





from typing import TYPE_CHECKING

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


a__      :     Tuple         =		{
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     Union[str, Any]         =		['FunnelTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[str]         =		[
          'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FunnelBaseModel',
          'FunnelForMaskedLM',
          'FunnelForMultipleChoice',
          'FunnelForPreTraining',
          'FunnelForQuestionAnswering',
          'FunnelForSequenceClassification',
          'FunnelForTokenClassification',
          'FunnelModel',
          'FunnelPreTrainedModel',
          'load_tf_weights_in_funnel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     Optional[Any]         =		[
          'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFFunnelBaseModel',
          'TFFunnelForMaskedLM',
          'TFFunnelForMultipleChoice',
          'TFFunnelForPreTraining',
          'TFFunnelForQuestionAnswering',
          'TFFunnelForSequenceClassification',
          'TFFunnelForTokenClassification',
          'TFFunnelModel',
          'TFFunnelPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
      from .tokenization_funnel import FunnelTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_funnel_fast import FunnelTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_funnel import (
                FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
                FunnelBaseModel,
                FunnelForMaskedLM,
                FunnelForMultipleChoice,
                FunnelForPreTraining,
                FunnelForQuestionAnswering,
                FunnelForSequenceClassification,
                FunnelForTokenClassification,
                FunnelModel,
                FunnelPreTrainedModel,
                load_tf_weights_in_funnel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_funnel import (
                TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFFunnelBaseModel,
                TFFunnelForMaskedLM,
                TFFunnelForMultipleChoice,
                TFFunnelForPreTraining,
                TFFunnelForQuestionAnswering,
                TFFunnelForSequenceClassification,
                TFFunnelForTokenClassification,
                TFFunnelModel,
                TFFunnelPreTrainedModel,
            )

else:
      import sys

      a__      :     List[Any]         =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










def      UpperCAmelCase_    (    _UpperCAmelCase							:list				)	->				list:
     '''simple docstring'''


     if len(_UpperCAmelCase				) < 2:
          return collection

     def circle_sort_util(_UpperCAmelCase							:list  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:int				) -> bool:
          A_          =       False

          if low == high:
               return swapped

          A_          =       low
          A_          =       high

          while left < right:
               if collection[left] > collection[right]:
                    A_   ,    A_          =       (
                        collection[right],
                        collection[left],
                    )
                    A_          =       True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               A_   ,    A_          =       (
                   collection[right + 1],
                   collection[left],
               )

               A_          =       True

          A_          =       low + int((high - low) / 2				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						mid + 1  ,						_UpperCAmelCase				)

          return swapped or left_swap or right_swap

     A_          =       True

     while is_not_sorted is True:
          A_          =       circle_sort_util(_UpperCAmelCase  ,						0  ,						len(_UpperCAmelCase				) - 1				)

     return collection


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



