
'''simple docstring'''


snake_case_ :  List[str]     =					'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def  __snake_case     (    ):
   UpperCamelCase       =  input('''Enter message: ''')
   UpperCamelCase       =  input('''Enter key [alphanumeric]: ''')
   UpperCamelCase       =  input('''Encrypt/Decrypt [e/d]: ''')

   if mode.lower().startswith('''e'''):
      UpperCamelCase       =  '''encrypt'''
      UpperCamelCase       =  encrypt_message(_UpperCAmelCase,      _UpperCAmelCase)
   elif mode.lower().startswith('''d'''):
      UpperCamelCase       =  '''decrypt'''
      UpperCamelCase       =  decrypt_message(_UpperCAmelCase,      _UpperCAmelCase)

   print(f'\n{mode.title()}ed message:')
   print(_UpperCAmelCase)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str):
   return translate_message(_UpperCAmelCase,      _UpperCAmelCase,      '''encrypt''')



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str):
   return translate_message(_UpperCAmelCase,      _UpperCAmelCase,      '''decrypt''')



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str):
   UpperCamelCase       =  []
   UpperCamelCase       =  0
   UpperCamelCase       =  key.upper()

   for symbol in message:
      UpperCamelCase       =  LETTERS.find(symbol.upper())
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index])
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index])

         num %= len(_UpperCAmelCase)

         if symbol.isupper():
            translated.append(LETTERS[num])
         elif symbol.islower():
            translated.append(LETTERS[num].lower())

         key_index += 1
         if key_index == len(_UpperCAmelCase):
            UpperCamelCase       =  0
      else:
         translated.append(_UpperCAmelCase)
   return "".join(_UpperCAmelCase)


if __name__ == "__main__":
   main()







'''simple docstring'''


from typing import TYPE_CHECKING

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


snake_case_ :  Any     =					{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					['ElectraTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Tuple     =					[
       'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ElectraForCausalLM',
       'ElectraForMaskedLM',
       'ElectraForMultipleChoice',
       'ElectraForPreTraining',
       'ElectraForQuestionAnswering',
       'ElectraForSequenceClassification',
       'ElectraForTokenClassification',
       'ElectraModel',
       'ElectraPreTrainedModel',
       'load_tf_weights_in_electra',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  int     =					[
       'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFElectraForMaskedLM',
       'TFElectraForMultipleChoice',
       'TFElectraForPreTraining',
       'TFElectraForQuestionAnswering',
       'TFElectraForSequenceClassification',
       'TFElectraForTokenClassification',
       'TFElectraModel',
       'TFElectraPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Optional[int]     =					[
       'FlaxElectraForCausalLM',
       'FlaxElectraForMaskedLM',
       'FlaxElectraForMultipleChoice',
       'FlaxElectraForPreTraining',
       'FlaxElectraForQuestionAnswering',
       'FlaxElectraForSequenceClassification',
       'FlaxElectraForTokenClassification',
       'FlaxElectraModel',
       'FlaxElectraPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   snake_case_ :  Union[str, Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





