





'''simple docstring'''






import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812
def   UpperCamelCase__ (						):





      """simple docstring"""


      print("""Making key files..."""    )
      make_key_files("""rsa"""				,      10_24    )
      print("""Key files generation successful."""    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      print("""Generating prime p..."""    )
      _lowerCAmelCase					=				rabinMiller.generate_large_prime(lowerCAmelCase    )
      print("""Generating prime q..."""    )
      _lowerCAmelCase					=				rabinMiller.generate_large_prime(lowerCAmelCase    )
      _lowerCAmelCase					=				p * q

      print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""    )
      while True:
            _lowerCAmelCase					=				random.randrange(2 ** (key_size - 1)				,      2 ** (key_size)    )
            if cryptoMath.gcd(lowerCAmelCase				,      (p - 1) * (q - 1)    ) == 1:
                  break

      print("""Calculating d that is mod inverse of e..."""    )
      _lowerCAmelCase					=				cryptoMath.find_mod_inverse(lowerCAmelCase				,      (p - 1) * (q - 1)    )

      _lowerCAmelCase					=				(n, e)
      _lowerCAmelCase					=				(n, d)
      return (public_key, private_key)
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      if os.path.exists(f"{name}_pubkey.txt"    ) or os.path.exists(f"{name}_privkey.txt"    ):
            print("""\nWARNING:"""    )
            print(
                f"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
                """Use a different name or delete these files and re-run this program."""    )
            sys.exit()

      _lowerCAmelCase	,				_lowerCAmelCase					=				generate_key(lowerCAmelCase    )
      print(f"\nWriting public key to file {name}_pubkey.txt..."    )
      with open(f"{name}_pubkey.txt"				,      """w"""    ) as out_file:
            out_file.write(f"{key_size},{public_key[0]},{public_key[1]}"    )

      print(f"Writing private key to file {name}_privkey.txt..."    )
      with open(f"{name}_privkey.txt"				,      """w"""    ) as out_file:
            out_file.write(f"{key_size},{private_key[0]},{private_key[1]}"    )


if __name__ == "__main__":
      main()













'''simple docstring'''






from typing import TYPE_CHECKING

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


A__							:  Any    ={
    '''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:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[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

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






