

_a	 =     [chr(i) for i in range(3_2, 1_2_6)]
_a	 =     list(range(len(alphabets)))
_a	 =     list(range(len(alphabets)))
_a	 =     list(range(len(alphabets)))
_a	 =     list(reversed(range(len(alphabets))))
_a	 =     []
_a	 =     _a	 =     _a	 =     0







def _a				( )					->					Optional[int]:

 """simple docstring"""






 global gear_one_pos
 global gear_two_pos
 global gear_three_pos
 __lowerCAmelCase:     Dict     			=   gear_one[0]
 gear_one.append(SCREAMING_SNAKE_CASE	)
 del gear_one[0]
 gear_one_pos += 1
 if gear_one_pos % int(len(SCREAMING_SNAKE_CASE	)	) == 0:
  __lowerCAmelCase:     str     			=   gear_two[0]
  gear_two.append(SCREAMING_SNAKE_CASE	)
  del gear_two[0]
  gear_two_pos += 1
  if gear_two_pos % int(len(SCREAMING_SNAKE_CASE	)	) == 0:
   __lowerCAmelCase:     List[str]     			=   gear_three[0]
   gear_three.append(SCREAMING_SNAKE_CASE	)
   del gear_three[0]
   gear_three_pos += 1







def _a				( SCREAMING_SNAKE_CASE	:    Union[str, Any]	)					->					Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   alphabets.index(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Any     			=   gear_one[target]
 __lowerCAmelCase:     Tuple     			=   gear_two[target]
 __lowerCAmelCase:     str     			=   gear_three[target]
 __lowerCAmelCase:     int     			=   reflector[target]
 __lowerCAmelCase:     Union[str, Any]     			=   gear_three.index(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     List[str]     			=   gear_two.index(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Optional[int]     			=   gear_one.index(SCREAMING_SNAKE_CASE	)
 code.append(alphabets[target]	)
 rotator()


if __name__ == "__main__":
  _a	 =     list(input('''Type your message:\n'''))
  while True:
    try:
      _a	 =     int(input('''Please set token:(must be only digits)\n'''))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print('''\n''' + ''''''.join(code))
  print(
      f"\nYour Token is {token} please write it down.\nIf you want to decode "
      '''this message again you should input same digits as token!'''
  )








from typing import TYPE_CHECKING

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


_a	 =     {
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''GPT2TokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GPT2DoubleHeadsModel''',
      '''GPT2ForQuestionAnswering''',
      '''GPT2ForSequenceClassification''',
      '''GPT2ForTokenClassification''',
      '''GPT2LMHeadModel''',
      '''GPT2Model''',
      '''GPT2PreTrainedModel''',
      '''load_tf_weights_in_gpt2''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFGPT2DoubleHeadsModel''',
      '''TFGPT2ForSequenceClassification''',
      '''TFGPT2LMHeadModel''',
      '''TFGPT2MainLayer''',
      '''TFGPT2Model''',
      '''TFGPT2PreTrainedModel''',
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''TFGPT2Tokenizer''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

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





