

'''simple docstring'''




import random

class     UpperCAmelCase_ :



	"""simple docstring"""





	@staticmethod
	def 	SCREAMING_SNAKE_CASE__							(    lowerCamelCase   )							->     Dict:




				'''simple docstring'''






				UpperCamelCase				:	Any    						=					[ord(a__   ) for i in text]
				UpperCamelCase				:	List[Any]    						=					[]
				UpperCamelCase				:	Optional[Any]    						=					[]
				for i in plain:
							UpperCamelCase				:	str    						=					random.randint(1				,			3_00   )
							UpperCamelCase				:	Tuple    						=					(i + k) * k
							cipher.append(a__   )
							key.append(a__   )
				return cipher, key



	@staticmethod
	def 	SCREAMING_SNAKE_CASE__							(    lowerCamelCase				,			lowerCamelCase   )							->     List[str]:




				'''simple docstring'''






				UpperCamelCase				:	Tuple    						=					[]
				for i in range(len(a__   )   ):
							UpperCamelCase				:	Union[str, Any]    						=					int((cipher[i] - (key[i]) ** 2) / key[i]   )
							plain.append(chr(a__   )   )
				return "".join(a__   )


if __name__ == "__main__":
				lowerCAmelCase_					,       lowerCAmelCase_							=	Onepad().encrypt('Hello')
				print(c, k)
				print(Onepad().decrypt(c, k))





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    	=		{
    "configuration_upernet": ["UperNetConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "UperNetForSemanticSegmentation",
       "UperNetPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_upernet import UperNetConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
