"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__						:						int		  =			{
    '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__						:						List[Any]		  =			['SpeechT5Tokenizer']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Optional[Any]		  =			[
						    '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__						:						str		  =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""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))



