



"""simple docstring"""




from typing import TYPE_CHECKING

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


_lowerCAmelCase    : str							=							{
    "configuration_xlm_roberta": [
        "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaConfig",
        "XLMRobertaOnnxConfig",
    ],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : int							=							["XLMRobertaTokenizer"]

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMRobertaForCausalLM",
     "XLMRobertaForMaskedLM",
     "XLMRobertaForMultipleChoice",
     "XLMRobertaForQuestionAnswering",
     "XLMRobertaForSequenceClassification",
     "XLMRobertaForTokenClassification",
     "XLMRobertaModel",
     "XLMRobertaPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Union[str, Any]							=							[
     "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXLMRobertaForCausalLM",
     "TFXLMRobertaForMaskedLM",
     "TFXLMRobertaForMultipleChoice",
     "TFXLMRobertaForQuestionAnswering",
     "TFXLMRobertaForSequenceClassification",
     "TFXLMRobertaForTokenClassification",
     "TFXLMRobertaModel",
     "TFXLMRobertaPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : List[Any]							=							[
     "FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaxXLMRobertaForMaskedLM",
     "FlaxXLMRobertaForCausalLM",
     "FlaxXLMRobertaForMultipleChoice",
     "FlaxXLMRobertaForQuestionAnswering",
     "FlaxXLMRobertaForSequenceClassification",
     "FlaxXLMRobertaForTokenClassification",
     "FlaxXLMRobertaModel",
     "FlaxXLMRobertaPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_xlm_roberta import (
     XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XLMRobertaConfig,
     XLMRobertaOnnxConfig,
 )

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_roberta import XLMRobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_roberta import (
      XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMRobertaForCausalLM,
      XLMRobertaForMaskedLM,
      XLMRobertaForMultipleChoice,
      XLMRobertaForQuestionAnswering,
      XLMRobertaForSequenceClassification,
      XLMRobertaForTokenClassification,
      XLMRobertaModel,
      XLMRobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm_roberta import (
      TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMRobertaForCausalLM,
      TFXLMRobertaForMaskedLM,
      TFXLMRobertaForMultipleChoice,
      TFXLMRobertaForQuestionAnswering,
      TFXLMRobertaForSequenceClassification,
      TFXLMRobertaForTokenClassification,
      TFXLMRobertaModel,
      TFXLMRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_xlm_roberta import (
      FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxXLMRobertaForCausalLM,
      FlaxXLMRobertaForMaskedLM,
      FlaxXLMRobertaForMultipleChoice,
      FlaxXLMRobertaForQuestionAnswering,
      FlaxXLMRobertaForSequenceClassification,
      FlaxXLMRobertaForTokenClassification,
      FlaxXLMRobertaModel,
      FlaxXLMRobertaPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase    : Optional[Any]							=							_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))
