





import random

class    _UpperCAmelCase							:
      @staticmethod
      def 		_lowerCamelCase      (		a__       ):
         A_     :				Any			      =	[ord(a__       ) for i in text]
         A_     :				Dict			      =	[]
         A_     :				Optional[int]			      =	[]
         for i in plain:
            A_     :				Union[str, Any]			      =	random.randint(1      ,       300       )
            A_     :				int			      =	(i + k) * k
            cipher.append(a__       )
            key.append(a__       )
         return cipher, key






      @staticmethod
      def 		_lowerCamelCase      (		a__      ,       a__       ):
         A_     :				Any			      =	[]
         for i in range(len(a__       )       ):
            A_     :				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_sentencepiece_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""SpeechT5Tokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SpeechT5ForSpeechToText""",
          """SpeechT5ForSpeechToSpeech""",
          """SpeechT5ForTextToSpeech""",
          """SpeechT5Model""",
          """SpeechT5PreTrainedModel""",
          """SpeechT5HifiGan""",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

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






