




"""simple docstring"""
def 						UpperCAmelCase				(			A__:    float							,				A__:    float     )  ->     float:
			if mass < 0:
						raise ValueError('The mass of a body cannot be negative'     )
			return 0.5 * mass * abs(A__     ) * abs(A__     )


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)









"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = False						,	__a = False						,	__a = None						,	__a = None						,	**__a						,	):
								super().__init__(
								    __a						,	split=__a						,	features=__a						,	cache_dir=__a						,	keep_in_memory=__a						,	streaming=__a						,	num_proc=__a						,	**__a						,	)
								__lowerCamelCase					:	List[Any]	       =				field
								__lowerCamelCase					:	Dict	       =				path_or_paths if isinstance(__a						,	__a     ) else {self.split: path_or_paths}
								__lowerCamelCase					:	str	       =				Json(
								    cache_dir=__a						,	data_files=__a						,	features=__a						,	field=__a						,	**__a						,	)





					def     snake_case_				(    self     ):
								# Build iterable dataset
								if self.streaming:
											__lowerCamelCase					:	str	       =				self.builder.as_streaming_dataset(split=self.split     )
								# Build regular (map-style) dataset
								else:
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	str	       =				None
											__lowerCamelCase					:	int	       =				None

											self.builder.download_and_prepare(
											    download_config=__a						,	download_mode=__a						,	verification_mode=__a						,	base_path=__a						,	num_proc=self.num_proc						,	)
											__lowerCamelCase					:	List[Any]	       =				self.builder.as_dataset(
											    split=self.split						,	verification_mode=__a						,	in_memory=self.keep_in_memory     )
								return dataset



class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a = None						,	__a = None						,	**__a						,	):
								if num_proc is not None and num_proc <= 0:
											raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''     )

								__lowerCamelCase					:	Optional[int]	       =				dataset
								__lowerCamelCase					:	Optional[int]	       =				path_or_buf
								__lowerCamelCase					:	List[str]	       =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								__lowerCamelCase					:	str	       =				num_proc
								__lowerCamelCase					:	Optional[Any]	       =				'utf-8'
								__lowerCamelCase					:	Optional[int]	       =				to_json_kwargs
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('path_or_buf'						,	__a     )
								__lowerCamelCase					:	Any	       =				self.to_json_kwargs.pop('orient'						,	'records'     )
								__lowerCamelCase					:	List[Any]	       =				self.to_json_kwargs.pop('lines'						,	True if orient == 'records' else False     )
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('index'						,	False if orient in ['split', 'table'] else True     )
								__lowerCamelCase					:	int	       =				self.to_json_kwargs.pop('compression'						,	__a     )

								if compression not in [None, "infer", "gzip", "bz2", "xz"]:
											raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''     )

								if isinstance(self.path_or_buf						,	(str, bytes, os.PathLike)     ):
											with fsspec.open(self.path_or_buf						,	'wb'						,	compression=__a     ) as buffer:
														__lowerCamelCase					:	int	       =				self._write(file_obj=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								else:
											if compression:
														raise NotImplementedError(
														    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
														    ' was passed. Please provide a local path instead.'     )
											__lowerCamelCase					:	Dict	       =				self._write(
											    file_obj=self.path_or_buf						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								return written
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				args

								__lowerCamelCase					:	int	       =				query_table(
								    table=self.dataset.data						,	key=slice(__a						,	offset + self.batch_size     )						,	indices=self.dataset._indices						,	)
								__lowerCamelCase					:	List[str]	       =				batch.to_pandas().to_json(
								    path_or_buf=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**__a     )
								if not json_str.endswith('\n'     ):
											json_str += "\n"
								return json_str.encode(self.encoding     )





					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	**__a						,	):
								__lowerCamelCase					:	Any	       =				0

								if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0						,	len(self.dataset     )						,	self.batch_size     )						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
														__lowerCamelCase					:	List[str]	       =				self._batch_json((offset, orient, lines, index, to_json_kwargs)     )
														written += file_obj.write(__a     )
								else:
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				len(self.dataset     ), self.batch_size
											with multiprocessing.Pool(self.num_proc     ) as pool:
														for json_str in logging.tqdm(
														    pool.imap(
														        self._batch_json						,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,	__a						,	__a     )]						,	)						,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
																	written += file_obj.write(__a     )

								return written



