
"""simple docstring"""




from __future__ import annotations

from math import pi, sqrt



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''







					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''		)

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''		)

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance		)))		),
										)


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 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						np.inf

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




							def __init__(    self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = None       ,**A__       ,):
												super().__init__(
												    A__       ,split=A__       ,features=A__       ,cache_dir=A__       ,keep_in_memory=A__       ,streaming=A__       ,num_proc=A__       ,**A__       ,)
												lowercase						     =						path_or_paths if isinstance(A__       ,A__) else {self.split: path_or_paths}
												lowercase						     =						_PACKAGED_DATASETS_MODULES['''parquet'''][1]
												lowercase						     =						Parquet(
												    cache_dir=A__       ,data_files=A__       ,features=A__       ,hash=A__       ,**A__       ,)





							def 			A__				(    self):
												# Build iterable dataset
												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=A__       ,download_mode=A__       ,verification_mode=A__       ,base_path=A__       ,num_proc=self.num_proc       ,)
																	lowercase						     =						self.builder.as_dataset(
																	    split=self.split       ,verification_mode=A__       ,in_memory=self.keep_in_memory)
												return dataset







class     lowercase		:




							def __init__(    self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
												lowercase						     =						dataset
												lowercase						     =						path_or_buf
												lowercase						     =						batch_size or get_writer_batch_size(dataset.features)
												lowercase						     =						parquet_writer_kwargs




							def 			A__				(    self):
												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=A__       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												else:
																	lowercase						     =						self._write(file_obj=self.path_or_buf       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												return written





							def 			A__				(    self       ,A__       ,A__       ,**A__):
												lowercase						     =						0
												lowercase						     =						parquet_writer_kwargs.pop('''path_or_buf'''       ,A__)
												lowercase						     =						self.dataset.features.arrow_schema

												lowercase						     =						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'''       ,):
																	lowercase						     =						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




