





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__						:      Tuple		=    {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      str		=    [
       'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OPTForCausalLM',
       'OPTModel',
       'OPTPreTrainedModel',
       'OPTForSequenceClassification',
       'OPTForQuestionAnswering',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      Dict		=    ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      Optional[int]		=    [
       'FlaxOPTForCausalLM',
       'FlaxOPTModel',
       'FlaxOPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_opt import (
          OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OPTForCausalLM,
          OPTForQuestionAnswering,
          OPTForSequenceClassification,
          OPTModel,
          OPTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
   import sys

   lowercase__						:      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))



