'''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 (       _snake_case				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = False   ,    snake_case_ = False   ,    snake_case_ = None   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    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:					str		= field
    __UpperCAmelCase:					Optional[Any]		= path_or_paths if isinstance(snake_case_   ,    snake_case_					) else {self.split: path_or_paths}
    __UpperCAmelCase:					Optional[int]		= Json(
        cache_dir=snake_case_   ,    data_files=snake_case_   ,    features=snake_case_   ,    field=snake_case_   ,    **snake_case_   ,    )







  def    lowercase_  (							self					):

    '''simple docstring'''







    if self.streaming:
      __UpperCAmelCase:					int		= self.builder.as_streaming_dataset(split=self.split					)
    # Build regular (map-style) dataset
    else:
      __UpperCAmelCase:					Optional[Any]		= None
      __UpperCAmelCase:					Tuple		= None
      __UpperCAmelCase:					List[Any]		= None
      __UpperCAmelCase:					Tuple		= 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:					Optional[Any]		= self.builder.as_dataset(
          split=self.split   ,    verification_mode=snake_case_   ,    in_memory=self.keep_in_memory					)
    return dataset




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    if num_proc is not None and num_proc <= 0:
      raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''					)

    __UpperCAmelCase:					Tuple		= dataset
    __UpperCAmelCase:					str		= path_or_buf
    __UpperCAmelCase:					List[str]		= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    __UpperCAmelCase:					Optional[int]		= num_proc
    __UpperCAmelCase:					List[str]		= """utf-8"""
    __UpperCAmelCase:					str		= to_json_kwargs







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.to_json_kwargs.pop("""path_or_buf"""   ,    snake_case_					)
    __UpperCAmelCase:					List[str]		= self.to_json_kwargs.pop("""orient"""   ,    """records"""					)
    __UpperCAmelCase:					Optional[Any]		= self.to_json_kwargs.pop("""lines"""   ,    True if orient == """records""" else False					)
    __UpperCAmelCase:					Optional[int]		= self.to_json_kwargs.pop("""index"""   ,    False if orient in ["""split""", """table"""] else True					)
    __UpperCAmelCase:					str		= 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:					Union[str, Any]		= 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:					Optional[int]		= 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   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					Tuple		= args

    __UpperCAmelCase:					Optional[Any]		= query_table(
        table=self.dataset.data   ,    key=slice(snake_case_   ,    offset + self.batch_size					)   ,    indices=self.dataset._indices   ,    )
    __UpperCAmelCase:					Dict		= 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   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, 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:					Any		= self._batch_json((offset, orient, lines, index, to_json_kwargs)					)
        written += file_obj.write(snake_case_					)
    else:
      __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= 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'''






import itertools
import math






def  UpperCamelCase__					(					_lowercase    :				int     )							->	bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5   ,    int(math.sqrt(_lowercase     ) + 1     )   ,    6     ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True






def  UpperCamelCase__					(					)							->	Optional[int]:
  __UpperCAmelCase:					Union[str, Any]		= 2
  while True:
    if is_prime(_lowercase     ):
      yield num
    num += 1






def  UpperCamelCase__					(					_lowercase    :				int = 1_0_0_0_1     )							->	int:
  return next(itertools.islice(prime_generator()   ,    nth - 1   ,    _lowercase     )     )


if __name__ == "__main__":
    print(F"""{solution() = }""")