"""simple docstring"""
from __future__ import annotations

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

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

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



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->List[str]:
    _lowerCamelCase	: str       =       ""
    _lowerCamelCase	: int
    _lowerCamelCase	: int
    _lowerCamelCase	: int

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

    return decoded



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->List[str]:
    _lowerCamelCase	: list[str]       =       []
    for key in product(_A						,	repeat=3 ):
        _lowerCamelCase	: Tuple       =       try_key(_A						,	_A )
        if encoded is not None:
            possibles.append(_A )
    return possibles



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->List[Any]:
    return [possible for possible in possibles if common_word in possible.lower()]



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ = "p059_cipher.txt" )      ->str:
    _lowerCamelCase	: list[int]
    _lowerCamelCase	: list[str]
    _lowerCamelCase	: str
    _lowerCamelCase	: str
    _lowerCamelCase	: str       =       Path(_A ).parent.joinpath(_A ).read_text(encoding='''utf-8''' )

    _lowerCamelCase	: Union[str, Any]       =       [int(_A ) for number in data.strip().split(''',''' )]

    _lowerCamelCase	: Union[str, Any]       =       filter_valid_chars(_A )
    for common_word in COMMON_WORDS:
        _lowerCamelCase	: int       =       filter_common_word(_A						,	_A )
        if len(_A ) == 1:
            break

    _lowerCamelCase	: Any       =       possibles[0]
    return sum(ord(_A ) for char in decoded_text )


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






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCAmelCase     :  Optional[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

