



"""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'		)





"""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)
