
'''simple docstring'''



import math
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 0    ,      SCREAMING_SNAKE_CASE_ = 0						)     ->     list:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				end or len(SCREAMING_SNAKE_CASE_						)
   for i in range(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				i
      _SCREAMING_SNAKE_CASE      =				array[i]
      while temp_index != start and temp_index_value < array[temp_index - 1]:
         _SCREAMING_SNAKE_CASE      =				array[temp_index - 1]
         temp_index -= 1
      _SCREAMING_SNAKE_CASE      =				temp_index_value
   return array
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     None:  # Max Heap




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				index
   _SCREAMING_SNAKE_CASE      =				2 * index + 1  # Left Node
   _SCREAMING_SNAKE_CASE      =				2 * index + 2  # Right Node

   if left_index < heap_size and array[largest] < array[left_index]:
      _SCREAMING_SNAKE_CASE      =				left_index

   if right_index < heap_size and array[largest] < array[right_index]:
      _SCREAMING_SNAKE_CASE      =				right_index

   if largest != index:
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				array[largest], array[index]
      heapify(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     list:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				len(SCREAMING_SNAKE_CASE_						)

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

   for i in range(n - 1    ,      0    ,      -1						):
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				array[0], array[i]
      heapify(SCREAMING_SNAKE_CASE_    ,      0    ,      SCREAMING_SNAKE_CASE_						)

   return array
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				low
   _SCREAMING_SNAKE_CASE      =				high
   while True:
      while array[i] < pivot:
         i += 1
      j -= 1
      while pivot < array[j]:
         j -= 1
      if i >= j:
         return i
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				array[j], array[i]
      i += 1
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     list:




   """simple docstring"""
   if len(SCREAMING_SNAKE_CASE_						) == 0:
      return array
   _SCREAMING_SNAKE_CASE      =				2 * math.ceil(math.loga(len(SCREAMING_SNAKE_CASE_						)						)						)
   _SCREAMING_SNAKE_CASE      =				16
   return intro_sort(SCREAMING_SNAKE_CASE_    ,      0    ,      len(SCREAMING_SNAKE_CASE_						)    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     list:




   """simple docstring"""
   while end - start > size_threshold:
      if max_depth == 0:
         return heap_sort(SCREAMING_SNAKE_CASE_						)
      max_depth -= 1
      _SCREAMING_SNAKE_CASE      =				median_of_a(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      start + ((end - start) // 2) + 1    ,      end - 1						)
      _SCREAMING_SNAKE_CASE      =				partition(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
      intro_sort(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				p
   return insertion_sort(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

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








'''simple docstring'''



import warnings

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

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ['image_processor', 'tokenizer']
       SCREAMING_SNAKE_CASE      =  'ChineseCLIPImageProcessor'
       SCREAMING_SNAKE_CASE      =  ('BertTokenizer', 'BertTokenizerFast')






       def __init__(							self       ,							A__=None       ,							A__=None       ,							**A__      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""       ,							A__       ,							)
             _SCREAMING_SNAKE_CASE      =				kwargs.pop("""feature_extractor"""      )

          _SCREAMING_SNAKE_CASE      =				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__(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				self.image_processor






       def __call__(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:

          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:
             _SCREAMING_SNAKE_CASE      =				self.tokenizer(A__       ,							return_tensors=A__       ,							**A__      )

          if images is not None:
             _SCREAMING_SNAKE_CASE      =				self.image_processor(A__       ,							return_tensors=A__       ,							**A__      )

          if text is not None and images is not None:
             _SCREAMING_SNAKE_CASE      =				image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**A__      )       ,							tensor_type=A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Dict:
          return self.tokenizer.batch_decode(*A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Optional[Any]:
          return self.tokenizer.decode(*A__       ,							**A__      )






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.tokenizer.model_input_names
          _SCREAMING_SNAKE_CASE      =				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       @property
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							A__       ,							)
          return self.image_processor_class






