


'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase     :  List[str]											=    {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    ["""ElectraTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  str											=    [
           """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ElectraForCausalLM""",
           """ElectraForMaskedLM""",
           """ElectraForMultipleChoice""",
           """ElectraForPreTraining""",
           """ElectraForQuestionAnswering""",
           """ElectraForSequenceClassification""",
           """ElectraForTokenClassification""",
           """ElectraModel""",
           """ElectraPreTrainedModel""",
           """load_tf_weights_in_electra""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFElectraForMaskedLM""",
           """TFElectraForMultipleChoice""",
           """TFElectraForPreTraining""",
           """TFElectraForQuestionAnswering""",
           """TFElectraForSequenceClassification""",
           """TFElectraForTokenClassification""",
           """TFElectraModel""",
           """TFElectraPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Union[str, Any]											=    [
           """FlaxElectraForCausalLM""",
           """FlaxElectraForMaskedLM""",
           """FlaxElectraForMultipleChoice""",
           """FlaxElectraForPreTraining""",
           """FlaxElectraForQuestionAnswering""",
           """FlaxElectraForSequenceClassification""",
           """FlaxElectraForTokenClassification""",
           """FlaxElectraModel""",
           """FlaxElectraPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
       from .tokenization_electra import ElectraTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_electra_fast import ElectraTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_electra import (
                  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ElectraForCausalLM,
                  ElectraForMaskedLM,
                  ElectraForMultipleChoice,
                  ElectraForPreTraining,
                  ElectraForQuestionAnswering,
                  ElectraForSequenceClassification,
                  ElectraForTokenClassification,
                  ElectraModel,
                  ElectraPreTrainedModel,
                  load_tf_weights_in_electra,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_electra import (
                  TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFElectraForMaskedLM,
                  TFElectraForMultipleChoice,
                  TFElectraForPreTraining,
                  TFElectraForQuestionAnswering,
                  TFElectraForSequenceClassification,
                  TFElectraForTokenClassification,
                  TFElectraModel,
                  TFElectraPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_electra import (
                  FlaxElectraForCausalLM,
                  FlaxElectraForMaskedLM,
                  FlaxElectraForMultipleChoice,
                  FlaxElectraForPreTraining,
                  FlaxElectraForQuestionAnswering,
                  FlaxElectraForSequenceClassification,
                  FlaxElectraForTokenClassification,
                  FlaxElectraModel,
                  FlaxElectraPreTrainedModel,
              )

else:
       import sys

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





'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							len(_A       )
       for i in range(length - 1       ):
              _lowerCAmelCase     :				List[Any]       =							i
              for k in range(i + 1 ,    _A       ):
                     if collection[k] < collection[least]:
                            _lowerCAmelCase     :				List[str]       =							k
              if least != i:
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							(collection[i], collection[least])
       return collection


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[Any]											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Tuple											=    [int(item) for item in user_input.split(""",""")]
       print(selection_sort(unsorted))

