





import math






def lowerCamelCase__   (							snake_case_     :  list	,				snake_case_     :  int = 0	,				snake_case_     :  int = 0	)				->    list:
   __snake_case								=       end or len(snake_case_	)
   for i in range(snake_case_	,				snake_case_	):
      __snake_case								=       i
      __snake_case								=       array[i]
      while temp_index != start and temp_index_value < array[temp_index - 1]:
         __snake_case								=       array[temp_index - 1]
         temp_index -= 1
      __snake_case								=       temp_index_value
   return array






def lowerCamelCase__   (							snake_case_     :  list	,				snake_case_     :  int	,				snake_case_     :  int	)				->    None:  # Max Heap
   __snake_case								=       index
   __snake_case								=       2 * index + 1  # Left Node
   __snake_case								=       2 * index + 2  # Right Node

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

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

   if largest != index:
      __snake_case	,     __snake_case								=       array[largest], array[index]
      heapify(snake_case_	,				snake_case_	,				snake_case_	)






def lowerCamelCase__   (							snake_case_     :  list	)				->    list:
   __snake_case								=       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	):
      __snake_case	,     __snake_case								=       array[0], array[i]
      heapify(snake_case_	,				0	,				snake_case_	)

   return array






def lowerCamelCase__   (							snake_case_     :  list	,				snake_case_     :  int	,				snake_case_     :  int	,				snake_case_     :  int	)				->    int:
   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__   (							snake_case_     :  list	,				snake_case_     :  int	,				snake_case_     :  int	,				snake_case_     :  int	)				->    int:
   __snake_case								=       low
   __snake_case								=       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								=       array[j], array[i]
      i += 1






def lowerCamelCase__   (							snake_case_     :  list	)				->    list:
   if len(snake_case_	) == 0:
      return array
   __snake_case								=       2 * math.ceil(math.loga(len(snake_case_	)	)	)
   __snake_case								=       16
   return intro_sort(snake_case_	,				0	,				len(snake_case_	)	,				snake_case_	,				snake_case_	)






def lowerCamelCase__   (							snake_case_     :  list	,				snake_case_     :  int	,				snake_case_     :  int	,				snake_case_     :  int	,				snake_case_     :  int	)				->    list:
   while end - start > size_threshold:
      if max_depth == 0:
         return heap_sort(snake_case_	)
      max_depth -= 1
      __snake_case								=       median_of_a(snake_case_	,				snake_case_	,				start + ((end - start) // 2) + 1	,				end - 1	)
      __snake_case								=       partition(snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	)
      intro_sort(snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	)
      __snake_case								=       p
   return insertion_sort(snake_case_	,				snake_case_	,				snake_case_	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



         return super().__call__(a__    ,      **a__				)


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

