"""simple docstring"""







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'Speech2TextFeatureExtractor'
   lowercase							=  'Speech2TextTokenizer'
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        super().__init__(__UpperCamelCase     ,__UpperCamelCase			)
        lowercase_	:				List[Any]													=	self.feature_extractor
        lowercase_	:				Optional[Any]													=	False
   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Optional[Any]:


        '''simple docstring'''


        if self._in_target_context_manager:
             return self.current_processor(*__UpperCamelCase     ,**__UpperCamelCase			)

        if "raw_speech" in kwargs:
             warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
             lowercase_	:				List[str]													=	kwargs.pop('raw_speech'			)
        else:
             lowercase_	:				str													=	kwargs.pop('audio'     ,__UpperCamelCase			)
        lowercase_	:				List[Any]													=	kwargs.pop('sampling_rate'     ,__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	kwargs.pop('text'     ,__UpperCamelCase			)
        if len(__UpperCamelCase			) > 0:
             lowercase_	:				int													=	args[0]
             lowercase_	:				Any													=	args[1:]

        if audio is None and text is None:
             raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

        if audio is not None:
             lowercase_	:				Tuple													=	self.feature_extractor(__UpperCamelCase     ,*__UpperCamelCase     ,sampling_rate=__UpperCamelCase     ,**__UpperCamelCase			)
        if text is not None:
             lowercase_	:				Tuple													=	self.tokenizer(__UpperCamelCase     ,**__UpperCamelCase			)

        if text is None:
             return inputs
        elif audio is None:
             return encodings
        else:
             lowercase_	:				Dict													=	encodings['input_ids']
             return inputs
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      int:


        '''simple docstring'''


        return self.tokenizer.batch_decode(*__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      int:


        '''simple docstring'''


        return self.tokenizer.decode(*__UpperCamelCase     ,**__UpperCamelCase			)







   @contextmanager
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        warnings.warn(
            '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
            'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
            'your audio inputs, or in a separate call.'			)
        lowercase_	:				Optional[Any]													=	True
        lowercase_	:				Any													=	self.tokenizer
        yield
        lowercase_	:				Optional[int]													=	self.feature_extractor
        lowercase_	:				Any													=	False


"""simple docstring"""







def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):

     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:
          lowercase_	:				Any													=	False

          if low == high:
               return swapped

          lowercase_	:				str													=	low
          lowercase_	:				int													=	high

          while left < right:
               if collection[left] > collection[right]:
                    lowercase_							,  lowercase_	:				Optional[Any]													=	(
                        collection[right],
                        collection[left],
                    )
                    lowercase_	:				Tuple													=	True

               left += 1
               right -= 1

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

               lowercase_	:				str													=	True

          lowercase_	:				Optional[Any]													=	low + int((high - low) / 2      )
          lowercase_	:				str													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          lowercase_	:				List[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			mid + 1     ,			__SCREAMING_SNAKE_CASE      )

          return swapped or left_swap or right_swap

     lowercase_	:				Dict													=	True

     while is_not_sorted is True:
          lowercase_	:				Optional[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      )

     return collection


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

