
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__)












import random

class 		UpperCamelCase__ :



     '''simple docstring'''

     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    str       )     ->		tuple[list[int], list[int]]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[ord(lowerCamelCase__       ) for i in text]
           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						[]
           for i in plain:
                 SCREAMING_SNAKE_CASE		=						random.randint(1						,300       )
                 SCREAMING_SNAKE_CASE		=						(i + k) * k
                 cipher.append(lowerCamelCase__       )
                 key.append(lowerCamelCase__       )
           return cipher, key






     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    list[int]						,lowerCamelCase__						:    list[int]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           for i in range(len(lowerCamelCase__       )       ):
                 SCREAMING_SNAKE_CASE		=						int((cipher[i] - (key[i]) ** 2) / key[i]       )
                 plain.append(chr(lowerCamelCase__       )       )
           return "".join(lowerCamelCase__       )


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


