code: 
  
  
  
  
  
  __lowerCAmelCase							:			Tuple				=[chr(i) for i in range(32, 126)]
  __lowerCAmelCase							:			List[Any]				=list(range(len(alphabets)))
  __lowerCAmelCase							:			str				=list(range(len(alphabets)))
  __lowerCAmelCase							:			str				=list(range(len(alphabets)))
  __lowerCAmelCase							:			Any				=list(reversed(range(len(alphabets))))
  __lowerCAmelCase							:			Tuple				=[]
  __lowerCAmelCase							:			int				=0
  
  
  
  
  
  def 		UpperCamelCase       (							):
      global gear_one_pos
      global gear_two_pos
      global gear_three_pos
      A__       =    gear_one[0]
      gear_one.append(lowercase_ )
      del gear_one[0]
      gear_one_pos += 1
      if gear_one_pos % int(len(lowercase_ ) ) == 0:
          A__       =    gear_two[0]
          gear_two.append(lowercase_ )
          del gear_two[0]
          gear_two_pos += 1
          if gear_two_pos % int(len(lowercase_ ) ) == 0:
              A__       =    gear_three[0]
              gear_three.append(lowercase_ )
              del gear_three[0]
              gear_three_pos += 1
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
      A__       =    alphabets.index(lowercase_ )
      A__       =    gear_one[target]
      A__       =    gear_two[target]
      A__       =    gear_three[target]
      A__       =    reflector[target]
      A__       =    gear_three.index(lowercase_ )
      A__       =    gear_two.index(lowercase_ )
      A__       =    gear_one.index(lowercase_ )
      code.append(alphabets[target] )
      rotator()
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			List[str]				=list(input("Type your message:\n"))
    while True:
      try:
        __lowerCAmelCase							:			Any				=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!"
    )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  