





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__						:      Tuple	=    {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      List[str]	=    ['SqueezeBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      Dict	=    [
       'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SqueezeBertForMaskedLM',
       'SqueezeBertForMultipleChoice',
       'SqueezeBertForQuestionAnswering',
       'SqueezeBertForSequenceClassification',
       'SqueezeBertForTokenClassification',
       'SqueezeBertModel',
       'SqueezeBertModule',
       'SqueezeBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   lowercase__						:      Optional[Any]	=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










'''simple docstring'''

import random
class     __lowerCAmelCase					:






       """simple docstring"""





       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		str					)   ->					tuple[list[int], list[int]]:

            '''simple docstring'''




            _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 snake_case__						(							lowerCAmelCase__       :		list[int]	,			lowerCAmelCase__       :		list[int]					)   ->					str:

            '''simple docstring'''




            _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__":
   lowercase__	,       lowercase__						:      List[str]	=    Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))



