






"""simple docstring"""






from string import ascii_uppercase

UpperCAmelCase_   : Tuple   			= {char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_   : Dict   			= dict(enumerate(ascii_uppercase))



def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		len(__a							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
    while True:
        if x == i:
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
        if len(__a							) == len(__a							):
            break
        key += key[i]
        i += 1
    return key



def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		''''''
    SCREAMING_SNAKE_CASE_      :  str    						=		0
    for letter in message:
        if letter == " ":
            cipher_text += " "
        else:
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		(dicta[letter] - dicta[key_new[i]]) % 26
            i += 1
            cipher_text += dicta[x]
    return cipher_text



def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		''''''
    SCREAMING_SNAKE_CASE_      :  Dict    						=		0
    for letter in cipher_text:
        if letter == " ":
            or_txt += " "
        else:
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		(dicta[letter] + dicta[key_new[i]] + 26) % 26
            i += 1
            or_txt += dicta[x]
    return or_txt



def 					_A			() ->       None:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''THE GERMAN ATTACK'''
    SCREAMING_SNAKE_CASE_      :  int    						=		'''SECRET'''
    SCREAMING_SNAKE_CASE_      :  Dict    						=		generate_key(__a    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		cipher_text(__a    ,				__a							)
    print(f'Encrypted Text = {s}'							)
    print(f'Original Text = {original_text(__a    ,				__a							)}'							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()










"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase_   : str   			= {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Dict   			= ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[str]   			= [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : Tuple   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


