





"""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 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      int						,				__lowercase       :      NestedDataStructureLike[PathLike]						,				__lowercase       :      Optional[NamedSplit] = None						,				__lowercase       :      Optional[Features] = None						,				__lowercase       :      str = None						,				__lowercase       :      bool = False						,				__lowercase       :      bool = False						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[int] = None						,				**__lowercase       :      int						,				):


        '''simple docstring'''




        super().__init__(
            __lowercase						,				split=__lowercase						,				features=__lowercase						,				cache_dir=__lowercase						,				keep_in_memory=__lowercase						,				streaming=__lowercase						,				num_proc=__lowercase						,				**__lowercase						,				)
        __UpperCAmelCase    :						Tuple       =						field
        __UpperCAmelCase    :						int       =						path_or_paths if isinstance(__lowercase						,				__lowercase ) else {self.split: path_or_paths}
        __UpperCAmelCase    :						List[Any]       =						Json(
            cache_dir=__lowercase						,				data_files=__lowercase						,				features=__lowercase						,				field=__lowercase						,				**__lowercase						,				)







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        if self.streaming:
          __UpperCAmelCase    :						Dict       =						self.builder.as_streaming_dataset(split=self.split )
        # Build regular (map-style) dataset
        else:
          __UpperCAmelCase    :						int       =						None
          __UpperCAmelCase    :						str       =						None
          __UpperCAmelCase    :						Union[str, Any]       =						None
          __UpperCAmelCase    :						List[Any]       =						None

          self.builder.download_and_prepare(
              download_config=__lowercase						,				download_mode=__lowercase						,				verification_mode=__lowercase						,				base_path=__lowercase						,				num_proc=self.num_proc						,				)
          __UpperCAmelCase    :						Optional[int]       =						self.builder.as_dataset(
              split=self.split						,				verification_mode=__lowercase						,				in_memory=self.keep_in_memory )
        return dataset




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      List[str]						,				__lowercase       :      Dataset						,				__lowercase       :      Union[PathLike, BinaryIO]						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				**__lowercase       :      int						,				):


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

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.to_json_kwargs.pop('''path_or_buf'''						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.to_json_kwargs.pop('''orient'''						,				'''records''' )
        __UpperCAmelCase    :						Optional[Any]       =						self.to_json_kwargs.pop('''lines'''						,				True if orient == '''records''' else False )
        __UpperCAmelCase    :						Dict       =						self.to_json_kwargs.pop('''index'''						,				False if orient in ['''split''', '''table'''] else True )
        __UpperCAmelCase    :						Tuple       =						self.to_json_kwargs.pop('''compression'''						,				__lowercase )

        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=__lowercase ) as buffer:
            __UpperCAmelCase    :						Any       =						self._write(file_obj=__lowercase						,				orient=__lowercase						,				lines=__lowercase						,				index=__lowercase						,				**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[Any]       =						self._write(
              file_obj=self.path_or_buf						,				orient=__lowercase						,				lines=__lowercase						,				index=__lowercase						,				**self.to_json_kwargs )
        return written

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						args

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







      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      BinaryIO						,				__lowercase       :      int						,				__lowercase       :      int						,				__lowercase       :      List[str]						,				**__lowercase       :      Dict						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						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    :						str       =						self._batch_json((offset, orient, lines, index, to_json_kwargs) )
            written += file_obj.write(__lowercase )
        else:
          __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						,				__lowercase						,				__lowercase )]						,				)						,				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(__lowercase )

        return written






"""simple docstring"""





from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase__				:str				   =       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase__				:list[int]				   =       [ord(letter) for letter in string.ascii_lowercase]
lowercase__				:set[int]				   =       {ord(char) for char in VALID_CHARS}

lowercase__				:list[str]				   =       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->str | None:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						""
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int

  for keychar, cipherchar in zip(cycle(UpperCAmelCase_  )  ,			UpperCAmelCase_  ):
    __UpperCAmelCase    :						Optional[Any]       =						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(UpperCAmelCase_  )

  return decoded


def       lowerCamelCase_       (   UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  __UpperCAmelCase    :						list[str]       =						[]
  for key in product(UpperCAmelCase_  ,			repeat=3  ):
    __UpperCAmelCase    :						List[Any]       =						try_key(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if encoded is not None:
      possibles.append(UpperCAmelCase_  )
  return possibles


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  return [possible for possible in possibles if common_word in possible.lower()]


def       lowerCamelCase_       (   UpperCAmelCase_ = "p059_cipher.txt"  )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						list[int]
  __UpperCAmelCase    :						list[str]
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str       =						Path(UpperCAmelCase_  ).parent.joinpath(UpperCAmelCase_  ).read_text(encoding='''utf-8'''  )

  __UpperCAmelCase    :						List[str]       =						[int(UpperCAmelCase_  ) for number in data.strip().split(''','''  )]

  __UpperCAmelCase    :						List[Any]       =						filter_valid_chars(UpperCAmelCase_  )
  for common_word in COMMON_WORDS:
    __UpperCAmelCase    :						Optional[int]       =						filter_common_word(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if len(UpperCAmelCase_  ) == 1:
      break

  __UpperCAmelCase    :						Optional[Any]       =						possibles[0]
  return sum(ord(UpperCAmelCase_  ) for char in decoded_text  )


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