"""simple docstring"""






import os
import sys

lowerCAmelCase__						:						List[str]		  =			os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCAmelCase__						:						Optional[int]		  =			[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]



@add_start_docstrings(AutoConfig.__doc__ )
def 						a_		(      *lowerCamelCase      ,    **lowerCamelCase ):
				return AutoConfig.from_pretrained(*lowerCamelCase      ,    **lowerCamelCase )



@add_start_docstrings(AutoTokenizer.__doc__ )
def 						a_		(      *lowerCamelCase      ,    **lowerCamelCase ):
				return AutoTokenizer.from_pretrained(*lowerCamelCase      ,    **lowerCamelCase )



@add_start_docstrings(AutoModel.__doc__ )
def 						a_		(      *lowerCamelCase      ,    **lowerCamelCase ):
				return AutoModel.from_pretrained(*lowerCamelCase      ,    **lowerCamelCase )



@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def 						a_		(      *lowerCamelCase      ,    **lowerCamelCase ):
				return AutoModelForCausalLM.from_pretrained(*lowerCamelCase      ,    **lowerCamelCase )



@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def 						a_		(      *lowerCamelCase      ,    **lowerCamelCase ):
				return AutoModelForMaskedLM.from_pretrained(*lowerCamelCase      ,    **lowerCamelCase )



@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def 						a_		(      *lowerCamelCase      ,    **lowerCamelCase ):
				return AutoModelForSequenceClassification.from_pretrained(*lowerCamelCase      ,    **lowerCamelCase )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def 						a_		(      *lowerCamelCase      ,    **lowerCamelCase ):
				return AutoModelForQuestionAnswering.from_pretrained(*lowerCamelCase      ,    **lowerCamelCase )




"""simple docstring"""






import random
class        snake_case :




					"""simple docstring"""

					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							[ord(lowerCamelCase__    ) for i in text]
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for i in plain:
													UpperCAmelCase__    =							random.randint(1					,300    )
													UpperCAmelCase__    =							(i + k) * k
													cipher.append(lowerCamelCase__    )
													key.append(lowerCamelCase__    )
									return cipher, key







					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			list[int]    ):
									UpperCAmelCase__    =							[]
									for i in range(len(lowerCamelCase__    )    ):
													UpperCAmelCase__    =							int((cipher[i] - (key[i]) ** 2) / key[i]    )
													plain.append(chr(lowerCamelCase__    )    )
									return "".join(lowerCamelCase__    )


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



