




import string



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Optional[int]      	=						""
  for i in sequence:
    lowercase    :Optional[Any]      	=						ord(lowerCamelCase   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Union[str, Any]      	=						string.ascii_letters
  lowercase    :Dict      	=						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       UpperCAmelCase__  (     ):
  from timeit import timeit

  print("Running performance benchmarks..."   )
  lowercase    :Dict      	=						"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()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase	:						Optional[int]			=     {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						int			=     [
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Optional[Any]			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



