code: 
  
  
  A_    :					str             =[chr(i) for i in range(32, 126)]
  A_    :					Dict             =list(range(len(alphabets)))
  A_    :					Union[str, Any]             =list(range(len(alphabets)))
  A_    :					Any             =list(range(len(alphabets)))
  A_    :					List[str]             =list(reversed(range(len(alphabets))))
  A_    :					Dict             =[]
  A_    :					Optional[int]             =0
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						global gear_one_pos
  						global gear_two_pos
  						global gear_three_pos
  						a_					=		gear_one[0]
  						gear_one.append(UpperCAmelCase__      )
  						del gear_one[0]
  						gear_one_pos += 1
  						if gear_one_pos % int(len(UpperCAmelCase__      )      ) == 0:
  												a_					=		gear_two[0]
  												gear_two.append(UpperCAmelCase__      )
  												del gear_two[0]
  												gear_two_pos += 1
  												if gear_two_pos % int(len(UpperCAmelCase__      )      ) == 0:
  																		a_					=		gear_three[0]
  																		gear_three.append(UpperCAmelCase__      )
  																		del gear_three[0]
  																		gear_three_pos += 1
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		alphabets.index(UpperCAmelCase__      )
  						a_					=		gear_one[target]
  						a_					=		gear_two[target]
  						a_					=		gear_three[target]
  						a_					=		reflector[target]
  						a_					=		gear_three.index(UpperCAmelCase__      )
  						a_					=		gear_two.index(UpperCAmelCase__      )
  						a_					=		gear_one.index(UpperCAmelCase__      )
  						code.append(alphabets[target]      )
  						rotator()
  
  
  if __name__ == "__main__":
  	A_    :					Dict             =list(input("""Type your message:\n"""))
  	while True:
  		try:
  			A_    :					Optional[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: 
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
  	import torch
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		['''image_processor''', '''tokenizer''']
  							snake_case_        =		'''OneFormerImageProcessor'''
  							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
  							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if image_processor is None:
  																			raise ValueError("""You need to specify an `image_processor`."""		)
  													if tokenizer is None:
  																			raise ValueError("""You need to specify a `tokenizer`."""		)
  
  													a_					=		max_seq_length
  													a_					=		task_seq_length
  
  													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if text_list is None:
  																			raise ValueError("""tokens cannot be None."""		)
  
  													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
  													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]
  
  													a_					=		[]
  													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
  																			token_inputs.append(token.unsqueeze(0		)		)
  
  													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  													return token_inputs
  
  
  							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)