





import string


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			''''''
    for i in sequence:
        SCREAMING_SNAKE_CASE__											=			ord(lowerCAmelCase_	)
        if 6_5 <= extract <= 9_0:
            output += chr(1_5_5 - extract	)
        elif 9_7 <= extract <= 1_2_2:
            output += chr(2_1_9 - extract	)
        else:
            output += i
    return output


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			string.ascii_letters
    SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    )				->						None:
    from timeit import timeit

    print('''Running performance benchmarks...'''	)
    SCREAMING_SNAKE_CASE__											=			'''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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A					:				Any				=			{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Optional[Any]				=			["""XGLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Any				=			["""XGLMTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				str				=			[
      """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XGLMForCausalLM""",
      """XGLMModel""",
      """XGLMPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Tuple				=			[
      """FlaxXGLMForCausalLM""",
      """FlaxXGLMModel""",
      """FlaxXGLMPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				List[Any]				=			[
      """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXGLMForCausalLM""",
      """TFXGLMModel""",
      """TFXGLMPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm import XGLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm_fast import XGLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
        TFXGLMPreTrainedModel,
    )


else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
