





'''simple docstring'''

from __future__ import annotations

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

a        =						(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a        =						[ord(letter) for letter in string.ascii_lowercase]
a        =						{ord(char) for char in VALID_CHARS}

a        =						["the", "be", "to", "of", "and", "in", "that", "have"]

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       ""
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42

   for keychar, cipherchar in zip(cycle(__UpperCAmelCase      )							,	__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       cipherchar ^ keychar
      if decodedchar not in VALID_INTS:
         return None
      decoded += chr(__UpperCAmelCase      )

   return decoded

def 						__magic_name__       (							__UpperCAmelCase      )						->					list[str]:

   '''simple docstring'''







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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					list[str]:

   '''simple docstring'''







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

def 						__magic_name__       (							__UpperCAmelCase = "p059_cipher.txt"      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       Path(__UpperCAmelCase      ).parent.joinpath(__UpperCAmelCase      ).read_text(encoding="""utf-8"""      )

   __SCREAMING_SNAKE_CASE      							=       [int(__UpperCAmelCase      ) for number in data.strip().split(""","""      )]

   __SCREAMING_SNAKE_CASE      							=       filter_valid_chars(__UpperCAmelCase      )
   for common_word in COMMON_WORDS:
      __SCREAMING_SNAKE_CASE      							=       filter_common_word(__UpperCAmelCase							,	__UpperCAmelCase      )
      if len(__UpperCAmelCase      ) == 1:
         break

   __SCREAMING_SNAKE_CASE      							=       possibles[0]
   return sum(ord(__UpperCAmelCase      ) for char in decoded_text      )


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












'''simple docstring'''

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       seq_shapes or {}
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(__UpperCAmelCase							,	_ArrayXD      ):
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(__UpperCAmelCase							,	datasets.Value      ):
            if v.dtype == "string":
               __SCREAMING_SNAKE_CASE      							=       """The small grey turtle was surprisingly fast when challenged."""
            else:
               __SCREAMING_SNAKE_CASE      							=       np.random.randint(10							,	size=1      ).astype(v.dtype      ).item()
         elif isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
            while isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
               __SCREAMING_SNAKE_CASE      							=       v.feature
            __SCREAMING_SNAKE_CASE      							=       seq_shapes[k]
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*__UpperCAmelCase      ).astype(v.dtype      )
         __SCREAMING_SNAKE_CASE      							=       data

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





