

import math







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = 0		,							SCREAMING_SNAKE_CASE = 0				)       ->      Any:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    end or len(A_				)
     for i in range(A_		,							A_				):
          _lowerCAmelCase   :					Optional[Any]        =    i
          _lowerCAmelCase   :					Dict        =    array[i]
          while temp_index != start and temp_index_value < array[temp_index - 1]:
               _lowerCAmelCase   :					List[Any]        =    array[temp_index - 1]
               temp_index -= 1
          _lowerCAmelCase   :					Tuple        =    temp_index_value
     return array







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:  # Max Heap

     """simple docstring"""







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

     if left_index < heap_size and array[largest] < array[left_index]:
          _lowerCAmelCase   :					str        =    left_index

     if right_index < heap_size and array[largest] < array[right_index]:
          _lowerCAmelCase   :					Union[str, Any]        =    right_index

     if largest != index:
          _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    array[largest], array[index]
          heapify(A_		,							A_		,							A_				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    len(A_				)

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

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

     return array







def 	_snake_case		(    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     if len(A_				) == 0:
          return array
     _lowerCAmelCase   :					int        =    2 * math.ceil(math.loga(len(A_				)				)				)
     _lowerCAmelCase   :					str        =    16
     return intro_sort(A_		,							0		,							len(A_				)		,							A_		,							A_				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     while end - start > size_threshold:
          if max_depth == 0:
               return heap_sort(A_				)
          max_depth -= 1
          _lowerCAmelCase   :					Tuple        =    median_of_a(A_		,							A_		,							start + ((end - start) // 2) + 1		,							end - 1				)
          _lowerCAmelCase   :					Optional[Any]        =    partition(A_		,							A_		,							A_		,							A_				)
          intro_sort(A_		,							A_		,							A_		,							A_		,							A_				)
          _lowerCAmelCase   :					Optional[Any]        =    p
     return insertion_sort(A_		,							A_		,							A_				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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









from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
  import PIL

if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else {"shortest_edge": 2_2_4}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Any        =    crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					str        =    resample
      _lowerCAmelCase   :					Optional[Any]        =    do_rescale
      _lowerCAmelCase   :					Dict        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_center_crop
      _lowerCAmelCase   :					List[Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_flip_channel_order

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Union[str, Any]        =    get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						default_to_square=A_       )
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Any     ,						):






      '''simple docstring'''




      return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Optional[Union[str, ChannelDimension]] = None       ):






      '''simple docstring'''




      return flip_channel_order(A_     ,						data_format=A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Dict        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					Tuple        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Optional[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Dict        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."       )

      if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."       )

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Optional[int]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					Any        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Tuple        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					Any        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
           _lowerCAmelCase   :					Dict        =    [self.flip_channel_order(image=A_       ) for image in images]

      _lowerCAmelCase   :					Optional[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(A_       ) != len(A_       ):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

           if is_torch_tensor(A_       ):
                _lowerCAmelCase   :					Dict        =    target_sizes.numpy()

           _lowerCAmelCase   :					List[Any]        =    []

           for idx in range(len(A_       )       ):
                _lowerCAmelCase   :					Tuple        =    torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0       )     ,						size=target_sizes[idx]     ,						mode="bilinear"     ,						align_corners=A_       )
                _lowerCAmelCase   :					Any        =    resized_logits[0].argmax(dim=0       )
                semantic_segmentation.append(A_       )
      else:
           _lowerCAmelCase   :					Tuple        =    logits.argmax(dim=1       )
           _lowerCAmelCase   :					Tuple        =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

      return semantic_segmentation






