





import math
def      _A       (		SCREAMING_SNAKE_CASE     :   list       ,       SCREAMING_SNAKE_CASE     :   int = 0       ,       SCREAMING_SNAKE_CASE     :   int = 0						):





  """simple docstring"""



  a__	:					Union[str, Any]										=end or len(SCREAMING_SNAKE_CASE						)
  for i in range(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    a__	:					Dict										=i
    a__	:					Optional[int]										=array[i]
    while temp_index != start and temp_index_value < array[temp_index - 1]:
      a__	:					Tuple										=array[temp_index - 1]
      temp_index -= 1
    a__	:					Any										=temp_index_value
  return array
def      _A       (		SCREAMING_SNAKE_CASE     :   list       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):  # Max Heap





  """simple docstring"""



  a__	:					Optional[int]										=index
  a__	:					Any										=2 * index + 1  # Left Node
  a__	:					Tuple										=2 * index + 2  # Right Node

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

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

  if largest != index:
    a__					,							a__	:					str										=array[largest], array[index]
    heapify(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  a__	:					Any										=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						):
    a__					,							a__	:					Optional[Any]										=array[0], array[i]
    heapify(SCREAMING_SNAKE_CASE       ,       0       ,       SCREAMING_SNAKE_CASE						)

  return array
def      _A       (		SCREAMING_SNAKE_CASE     :   list       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int       ,       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      _A       (		SCREAMING_SNAKE_CASE     :   list       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  a__	:					str										=low
  a__	:					List[Any]										=high
  while True:
    while array[i] < pivot:
      i += 1
    j -= 1
    while pivot < array[j]:
      j -= 1
    if i >= j:
      return i
    a__					,							a__	:					Tuple										=array[j], array[i]
    i += 1
def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if len(SCREAMING_SNAKE_CASE						) == 0:
    return array
  a__	:					str										=2 * math.ceil(math.loga(len(SCREAMING_SNAKE_CASE						)						)						)
  a__	:					str										=16
  return intro_sort(SCREAMING_SNAKE_CASE       ,       0       ,       len(SCREAMING_SNAKE_CASE						)       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   list       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  while end - start > size_threshold:
    if max_depth == 0:
      return heap_sort(SCREAMING_SNAKE_CASE						)
    max_depth -= 1
    a__	:					Dict										=median_of_a(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       start + ((end - start) // 2) + 1       ,       end - 1						)
    a__	:					Any										=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						)
    a__	:					Tuple										=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	:	Dict		     =       [float(item) for item in user_input.split(""",""")]
     print(sort(unsorted))










from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase       =							{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							['DPTFeatureExtractor']
			lowerCAmelCase       =							['DPTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPTForDepthEstimation',
			    'DPTForSemanticSegmentation',
			    'DPTModel',
			    'DPTPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_dpt import DPTFeatureExtractor
						from .image_processing_dpt import DPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dpt import (
						    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPTForDepthEstimation,
						    DPTForSemanticSegmentation,
						    DPTModel,
						    DPTPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


