'''simple docstring'''





from __future__ import annotations

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

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

UpperCAmelCase_			    =							['''the''', '''be''', '''to''', '''of''', '''and''', '''in''', '''that''', '''have''']




def       lowerCAmelCase_     (		lowercase:							list[int]  ,					lowercase:							tuple[int, ...]		)    ->				str | None:







 '''simple docstring'''
 _UpperCamelCase:     str							= ""
 _UpperCamelCase:     int
 _UpperCamelCase:     int
 _UpperCamelCase:     int

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

 return decoded




def       lowerCAmelCase_     (		lowercase:							list[int]		)    ->				list[str]:







 '''simple docstring'''
 _UpperCamelCase:     list[str]							= []
 for key in product(lowercase  ,					repeat=3		):
  _UpperCamelCase:     Tuple							= try_key(lowercase  ,					lowercase		)
  if encoded is not None:
   possibles.append(lowercase		)
 return possibles




def       lowerCAmelCase_     (		lowercase:							list[str]  ,					lowercase:							str		)    ->				list[str]:







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




def       lowerCAmelCase_     (		lowercase:							str = "p059_cipher.txt"		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     list[int]
 _UpperCamelCase:     list[str]
 _UpperCamelCase:     str
 _UpperCamelCase:     str
 _UpperCamelCase:     str							= Path(lowercase		).parent.joinpath(lowercase		).read_text(encoding='''utf-8'''		)

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

 _UpperCamelCase:     Any							= filter_valid_chars(lowercase		)
 for common_word in COMMON_WORDS:
  _UpperCamelCase:     List[Any]							= filter_common_word(lowercase  ,					lowercase		)
  if len(lowercase		) == 1:
   break

 _UpperCamelCase:     Optional[int]							= possibles[0]
 return sum(ord(lowercase		) for char in decoded_text		)


if __name__ == "__main__":
  print(f"""{solution() = }""")
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLNetForMultipleChoice''',
      '''XLNetForQuestionAnswering''',
      '''XLNetForQuestionAnsweringSimple''',
      '''XLNetForSequenceClassification''',
      '''XLNetForTokenClassification''',
      '''XLNetLMHeadModel''',
      '''XLNetModel''',
      '''XLNetPreTrainedModel''',
      '''load_tf_weights_in_xlnet''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLNetForMultipleChoice''',
      '''TFXLNetForQuestionAnsweringSimple''',
      '''TFXLNetForSequenceClassification''',
      '''TFXLNetForTokenClassification''',
      '''TFXLNetLMHeadModel''',
      '''TFXLNetMainLayer''',
      '''TFXLNetModel''',
      '''TFXLNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_			    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)