




from __future__ import annotations

from random import choice




def 					lowerCamelCase__       (						A__       :					Any	):





       '''simple docstring'''



       return choice(_lowerCamelCase	)




def 					lowerCamelCase__       (						A__       :					list[int]  ,			A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						random_pivot(_lowerCamelCase	)

       # partition based on pivot
       # linear time
       __lowerCamelCase   =						[e for e in lst if e < pivot]
       __lowerCamelCase   =						[e for e in lst if e > pivot]

       # if we get lucky, pivot might be the element we want.
       # we can easily see this:
       # small (elements smaller than k)
       # + pivot (kth element)
       # + big (elements larger than k)
       if len(_lowerCamelCase	) == k - 1:
              return pivot
       # pivot is in elements bigger than k
       elif len(_lowerCamelCase	) < k - 1:
              return kth_number(_lowerCamelCase  ,			k - len(_lowerCamelCase	) - 1	)
       # pivot is in elements smaller than k
       else:
              return kth_number(_lowerCamelCase  ,			_lowerCamelCase	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
		lowercase  :					int													=       {
		    'linear': PIL.Image.Resampling.BILINEAR,
		    'bilinear': PIL.Image.Resampling.BILINEAR,
		    'bicubic': PIL.Image.Resampling.BICUBIC,
		    'lanczos': PIL.Image.Resampling.LANCZOS,
		    'nearest': PIL.Image.Resampling.NEAREST,
		}
else:
		lowercase  :					Any													=       {
		    'linear': PIL.Image.LINEAR,
		    'bilinear': PIL.Image.BILINEAR,
		    'bicubic': PIL.Image.BICUBIC,
		    'lanczos': PIL.Image.LANCZOS,
		    'nearest': PIL.Image.NEAREST,
		}




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					(images / 2 + 0.5).clamp(0						,  1)
							__UpperCamelCase	:   int			   =					images.cpu().permute(0						,  2						,  3						,  1).float().numpy()
							__UpperCamelCase	:   Dict			   =					numpy_to_pil(_lowerCamelCase)
							return images




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    Optional[Any]:






							'''simple docstring'''





							if images.ndim == 3:
														__UpperCamelCase	:   Optional[int]			   =					images[None, ...]
							__UpperCamelCase	:   Dict			   =					(images * 255).round().astype("uint8")
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														__UpperCamelCase	:   int			   =					[Image.fromarray(image.squeeze()						,  mode="L") for image in images]
							else:
														__UpperCamelCase	:   Union[str, Any]			   =					[Image.fromarray(_lowerCamelCase) for image in images]

							return pil_images