





'''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   A_	(  __UpperCAmelCase			):




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




 def      lowercase	(					self       :    Optional[Any]    ):
      # Build iterable dataset
      if self.streaming:
           _UpperCAmelCase     							=   self.builder.as_streaming_dataset(split=self.split    )
      # Build regular (map-style) dataset
      else:
           _UpperCAmelCase     							=   None
           _UpperCAmelCase     							=   None
           _UpperCAmelCase     							=   None
           _UpperCAmelCase     							=   None

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







class   A_	:




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

      _UpperCAmelCase     							=   dataset
      _UpperCAmelCase     							=   path_or_buf
      _UpperCAmelCase     							=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      _UpperCAmelCase     							=   num_proc
      _UpperCAmelCase     							=   """utf-8"""
      _UpperCAmelCase     							=   to_json_kwargs




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.to_json_kwargs.pop("path_or_buf"	,			snake_case_    )
      _UpperCAmelCase     							=   self.to_json_kwargs.pop("orient"	,			"records"    )
      _UpperCAmelCase     							=   self.to_json_kwargs.pop("lines"	,			True if orient == "records" else False    )
      _UpperCAmelCase     							=   self.to_json_kwargs.pop("index"	,			False if orient in ["split", "table"] else True    )
      _UpperCAmelCase     							=   self.to_json_kwargs.pop("compression"	,			snake_case_    )

      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=snake_case_    ) as buffer:
                _UpperCAmelCase     							=   self._write(file_obj=snake_case_	,			orient=snake_case_	,			lines=snake_case_	,			index=snake_case_	,			**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     							=   self._write(
               file_obj=self.path_or_buf	,			orient=snake_case_	,			lines=snake_case_	,			index=snake_case_	,			**self.to_json_kwargs    )
      return written




 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    Any    ):
      _UpperCAmelCase     							=   args

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




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    List[str]	,			snake_case_       :    Tuple	,			snake_case_       :    Tuple	,			snake_case_       :    int	,			**snake_case_       :    List[Any]	,			):
      _UpperCAmelCase     							=   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     							=   self._batch_json((offset, orient, lines, index, to_json_kwargs)    )
                written += file_obj.write(snake_case_    )
      else:
           _UpperCAmelCase     							=   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	,			snake_case_	,			snake_case_    )]	,			)	,			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(snake_case_    )

      return written









'''simple docstring'''




from __future__ import annotations

import math



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if num <= 0:
						A_	:						List[Any]	=     f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(lowerCamelCase__    )

			A_	:						Dict	=     [True] * (num + 1)
			A_	:						List[Any]	=     []
			A_	:						Tuple	=     2
			A_	:						Optional[int]	=     int(math.sqrt(lowerCamelCase__    )    )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(lowerCamelCase__    )

									# Set multiples of start be False
									for i in range(start * start						,      num + 1						,      lowerCamelCase__    ):
												if sieve[i] is True:
															A_	:						List[Any]	=     False

						start += 1

			for j in range(end + 1						,      num + 1    ):
						if sieve[j] is True:
									prime.append(lowerCamelCase__    )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))