





"""simple docstring"""


from __future__ import annotations

from string import ascii_letters





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Any						,  snake_case   :					Union[str, Any]						,  snake_case   :					str = None		)->		str:
       _lowerCamelCase    	=					alphabet or ascii_letters

       # The final result string
       _lowerCamelCase    	=					''

       for character in input_string:
              if character not in alpha:
                     # Append without encryption if character is not in the alphabet
                     result += character
              else:
                     # Get the index of the new key and make sure it isn't too large
                     _lowerCamelCase    	=					(alpha.index(UpperCamelCase_		) + key) % len(UpperCamelCase_		)

                     # Append the encoded character to the alphabet
                     result += alpha[new_key]

       return result





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					List[Any]						,  snake_case   :					Dict = None		)->		str:
       key *= -1

       return encrypt(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					str						,  snake_case   :					Optional[Any] = None		)->		dict[int, str]:
       _lowerCamelCase    	=					alphabet or ascii_letters

       # To store data on all the combinations
       _lowerCamelCase    	=					{}

       # Cycle through each combination
       for key in range(1						,  len(UpperCamelCase_		) + 1		):
              # Decrypt the message and store the result in the data
              _lowerCamelCase    	=					decrypt(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_		)

       return brute_force_data


if __name__ == "__main__":
      while True:
            print(f'\n{"-" * 1_0}\n Menu\n{"-" * 1_0}')
            print(*["""1.Encrypt""", """2.Decrypt""", """3.BruteForce""", """4.Quit"""], sep="""\n""")

            # get user input
            A_     :      Dict								=input("""\nWhat would you like to do?: """).strip() or """4"""

            # run functions based on what the user chose
            if choice not in ("1", "2", "3", "4"):
                  print("""Invalid choice, please enter a valid choice""")
            elif choice == "1":
                  A_     :      Dict								=input("""Please enter the string to be encrypted: """)
                  A_     :      Tuple								=int(input("""Please enter off-set: """).strip())

                  print(encrypt(input_string, key))
            elif choice == "2":
                  A_     :      Optional[int]								=input("""Please enter the string to be decrypted: """)
                  A_     :      Optional[Any]								=int(input("""Please enter off-set: """).strip())

                  print(decrypt(input_string, key))
            elif choice == "3":
                  A_     :      Tuple								=input("""Please enter the string to be decrypted: """)
                  A_     :      Any								=brute_force(input_string)

                  for key, value in brute_force_data.items():
                        print(f'Key: {key} | Message: {value}')

            elif choice == "4":
                  print("""Goodbye.""")
                  break













from typing import TYPE_CHECKING

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


_UpperCAmelCase				       = {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MPNetForMaskedLM',
         'MPNetForMultipleChoice',
         'MPNetForQuestionAnswering',
         'MPNetForSequenceClassification',
         'MPNetForTokenClassification',
         'MPNetLayer',
         'MPNetModel',
         'MPNetPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMPNetEmbeddings',
         'TFMPNetForMaskedLM',
         'TFMPNetForMultipleChoice',
         'TFMPNetForQuestionAnswering',
         'TFMPNetForSequenceClassification',
         'TFMPNetForTokenClassification',
         'TFMPNetMainLayer',
         'TFMPNetModel',
         'TFMPNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
     from .tokenization_mpnet import MPNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mpnet_fast import MPNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mpnet import (
              MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetLayer,
              MPNetModel,
              MPNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mpnet import (
              TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMPNetEmbeddings,
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetMainLayer,
              TFMPNetModel,
              TFMPNetPreTrainedModel,
          )

else:
     import sys

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

