






"""simple docstring"""






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


A_      	=     logging.get_logger(__name__)
class     __lowerCAmelCase (      A_							):







  '''simple docstring'''


  __lowerCamelCase							:     List[str]  						=      ["pixel_values"]
  def __init__(   self:		Tuple					,      UpperCamelCase_:		Optional[Any] = True					,      UpperCamelCase_:		Dict = 1 / 255					,      UpperCamelCase_:		Any = True					,      UpperCamelCase_:		Union[str, Any] = 8					,      **UpperCamelCase_:		Optional[Any]					,      ):
     super().__init__(**UpperCamelCase_       )

     UpperCamelCase_			     =do_rescale
     UpperCamelCase_			     =rescale_factor
     UpperCamelCase_			     =do_pad
     UpperCamelCase_			     =pad_size
  def        UpperCamelCase__ (   self:		Union[str, Any]					,      UpperCamelCase_:		Tuple					,      UpperCamelCase_:		Optional[Any]					,      UpperCamelCase_:		Dict = None					,      **UpperCamelCase_:		str       ):
     return rescale(UpperCamelCase_					,      scale=UpperCamelCase_					,      data_format=UpperCamelCase_					,      **UpperCamelCase_       )
  def        UpperCamelCase__ (   self:		Union[str, Any]					,      UpperCamelCase_:		List[Any]					,      UpperCamelCase_:		Optional[Any]					,      UpperCamelCase_:		Dict = None       ):
     UpperCamelCase_			     =get_image_size(UpperCamelCase_       )
     UpperCamelCase_			     =(old_height // size + 1) * size - old_height
     UpperCamelCase_			     =(old_width // size + 1) * size - old_width

     return pad(UpperCamelCase_					,      ((0, pad_height), (0, pad_width))					,      mode="symmetric"					,      data_format=UpperCamelCase_       )





  def        UpperCamelCase__ (   self:		str					,      UpperCamelCase_:		Any					,      UpperCamelCase_:		Tuple = None					,      UpperCamelCase_:		int = None					,      UpperCamelCase_:		Tuple = None					,      UpperCamelCase_:		Optional[int] = None					,      UpperCamelCase_:		int = None					,      UpperCamelCase_:		Dict = ChannelDimension.FIRST					,      **UpperCamelCase_:		Union[str, Any]					,      ):
     UpperCamelCase_			     =do_rescale if do_rescale is not None else self.do_rescale
     UpperCamelCase_			     =rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCamelCase_			     =do_pad if do_pad is not None else self.do_pad
     UpperCamelCase_			     =pad_size if pad_size is not None else self.pad_size

     UpperCamelCase_			     =make_list_of_images(UpperCamelCase_       )

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

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

     # All transformations expect numpy arrays.
     UpperCamelCase_			     =[to_numpy_array(UpperCamelCase_       ) for image in images]

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

     if do_pad:
        UpperCamelCase_			     =[self.pad(UpperCamelCase_					,      size=UpperCamelCase_       ) for image in images]

     UpperCamelCase_			     =[to_channel_dimension_format(UpperCamelCase_					,      UpperCamelCase_       ) for image in images]

     UpperCamelCase_			     ={"""pixel_values""": images}
     return BatchFeature(data=UpperCamelCase_					,      tensor_type=UpperCamelCase_       )




"""simple docstring"""
import random

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      List[Any]						, __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		a[left_index]
   _lowerCamelCase	:					Dict   =		left_index + 1
   for j in range(left_index + 1						, __snake_case   ):
      if a[j] < pivot:
         _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		a[i], a[j]
         i += 1
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		a[i - 1], a[left_index]
   return i - 1

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]						, __snake_case							:      List[str]   ):




   """simple docstring"""







   if left < right:
      _lowerCamelCase	:					Any   =		random.randint(__snake_case						, right - 1   )
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		(
          a[left],
          a[pivot],
      )  # switches the pivot with the left most bound
      _lowerCamelCase	:					List[str]   =		partition(__snake_case						, __snake_case						, __snake_case   )
      quick_sort_random(
          __snake_case						, __snake_case						, __snake_case   )  # recursive quicksort to the left of the pivot point
      quick_sort_random(
          __snake_case						, pivot_index + 1						, __snake_case   )  # recursive quicksort to the right of the pivot point

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		input("""Enter numbers separated by a comma:\n"""   ).strip()
   _lowerCamelCase	:					int   =		[int(__snake_case   ) for item in user_input.split(""","""   )]

   quick_sort_random(__snake_case						, 0						, len(__snake_case   )   )

   print(__snake_case   )


if __name__ == "__main__":
 main()

