
"""simple docstring"""





import math
def 				A_  (__a					, __a = 0					, __a = 0					):

					'''simple docstring'''





					A_								=    end or len(__a					)
					for i in range(__a					, __a					):
										A_								=    i
										A_								=    array[i]
										while temp_index != start and temp_index_value < array[temp_index - 1]:
															A_								=    array[temp_index - 1]
															temp_index -= 1
										A_								=    temp_index_value
					return array
def 				A_  (__a					, __a					, __a					):  # Max Heap

					'''simple docstring'''





					A_								=    index
					A_								=    2 * index + 1  # Left Node
					A_								=    2 * index + 2  # Right Node

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

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

					if largest != index:
										A_			,     A_								=    array[largest], array[index]
										heapify(__a					, __a					, __a					)
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    len(__a					)

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

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

					return array
def 				A_  (__a					, __a					, __a					, __a					):

					'''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 				A_  (__a					, __a					, __a					, __a					):

					'''simple docstring'''





					A_								=    low
					A_								=    high
					while True:
										while array[i] < pivot:
															i += 1
										j -= 1
										while pivot < array[j]:
															j -= 1
										if i >= j:
															return i
										A_			,     A_								=    array[j], array[i]
										i += 1
def 				A_  (__a					):

					'''simple docstring'''





					if len(__a					) == 0:
										return array
					A_								=    2 * math.ceil(math.loga(len(__a					)					)					)
					A_								=    16
					return intro_sort(__a					, 0					, len(__a					)					, __a					, __a					)
def 				A_  (__a					, __a					, __a					, __a					, __a					):

					'''simple docstring'''





					while end - start > size_threshold:
										if max_depth == 0:
															return heap_sort(__a					)
										max_depth -= 1
										A_								=    median_of_a(__a					, __a					, start + ((end - start) // 2) + 1					, end - 1					)
										A_								=    partition(__a					, __a					, __a					, __a					)
										intro_sort(__a					, __a					, __a					, __a					, __a					)
										A_								=    p
					return insertion_sort(__a					, __a					, __a					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    List[str]						      =			{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Tuple						      =			[
		    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWForCTC''',
		    '''SEWForSequenceClassification''',
		    '''SEWModel''',
		    '''SEWPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    List[str]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




