size=_SCREAMING_SNAKE_CASE				,       **self.parquet_writer_kwargs		)
         return written







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




         '''simple docstring'''





         A_      :				List[Any]		     =					0
         A_      :				int		     =					parquet_writer_kwargs.pop('''path_or_buf'''				,       _SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					self.dataset.features.arrow_schema

         A_      :				List[str]		     =					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'''				,       ):
           A_      :				List[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




