
from typing import TYPE_CHECKING

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


_A	  =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A	  =					['''MPNetTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A	  =					[
						    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MPNetForMaskedLM''',
						    '''MPNetForMultipleChoice''',
						    '''MPNetForQuestionAnswering''',
						    '''MPNetForSequenceClassification''',
						    '''MPNetForTokenClassification''',
						    '''MPNetLayer''',
						    '''MPNetModel''',
						    '''MPNetPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A	  =					[
						    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFMPNetEmbeddings''',
						    '''TFMPNetForMaskedLM''',
						    '''TFMPNetForMultipleChoice''',
						    '''TFMPNetForQuestionAnswering''',
						    '''TFMPNetForSequenceClassification''',
						    '''TFMPNetForTokenClassification''',
						    '''TFMPNetMainLayer''',
						    '''TFMPNetModel''',
						    '''TFMPNetPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
						from .tokenization_mpnet import MPNetTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mpnet_fast import MPNetTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mpnet import (
												    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MPNetForMaskedLM,
												    MPNetForMultipleChoice,
												    MPNetForQuestionAnswering,
												    MPNetForSequenceClassification,
												    MPNetForTokenClassification,
												    MPNetLayer,
												    MPNetModel,
												    MPNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mpnet import (
												    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMPNetEmbeddings,
												    TFMPNetForMaskedLM,
												    TFMPNetForMultipleChoice,
												    TFMPNetForQuestionAnswering,
												    TFMPNetForSequenceClassification,
												    TFMPNetForTokenClassification,
												    TFMPNetMainLayer,
												    TFMPNetModel,
												    TFMPNetPreTrainedModel,
												)

else:
						import sys

						_A	  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





def 				lowerCamelCase__				(						a__  :  list     )		->	list:
       UpperCamelCase_							       =       1.3
       UpperCamelCase_							       =       len(a__     )
       UpperCamelCase_							       =       False

       while not completed:
              # Update the gap value for a next comb
              UpperCamelCase_							       =       int(gap / shrink_factor     )
              if gap <= 1:
                     UpperCamelCase_							       =       True

              UpperCamelCase_							       =       0
              while index + gap < len(a__     ):
                     if data[index] > data[index + gap]:
                            # Swap values
                            UpperCamelCase_       ,				UpperCamelCase_							       =       data[index + gap], data[index]
                            UpperCamelCase_							       =       False
                     index += 1

       return data


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      _A	  =					input('''Enter numbers separated by a comma:\n''').strip()
      _A	  =					[int(item) for item in user_input.split(''',''')]
      print(comb_sort(unsorted))



