

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 				snake_case_(_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  _snake_case						=							np.inf

  def set_batch_size(_UpperCamelCase					) -> None:
    nonlocal batch_size
    if isinstance(_UpperCAmelCase			,	_UpperCAmelCase					):
      _snake_case						=							min(_UpperCAmelCase			,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
    elif isinstance(_UpperCAmelCase			,	_UpperCAmelCase					):
      _snake_case						=							min(_UpperCAmelCase			,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
    elif isinstance(_UpperCAmelCase			,	_UpperCAmelCase					) and feature.dtype == "binary":
      _snake_case						=							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   lowercase_   (  SCREAMING_SNAKE_CASE__					):



      def __init__(      self				:    Union[str, Any]      , A__				:    NestedDataStructureLike[PathLike]      , A__				:    Optional[NamedSplit] = None      , A__				:    Optional[Features] = None      , A__				:    str = None      , A__				:    bool = False      , A__				:    bool = False      , A__				:    Optional[int] = None      , **A__				:    int      , )   ->					List[Any]:
        super().__init__(
            A__      , split=A__      , features=A__      , cache_dir=A__      , keep_in_memory=A__      , streaming=A__      , num_proc=A__      , **A__      , )
        _snake_case						=							path_or_paths if isinstance(A__      , A__		) else {self.split: path_or_paths}
        _snake_case						=							_PACKAGED_DATASETS_MODULES['parquet'][1]
        _snake_case						=							Parquet(
            cache_dir=A__      , data_files=A__      , features=A__      , hash=A__      , **A__      , )







      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Union[str, Any]:
        # Build iterable dataset
        if self.streaming:
          _snake_case						=							self.builder.as_streaming_dataset(split=self.split		)
        # Build regular (map-style) dataset
        else:
          _snake_case						=							None
          _snake_case						=							None
          _snake_case						=							None
          _snake_case						=							None

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



class   lowercase_   :



      def __init__(      self				:    Dict      , A__				:    Dataset      , A__				:    Union[PathLike, BinaryIO]      , A__				:    Optional[int] = None      , **A__				:    Optional[Any]      , )   ->					Optional[Any]:
        _snake_case						=							dataset
        _snake_case						=							path_or_buf
        _snake_case						=							batch_size or get_writer_batch_size(dataset.features		)
        _snake_case						=							parquet_writer_kwargs



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					int:
        _snake_case						=							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:
            _snake_case						=							self._write(file_obj=A__      , batch_size=A__      , **self.parquet_writer_kwargs		)
        else:
          _snake_case						=							self._write(file_obj=self.path_or_buf      , batch_size=A__      , **self.parquet_writer_kwargs		)
        return written







      def 						UpperCamelCase_		(      self				:    int      , A__				:    BinaryIO      , A__				:    int      , **A__				:    Union[str, Any]		)   ->					int:
        _snake_case						=							0
        _snake_case						=							parquet_writer_kwargs.pop('''path_or_buf'''      , A__		)
        _snake_case						=							self.dataset.features.arrow_schema

        _snake_case						=							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'''      , ):
          _snake_case						=							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




import cmath
import math



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     complex:



  """simple docstring"""






  _snake_case						=							math.radians(_UpperCamelCase					)
  _snake_case						=							math.radians(_UpperCamelCase					)

  # Convert voltage and current to rectangular form
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)

  # Calculate apparent power
  return voltage_rect * current_rect


if __name__ == "__main__":
 import doctest

 doctest.testmod()

