code: 
  
  
  
  def _lowercase   (  __A							,__A = 13       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  """"""
   for c in s:
    if "A" <= c <= "Z":
     out += chr(ord("""A"""       ) + (ord(__A       ) - ord("""A"""       ) + n) % 26       )
    elif "a" <= c <= "z":
     out += chr(ord("""a"""       ) + (ord(__A       ) - ord("""a"""       ) + n) % 26       )
    else:
     out += c
   return out
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  input("""Enter message: """       )
  
   __UpperCamelCase				      =  dencrypt(__A							,13       )
   print("""Encryption:"""							,__A       )
  
   __UpperCamelCase				      =  dencrypt(__A							,13       )
   print("""Decryption: """							,__A       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
   main()
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  a__						:			Optional[int]										=			{
      'configuration_bark': [
          'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'BarkCoarseConfig',
          'BarkConfig',
          'BarkFineConfig',
          'BarkSemanticConfig',
      ],
      'processing_bark': ['BarkProcessor'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Any										=			[
       'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BarkFineModel',
       'BarkSemanticModel',
       'BarkCoarseModel',
       'BarkModel',
       'BarkPreTrainedModel',
       'BarkCausalModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_bark import (
       BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BarkCoarseConfig,
       BarkConfig,
       BarkFineConfig,
       BarkSemanticConfig,
   )
   from .processing_bark import BarkProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_bark import (
        BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
        BarkCausalModel,
        BarkCoarseModel,
        BarkFineModel,
        BarkModel,
        BarkPreTrainedModel,
        BarkSemanticModel,
    )
  
  else:
   import sys
  
   a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  