


'''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        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


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


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

						self.builder.download_and_prepare(
						    download_config=a_							,      download_mode=a_							,      verification_mode=a_							,      base_path=a_							,      num_proc=self.num_proc							,      )
						lowercase						:		List[str]   					=		self.builder.as_dataset(
						    split=self.split							,      verification_mode=a_							,      in_memory=self.keep_in_memory							)
					return dataset




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_							,      a_ = None							,      a_ = None							,      **a_							,      )    ->  Union[str, Any]:
					if num_proc is not None and num_proc <= 0:
						raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''							)

					lowercase						:		Any   					=		dataset
					lowercase						:		List[str]   					=		path_or_buf
					lowercase						:		Union[str, Any]   					=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					lowercase						:		Dict   					=		num_proc
					lowercase						:		Dict   					=		"utf-8"
					lowercase						:		Any   					=		to_json_kwargs

				def 	a__		(     self							)    ->  int:
					lowercase						:		List[Any]   					=		self.to_json_kwargs.pop("path_or_buf"							,      a_							)
					lowercase						:		List[str]   					=		self.to_json_kwargs.pop("orient"							,      "records"							)
					lowercase						:		Union[str, Any]   					=		self.to_json_kwargs.pop("lines"							,      True if orient == "records" else False							)
					lowercase						:		Optional[int]   					=		self.to_json_kwargs.pop("index"							,      False if orient in ["split", "table"] else True							)
					lowercase						:		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:
							lowercase						:		Tuple   					=		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."							)
						lowercase						:		Any   					=		self._write(
						    file_obj=self.path_or_buf							,      orient=a_							,      lines=a_							,      index=a_							,      **self.to_json_kwargs							)
					return written

				def 	a__		(     self							,      a_							)    ->  int:
					lowercase					,		lowercase					,		lowercase					,		lowercase					,		lowercase						:		int   					=		args

					lowercase						:		List[str]   					=		query_table(
					    table=self.dataset.data							,      key=slice(a_							,      offset + self.batch_size							)							,      indices=self.dataset._indices							,      )
					lowercase						:		Optional[Any]   					=		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 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      **a_							,      )    ->  int:
					lowercase						:		int   					=		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"							,      ):
							lowercase						:		Tuple   					=		self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
							written += file_obj.write(a_							)
					else:
						lowercase					,		lowercase						:		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






'''simple docstring'''
def 	_A  (       A							)  ->				list:

	if len(A							) <= 1:
		return [tuple(A							)]

	lowercase						:		Dict   					=		[]

	def generate(A							,A							):
		lowercase						:		List[Any]   					=		[0] * n
		res.append(tuple(A							)							)

		lowercase						:		Tuple   					=		0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					lowercase					,		lowercase						:		List[Any]   					=		arr[i], arr[0]
				else:
					lowercase					,		lowercase						:		Optional[Any]   					=		arr[i], arr[c[i]]
				res.append(tuple(A							)							)
				c[i] += 1
				lowercase						:		Optional[Any]   					=		0
			else:
				lowercase						:		Any   					=		0
				i += 1

	generate(len(A							)							,A							)
	return res


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase   : List[str]             =				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))


