code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				CONFIG_NAME
  
  
  
        @register_to_config
        def __init__(  self  :			List[str]  ,			A  :			bool = True  ,			A  :			int = 8  ,			A  :			str = "lanczos"  ,			A  :			bool = True  ,			A  :			bool = False  ,			):
             super().__init__()
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			np.ndarray		):
             if images.ndim == 3:
                  _UpperCAmelCase    :			str              =					images[None, ...]
             _UpperCAmelCase    :			Optional[int]              =					(images * 2_5_5).round().astype("uint8"		)
             if images.shape[-1] == 1:
                  # special case for grayscale (single channel) images
                  _UpperCAmelCase    :			str              =					[Image.fromarray(image.squeeze()  ,			mode="L"		) for image in images]
             else:
                  _UpperCAmelCase    :			Tuple              =					[Image.fromarray(A		) for image in images]
  
             return pil_images
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			Union[List[PIL.Image.Image], PIL.Image.Image]		):
             if not isinstance(A  ,			A		):
                  _UpperCAmelCase    :			int              =					[images]
             _UpperCAmelCase    :			List[Any]              =					[np.array(A		).astype(np.floataa		) / 255.0 for image in images]
             _UpperCAmelCase    :			List[str]              =					np.stack(A  ,			axis=0		)
  
             return images
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			np.ndarray		):
             if images.ndim == 3:
                  _UpperCAmelCase    :			int              =					images[..., None]
  
             _UpperCAmelCase    :			List[Any]              =					torch.from_numpy(images.transpose(0  ,			3  ,			1  ,			2		)		)
             return images
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			torch.FloatTensor		):
             _UpperCAmelCase    :			Optional[Any]              =					images.cpu().permute(0  ,			2  ,			3  ,			1		).float().numpy()
             return images
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			str		):
             return 2.0 * images - 1.0
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			str		):
             return (images / 2 + 0.5).clamp(0  ,			1		)
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			PIL.Image.Image		):
             _UpperCAmelCase    :			List[str]              =					image.convert("RGB"		)
             return image
  
  
  
        def 		snake_case_      (  self  :			List[Any]  ,			A  :			PIL.Image.Image  ,			A  :			Optional[int] = None  ,			A  :			Optional[int] = None  ,			):
             if height is None:
                  _UpperCAmelCase    :			List[str]              =					image.height
             if width is None:
                  _UpperCAmelCase    :			Any              =					image.width
  
             _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					(
                 x - x % self.config.vae_scale_factor for x in (width, height)
             )  # resize to integer multiple of vae_scale_factor
             _UpperCAmelCase    :			Any              =					image.resize((width, height)  ,			resample=PIL_INTERPOLATION[self.config.resample]		)
             return image
  
  
  
        def 		snake_case_      (  self  :			int  ,			A  :			Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]  ,			A  :			Optional[int] = None  ,			A  :			Optional[int] = None  ,			):
             _UpperCAmelCase    :			int              =					(PIL.Image.Image, np.ndarray, torch.Tensor)
             if isinstance(A  ,			A		):
                  _UpperCAmelCase    :			int              =					[image]
             elif not (isinstance(A  ,			A		) and all(isinstance(A  ,			A		) for i in image		)):
                  raise ValueError(
                      f'Input is in incorrect format: {[type(A		) for i in image]}. Currently, we only support {", ".join(A		)}'		)
  
             if isinstance(image[0]  ,			PIL.Image.Image		):
                  if self.config.do_convert_rgb:
                       _UpperCAmelCase    :			Any              =					[self.convert_to_rgb(A		) for i in image]
                  if self.config.do_resize:
                       _UpperCAmelCase    :			Optional[Any]              =					[self.resize(A  ,			A  ,			A		) for i in image]
                  _UpperCAmelCase    :			Optional[int]              =					self.pil_to_numpy(A		)  # to np
                  _UpperCAmelCase    :			Optional[int]              =					self.numpy_to_pt(A		)  # to pt
  
             elif isinstance(image[0]  ,			np.ndarray		):
                  _UpperCAmelCase    :			Dict              =					np.concatenate(A  ,			axis=0		) if image[0].ndim == 4 else np.stack(A  ,			axis=0		)
                  _UpperCAmelCase    :			Optional[int]              =					self.numpy_to_pt(A		)
                  _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					image.shape
                  if self.config.do_resize and (
                      height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                  ):
                       raise ValueError(
                           f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
                           f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'		)
  
             elif isinstance(image[0]  ,			torch.Tensor		):
                  _UpperCAmelCase    :			Any              =					torch.cat(A  ,			axis=0		) if image[0].ndim == 4 else torch.stack(A  ,			axis=0		)
                  _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					image.shape
  
                  # don't need any preprocess if the image is latents
                  if channel == 4:
                       return image
  
                  if self.config.do_resize and (
                      height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                  ):
                       raise ValueError(
                           f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
                           f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'		)
  
          # expected range [0,1], normalize to [-1,1]
             _UpperCAmelCase    :			int              =					self.config.do_normalize
             if image.min() < 0:
                  warnings.warn(
                      "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
                      f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'  ,			A  ,			)
                  _UpperCAmelCase    :			Optional[int]              =					False
  
             if do_normalize:
                  _UpperCAmelCase    :			List[str]              =					self.normalize(A		)
  
             return image
  
  
  
        def 		snake_case_      (  self  :			Optional[int]  ,			A  :			torch.FloatTensor  ,			A  :			str = "pil"  ,			A  :			Optional[List[bool]] = None  ,			):
             if not isinstance(A  ,			torch.Tensor		):
                  raise ValueError(
                      f'Input for postprocessing is in incorrect format: {type(A		)}. We only support pytorch tensor'		)
             if output_type not in ["latent", "pt", "np", "pil"]:
                  _UpperCAmelCase    :			int              =					(
                      f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
                      "`pil`, `np`, `pt`, `latent`"
                  )
                  deprecate("Unsupported output_type"  ,			"1.0.0"  ,			A  ,			standard_warn=A		)
                  _UpperCAmelCase    :			Union[str, Any]              =					"np"
  
             if output_type == "latent":
                  return image
  
             if do_denormalize is None:
                  _UpperCAmelCase    :			Optional[int]              =					[self.config.do_normalize] * image.shape[0]
  
             _UpperCAmelCase    :			Tuple              =					torch.stack(
                 [self.denormalize(image[i]		) if do_denormalize[i] else image[i] for i in range(image.shape[0]		)]		)
  
             if output_type == "pt":
                  return image
  
             _UpperCAmelCase    :			List[str]              =					self.pt_to_numpy(A		)
  
             if output_type == "np":
                  return image
  
             if output_type == "pil":
                  return self.numpy_to_pil(A		)
  
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Any				  =				CONFIG_NAME
  
  
  
        @register_to_config
        def __init__(  self  :			int  ,			A  :			bool = True  ,			A  :			int = 8  ,			A  :			str = "lanczos"  ,			A  :			bool = True  ,			):
             super().__init__()
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			Any		):
             if images.ndim == 3:
                  _UpperCAmelCase    :			Optional[Any]              =					images[None, ...]
             _UpperCAmelCase    :			Optional[Any]              =					(images * 2_5_5).round().astype("uint8"		)
             if images.shape[-1] == 1:
                  # special case for grayscale (single channel) images
                  _UpperCAmelCase    :			Union[str, Any]              =					[Image.fromarray(image.squeeze()  ,			mode="L"		) for image in images]
             else:
                  _UpperCAmelCase    :			str              =					[Image.fromarray(image[:, :, :3]		) for image in images]
  
             return pil_images
  
  
  
        @staticmethod
        def 		snake_case_      (  A  :			List[Any]		):
             return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			List[str]		):
             if images.ndim == 3:
                  _UpperCAmelCase    :			Union[str, Any]              =					images[None, ...]
             _UpperCAmelCase    :			Tuple              =					images[:, :, :, 3:]
             if images.shape[-1] == 6:
                  _UpperCAmelCase    :			Optional[int]              =					(images_depth * 2_5_5).round().astype("uint8"		)
                  _UpperCAmelCase    :			Dict              =					[
                      Image.fromarray(self.rgblike_to_depthmap(A		)  ,			mode="I;16"		) for image_depth in images_depth
                  ]
             elif images.shape[-1] == 4:
                  _UpperCAmelCase    :			Optional[Any]              =					(images_depth * 65_535.0).astype(np.uintaa		)
                  _UpperCAmelCase    :			str              =					[Image.fromarray(A  ,			mode="I;16"		) for image_depth in images_depth]
             else:
                  raise Exception("Not supported"		)
  
             return pil_images
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			torch.FloatTensor  ,			A  :			str = "pil"  ,			A  :			Optional[List[bool]] = None  ,			):
             if not isinstance(A  ,			torch.Tensor		):
                  raise ValueError(
                      f'Input for postprocessing is in incorrect format: {type(A		)}. We only support pytorch tensor'		)
             if output_type not in ["latent", "pt", "np", "pil"]:
                  _UpperCAmelCase    :			Optional[int]              =					(
                      f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
                      "`pil`, `np`, `pt`, `latent`"
                  )
                  deprecate("Unsupported output_type"  ,			"1.0.0"  ,			A  ,			standard_warn=A		)
                  _UpperCAmelCase    :			Optional[Any]              =					"np"
  
             if do_denormalize is None:
                  _UpperCAmelCase    :			str              =					[self.config.do_normalize] * image.shape[0]
  
             _UpperCAmelCase    :			Dict              =					torch.stack(
                 [self.denormalize(image[i]		) if do_denormalize[i] else image[i] for i in range(image.shape[0]		)]		)
  
             _UpperCAmelCase    :			Optional[int]              =					self.pt_to_numpy(A		)
  
             if output_type == "np":
                  if image.shape[-1] == 6:
                       _UpperCAmelCase    :			Tuple              =					np.stack([self.rgblike_to_depthmap(im[:, :, 3:]		) for im in image]  ,			axis=0		)
                  else:
                       _UpperCAmelCase    :			Union[str, Any]              =					image[:, :, :, 3:]
                  return image[:, :, :, :3], image_depth
  
             if output_type == "pil":
                  return self.numpy_to_pil(A		), self.numpy_to_depth(A		)
             else:
                  raise Exception(f'This type {output_type} is not supported'		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if right <= left:
            return
       _UpperCAmelCase    :			Any              =					left
       _UpperCAmelCase    :			Tuple              =					right
       _UpperCAmelCase    :			List[Any]              =					sorting[left]
       while i <= b:
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					sorting[i], sorting[a]
                 a += 1
                 i += 1
            elif sorting[i] > pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					sorting[i], sorting[b]
                 b -= 1
            else:
                 i += 1
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						a - 1       )
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						b + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if left < right:
            _UpperCAmelCase    :			Optional[int]              =					lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						pivot_index - 1       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						pivot_index + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					sorting[right]
       _UpperCAmelCase    :			Any              =					left
       for i in range(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					sorting[i], sorting[store_index]
                 store_index += 1
       _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					sorting[store_index], sorting[right]
       return store_index
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     list:
  
  
  
  
  
       '''simple docstring'''
  
       if len(SCREAMING_SNAKE_CASE__       ) <= 1:
            return sorting
       return (
           three_way_radix_quicksort([i for i in sorting if i < sorting[0]]       )
           + [i for i in sorting if i == sorting[0]]
           + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]       )
       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod(verbose=True)
  
   _lowerCAmelCase       :      Union[str, Any]							=							input("Enter numbers separated by a comma:\n").strip()
   _lowerCAmelCase       :      List[str]							=							[int(item) for item in user_input.split(",")]
   quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
   print(unsorted)
  