






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   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



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



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

												self.builder.download_and_prepare(
												    download_config=_UpperCamelCase    ,		download_mode=_UpperCamelCase    ,		verification_mode=_UpperCamelCase    ,		base_path=_UpperCamelCase    ,		num_proc=self.num_proc    ,		)
												UpperCAmelCase_   :				Dict						=						self.builder.as_dataset(
												    split=self.split    ,		verification_mode=_UpperCamelCase    ,		in_memory=self.keep_in_memory       )
							return dataset





class   lowerCamelCase				:







		'''simple docstring'''



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

							UpperCAmelCase_   :				Dict						=						dataset
							UpperCAmelCase_   :				Optional[Any]						=						path_or_buf
							UpperCAmelCase_   :				int						=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							UpperCAmelCase_   :				Optional[int]						=						num_proc
							UpperCAmelCase_   :				List[Any]						=						'utf-8'
							UpperCAmelCase_   :				Any						=						to_json_kwargs



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				int						=						self.to_json_kwargs.pop('path_or_buf'    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				Tuple						=						self.to_json_kwargs.pop('orient'    ,		'records'       )
							UpperCAmelCase_   :				List[str]						=						self.to_json_kwargs.pop('lines'    ,		True if orient == 'records' else False       )
							UpperCAmelCase_   :				Any						=						self.to_json_kwargs.pop('index'    ,		False if orient in ['split', 'table'] else True       )
							UpperCAmelCase_   :				Dict						=						self.to_json_kwargs.pop('compression'    ,		_UpperCamelCase       )

							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=_UpperCamelCase       ) as buffer:
																	UpperCAmelCase_   :				List[str]						=						self._write(file_obj=_UpperCamelCase    ,		orient=_UpperCamelCase    ,		lines=_UpperCamelCase    ,		index=_UpperCamelCase    ,		**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.'       )
												UpperCAmelCase_   :				Optional[int]						=						self._write(
												    file_obj=self.path_or_buf    ,		orient=_UpperCamelCase    ,		lines=_UpperCamelCase    ,		index=_UpperCamelCase    ,		**self.to_json_kwargs       )
							return written



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Optional[Any]:
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				List[str]						=						args

							UpperCAmelCase_   :				Tuple						=						query_table(
							    table=self.dataset.data    ,		key=slice(_UpperCamelCase    ,		offset + self.batch_size       )    ,		indices=self.dataset._indices    ,		)
							UpperCAmelCase_   :				Tuple						=						batch.to_pandas().to_json(
							    path_or_buf=_UpperCamelCase    ,		orient=_UpperCamelCase    ,		lines=_UpperCamelCase    ,		index=_UpperCamelCase    ,		**_UpperCamelCase       )
							if not json_str.endswith('\n'       ):
												json_str += "\n"
							return json_str.encode(self.encoding       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase    ,		)     ->							int:
							UpperCAmelCase_   :				Optional[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'    ,		):
																	UpperCAmelCase_   :				Tuple						=						self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
																	written += file_obj.write(_UpperCamelCase       )
							else:
												UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						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    ,		_UpperCamelCase    ,		_UpperCamelCase       )]    ,		)    ,		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(_UpperCamelCase       )

							return written














from __future__ import annotations




def 					lowercase__				(					__snake_case   :							list[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					if len(__snake_case  ) == 0:
										return False
					UpperCAmelCase_   :				Optional[int]						=						len(__snake_case  ) // 2
					if a_list[midpoint] == item:
										return True
					if item < a_list[midpoint]:
										return binary_search(a_list[:midpoint]					,	__snake_case  )
					else:
										return binary_search(a_list[midpoint + 1 :]					,	__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						input('Enter numbers separated by comma:\n').strip()
		__UpperCAmelCase     							=						[int(item.strip()) for item in user_input.split(',')]
		__UpperCAmelCase     							=						int(input('Enter the number to be found in the list:\n').strip())
		__UpperCAmelCase     							=						'' if binary_search(sequence, target) else 'not '
		print(F'{target} was {not_str}found in {sequence}')






