






"""simple docstring"""





UpperCamelCase					=     [chr(i) for i in range(32, 1_26)]
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(reversed(range(len(alphabets))))
UpperCamelCase					=     []
UpperCamelCase					=     UpperCamelCase					=     UpperCamelCase					=     0






def 							A(    ):
       """simple docstring"""





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






def 							A(    snake_case_						):
       """simple docstring"""





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


if __name__ == "__main__":
  UpperCamelCase					=     list(input("""Type your message:\n"""))
  while True:
    try:
      UpperCamelCase					=     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!"""
  )











'''simple docstring'''



import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


_lowercase       :						Dict						=logging.get_logger(__name__)

if is_vision_available():
				import PIL.Image
				from PIL import Image
				from PIL.Image import Image as ImageType
else:
				_lowercase       :						List[Any]						=object

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   int      =    value



			def __str__(     self		:						int			)      ->      str:
							"""simple docstring"""





							return self.to_string()



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      List[str]:
							"""simple docstring"""





							logger.error(
							    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''			)
							return self._value







			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      str:
							"""simple docstring"""





							logger.error(
							    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''			)
							return str(self._value			)






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def 				lowerCAmelCase (     self		:						str			)      ->      Tuple:
							"""simple docstring"""





							return self._value







			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Union[str, Any]:
							"""simple docstring"""





							return self._value






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Dict:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

							if not is_vision_available():
											raise ImportError('''PIL must be installed in order to handle images.'''			)

							snake_case__      :   str      =    None
							snake_case__      :   List[Any]      =    None
							snake_case__      :   Tuple      =    None

							if isinstance(__UpperCamelCase    ,			__UpperCamelCase			):
											snake_case__      :   List[Any]      =    value
							elif isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   Union[str, Any]      =    value
							else:
											raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						Any=None			)      ->      List[Any]:
							"""simple docstring"""





							from IPython.display import Image, display

							display(Image(self.to_string()			)			)



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""simple docstring"""





							if self._raw is not None:
											return self._raw

							if self._path is not None:
											snake_case__      :   str      =    Image.open(self._path			)
											return self._raw







			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





							if self._path is not None:
											return self._path

							if self._raw is not None:
											snake_case__      :   Any      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)
											self._raw.save(self._path			)

											return self._path

							if self._tensor is not None:
											snake_case__      :   List[str]      =    self._tensor.cpu().detach().numpy()

											# There is likely simpler than load into image into save
											snake_case__      :   List[Any]      =    Image.fromarray((array * 255).astype(np.uinta			)			)

											snake_case__      :   str      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)

											img.save(self._path			)

											return self._path






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						str=16000			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

							if not is_soundfile_availble():
											raise ImportError('''soundfile must be installed in order to handle audio.'''			)

							snake_case__      :   List[str]      =    None
							snake_case__      :   Optional[int]      =    None

							snake_case__      :   Any      =    samplerate

							if isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   int      =    value
							else:
											raise ValueError(F'''Unsupported audio type: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      Any:
							"""simple docstring"""





							from IPython.display import Audio, display

							display(Audio(self.to_string()    ,			rate=self.samplerate			)			)



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							if self._tensor is not None:
											return self._tensor

							if self._path is not None:
											snake_case__  ,      snake_case__      :   Tuple      =    sf.read(self._path			)
											snake_case__      :   Tuple      =    torch.tensor(__UpperCamelCase			)
											return self._tensor







			def 				lowerCAmelCase (     self		:						Any			)      ->      Dict:
							"""simple docstring"""





							if self._path is not None:
											return self._path

							if self._tensor is not None:
											snake_case__      :   Tuple      =    tempfile.mkdtemp()
											snake_case__      :   int      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.wav'''			)
											sf.write(self._path    ,			self._tensor    ,			samplerate=self.samplerate			)
											return self._path



_lowercase       :						Union[str, Any]						={"text": AgentText, "image": AgentImage, "audio": AgentAudio}
_lowercase       :						List[Any]						={str: AgentText}

if is_vision_available():
				_lowercase       :						Optional[Any]						=AgentImage


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Optional[int]   ,				**UpperCamelCase__				:List[Any]			)		->						Optional[Any]:
				snake_case__      :   List[str]      =    [(arg.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else arg) for arg in args]
				snake_case__      :   Any      =    {k: (v.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else v) for k, v in kwargs.items()}
				return args, kwargs



def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Tuple=None			)		->						Dict:
				if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								snake_case__      :   Any      =    {}
								for i, (k, v) in enumerate(outputs.items()			):
												if output_types is not None:
																# If the class has defined outputs, we can map directly according to the class definition
																if output_types[i] in AGENT_TYPE_MAPPING:
																				snake_case__      :   List[Any]      =    AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)
																else:
																				snake_case__      :   Optional[int]      =    AgentType(UpperCamelCase__			)

												else:
																# If the class does not have defined output, then we map according to the type
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(UpperCamelCase__   ,				_k			):
																								snake_case__      :   str      =    _v(UpperCamelCase__			)
																if k not in decoded_outputs:
																				snake_case__      :   Any      =    AgentType[v]

				elif isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    type(UpperCamelCase__			)()
								for i, v in enumerate(UpperCamelCase__			):
												if output_types is not None:
																# If the class has defined outputs, we can map directly according to the class definition
																if output_types[i] in AGENT_TYPE_MAPPING:
																				decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)			)
																else:
																				decoded_outputs.append(AgentType(UpperCamelCase__			)			)
												else:
																# If the class does not have defined output, then we map according to the type
																snake_case__      :   Any      =    False
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(UpperCamelCase__   ,				_k			):
																								decoded_outputs.append(_v(UpperCamelCase__			)			)
																								snake_case__      :   str      =    True

																if not found:
																				decoded_outputs.append(AgentType(UpperCamelCase__			)			)

				else:
								if output_types[0] in AGENT_TYPE_MAPPING:
												# If the class has defined outputs, we can map directly according to the class definition
												snake_case__      :   Tuple      =    AGENT_TYPE_MAPPING[output_types[0]](UpperCamelCase__			)

								else:
												# If the class does not have defined output, then we map according to the type
												for _k, _v in INSTANCE_TYPE_MAPPING.items():
																if isinstance(UpperCamelCase__   ,				_k			):
																				return _v(UpperCamelCase__			)
												return AgentType(UpperCamelCase__			)

				return decoded_outputs




