
"""simple docstring"""





from typing import TYPE_CHECKING

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


A__					: Dict=	{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: List[Any]=	['XLNetTokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: int=	['XLNetTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: List[Any]=	[
      'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLNetForMultipleChoice',
      'XLNetForQuestionAnswering',
      'XLNetForQuestionAnsweringSimple',
      'XLNetForSequenceClassification',
      'XLNetForTokenClassification',
      'XLNetLMHeadModel',
      'XLNetModel',
      'XLNetPreTrainedModel',
      'load_tf_weights_in_xlnet',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: Dict=	[
      'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLNetForMultipleChoice',
      'TFXLNetForQuestionAnsweringSimple',
      'TFXLNetForSequenceClassification',
      'TFXLNetForTokenClassification',
      'TFXLNetLMHeadModel',
      'TFXLNetMainLayer',
      'TFXLNetModel',
      'TFXLNetPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  A__					: int=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	list[int]:
    """simple docstring"""

    UpperCamelCase__         =    len(SCREAMING_SNAKE_CASE					)
    for i in range(SCREAMING_SNAKE_CASE					):
        for j in range(i + 1 ,	SCREAMING_SNAKE_CASE					):
            if numbers[j] < numbers[i]:
                UpperCamelCase__       ,						UpperCamelCase__         =    numbers[j], numbers[i]
    return numbers


if __name__ == "__main__":
  A__					: Union[str, Any]=	input("""Enter numbers separated by a comma:\n""").strip()
  A__					: List[Any]=	[int(item) for item in user_input.split(""",""")]
  print(exchange_sort(unsorted))




