






import math

def   __lowercase       (			snake_case,	snake_case = 0,	snake_case = 0			):







				"""simple docstring"""






				__magic_name__		:int   =   end or len(snake_case			)
				for i in range(snake_case,	snake_case			):
								__magic_name__		:Any   =   i
								__magic_name__		:Union[str, Any]   =   array[i]
								while temp_index != start and temp_index_value < array[temp_index - 1]:
												__magic_name__		:Optional[int]   =   array[temp_index - 1]
												temp_index -= 1
								__magic_name__		:Optional[Any]   =   temp_index_value
				return array

def   __lowercase       (			snake_case,	snake_case,	snake_case			):  # Max Heap







				"""simple docstring"""






				__magic_name__		:int   =   index
				__magic_name__		:str   =   2 * index + 1  # Left Node
				__magic_name__		:List[Any]   =   2 * index + 2  # Right Node

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

				if right_index < heap_size and array[largest] < array[right_index]:
								__magic_name__		:Any   =   right_index

				if largest != index:
								__magic_name__  ,					__magic_name__		:Tuple   =   array[largest], array[index]
								heapify(snake_case,	snake_case,	snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   len(snake_case			)

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

				for i in range(n - 1,	0,	-1			):
								__magic_name__  ,					__magic_name__		:Union[str, Any]   =   array[0], array[i]
								heapify(snake_case,	0,	snake_case			)

				return array

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""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   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   low
				__magic_name__		:List[Any]   =   high
				while True:
								while array[i] < pivot:
												i += 1
								j -= 1
								while pivot < array[j]:
												j -= 1
								if i >= j:
												return i
								__magic_name__  ,					__magic_name__		:Tuple   =   array[j], array[i]
								i += 1

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if len(snake_case			) == 0:
								return array
				__magic_name__		:List[str]   =   2 * math.ceil(math.loga(len(snake_case			)			)			)
				__magic_name__		:Union[str, Any]   =   1_6
				return intro_sort(snake_case,	0,	len(snake_case			),	snake_case,	snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				while end - start > size_threshold:
								if max_depth == 0:
												return heap_sort(snake_case			)
								max_depth -= 1
								__magic_name__		:int   =   median_of_a(snake_case,	snake_case,	start + ((end - start) // 2) + 1,	end - 1			)
								__magic_name__		:Tuple   =   partition(snake_case,	snake_case,	snake_case,	snake_case			)
								intro_sort(snake_case,	snake_case,	snake_case,	snake_case,	snake_case			)
								__magic_name__		:Dict   =   p
				return insertion_sort(snake_case,	snake_case,	snake_case			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE__   :      Any				=		input("""Enter numbers separated by a comma : """).strip()
					SCREAMING_SNAKE_CASE__   :      List[Any]				=		[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     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   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							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   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  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""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:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, Any]   =   image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__lowerCAmelCase  )							,       tensor_type=__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""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






