



import math



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any] = 0				,       SCREAMING_SNAKE_CASE__       :	Optional[Any] = 0				)  ->     Tuple:
 _snake_case    :  Dict					=					end or len(lowercase__				)
 for i in range(lowercase__				,       lowercase__				):
  _snake_case    :  Tuple					=					i
  _snake_case    :  int					=					array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   _snake_case    :  str					=					array[temp_index - 1]
   temp_index -= 1
  _snake_case    :  Optional[Any]					=					temp_index_value
 return array



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Any				)  ->     int:  # Max Heap
 _snake_case    :  Optional[int]					=					index
 _snake_case    :  List[str]					=					2 * index + 1  # Left Node
 _snake_case    :  Optional[Any]					=					2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  _snake_case    :  int					=					left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  _snake_case    :  int					=					right_index

 if largest != index:
  _snake_case	,    _snake_case    :  Optional[Any]					=					array[largest], array[index]
  heapify(lowercase__				,       lowercase__				,       lowercase__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				)  ->     Any:
 _snake_case    :  List[Any]					=					len(lowercase__				)

 for i in range(n // 2				,       -1				,       -1				):
  heapify(lowercase__				,       lowercase__				,       lowercase__				)

 for i in range(n - 1				,       0				,       -1				):
  _snake_case	,    _snake_case    :  Dict					=					array[0], array[i]
  heapify(lowercase__				,       0				,       lowercase__				)

 return array



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Tuple:







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     Optional[int]:
 _snake_case    :  List[Any]					=					low
 _snake_case    :  List[str]					=					high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  _snake_case	,    _snake_case    :  Optional[int]					=					array[j], array[i]
  i += 1



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     str:







 if len(lowercase__				) == 0:
  return array
 _snake_case    :  Optional[Any]					=					2 * math.ceil(math.loga(len(lowercase__				)				)				)
 _snake_case    :  Any					=					16
 return intro_sort(lowercase__				,       0				,       len(lowercase__				)				,       lowercase__				,       lowercase__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Union[str, Any]:







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(lowercase__				)
  max_depth -= 1
  _snake_case    :  Optional[Any]					=					median_of_a(lowercase__				,       lowercase__				,       start + ((end - start) // 2) + 1				,       end - 1				)
  _snake_case    :  Optional[Any]					=					partition(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				)
  intro_sort(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				)
  _snake_case    :  Optional[int]					=					p
 return insertion_sort(lowercase__				,       lowercase__				,       lowercase__				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a__    =	input("""Enter numbers separated by a comma : """).strip()
  a__    =	[float(item) for item in user_input.split(""",""")]
  print(sort(unsorted))






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		["""image_processor""", """tokenizer"""]
   snake_case_					: str          =		"""ChineseCLIPImageProcessor"""
   snake_case_					: Tuple          =		("""BertTokenizer""", """BertTokenizerFast""")

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""				,		lowerCAmelCase				,		)
     _snake_case    :  Tuple					=					kwargs.pop("""feature_extractor""")

    _snake_case    :  Dict					=					image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`.""")
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`.""")

    super().__init__(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					self.image_processor

   def __call__(					self							:			List[Any]				,		lowerCAmelCase							:			str=None				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			Optional[int])				->				Optional[Any]:

    """simple docstring"""







    if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

    if text is not None:
     _snake_case    :  Dict					=					self.tokenizer(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if images is not None:
     _snake_case    :  Any					=					self.image_processor(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if text is not None and images is not None:
     _snake_case    :  Optional[Any]					=					image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    else:
     return BatchEncoding(data=dict(**lowerCAmelCase)				,		tensor_type=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Dict				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







    return self.tokenizer.batch_decode(*lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			int)				->				str:

    """simple docstring"""







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

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.tokenizer.model_input_names
    _snake_case    :  int					=					self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,		lowerCAmelCase				,		)
    return self.image_processor_class
