





import string






def      lowerCamelCase__      (__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				""
     for i in sequence:
          _SCREAMING_SNAKE_CASE							:						str         =				ord(__lowerCamelCase						)
          if 65 <= extract <= 90:
               output += chr(155 - extract						)
          elif 97 <= extract <= 122:
               output += chr(219 - extract						)
          else:
               output += i
     return output






def      lowerCamelCase__      (__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						str         =				string.ascii_letters
     _SCREAMING_SNAKE_CASE							:						Tuple         =				string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
     return "".join(
         letters_reversed[letters.index(__lowerCamelCase						)] if c in letters else c for c in sequence						)






def      lowerCamelCase__      ():
     from timeit import timeit

     print("Running performance benchmarks..."						)
     _SCREAMING_SNAKE_CASE							:						Any         =				"from string import printable ; from __main__ import atbash, atbash_slow"
     print(f"""> atbash_slow(): {timeit('atbash_slow(printable)',							setup=__lowerCamelCase						)} seconds"""						)
     print(f""">      atbash(): {timeit('atbash(printable)',							setup=__lowerCamelCase						)} seconds"""						)


if __name__ == "__main__":
       for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
              print(f"{example} encrypted in atbash: {atbash(example)}")
       benchmark()






'''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__)






