



"""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    : Union[str, Any]							=							{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCAmelCase    : Any							=							[
       "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RoFormerForCausalLM",
       "RoFormerForMaskedLM",
       "RoFormerForMultipleChoice",
       "RoFormerForQuestionAnswering",
       "RoFormerForSequenceClassification",
       "RoFormerForTokenClassification",
       "RoFormerLayer",
       "RoFormerModel",
       "RoFormerPreTrainedModel",
       "load_tf_weights_in_roformer",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCAmelCase    : Optional[int]							=							[
       "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFRoFormerForCausalLM",
       "TFRoFormerForMaskedLM",
       "TFRoFormerForMultipleChoice",
       "TFRoFormerForQuestionAnswering",
       "TFRoFormerForSequenceClassification",
       "TFRoFormerForTokenClassification",
       "TFRoFormerLayer",
       "TFRoFormerModel",
       "TFRoFormerPreTrainedModel",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCAmelCase    : List[str]							=							[
       "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "FlaxRoFormerForMaskedLM",
       "FlaxRoFormerForMultipleChoice",
       "FlaxRoFormerForQuestionAnswering",
       "FlaxRoFormerForSequenceClassification",
       "FlaxRoFormerForTokenClassification",
       "FlaxRoFormerModel",
       "FlaxRoFormerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

   _lowerCAmelCase    : List[str]							=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list    )  ->       list:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__    ) < 2:
          return collection

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

          if low == high:
               return swapped

          _UpperCAmelCase      :     str        =   low
          _UpperCAmelCase      :     Dict        =   high

          while left < right:
               if collection[left] > collection[right]:
                    _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   (
                        collection[right],
                        collection[left],
                    )
                    _UpperCAmelCase      :     Optional[int]        =   True

               left += 1
               right -= 1

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

               _UpperCAmelCase      :     Union[str, Any]        =   True

          _UpperCAmelCase      :     Dict        =   low + int((high - low) / 2    )
          _UpperCAmelCase      :     Any        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Optional[Any]        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  mid + 1						,  SCREAMING_SNAKE_CASE__    )

          return swapped or left_swap or right_swap

     _UpperCAmelCase      :     Tuple        =   True

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

     return collection


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